From 05a01668ea2e3fbe8e3bda2ec9682bca64a5bdb4 Mon Sep 17 00:00:00 2001 From: "wenfeng.zhang" Date: Thu, 16 Feb 2023 18:41:30 +0800 Subject: [PATCH 1/2] add pointpillars, yolov7, yolact and deepsort model link #I6BFUO add pointpillars, yolov7, yolact and deepsort model Signed-off-by: wenfeng.zhang --- .../pointpillars/pytorch/.gitignore | 11 + .../pointpillars/pytorch/README.md | 120 + .../pointpillars/pytorch/__init__.py | 0 .../pointpillars/pytorch/dataset/__init__.py | 17 + .../pointpillars/pytorch/dataset/data_aug.py | 357 +++ .../pytorch/dataset/dataloader.py | 77 + .../pointpillars/pytorch/dataset/kitti.py | 144 ++ .../pointpillars/pytorch/evaluate.py | 386 ++++ .../pytorch/figures/img_3dbbox_000134.png | Bin 0 -> 807160 bytes .../pytorch/figures/pc_pred_000134.png | Bin 0 -> 277584 bytes .../pointpillars/pytorch/loss/__init__.py | 1 + .../pointpillars/pytorch/loss/loss.py | 66 + .../pointpillars/pytorch/misc/log.md | 103 + .../pointpillars/pytorch/misc/vis_data_gt.py | 84 + 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cv/tracking/deep_sort/pytorch/yolov3_deepsort_eval.py diff --git a/cv/3d_detection/pointpillars/pytorch/.gitignore b/cv/3d_detection/pointpillars/pytorch/.gitignore new file mode 100755 index 000000000..20ee3d049 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/.gitignore @@ -0,0 +1,11 @@ +__pycache__ +build/ +*.egg-info +*.so +*_logs* +tmp/ +results/ +data/ +pillar_logs/ +pretrained/ +dataset/ImageSets/ diff --git a/cv/3d_detection/pointpillars/pytorch/README.md b/cv/3d_detection/pointpillars/pytorch/README.md new file mode 100755 index 000000000..fa7eca008 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/README.md @@ -0,0 +1,120 @@ +# [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://arxiv.org/abs/1812.05784) + +A Simple PointPillars PyTorch Implenmentation for 3D Lidar(KITTI) Detection. + +- It can be run without installing [mmcv](https://github.com/open-mmlab/mmcv), [Spconv](https://github.com/traveller59/spconv), [mmdet](https://github.com/open-mmlab/mmdetection) or [mmdet3d](https://github.com/open-mmlab/mmdetection3d). +- Only one detection network (PointPillars) was implemented in this repo, so the code may be more easy to read. +- Sincere thanks for the great open-souce architectures [mmcv](https://github.com/open-mmlab/mmcv), [mmdet](https://github.com/open-mmlab/mmdetection) and [mmdet3d](https://github.com/open-mmlab/mmdetection3d), which helps me to learn 3D detetion and implement this repo. + +## Detection Visualization + +![](./figures/pc_pred_000134.png) +![](./figures/img_3dbbox_000134.png) + +## [Compile] + +``` +cd ops +python3 setup.py develop +``` + +## [Datasets] + +1. Download + + Download [point cloud](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_velodyne.zip)(29GB), [images](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_image_2.zip)(12 GB), [calibration files](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_calib.zip)(16 MB)和[labels](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_label_2.zip)(5 MB)。 + Or you can download from fileserve, + ``` + wget http://10.150.9.95/swapp/datasets/cv/3d_detection/kitti.tar.gz + tar -xzvf kitti.tar.gz + ``` + Format the datasets as follows: + ``` + kitti + |- training + |- calib (#7481 .txt) + |- image_2 (#7481 .png) + |- label_2 (#7481 .txt) + |- velodyne (#7481 .bin) + |- testing + |- calib (#7518 .txt) + |- image_2 (#7518 .png) + |- velodyne (#7418 .bin) + ``` + +2. Pre-process KITTI datasets First + + ``` + cd PointPillars/ + python pre_process_kitti.py --data_root your_path_to_kitti + ``` + + Now, we have datasets as follows: + ``` + kitti + |- training + |- calib (#7481 .txt) + |- image_2 (#7481 .png) + |- label_2 (#7481 .txt) + |- velodyne (#7481 .bin) + |- velodyne_reduced (#7481 .bin) + |- testing + |- calib (#7518 .txt) + |- image_2 (#7518 .png) + |- velodyne (#7518 .bin) + |- velodyne_reduced (#7518 .bin) + |- kitti_gt_database (# 19700 .bin) + |- kitti_infos_train.pkl + |- kitti_infos_val.pkl + |- kitti_infos_trainval.pkl + |- kitti_infos_test.pkl + |- kitti_dbinfos_train.pkl + + ``` + +## [Training] + +### Single GPU training +``` +cd PointPillars/ +python3 train.py --data_root your_path_to_kitti +``` +### Multiple GPU training +``` +python3 -m torch.distributed.launch --nproc_per_node 8 train_dist.py --data_root your_path_to_kitti +``` +## [Evaluation] + +``` +cd PointPillars/ +python3 evaluate.py --ckpt pretrained/epoch_160.pth --data_root your_path_to_kitti +``` + +## [Test] + +``` +cd PointPillars/ + +# 1. infer and visualize point cloud detection +python3 test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path + +# 2. infer and visualize point cloud detection and gound truth. +python3 test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path --gt_path your_gt_path + +# 3. infer and visualize point cloud & image detection +python3 test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path --img_path your_img_path + + +e.g. [infer on val set 000134] + +python3 test.py --ckpt pretrained/epoch_160.pth --pc_path /home/lifa/data/KITTI/training/velodyne_reduced/000134.bin + +or + +python3 test.py --ckpt pretrained/epoch_160.pth --pc_path data/kitti/training/velodyne_reduced/000134.bin --calib_path data/kitti/training/calib/000134.txt --img_path data/kitti/training/image_2/000134.png --gt_path data/kitti/training/label_2/000134.txt + +``` + +## Acknowledements + +Thanks for the open souce code [mmcv](https://github.com/open-mmlab/mmcv), [mmdet](https://github.com/open-mmlab/mmdetection) and [mmdet3d](https://github.com/open-mmlab/mmdetection3d). diff --git a/cv/3d_detection/pointpillars/pytorch/__init__.py b/cv/3d_detection/pointpillars/pytorch/__init__.py new file mode 100755 index 000000000..e69de29bb diff --git a/cv/3d_detection/pointpillars/pytorch/dataset/__init__.py b/cv/3d_detection/pointpillars/pytorch/dataset/__init__.py new file mode 100755 index 000000000..fbc52c3db --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/dataset/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2023, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. You may obtain +# a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations +# under the License. +from .data_aug import point_range_filter, data_augment +from .kitti import Kitti +from .dataloader import get_dataloader, get_dataloader_dist diff --git a/cv/3d_detection/pointpillars/pytorch/dataset/data_aug.py b/cv/3d_detection/pointpillars/pytorch/dataset/data_aug.py new file mode 100755 index 000000000..7e060feb6 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/dataset/data_aug.py @@ -0,0 +1,357 @@ +import copy +import numba +import numpy as np +import os +import pdb +from utils import bbox3d2bevcorners, box_collision_test, read_points, \ + remove_pts_in_bboxes, limit_period + + +def dbsample(CLASSES, data_root, data_dict, db_sampler, sample_groups): + ''' + CLASSES: dict(Pedestrian=0, Cyclist=1, Car=2) + data_root: str, data root + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + db_infos: dict(Pedestrian, Cyclist, Car, ...) + return: data_dict + ''' + pts, gt_bboxes_3d = data_dict['pts'], data_dict['gt_bboxes_3d'] + gt_labels, gt_names = data_dict['gt_labels'], data_dict['gt_names'] + gt_difficulty = data_dict['difficulty'] + image_info, calib_info = data_dict['image_info'], data_dict['calib_info'] + + sampled_pts, sampled_names, sampled_labels = [], [], [] + sampled_bboxes, sampled_difficulty = [], [] + + avoid_coll_boxes = copy.deepcopy(gt_bboxes_3d) + for name, v in sample_groups.items(): + # 1. calculate sample numbers + sampled_num = v - np.sum(gt_names == name) + if sampled_num <= 0: + continue + + # 2. sample databases bboxes + sampled_cls_list = db_sampler[name].sample(sampled_num) + sampled_cls_bboxes = np.array([item['box3d_lidar'] for item in sampled_cls_list], dtype=np.float32) + + # 3. box_collision_test + avoid_coll_boxes_bv_corners = bbox3d2bevcorners(avoid_coll_boxes) + sampled_cls_bboxes_bv_corners = bbox3d2bevcorners(sampled_cls_bboxes) + coll_query_matrix = np.concatenate([avoid_coll_boxes_bv_corners, sampled_cls_bboxes_bv_corners], axis=0) + coll_mat = box_collision_test(coll_query_matrix, coll_query_matrix) + n_gt, tmp_bboxes = len(avoid_coll_boxes_bv_corners), [] + for i in range(n_gt, len(coll_mat)): + if any(coll_mat[i]): + coll_mat[i] = False + coll_mat[:, i] = False + else: + cur_sample = sampled_cls_list[i - n_gt] + pt_path = os.path.join(data_root, cur_sample['path']) + sampled_pts_cur = read_points(pt_path) + sampled_pts_cur[:, :3] += cur_sample['box3d_lidar'][:3] + sampled_pts.append(sampled_pts_cur) + sampled_names.append(cur_sample['name']) + sampled_labels.append(CLASSES[cur_sample['name']]) + sampled_bboxes.append(cur_sample['box3d_lidar']) + tmp_bboxes.append(cur_sample['box3d_lidar']) + sampled_difficulty.append(cur_sample['difficulty']) + if len(tmp_bboxes) == 0: + tmp_bboxes = np.array(tmp_bboxes).reshape(-1, 7) + else: + tmp_bboxes = np.array(tmp_bboxes) + avoid_coll_boxes = np.concatenate([avoid_coll_boxes, tmp_bboxes], axis=0) + + # merge sampled database + # remove raw points in sampled_bboxes firstly + pts = remove_pts_in_bboxes(pts, np.stack(sampled_bboxes, axis=0)) + # pts = np.concatenate([pts, np.concatenate(sampled_pts, axis=0)], axis=0) + pts = np.concatenate([np.concatenate(sampled_pts, axis=0), pts], axis=0) + gt_bboxes_3d = avoid_coll_boxes.astype(np.float32) + gt_labels = np.concatenate([gt_labels, np.array(sampled_labels)], axis=0) + gt_names = np.concatenate([gt_names, np.array(sampled_names)], axis=0) + difficulty = np.concatenate([gt_difficulty, np.array(sampled_difficulty)], axis=0) + data_dict = { + 'pts': pts, + 'gt_bboxes_3d': gt_bboxes_3d, + 'gt_labels': gt_labels, + 'gt_names': gt_names, + 'difficulty': difficulty, + 'image_info': image_info, + 'calib_info': calib_info + } + return data_dict + + +@numba.jit(nopython=True) +def object_noise_core(pts, gt_bboxes_3d, bev_corners, trans_vec, rot_angle, rot_mat, masks): + ''' + pts: (N, 4) + gt_bboxes_3d: (n_bbox, 7) + bev_corners: ((n_bbox, 4, 2)) + trans_vec: (n_bbox, num_try, 3) + rot_mat: (n_bbox, num_try, 2, 2) + masks: (N, n_bbox), bool + return: gt_bboxes_3d, pts + ''' + # 1. select the noise of num_try for each bbox under the collision test + n_bbox, num_try = trans_vec.shape[:2] + + # succ_mask: (n_bbox, ), whether each bbox can be added noise successfully. -1 denotes failure. + succ_mask = -np.ones((n_bbox, ), dtype=np.int_) + for i in range(n_bbox): + for j in range(num_try): + cur_bbox = bev_corners[i] - np.expand_dims(gt_bboxes_3d[i, :2], 0) # (4, 2) - (1, 2) -> (4, 2) + rot = np.zeros((2, 2), dtype=np.float32) + rot[:] = rot_mat[i, j] # (2, 2) + trans = trans_vec[i, j] # (3, ) + cur_bbox = cur_bbox @ rot + cur_bbox += gt_bboxes_3d[i, :2] + cur_bbox += np.expand_dims(trans[:2], 0) # (4, 2) + coll_mat = box_collision_test(np.expand_dims(cur_bbox, 0), bev_corners) + coll_mat[0, i] = False + if coll_mat.any(): + continue + else: + bev_corners[i] = cur_bbox # update the bev_corners when adding noise succseefully. + succ_mask[i] = j + break + # 2. points and bboxes noise + visit = {} + for i in range(n_bbox): + jj = succ_mask[i] + if jj == -1: + continue + cur_trans, cur_angle = trans_vec[i, jj], rot_angle[i, jj] + cur_rot_mat = np.zeros((2, 2), dtype=np.float32) + cur_rot_mat[:] = rot_mat[i, jj] + for k in range(len(pts)): + if masks[k][i] and k not in visit: + cur_pt = pts[k] # (4, ) + cur_pt_xyz = np.zeros((1, 3), dtype=np.float32) + cur_pt_xyz[0] = cur_pt[:3] - gt_bboxes_3d[i][:3] + tmp_cur_pt_xy = np.zeros((1, 2), dtype=np.float32) + tmp_cur_pt_xy[:] = cur_pt_xyz[:, :2] + cur_pt_xyz[:, :2] = tmp_cur_pt_xy @ cur_rot_mat # (1, 2) + cur_pt_xyz[0] = cur_pt_xyz[0] + gt_bboxes_3d[i][:3] + cur_pt_xyz[0] = cur_pt_xyz[0] + cur_trans[:3] + cur_pt[:3] = cur_pt_xyz[0] + visit[k] = 1 + + gt_bboxes_3d[i, :3] += cur_trans[:3] + gt_bboxes_3d[i, 6] += cur_angle + + return gt_bboxes_3d, pts + + +def object_noise(data_dict, num_try, translation_std, rot_range): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + num_try: int, 100 + translation_std: shape=[3, ] + rot_range: shape=[2, ] + return: data_dict + ''' + pts, gt_bboxes_3d = data_dict['pts'], data_dict['gt_bboxes_3d'] + n_bbox = len(gt_bboxes_3d) + + # 1. generate rotation vectors and rotation matrices + trans_vec = np.random.normal(scale=translation_std, size=(n_bbox, num_try, 3)).astype(np.float32) + rot_angle = np.random.uniform(rot_range[0], rot_range[1], size=(n_bbox, num_try)).astype(np.float32) + rot_cos, rot_sin = np.cos(rot_angle), np.sin(rot_angle) + # in fact, - rot_angle + rot_mat = np.array([[rot_cos, rot_sin], + [-rot_sin, rot_cos]]) # (2, 2, n_bbox, num_try) + rot_mat = np.transpose(rot_mat, (2, 3, 1, 0)) # (n_bbox, num_try, 2, 2) + + # 2. generate noise for each bbox and the points inside the bbox. + bev_corners = bbox3d2bevcorners(gt_bboxes_3d) # (n_bbox, 4, 2) # for collision test + masks = remove_pts_in_bboxes(pts, gt_bboxes_3d, rm=False) # identify which point should be added noise + gt_bboxes_3d, pts = object_noise_core(pts=pts, + gt_bboxes_3d=gt_bboxes_3d, + bev_corners=bev_corners, + trans_vec=trans_vec, + rot_angle=rot_angle, + rot_mat=rot_mat, + masks=masks) + data_dict.update({'gt_bboxes_3d': gt_bboxes_3d}) + data_dict.update({'pts': pts}) + + return data_dict + + +def random_flip(data_dict, random_flip_ratio): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + random_flip_ratio: float, 0-1 + return: data_dict + ''' + random_flip_state = np.random.choice([True, False], p=[random_flip_ratio, 1-random_flip_ratio]) + if random_flip_state: + pts, gt_bboxes_3d = data_dict['pts'], data_dict['gt_bboxes_3d'] + pts[:, 1] = -pts[:, 1] + gt_bboxes_3d[:, 1] = -gt_bboxes_3d[:, 1] + gt_bboxes_3d[:, 6] = -gt_bboxes_3d[:, 6] + np.pi + data_dict.update({'gt_bboxes_3d': gt_bboxes_3d}) + data_dict.update({'pts': pts}) + return data_dict + + +def global_rot_scale_trans(data_dict, rot_range, scale_ratio_range, translation_std): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + rot_range: [a, b] + scale_ratio_range: [c, d] + translation_std: [e, f, g] + return: data_dict + ''' + pts, gt_bboxes_3d = data_dict['pts'], data_dict['gt_bboxes_3d'] + + # 1. rotation + rot_angle = np.random.uniform(rot_range[0], rot_range[1]) + rot_cos, rot_sin = np.cos(rot_angle), np.sin(rot_angle) + # in fact, - rot_angle + rot_mat = np.array([[rot_cos, rot_sin], + [-rot_sin, rot_cos]]) # (2, 2) + # 1.1 bbox rotation + gt_bboxes_3d[:, :2] = gt_bboxes_3d[:, :2] @ rot_mat.T + gt_bboxes_3d[:, 6] += rot_angle + # 1.2 point rotation + pts[:, :2] = pts[:, :2] @ rot_mat.T + + # 2. scaling + scale_fator = np.random.uniform(scale_ratio_range[0], scale_ratio_range[1]) + gt_bboxes_3d[:, :6] *= scale_fator + pts[:, :3] *= scale_fator + + # 3. translation + trans_factor = np.random.normal(scale=translation_std, size=(1, 3)) + gt_bboxes_3d[:, :3] += trans_factor + pts[:, :3] += trans_factor + data_dict.update({'gt_bboxes_3d': gt_bboxes_3d}) + data_dict.update({'pts': pts}) + return data_dict + + +def point_range_filter(data_dict, point_range): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + point_range: [x1, y1, z1, x2, y2, z2] + ''' + pts = data_dict['pts'] + flag_x_low = pts[:, 0] > point_range[0] + flag_y_low = pts[:, 1] > point_range[1] + flag_z_low = pts[:, 2] > point_range[2] + flag_x_high = pts[:, 0] < point_range[3] + flag_y_high = pts[:, 1] < point_range[4] + flag_z_high = pts[:, 2] < point_range[5] + keep_mask = flag_x_low & flag_y_low & flag_z_low & flag_x_high & flag_y_high & flag_z_high + pts = pts[keep_mask] + data_dict.update({'pts': pts}) + return data_dict + + +def object_range_filter(data_dict, object_range): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + point_range: [x1, y1, z1, x2, y2, z2] + ''' + gt_bboxes_3d, gt_labels = data_dict['gt_bboxes_3d'], data_dict['gt_labels'] + gt_names, difficulty = data_dict['gt_names'], data_dict['difficulty'] + + # bev filter + flag_x_low = gt_bboxes_3d[:, 0] > object_range[0] + flag_y_low = gt_bboxes_3d[:, 1] > object_range[1] + flag_x_high = gt_bboxes_3d[:, 0] < object_range[3] + flag_y_high = gt_bboxes_3d[:, 1] < object_range[4] + keep_mask = flag_x_low & flag_y_low & flag_x_high & flag_y_high + + gt_bboxes_3d, gt_labels = gt_bboxes_3d[keep_mask], gt_labels[keep_mask] + gt_names, difficulty = gt_names[keep_mask], difficulty[keep_mask] + gt_bboxes_3d[:, 6] = limit_period(gt_bboxes_3d[:, 6], 0.5, 2 * np.pi) + data_dict.update({'gt_bboxes_3d': gt_bboxes_3d}) + data_dict.update({'gt_labels': gt_labels}) + data_dict.update({'gt_names': gt_names}) + data_dict.update({'difficulty': difficulty}) + return data_dict + + +def points_shuffle(data_dict): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + ''' + pts = data_dict['pts'] + indices = np.arange(0, len(pts)) + np.random.shuffle(indices) + pts = pts[indices] + data_dict.update({'pts': pts}) + return data_dict + + +def filter_bboxes_with_labels(data_dict, label=-1): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + label: int + ''' + gt_bboxes_3d, gt_labels = data_dict['gt_bboxes_3d'], data_dict['gt_labels'] + gt_names, difficulty = data_dict['gt_names'], data_dict['difficulty'] + idx = gt_labels != label + gt_bboxes_3d = gt_bboxes_3d[idx] + gt_labels = gt_labels[idx] + gt_names = gt_names[idx] + difficulty = difficulty[idx] + data_dict.update({'gt_bboxes_3d': gt_bboxes_3d}) + data_dict.update({'gt_labels': gt_labels}) + data_dict.update({'gt_names': gt_names}) + data_dict.update({'difficulty': difficulty}) + return data_dict + + +def data_augment(CLASSES, data_root, data_dict, data_aug_config): + ''' + CLASSES: dict(Pedestrian=0, Cyclist=1, Car=2) + data_root: str, data root + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + data_aug_config: dict() + return: data_dict + ''' + + # 1. sample databases and merge into the data + db_sampler_config = data_aug_config['db_sampler'] + data_dict = dbsample(CLASSES, + data_root, + data_dict, + db_sampler=db_sampler_config['db_sampler'], + sample_groups=db_sampler_config['sample_groups']) + # 2. object noise + object_noise_config = data_aug_config['object_noise'] + data_dict = object_noise(data_dict, + num_try=object_noise_config['num_try'], + translation_std=object_noise_config['translation_std'], + rot_range=object_noise_config['rot_range']) + + # 3. random flip + random_flip_ratio = data_aug_config['random_flip_ratio'] + data_dict = random_flip(data_dict, random_flip_ratio) + + # 4. global rotation, scaling and translation + global_rot_scale_trans_config = data_aug_config['global_rot_scale_trans'] + rot_range = global_rot_scale_trans_config['rot_range'] + scale_ratio_range = global_rot_scale_trans_config['scale_ratio_range'] + translation_std = global_rot_scale_trans_config['translation_std'] + data_dict = global_rot_scale_trans(data_dict, rot_range, scale_ratio_range, translation_std) + + # 5. points range filter + point_range = data_aug_config['point_range_filter'] + data_dict = point_range_filter(data_dict, point_range) + + # 6. object range filter + object_range = data_aug_config['object_range_filter'] + data_dict = object_range_filter(data_dict, object_range) + + # 7. points shuffle + data_dict = points_shuffle(data_dict) + + # # 8. filter bboxes with label=-1 + # data_dict = filter_bboxes_with_labels(data_dict) + + return data_dict diff --git a/cv/3d_detection/pointpillars/pytorch/dataset/dataloader.py b/cv/3d_detection/pointpillars/pytorch/dataset/dataloader.py new file mode 100755 index 000000000..bb8fb6eec --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/dataset/dataloader.py @@ -0,0 +1,77 @@ +# Copyright (c) 2023, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. You may obtain +# a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations +# under the License. +import random +import numpy as np +import torch +from torch.utils.data import DataLoader +from functools import partial + + +def collate_fn(list_data): + batched_pts_list, batched_gt_bboxes_list = [], [] + batched_labels_list, batched_names_list = [], [] + batched_difficulty_list = [] + batched_img_list, batched_calib_list = [], [] + for data_dict in list_data: + pts, gt_bboxes_3d = data_dict['pts'], data_dict['gt_bboxes_3d'] + gt_labels, gt_names = data_dict['gt_labels'], data_dict['gt_names'] + difficulty = data_dict['difficulty'] + image_info, calbi_info = data_dict['image_info'], data_dict['calib_info'] + + batched_pts_list.append(torch.from_numpy(pts)) + batched_gt_bboxes_list.append(torch.from_numpy(gt_bboxes_3d)) + batched_labels_list.append(torch.from_numpy(gt_labels)) + batched_names_list.append(gt_names) # List(str) + batched_difficulty_list.append(torch.from_numpy(difficulty)) + batched_img_list.append(image_info) + batched_calib_list.append(calbi_info) + + rt_data_dict = dict( + batched_pts=batched_pts_list, + batched_gt_bboxes=batched_gt_bboxes_list, + batched_labels=batched_labels_list, + batched_names=batched_names_list, + batched_difficulty=batched_difficulty_list, + batched_img_info=batched_img_list, + batched_calib_info=batched_calib_list + ) + + return rt_data_dict + + +def get_dataloader(dataset, batch_size, num_workers, shuffle=True, drop_last=False): + collate = collate_fn + dataloader = DataLoader( + dataset=dataset, + batch_size=batch_size, + shuffle=shuffle, + num_workers=num_workers, + drop_last=drop_last, + collate_fn=collate, + ) + return dataloader + +def get_dataloader_dist(dataset, batch_size, num_workers, sampler, drop_last=False): + collate = collate_fn + dataloader = DataLoader( + dataset=dataset, + batch_size=batch_size, + # shuffle=shuffle, + num_workers=num_workers, + drop_last=drop_last, + collate_fn=collate, + sampler=sampler + ) + return dataloader diff --git a/cv/3d_detection/pointpillars/pytorch/dataset/kitti.py b/cv/3d_detection/pointpillars/pytorch/dataset/kitti.py new file mode 100755 index 000000000..9757317ee --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/dataset/kitti.py @@ -0,0 +1,144 @@ +import numpy as np +import os +import torch +from torch.utils.data import Dataset + +import sys +BASE = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(os.path.dirname(BASE)) + +from utils import read_pickle, read_points, bbox_camera2lidar +from dataset import point_range_filter, data_augment + + +class BaseSampler(): + def __init__(self, sampled_list, shuffle=True): + self.total_num = len(sampled_list) + self.sampled_list = np.array(sampled_list) + self.indices = np.arange(self.total_num) + if shuffle: + np.random.shuffle(self.indices) + self.shuffle = shuffle + self.idx = 0 + + def sample(self, num): + if self.idx + num < self.total_num: + ret = self.sampled_list[self.indices[self.idx:self.idx+num]] + self.idx += num + else: + ret = self.sampled_list[self.indices[self.idx:]] + self.idx = 0 + if self.shuffle: + np.random.shuffle(self.indices) + return ret + + +class Kitti(Dataset): + + CLASSES = { + 'Pedestrian': 0, + 'Cyclist': 1, + 'Car': 2 + } + + def __init__(self, data_root, split, pts_prefix='velodyne_reduced'): + assert split in ['train', 'val', 'trainval', 'test'] + self.data_root = data_root + self.split = split + self.pts_prefix = pts_prefix + self.data_infos = read_pickle(os.path.join(data_root, f'kitti_infos_{split}.pkl')) + self.sorted_ids = list(self.data_infos.keys()) + db_infos = read_pickle(os.path.join(data_root, 'kitti_dbinfos_train.pkl')) + db_infos = self.filter_db(db_infos) + + db_sampler = {} + for cat_name in self.CLASSES: + db_sampler[cat_name] = BaseSampler(db_infos[cat_name], shuffle=True) + self.data_aug_config=dict( + db_sampler=dict( + db_sampler=db_sampler, + sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10) + ), + object_noise=dict( + num_try=100, + translation_std=[0.25, 0.25, 0.25], + rot_range=[-0.15707963267, 0.15707963267] + ), + random_flip_ratio=0.5, + global_rot_scale_trans=dict( + rot_range=[-0.78539816, 0.78539816], + scale_ratio_range=[0.95, 1.05], + translation_std=[0, 0, 0] + ), + point_range_filter=[0, -39.68, -3, 69.12, 39.68, 1], + object_range_filter=[0, -39.68, -3, 69.12, 39.68, 1] + ) + + def remove_dont_care(self, annos_info): + keep_ids = [i for i, name in enumerate(annos_info['name']) if name != 'DontCare'] + for k, v in annos_info.items(): + annos_info[k] = v[keep_ids] + return annos_info + + def filter_db(self, db_infos): + # 1. filter_by_difficulty + for k, v in db_infos.items(): + db_infos[k] = [item for item in v if item['difficulty'] != -1] + + # 2. filter_by_min_points, dict(Car=5, Pedestrian=10, Cyclist=10) + filter_thrs = dict(Car=5, Pedestrian=10, Cyclist=10) + for cat in self.CLASSES: + filter_thr = filter_thrs[cat] + db_infos[cat] = [item for item in db_infos[cat] if item['num_points_in_gt'] >= filter_thr] + + return db_infos + + def __getitem__(self, index): + data_info = self.data_infos[self.sorted_ids[index]] + image_info, calib_info, annos_info = \ + data_info['image'], data_info['calib'], data_info['annos'] + + # point cloud input + velodyne_path = data_info['velodyne_path'].replace('velodyne', self.pts_prefix) + pts_path = os.path.join(self.data_root, velodyne_path) + pts = read_points(pts_path) + + # calib input: for bbox coordinates transformation between Camera and Lidar. + # because + tr_velo_to_cam = calib_info['Tr_velo_to_cam'].astype(np.float32) + r0_rect = calib_info['R0_rect'].astype(np.float32) + + # annotations input + annos_info = self.remove_dont_care(annos_info) + annos_name = annos_info['name'] + annos_location = annos_info['location'] + annos_dimension = annos_info['dimensions'] + rotation_y = annos_info['rotation_y'] + gt_bboxes = np.concatenate([annos_location, annos_dimension, rotation_y[:, None]], axis=1).astype(np.float32) + gt_bboxes_3d = bbox_camera2lidar(gt_bboxes, tr_velo_to_cam, r0_rect) + gt_labels = [self.CLASSES.get(name, -1) for name in annos_name] + data_dict = { + 'pts': pts, + 'gt_bboxes_3d': gt_bboxes_3d, + 'gt_labels': np.array(gt_labels), + 'gt_names': annos_name, + 'difficulty': annos_info['difficulty'], + 'image_info': image_info, + 'calib_info': calib_info + } + if self.split in ['train', 'trainval']: + data_dict = data_augment(self.CLASSES, self.data_root, data_dict, self.data_aug_config) + else: + data_dict = point_range_filter(data_dict, point_range=self.data_aug_config['point_range_filter']) + + return data_dict + + def __len__(self): + return len(self.data_infos) + + +if __name__ == '__main__': + + kitti_data = Kitti(data_root='/mnt/ssd1/lifa_rdata/det/kitti', + split='train') + kitti_data.__getitem__(9) diff --git a/cv/3d_detection/pointpillars/pytorch/evaluate.py b/cv/3d_detection/pointpillars/pytorch/evaluate.py new file mode 100755 index 000000000..e4fd746c4 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/evaluate.py @@ -0,0 +1,386 @@ +import argparse +import numpy as np +import os +import torch +import pdb +from tqdm import tqdm + +from utils import setup_seed, keep_bbox_from_image_range, \ + keep_bbox_from_lidar_range, write_pickle, write_label, \ + iou2d, iou3d_camera, iou_bev +from dataset import Kitti, get_dataloader +from model import PointPillars + + +def get_score_thresholds(tp_scores, total_num_valid_gt, num_sample_pts=41): + score_thresholds = [] + tp_scores = sorted(tp_scores)[::-1] + cur_recall, pts_ind = 0, 0 + for i, score in enumerate(tp_scores): + lrecall = (i + 1) / total_num_valid_gt + rrecall = (i + 2) / total_num_valid_gt + + if i == len(tp_scores) - 1: + score_thresholds.append(score) + break + + if (lrecall + rrecall) / 2 < cur_recall: + continue + + score_thresholds.append(score) + pts_ind += 1 + cur_recall = pts_ind / (num_sample_pts - 1) + return score_thresholds + + +def do_eval(det_results, gt_results, CLASSES, saved_path): + ''' + det_results: list, + gt_results: dict(id -> det_results) + CLASSES: dict + ''' + assert len(det_results) == len(gt_results) + f = open(os.path.join(saved_path, 'eval_results.txt'), 'w') + + # 1. calculate iou + ious = { + 'bbox_2d': [], + 'bbox_bev': [], + 'bbox_3d': [] + } + ids = list(sorted(gt_results.keys())) + for id in ids: + gt_result = gt_results[id]['annos'] + det_result = det_results[id] + + # 1.1, 2d bboxes iou + gt_bboxes2d = gt_result['bbox'].astype(np.float32) + det_bboxes2d = det_result['bbox'].astype(np.float32) + iou2d_v = iou2d(torch.from_numpy(gt_bboxes2d).cuda(), torch.from_numpy(det_bboxes2d).cuda()) + ious['bbox_2d'].append(iou2d_v.cpu().numpy()) + + # 1.2, bev iou + gt_location = gt_result['location'].astype(np.float32) + gt_dimensions = gt_result['dimensions'].astype(np.float32) + gt_rotation_y = gt_result['rotation_y'].astype(np.float32) + det_location = det_result['location'].astype(np.float32) + det_dimensions = det_result['dimensions'].astype(np.float32) + det_rotation_y = det_result['rotation_y'].astype(np.float32) + + gt_bev = np.concatenate([gt_location[:, [0, 2]], gt_dimensions[:, [0, 2]], gt_rotation_y[:, None]], axis=-1) + det_bev = np.concatenate([det_location[:, [0, 2]], det_dimensions[:, [0, 2]], det_rotation_y[:, None]], axis=-1) + iou_bev_v = iou_bev(torch.from_numpy(gt_bev).cuda(), torch.from_numpy(det_bev).cuda()) + ious['bbox_bev'].append(iou_bev_v.cpu().numpy()) + + # 1.3, 3dbboxes iou + gt_bboxes3d = np.concatenate([gt_location, gt_dimensions, gt_rotation_y[:, None]], axis=-1) + det_bboxes3d = np.concatenate([det_location, det_dimensions, det_rotation_y[:, None]], axis=-1) + iou3d_v = iou3d_camera(torch.from_numpy(gt_bboxes3d).cuda(), torch.from_numpy(det_bboxes3d).cuda()) + ious['bbox_3d'].append(iou3d_v.cpu().numpy()) + + MIN_IOUS = { + 'Pedestrian': [0.5, 0.5, 0.5], + 'Cyclist': [0.5, 0.5, 0.5], + 'Car': [0.7, 0.7, 0.7] + } + MIN_HEIGHT = [40, 25, 25] + + overall_results = {} + for e_ind, eval_type in enumerate(['bbox_2d', 'bbox_bev', 'bbox_3d']): + eval_ious = ious[eval_type] + eval_ap_results, eval_aos_results = {}, {} + for cls in CLASSES: + eval_ap_results[cls] = [] + eval_aos_results[cls] = [] + CLS_MIN_IOU = MIN_IOUS[cls][e_ind] + for difficulty in [0, 1, 2]: + # 1. bbox property + total_gt_ignores, total_det_ignores, total_dc_bboxes, total_scores = [], [], [], [] + total_gt_alpha, total_det_alpha = [], [] + for id in ids: + gt_result = gt_results[id]['annos'] + det_result = det_results[id] + + # 1.1 gt bbox property + cur_gt_names = gt_result['name'] + cur_difficulty = gt_result['difficulty'] + gt_ignores, dc_bboxes = [], [] + for j, cur_gt_name in enumerate(cur_gt_names): + ignore = cur_difficulty[j] < 0 or cur_difficulty[j] > difficulty + if cur_gt_name == cls: + valid_class = 1 + elif cls == 'Pedestrian' and cur_gt_name == 'Person_sitting': + valid_class = 0 + elif cls == 'Car' and cur_gt_name == 'Van': + valid_class = 0 + else: + valid_class = -1 + + if valid_class == 1 and not ignore: + gt_ignores.append(0) + elif valid_class == 0 or (valid_class == 1 and ignore): + gt_ignores.append(1) + else: + gt_ignores.append(-1) + + if cur_gt_name == 'DontCare': + dc_bboxes.append(gt_result['bbox'][j]) + total_gt_ignores.append(gt_ignores) + total_dc_bboxes.append(np.array(dc_bboxes)) + total_gt_alpha.append(gt_result['alpha']) + + # 1.2 det bbox property + cur_det_names = det_result['name'] + cur_det_heights = det_result['bbox'][:, 3] - det_result['bbox'][:, 1] + det_ignores = [] + for j, cur_det_name in enumerate(cur_det_names): + if cur_det_heights[j] < MIN_HEIGHT[difficulty]: + det_ignores.append(1) + elif cur_det_name == cls: + det_ignores.append(0) + else: + det_ignores.append(-1) + total_det_ignores.append(det_ignores) + total_scores.append(det_result['score']) + total_det_alpha.append(det_result['alpha']) + + # 2. calculate scores thresholds for PR curve + tp_scores = [] + for i, id in enumerate(ids): + cur_eval_ious = eval_ious[i] + gt_ignores, det_ignores = total_gt_ignores[i], total_det_ignores[i] + scores = total_scores[i] + + nn, mm = cur_eval_ious.shape + assigned = np.zeros((mm, ), dtype=np.bool_) + for j in range(nn): + if gt_ignores[j] == -1: + continue + match_id, match_score = -1, -1 + for k in range(mm): + if not assigned[k] and det_ignores[k] >= 0 and cur_eval_ious[j, k] > CLS_MIN_IOU and scores[k] > match_score: + match_id = k + match_score = scores[k] + if match_id != -1: + assigned[match_id] = True + if det_ignores[match_id] == 0 and gt_ignores[j] == 0: + tp_scores.append(match_score) + total_num_valid_gt = np.sum([np.sum(np.array(gt_ignores) == 0) for gt_ignores in total_gt_ignores]) + score_thresholds = get_score_thresholds(tp_scores, total_num_valid_gt) + + # 3. draw PR curve and calculate mAP + tps, fns, fps, total_aos = [], [], [], [] + + for score_threshold in score_thresholds: + tp, fn, fp = 0, 0, 0 + aos = 0 + for i, id in enumerate(ids): + cur_eval_ious = eval_ious[i] + gt_ignores, det_ignores = total_gt_ignores[i], total_det_ignores[i] + gt_alpha, det_alpha = total_gt_alpha[i], total_det_alpha[i] + scores = total_scores[i] + + nn, mm = cur_eval_ious.shape + assigned = np.zeros((mm, ), dtype=np.bool_) + for j in range(nn): + if gt_ignores[j] == -1: + continue + match_id, match_iou = -1, -1 + for k in range(mm): + if not assigned[k] and det_ignores[k] >= 0 and scores[k] >= score_threshold and cur_eval_ious[j, k] > CLS_MIN_IOU: + + if det_ignores[k] == 0 and cur_eval_ious[j, k] > match_iou: + match_iou = cur_eval_ious[j, k] + match_id = k + elif det_ignores[k] == 1 and match_iou == -1: + match_id = k + + if match_id != -1: + assigned[match_id] = True + if det_ignores[match_id] == 0 and gt_ignores[j] == 0: + tp += 1 + if eval_type == 'bbox_2d': + aos += (1 + np.cos(gt_alpha[j] - det_alpha[match_id])) / 2 + else: + if gt_ignores[j] == 0: + fn += 1 + + for k in range(mm): + if det_ignores[k] == 0 and scores[k] >= score_threshold and not assigned[k]: + fp += 1 + + # In case 2d bbox evaluation, we should consider dontcare bboxes + if eval_type == 'bbox_2d': + dc_bboxes = total_dc_bboxes[i] + det_bboxes = det_results[id]['bbox'] + if len(dc_bboxes) > 0: + ious_dc_det = iou2d(torch.from_numpy(det_bboxes), torch.from_numpy(dc_bboxes), metric=1).numpy().T + for j in range(len(dc_bboxes)): + for k in range(len(det_bboxes)): + if det_ignores[k] == 0 and scores[k] >= score_threshold and not assigned[k]: + if ious_dc_det[j, k] > CLS_MIN_IOU: + fp -= 1 + assigned[k] = True + + tps.append(tp) + fns.append(fn) + fps.append(fp) + if eval_type == 'bbox_2d': + total_aos.append(aos) + + tps, fns, fps = np.array(tps), np.array(fns), np.array(fps) + + recalls = tps / (tps + fns) + precisions = tps / (tps + fps) + for i in range(len(score_thresholds)): + precisions[i] = np.max(precisions[i:]) + + sums_AP = 0 + for i in range(0, len(score_thresholds), 4): + sums_AP += precisions[i] + mAP = sums_AP / 11 * 100 + eval_ap_results[cls].append(mAP) + + if eval_type == 'bbox_2d': + total_aos = np.array(total_aos) + similarity = total_aos / (tps + fps) + for i in range(len(score_thresholds)): + similarity[i] = np.max(similarity[i:]) + sums_similarity = 0 + for i in range(0, len(score_thresholds), 4): + sums_similarity += similarity[i] + mSimilarity = sums_similarity / 11 * 100 + eval_aos_results[cls].append(mSimilarity) + + print(f'=========={eval_type.upper()}==========') + print(f'=========={eval_type.upper()}==========', file=f) + for k, v in eval_ap_results.items(): + print(f'{k} AP@{MIN_IOUS[k][e_ind]}: {v[0]:.4f} {v[1]:.4f} {v[2]:.4f}') + print(f'{k} AP@{MIN_IOUS[k][e_ind]}: {v[0]:.4f} {v[1]:.4f} {v[2]:.4f}', file=f) + if eval_type == 'bbox_2d': + print(f'==========AOS==========') + print(f'==========AOS==========', file=f) + for k, v in eval_aos_results.items(): + print(f'{k} AOS@{MIN_IOUS[k][e_ind]}: {v[0]:.4f} {v[1]:.4f} {v[2]:.4f}') + print(f'{k} AOS@{MIN_IOUS[k][e_ind]}: {v[0]:.4f} {v[1]:.4f} {v[2]:.4f}', file=f) + + overall_results[eval_type] = np.mean(list(eval_ap_results.values()), 0) + if eval_type == 'bbox_2d': + overall_results['AOS'] = np.mean(list(eval_aos_results.values()), 0) + + print(f'\n==========Overall==========') + print(f'\n==========Overall==========', file=f) + for k, v in overall_results.items(): + print(f'{k} AP: {v[0]:.4f} {v[1]:.4f} {v[2]:.4f}') + print(f'{k} AP: {v[0]:.4f} {v[1]:.4f} {v[2]:.4f}', file=f) + f.close() + + +def main(args): + val_dataset = Kitti(data_root=args.data_root, + split='val') + val_dataloader = get_dataloader(dataset=val_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + CLASSES = Kitti.CLASSES + LABEL2CLASSES = {v:k for k, v in CLASSES.items()} + + if not args.no_cuda: + model = PointPillars(nclasses=args.nclasses).cuda() + model.load_state_dict(torch.load(args.ckpt)) + else: + model = PointPillars(nclasses=args.nclasses) + model.load_state_dict( + torch.load(args.ckpt, map_location=torch.device('cpu'))) + + saved_path = args.saved_path + os.makedirs(saved_path, exist_ok=True) + saved_submit_path = os.path.join(saved_path, 'submit') + os.makedirs(saved_submit_path, exist_ok=True) + + pcd_limit_range = np.array([0, -40, -3, 70.4, 40, 0.0], dtype=np.float32) + + model.eval() + with torch.no_grad(): + format_results = {} + print('Predicting and Formatting the results.') + for i, data_dict in enumerate(tqdm(val_dataloader)): + if not args.no_cuda: + # move the tensors to the cuda + for key in data_dict: + for j, item in enumerate(data_dict[key]): + if torch.is_tensor(item): + data_dict[key][j] = data_dict[key][j].cuda() + + batched_pts = data_dict['batched_pts'] + batched_gt_bboxes = data_dict['batched_gt_bboxes'] + batched_labels = data_dict['batched_labels'] + batched_difficulty = data_dict['batched_difficulty'] + batch_results = model(batched_pts=batched_pts, + mode='val', + batched_gt_bboxes=batched_gt_bboxes, + batched_gt_labels=batched_labels) + # pdb.set_trace() + for j, result in enumerate(batch_results): + format_result = { + 'name': [], + 'truncated': [], + 'occluded': [], + 'alpha': [], + 'bbox': [], + 'dimensions': [], + 'location': [], + 'rotation_y': [], + 'score': [] + } + + calib_info = data_dict['batched_calib_info'][j] + tr_velo_to_cam = calib_info['Tr_velo_to_cam'].astype(np.float32) + r0_rect = calib_info['R0_rect'].astype(np.float32) + P2 = calib_info['P2'].astype(np.float32) + image_shape = data_dict['batched_img_info'][j]['image_shape'] + idx = data_dict['batched_img_info'][j]['image_idx'] + result_filter = keep_bbox_from_image_range(result, tr_velo_to_cam, r0_rect, P2, image_shape) + result_filter = keep_bbox_from_lidar_range(result_filter, pcd_limit_range) + + lidar_bboxes = result_filter['lidar_bboxes'] + labels, scores = result_filter['labels'], result_filter['scores'] + bboxes2d, camera_bboxes = result_filter['bboxes2d'], result_filter['camera_bboxes'] + for lidar_bbox, label, score, bbox2d, camera_bbox in \ + zip(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes): + format_result['name'].append(LABEL2CLASSES[label]) + format_result['truncated'].append(0.0) + format_result['occluded'].append(0) + alpha = camera_bbox[6] - np.arctan2(camera_bbox[0], camera_bbox[2]) + format_result['alpha'].append(alpha) + format_result['bbox'].append(bbox2d) + format_result['dimensions'].append(camera_bbox[3:6]) + format_result['location'].append(camera_bbox[:3]) + format_result['rotation_y'].append(camera_bbox[6]) + format_result['score'].append(score) + + write_label(format_result, os.path.join(saved_submit_path, f'{idx:06d}.txt')) + + format_results[idx] = {k:np.array(v) for k, v in format_result.items()} + + write_pickle(format_results, os.path.join(saved_path, 'results.pkl')) + + print('Evaluating.. Please wait several seconds.') + do_eval(format_results, val_dataset.data_infos, CLASSES, saved_path) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Configuration Parameters') + parser.add_argument('--data_root', default='/mnt/ssd1/lifa_rdata/det/kitti', + help='your data root for kitti') + parser.add_argument('--ckpt', default='pretrained/epoch_160.pth', help='your checkpoint for kitti') + parser.add_argument('--saved_path', default='results', help='your saved path for predicted results') + parser.add_argument('--batch_size', type=int, default=1) + parser.add_argument('--num_workers', type=int, default=4) + parser.add_argument('--nclasses', type=int, default=3) + parser.add_argument('--no_cuda', action='store_true', + help='whether to use cuda') + args = parser.parse_args() + + main(args) diff --git a/cv/3d_detection/pointpillars/pytorch/figures/img_3dbbox_000134.png b/cv/3d_detection/pointpillars/pytorch/figures/img_3dbbox_000134.png new file mode 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+from .loss import Loss \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/loss/loss.py b/cv/3d_detection/pointpillars/pytorch/loss/loss.py new file mode 100755 index 000000000..016e0460a --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/loss/loss.py @@ -0,0 +1,66 @@ +import pdb +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Loss(nn.Module): + def __init__(self, alpha=0.25, gamma=2.0, beta=1/9, cls_w=1.0, reg_w=2.0, dir_w=0.2): + super().__init__() + self.alpha = 0.25 + self.gamma = 2.0 + self.cls_w = cls_w + self.reg_w = reg_w + self.dir_w = dir_w + self.smooth_l1_loss = nn.SmoothL1Loss(reduction='none', + beta=beta) + self.dir_cls = nn.CrossEntropyLoss() + + def forward(self, + bbox_cls_pred, + bbox_pred, + bbox_dir_cls_pred, + batched_labels, + num_cls_pos, + batched_bbox_reg, + batched_dir_labels): + ''' + bbox_cls_pred: (n, 3) + bbox_pred: (n, 7) + bbox_dir_cls_pred: (n, 2) + batched_labels: (n, ) + num_cls_pos: int + batched_bbox_reg: (n, 7) + batched_dir_labels: (n, ) + return: loss, float. + ''' + # 1. bbox cls loss + # focal loss: FL = - \alpha_t (1 - p_t)^\gamma * log(p_t) + # y == 1 -> p_t = p + # y == 0 -> p_t = 1 - p + nclasses = bbox_cls_pred.size(1) + batched_labels = F.one_hot(batched_labels, nclasses + 1)[:, :nclasses].float() # (n, 3) + + bbox_cls_pred_sigmoid = torch.sigmoid(bbox_cls_pred) + weights = self.alpha * (1 - bbox_cls_pred_sigmoid).pow(self.gamma) * batched_labels + \ + (1 - self.alpha) * bbox_cls_pred_sigmoid.pow(self.gamma) * (1 - batched_labels) # (n, 3) + cls_loss = F.binary_cross_entropy(bbox_cls_pred_sigmoid, batched_labels, reduction='none') + cls_loss = cls_loss * weights + cls_loss = cls_loss.sum() / num_cls_pos + + # 2. regression loss + reg_loss = self.smooth_l1_loss(bbox_pred, batched_bbox_reg) + reg_loss = reg_loss.sum() / reg_loss.size(0) + + # 3. direction cls loss + dir_cls_loss = self.dir_cls(bbox_dir_cls_pred, batched_dir_labels) + + # 4. total loss + total_loss = self.cls_w * cls_loss + self.reg_w * reg_loss + self.dir_w * dir_cls_loss + + loss_dict={'cls_loss': cls_loss, + 'reg_loss': reg_loss, + 'dir_cls_loss': dir_cls_loss, + 'total_loss': total_loss} + return loss_dict + \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/misc/log.md b/cv/3d_detection/pointpillars/pytorch/misc/log.md new file mode 100755 index 000000000..280d0445b --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/misc/log.md @@ -0,0 +1,103 @@ +- 难点: 碰撞检测 +- 代码难点: nms + +## Datasets + +- database 随机采样增强 + - 核心: 碰撞检测, 点是否在立方体内 + - 输入: gt_bboxes_3d, pts, gt_labels + - 输出: gt_bboxes_3d, pts, gt_labels + - 逻辑: + 1. 从Car, Pedestrian, Cyclist的database数据集中随机采集一定数量的bbox及inside points, 使每类bboxes的数量分别达到15, 10, 10. + 2. 将这些采样的bboxes进行碰撞检测, 满足碰撞检测的bboxes和对应labels加到gt_bboxes_3d, gt_labels + 3. 把位于这些采样bboxes内点删除掉, 替换成bboxes内部的点. +- object 随机旋转平移 + - 核心: 碰撞检测, 点是否在立方体内 + - 输入: gt_bboxes_3d, pts + - 输出: gt_bboxes_3d, pts + - 逻辑: + 1. 以某个bbox为例, 随机产生num_try个平移向量t和旋转角度r, 旋转角度可以转成旋转矩阵(mat). + 2. 对bbox进行旋转和平移, 找到num_try中第一个通过碰撞测试的平移向量t和旋转角度r(mat). + 3. 对bbox内部的点进行旋转和平移. + 4. 对bbox进行旋转和平移. +- 整体随机水平翻转 + - object: 水平翻转 (注意角度) + - point: 水平翻转 +- 整体旋转, 缩放和平移 + - object: 旋转, 缩放和平移, object的旋转需要注意, (x, y, z)和angle都需要变化. + - point: 旋转, 缩放和平移 +- 删除范围外的point +- 删除范围外的bbox + - 需要注意的是: 这里做了旋转角度的归一化 (-pi, pi) +- point shuffle +- dataloader实现, `目前为了调试shuffle设置成了False, 之后需要设置成True` + +## Model + +- Pillar + - cuda拓展 + ``` + cd ops + python setup.py develop + ``` + - Voxelization类实现, 这里基本是从mmdet3d复制过来的; 修改了`coors_out`的顺序 (z, y, x) -> (x, y, z) + - PillarLayer: 整合batch数据整pillars + - PillarEncoder: pillar 编码((N, 4) -> (N, 9) -> (1, 64)) + - PillarScatter +- Backbone + - 完成 + - nn.init.kaiming_normal_初始化方式 (`删除cuda seed`) +- Neck + - 完成 + - nn.init.kaiming_normal_初始化方式 (`删除cuda seed`) +- Head + - 完成 + - nn.init.normal_(m.weight, mean=0, std=0.01)初始化方式 (`删除cuda seed`) + +## Bbox (bottom center for z) + +- Anchor + - 生成完成 +- 编码, 解码完成 +- iou3d (完成) + - bev overlap + - height overlap + - iou3d +- anchor生成label + +## Train + +- loss + - cls loss + - pos: 与0, 1, 2类bboxes具有很大iou(大于某个阈值)的anchors; 与0, 1, 2类具有最大iou的anchors. + - neg: 与任何bboxes最有非常小iou(小于某个阈值)的anchors; 与-1类bboxes具有很大iou或者最大iou的anchors. + - reg loss + - 训练样本: 与0, 1, 2类bboxes具有很大iou(大于某个阈值)的anchors; 与0, 1, 2类具有最大iou的anchors. + - dir cls loss + - 训练样本: 与0, 1, 2类bboxes具有很大iou(大于某个阈值)的anchors; 与0, 1, 2类具有最大iou的anchors. +- 优化器 +- 开始训练 + +## Test and evaluate + +- 预测bbox +- 可视化 +- 评估 + - label_2的格式, mmdet3d .pkl的格式 + - iou 计算(bbox, bev, 3dbbox) + - gt_bbox, dt_bbox分类 (ignore, normal, remove) + - 基于tp, 计算score threshold + - 计算precision, recall + - 计算AP, mAP +- 性能优化 +- 代码优化 (不急) + +## bbox property evaluation + +以 `label='Pedestrian', difficulty=1`为例, + +| ignore level | gt | det | +|:---:|:---:|:---:| +| 1 | (cur_label == 'Pedestrian' && cur_difficulty > 1) or cur_label == 'Person_sitting' | cur_heigh < MIN_HEIGHTS[1] | +| 0 | cur_label == 'Pedestrian' && cur_difficulty <= 1 | cur_label == 'Pedestrian' | +| - 1 | Others | Others | diff --git a/cv/3d_detection/pointpillars/pytorch/misc/vis_data_gt.py b/cv/3d_detection/pointpillars/pytorch/misc/vis_data_gt.py new file mode 100755 index 000000000..741a010eb --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/misc/vis_data_gt.py @@ -0,0 +1,84 @@ +import argparse +from ast import arg +import cv2 +import copy +import numpy as np +import os +import sys + +from yaml import parse +CUR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(os.path.dirname(CUR)) +from utils import read_points, read_calib, read_label, bbox_camera2lidar, vis_pc, bbox3d2corners,\ + points_lidar2image, vis_img_3d + + +def vis_gt(root, id, saved_root): + img_path = os.path.join(root, 'image_2', f'{id}.png') + lidar_path = os.path.join(root, 'velodyne', f'{id}.bin') + calib_path = os.path.join(root, 'calib', f'{id}.txt') + label_path = os.path.join(root, 'label_2', f'{id}.txt') + + img = cv2.imread(img_path) + img3d = copy.deepcopy(img) + lidar_points = read_points(lidar_path) + calib_dict = read_calib(calib_path) + annotation_dict = read_label(label_path) + + bboxes = annotation_dict['bbox'] + names = annotation_dict['name'] + colors = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 255, 0]] + CLASSES = { + 'Pedestrian': 0, + 'Cyclist': 1, + 'Car': 2 + } + + + ## 1. visualize 2d + for i, bbox in enumerate(bboxes): + x1, y1, x2, y2 = bbox + x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) + cv2.rectangle(img, (x1, y1), (x2, y2), colors[CLASSES.get(names[i], -1)], 2) + cv2.imwrite(os.path.join(saved_root, f'{id}-2d.png'), img) + cv2.imshow(f'{id}-2d bbox', img) + cv2.waitKey(0) + + ## 2. visualize 3d bbox in point cloud + + # 2.1 camera coordinates + dimensions = annotation_dict['dimensions'] + location = annotation_dict['location'] + rotation_y = annotation_dict['rotation_y'] + names = annotation_dict['name'] + + # 2.2 lidar coordinates + bboxes_camera = np.concatenate([location, dimensions, rotation_y[:, None]], axis=-1) + tr_velo_to_cam = calib_dict['Tr_velo_to_cam'] + r0_rect = calib_dict['R0_rect'] + bboxes_lidar = bbox_camera2lidar(bboxes_camera, tr_velo_to_cam, r0_rect) + lidar_bboxes_points = bbox3d2corners(bboxes_lidar) # (N, 8, 3) + labels = [CLASSES.get(name, -1) for name in names] + vis_pc(lidar_points, lidar_bboxes_points, labels) # (N, 8, 2) + + ## 3. visualize 3d bbox in image + P2 = calib_dict['P2'] + image_points = points_lidar2image(lidar_bboxes_points, tr_velo_to_cam, r0_rect, P2) + img3d = vis_img_3d(img3d, image_points, labels) + cv2.imwrite(os.path.join(saved_root, f'{id}-3d.png'), img3d) + cv2.imshow(f'{id}-3d bbox', img3d) + cv2.waitKey(0) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Data information') + parser.add_argument('--root', default='/home/lifa/data/KITTI/training') + parser.add_argument('--id', default='000134') + parser.add_argument('--saved_root', default='tmp') + args = parser.parse_args() + + root = args.root + id = args.id + saved_root = args.saved_root + os.makedirs(saved_root, exist_ok=True) + vis_gt(root, id, saved_root) diff --git a/cv/3d_detection/pointpillars/pytorch/model/__init__.py b/cv/3d_detection/pointpillars/pytorch/model/__init__.py new file mode 100755 index 000000000..340ddce01 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/model/__init__.py @@ -0,0 +1,2 @@ +from .anchors import Anchors, anchors2bboxes, bboxes2deltas +from .pointpillars import PointPillars diff --git a/cv/3d_detection/pointpillars/pytorch/model/anchors.py b/cv/3d_detection/pointpillars/pytorch/model/anchors.py new file mode 100755 index 000000000..23a48534d --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/model/anchors.py @@ -0,0 +1,231 @@ +import pdb +import numpy as np +import torch +from utils import limit_period, iou2d_nearest + + +class Anchors(): + def __init__(self, ranges, sizes, rotations): + assert len(ranges) == len(sizes) + self.ranges = ranges + self.sizes = sizes + self.rotations = rotations + + def get_anchors(self, feature_map_size, anchor_range, anchor_size, rotations): + ''' + feature_map_size: (y_l, x_l) + anchor_range: [x1, y1, z1, x2, y2, z2] + anchor_size: [w, l, h] + rotations: [0, 1.57] + return: shape=(y_l, x_l, 2, 7) + ''' + device = feature_map_size.device + x_centers = torch.linspace(anchor_range[0], anchor_range[3], feature_map_size[1] + 1, device=device) + y_centers = torch.linspace(anchor_range[1], anchor_range[4], feature_map_size[0] + 1, device=device) + z_centers = torch.linspace(anchor_range[2], anchor_range[5], 1 + 1, device=device) + + x_shift = (x_centers[1] - x_centers[0]) / 2 + y_shift = (y_centers[1] - y_centers[0]) / 2 + z_shift = (z_centers[1] - z_centers[0]) / 2 + x_centers = x_centers[:feature_map_size[1]] + x_shift # (feature_map_size[1], ) + y_centers = y_centers[:feature_map_size[0]] + y_shift # (feature_map_size[0], ) + z_centers = z_centers[:1] + z_shift # (1, ) + + # [feature_map_size[1], feature_map_size[0], 1, 2] * 4 + meshgrids = torch.meshgrid(x_centers, y_centers, z_centers, rotations) + meshgrids = list(meshgrids) + for i in range(len(meshgrids)): + meshgrids[i] = meshgrids[i][..., None] # [feature_map_size[1], feature_map_size[0], 1, 2, 1] + + anchor_size = anchor_size[None, None, None, None, :] + repeat_shape = [feature_map_size[1], feature_map_size[0], 1, len(rotations), 1] + anchor_size = anchor_size.repeat(repeat_shape) # [feature_map_size[1], feature_map_size[0], 1, 2, 3] + meshgrids.insert(3, anchor_size) + anchors = torch.cat(meshgrids, dim=-1).permute(2, 1, 0, 3, 4).contiguous() # [1, feature_map_size[0], feature_map_size[1], 2, 7] + return anchors.squeeze(0) + + + def get_multi_anchors(self, feature_map_size): + ''' + feature_map_size: (y_l, x_l) + ranges: [[x1, y1, z1, x2, y2, z2], [x1, y1, z1, x2, y2, z2], [x1, y1, z1, x2, y2, z2]] + sizes: [[w, l, h], [w, l, h], [w, l, h]] + rotations: [0, 1.57] + return: shape=(y_l, x_l, 3, 2, 7) + ''' + device = feature_map_size.device + ranges = torch.tensor(self.ranges, device=device) + sizes = torch.tensor(self.sizes, device=device) + rotations = torch.tensor(self.rotations, device=device) + multi_anchors = [] + for i in range(len(ranges)): + anchors = self.get_anchors(feature_map_size=feature_map_size, + anchor_range=ranges[i], + anchor_size=sizes[i], + rotations=rotations) + multi_anchors.append(anchors[:, :, None, :, :]) + multi_anchors = torch.cat(multi_anchors, dim=2) + + return multi_anchors + + +def anchors2bboxes(anchors, deltas): + ''' + anchors: (M, 7), (x, y, z, w, l, h, theta) + deltas: (M, 7) + return: (M, 7) + ''' + da = torch.sqrt(anchors[:, 3] ** 2 + anchors[:, 4] ** 2) + x = deltas[:, 0] * da + anchors[:, 0] + y = deltas[:, 1] * da + anchors[:, 1] + z = deltas[:, 2] * anchors[:, 5] + anchors[:, 2] + anchors[:, 5] / 2 + + w = anchors[:, 3] * torch.exp(deltas[:, 3]) + l = anchors[:, 4] * torch.exp(deltas[:, 4]) + h = anchors[:, 5] * torch.exp(deltas[:, 5]) + + z = z - h / 2 + + theta = anchors[:, 6] + deltas[:, 6] + + bboxes = torch.stack([x, y, z, w, l, h, theta], dim=1) + return bboxes + + +def bboxes2deltas(bboxes, anchors): + ''' + bboxes: (M, 7), (x, y, z, w, l, h, theta) + anchors: (M, 7) + return: (M, 7) + ''' + da = torch.sqrt(anchors[:, 3] ** 2 + anchors[:, 4] ** 2) + + dx = (bboxes[:, 0] - anchors[:, 0]) / da + dy = (bboxes[:, 1] - anchors[:, 1]) / da + + zb = bboxes[:, 2] + bboxes[:, 5] / 2 # bottom center + za = anchors[:, 2] + anchors[:, 5] / 2 # bottom center + dz = (zb - za) / anchors[:, 5] # bottom center + + dw = torch.log(bboxes[:, 3] / anchors[:, 3]) + dl = torch.log(bboxes[:, 4] / anchors[:, 4]) + dh = torch.log(bboxes[:, 5] / anchors[:, 5]) + dtheta = bboxes[:, 6] - anchors[:, 6] + + deltas = torch.stack([dx, dy, dz, dw, dl, dh, dtheta], dim=1) + return deltas + + +def anchor_target(batched_anchors, batched_gt_bboxes, batched_gt_labels, assigners, nclasses): + ''' + batched_anchors: [(y_l, x_l, 3, 2, 7), (y_l, x_l, 3, 2, 7), ... ] + batched_gt_bboxes: [(n1, 7), (n2, 7), ...] + batched_gt_labels: [(n1, ), (n2, ), ...] + return: + dict = {batched_anchors_labels: (bs, n_anchors), + batched_labels_weights: (bs, n_anchors), + batched_anchors_reg: (bs, n_anchors, 7), + batched_reg_weights: (bs, n_anchors), + batched_anchors_dir: (bs, n_anchors), + batched_dir_weights: (bs, n_anchors)} + ''' + assert len(batched_anchors) == len(batched_gt_bboxes) == len(batched_gt_labels) + batch_size = len(batched_anchors) + n_assigners = len(assigners) + batched_labels, batched_label_weights = [], [] + batched_bbox_reg, batched_bbox_reg_weights = [], [] + batched_dir_labels, batched_dir_labels_weights = [], [] + for i in range(batch_size): + anchors = batched_anchors[i] + gt_bboxes, gt_labels = batched_gt_bboxes[i], batched_gt_labels[i] + # what we want to get next ? + # 1. identify positive anchors and negative anchors -> cls + # 2. identify the regresstion values -> reg + # 3. indentify the direction -> dir_cls + multi_labels, multi_label_weights = [], [] + multi_bbox_reg, multi_bbox_reg_weights = [], [] + multi_dir_labels, multi_dir_labels_weights = [], [] + d1, d2, d3, d4, d5 = anchors.size() + for j in range(n_assigners): # multi anchors + assigner = assigners[j] + pos_iou_thr, neg_iou_thr, min_iou_thr = \ + assigner['pos_iou_thr'], assigner['neg_iou_thr'], assigner['min_iou_thr'] + cur_anchors = anchors[:, :, j, :, :].reshape(-1, 7) + overlaps = iou2d_nearest(gt_bboxes, cur_anchors) + max_overlaps, max_overlaps_idx = torch.max(overlaps, dim=0) + gt_max_overlaps, _ = torch.max(overlaps, dim=1) + + assigned_gt_inds = -torch.ones_like(cur_anchors[:, 0], dtype=torch.long) + # a. negative anchors + assigned_gt_inds[max_overlaps < neg_iou_thr] = 0 + + # b. positive anchors + # rule 1 + assigned_gt_inds[max_overlaps >= pos_iou_thr] = max_overlaps_idx[max_overlaps >= pos_iou_thr] + 1 + + # rule 2 + # support one bbox to multi anchors, only if the anchors are with the highest iou. + # rule2 may modify the labels generated by rule 1 + for i in range(len(gt_bboxes)): + if gt_max_overlaps[i] >= min_iou_thr: + assigned_gt_inds[overlaps[i] == gt_max_overlaps[i]] = i + 1 + + pos_flag = assigned_gt_inds > 0 + neg_flag = assigned_gt_inds == 0 + # 1. anchor labels + assigned_gt_labels = torch.zeros_like(cur_anchors[:, 0], dtype=torch.long) + nclasses # -1 is not optimal, for some bboxes are with labels -1 + assigned_gt_labels[pos_flag] = gt_labels[assigned_gt_inds[pos_flag] - 1].long() + assigned_gt_labels_weights = torch.zeros_like(cur_anchors[:, 0]) + assigned_gt_labels_weights[pos_flag] = 1 + assigned_gt_labels_weights[neg_flag] = 1 + + # 2. anchor regression + assigned_gt_reg_weights = torch.zeros_like(cur_anchors[:, 0]) + assigned_gt_reg_weights[pos_flag] = 1 + + assigned_gt_reg = torch.zeros_like(cur_anchors) + positive_anchors = cur_anchors[pos_flag] + corr_gt_bboxes = gt_bboxes[assigned_gt_inds[pos_flag] - 1] + assigned_gt_reg[pos_flag] = bboxes2deltas(corr_gt_bboxes, positive_anchors) + + # 3. anchor direction + assigned_gt_dir_weights = torch.zeros_like(cur_anchors[:, 0]) + assigned_gt_dir_weights[pos_flag] = 1 + + assigned_gt_dir = torch.zeros_like(cur_anchors[:, 0], dtype=torch.long) + dir_cls_targets = limit_period(corr_gt_bboxes[:, 6].cpu(), 0, 2 * np.pi).to(corr_gt_bboxes) + dir_cls_targets = torch.floor(dir_cls_targets / np.pi).long() + assigned_gt_dir[pos_flag] = torch.clamp(dir_cls_targets, min=0, max=1) + + multi_labels.append(assigned_gt_labels.reshape(d1, d2, 1, d4)) + multi_label_weights.append(assigned_gt_labels_weights.reshape(d1, d2, 1, d4)) + multi_bbox_reg.append(assigned_gt_reg.reshape(d1, d2, 1, d4, -1)) + multi_bbox_reg_weights.append(assigned_gt_reg_weights.reshape(d1, d2, 1, d4)) + multi_dir_labels.append(assigned_gt_dir.reshape(d1, d2, 1, d4)) + multi_dir_labels_weights.append(assigned_gt_dir_weights.reshape(d1, d2, 1, d4)) + + multi_labels = torch.cat(multi_labels, dim=-2).reshape(-1) + multi_label_weights = torch.cat(multi_label_weights, dim=-2).reshape(-1) + multi_bbox_reg = torch.cat(multi_bbox_reg, dim=-3).reshape(-1, d5) + multi_bbox_reg_weights = torch.cat(multi_bbox_reg_weights, dim=-2).reshape(-1) + multi_dir_labels = torch.cat(multi_dir_labels, dim=-2).reshape(-1) + multi_dir_labels_weights = torch.cat(multi_dir_labels_weights, dim=-2).reshape(-1) + + batched_labels.append(multi_labels) + batched_label_weights.append(multi_label_weights) + batched_bbox_reg.append(multi_bbox_reg) + batched_bbox_reg_weights.append(multi_bbox_reg_weights) + batched_dir_labels.append(multi_dir_labels) + batched_dir_labels_weights.append(multi_dir_labels_weights) + + rt_dict = dict( + batched_labels=torch.stack(batched_labels, 0), # (bs, y_l * x_l * 3 * 2) + batched_label_weights=torch.stack(batched_label_weights, 0), # (bs, y_l * x_l * 3 * 2) + batched_bbox_reg=torch.stack(batched_bbox_reg, 0), # (bs, y_l * x_l * 3 * 2, 7) + batched_bbox_reg_weights=torch.stack(batched_bbox_reg_weights, 0), # (bs, y_l * x_l * 3 * 2) + batched_dir_labels=torch.stack(batched_dir_labels, 0), # (bs, y_l * x_l * 3 * 2) + batched_dir_labels_weights=torch.stack(batched_dir_labels_weights, 0) # (bs, y_l * x_l * 3 * 2) + ) + + return rt_dict + \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/model/pointpillars.py b/cv/3d_detection/pointpillars/pytorch/model/pointpillars.py new file mode 100755 index 000000000..e2d8ccf74 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/model/pointpillars.py @@ -0,0 +1,427 @@ +import numpy as np +import pdb +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.anchors import Anchors, anchor_target, anchors2bboxes +from ops import Voxelization, nms_cuda +from utils import limit_period + + +class PillarLayer(nn.Module): + def __init__(self, voxel_size, point_cloud_range, max_num_points, max_voxels): + super().__init__() + self.voxel_layer = Voxelization(voxel_size=voxel_size, + point_cloud_range=point_cloud_range, + max_num_points=max_num_points, + max_voxels=max_voxels) + + @torch.no_grad() + def forward(self, batched_pts): + ''' + batched_pts: list[tensor], len(batched_pts) = bs + return: + pillars: (p1 + p2 + ... + pb, num_points, c), + coors_batch: (p1 + p2 + ... + pb, 1 + 3), + num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size) + ''' + pillars, coors, npoints_per_pillar = [], [], [] + for i, pts in enumerate(batched_pts): + voxels_out, coors_out, num_points_per_voxel_out = self.voxel_layer(pts) + # voxels_out: (max_voxel, num_points, c), coors_out: (max_voxel, 3) + # num_points_per_voxel_out: (max_voxel, ) + pillars.append(voxels_out) + coors.append(coors_out.long()) + npoints_per_pillar.append(num_points_per_voxel_out) + + pillars = torch.cat(pillars, dim=0) # (p1 + p2 + ... + pb, num_points, c) + npoints_per_pillar = torch.cat(npoints_per_pillar, dim=0) # (p1 + p2 + ... + pb, ) + coors_batch = [] + for i, cur_coors in enumerate(coors): + coors_batch.append(F.pad(cur_coors, (1, 0), value=i)) + coors_batch = torch.cat(coors_batch, dim=0) # (p1 + p2 + ... + pb, 1 + 3) + + return pillars, coors_batch, npoints_per_pillar + + +class PillarEncoder(nn.Module): + def __init__(self, voxel_size, point_cloud_range, in_channel, out_channel): + super().__init__() + self.out_channel = out_channel + self.vx, self.vy = voxel_size[0], voxel_size[1] + self.x_offset = voxel_size[0] / 2 + point_cloud_range[0] + self.y_offset = voxel_size[1] / 2 + point_cloud_range[1] + self.x_l = int((point_cloud_range[3] - point_cloud_range[0]) / voxel_size[0]) + self.y_l = int((point_cloud_range[4] - point_cloud_range[1]) / voxel_size[1]) + + self.conv = nn.Conv1d(in_channel, out_channel, 1, bias=False) + self.bn = nn.BatchNorm1d(out_channel, eps=1e-3, momentum=0.01) + + def forward(self, pillars, coors_batch, npoints_per_pillar): + ''' + pillars: (p1 + p2 + ... + pb, num_points, c), c = 4 + coors_batch: (p1 + p2 + ... + pb, 1 + 3) + npoints_per_pillar: (p1 + p2 + ... + pb, ) + return: (bs, out_channel, y_l, x_l) + ''' + device = pillars.device + # 1. calculate offset to the points center (in each pillar) + offset_pt_center = pillars[:, :, :3] - torch.sum(pillars[:, :, :3], dim=1, keepdim=True) / npoints_per_pillar[:, None, None] # (p1 + p2 + ... + pb, num_points, 3) + + # 2. calculate offset to the pillar center + x_offset_pi_center = pillars[:, :, :1] - (coors_batch[:, None, 1:2] * self.vx + self.x_offset) # (p1 + p2 + ... + pb, num_points, 1) + y_offset_pi_center = pillars[:, :, 1:2] - (coors_batch[:, None, 2:3] * self.vy + self.y_offset) # (p1 + p2 + ... + pb, num_points, 1) + + # 3. encoder + features = torch.cat([pillars, offset_pt_center, x_offset_pi_center, y_offset_pi_center], dim=-1) # (p1 + p2 + ... + pb, num_points, 9) + features[:, :, 0:1] = x_offset_pi_center # tmp + features[:, :, 1:2] = y_offset_pi_center # tmp + # In consitent with mmdet3d. + # The reason can be referenced to https://github.com/open-mmlab/mmdetection3d/issues/1150 + + # 4. find mask for (0, 0, 0) and update the encoded features + # a very beautiful implementation + voxel_ids = torch.arange(0, pillars.size(1)).to(device) # (num_points, ) + mask = voxel_ids[:, None] < npoints_per_pillar[None, :] # (num_points, p1 + p2 + ... + pb) + mask = mask.permute(1, 0).contiguous() # (p1 + p2 + ... + pb, num_points) + features *= mask[:, :, None] + + # 5. embedding + features = features.permute(0, 2, 1).contiguous() # (p1 + p2 + ... + pb, 9, num_points) + features = F.relu(self.bn(self.conv(features))) # (p1 + p2 + ... + pb, out_channels, num_points) + pooling_features = torch.max(features, dim=-1)[0] # (p1 + p2 + ... + pb, out_channels) + + # 6. pillar scatter + batched_canvas = [] + bs = coors_batch[-1, 0] + 1 + for i in range(bs): + cur_coors_idx = coors_batch[:, 0] == i + cur_coors = coors_batch[cur_coors_idx, :] + cur_features = pooling_features[cur_coors_idx] + + canvas = torch.zeros((self.x_l, self.y_l, self.out_channel), dtype=torch.float32, device=device) + canvas[cur_coors[:, 1], cur_coors[:, 2]] = cur_features + canvas = canvas.permute(2, 1, 0).contiguous() + batched_canvas.append(canvas) + batched_canvas = torch.stack(batched_canvas, dim=0) # (bs, in_channel, self.y_l, self.x_l) + return batched_canvas + + +class Backbone(nn.Module): + def __init__(self, in_channel, out_channels, layer_nums, layer_strides=[2, 2, 2]): + super().__init__() + assert len(out_channels) == len(layer_nums) + assert len(out_channels) == len(layer_strides) + + self.multi_blocks = nn.ModuleList() + for i in range(len(layer_strides)): + blocks = [] + blocks.append(nn.Conv2d(in_channel, out_channels[i], 3, stride=layer_strides[i], bias=False, padding=1)) + blocks.append(nn.BatchNorm2d(out_channels[i], eps=1e-3, momentum=0.01)) + blocks.append(nn.ReLU(inplace=True)) + + for _ in range(layer_nums[i]): + blocks.append(nn.Conv2d(out_channels[i], out_channels[i], 3, bias=False, padding=1)) + blocks.append(nn.BatchNorm2d(out_channels[i], eps=1e-3, momentum=0.01)) + blocks.append(nn.ReLU(inplace=True)) + + in_channel = out_channels[i] + self.multi_blocks.append(nn.Sequential(*blocks)) + + # in consitent with mmdet3d + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + + def forward(self, x): + ''' + x: (b, c, y_l, x_l). Default: (6, 64, 496, 432) + return: list[]. Default: [(6, 64, 248, 216), (6, 128, 124, 108), (6, 256, 62, 54)] + ''' + outs = [] + for i in range(len(self.multi_blocks)): + x = self.multi_blocks[i](x) + outs.append(x) + return outs + + +class Neck(nn.Module): + def __init__(self, in_channels, upsample_strides, out_channels): + super().__init__() + assert len(in_channels) == len(upsample_strides) + assert len(upsample_strides) == len(out_channels) + + self.decoder_blocks = nn.ModuleList() + for i in range(len(in_channels)): + decoder_block = [] + decoder_block.append(nn.ConvTranspose2d(in_channels[i], + out_channels[i], + upsample_strides[i], + stride=upsample_strides[i], + bias=False)) + decoder_block.append(nn.BatchNorm2d(out_channels[i], eps=1e-3, momentum=0.01)) + decoder_block.append(nn.ReLU(inplace=True)) + + self.decoder_blocks.append(nn.Sequential(*decoder_block)) + + # in consitent with mmdet3d + for m in self.modules(): + if isinstance(m, nn.ConvTranspose2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + + def forward(self, x): + ''' + x: [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)] + return: (bs, 384, 248, 216) + ''' + outs = [] + for i in range(len(self.decoder_blocks)): + xi = self.decoder_blocks[i](x[i]) # (bs, 128, 248, 216) + outs.append(xi) + out = torch.cat(outs, dim=1) + return out + + +class Head(nn.Module): + def __init__(self, in_channel, n_anchors, n_classes): + super().__init__() + + self.conv_cls = nn.Conv2d(in_channel, n_anchors*n_classes, 1) + self.conv_reg = nn.Conv2d(in_channel, n_anchors*7, 1) + self.conv_dir_cls = nn.Conv2d(in_channel, n_anchors*2, 1) + + # in consitent with mmdet3d + conv_layer_id = 0 + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight, mean=0, std=0.01) + if conv_layer_id == 0: + prior_prob = 0.01 + bias_init = float(-np.log((1 - prior_prob) / prior_prob)) + nn.init.constant_(m.bias, bias_init) + else: + nn.init.constant_(m.bias, 0) + conv_layer_id += 1 + + def forward(self, x): + ''' + x: (bs, 384, 248, 216) + return: + bbox_cls_pred: (bs, n_anchors*3, 248, 216) + bbox_pred: (bs, n_anchors*7, 248, 216) + bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216) + ''' + bbox_cls_pred = self.conv_cls(x) + bbox_pred = self.conv_reg(x) + bbox_dir_cls_pred = self.conv_dir_cls(x) + return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred + + +class PointPillars(nn.Module): + def __init__(self, + nclasses=3, + voxel_size=[0.16, 0.16, 4], + point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1], + max_num_points=32, + max_voxels=(16000, 40000)): + super().__init__() + self.nclasses = nclasses + self.pillar_layer = PillarLayer(voxel_size=voxel_size, + point_cloud_range=point_cloud_range, + max_num_points=max_num_points, + max_voxels=max_voxels) + self.pillar_encoder = PillarEncoder(voxel_size=voxel_size, + point_cloud_range=point_cloud_range, + in_channel=9, + out_channel=64) + self.backbone = Backbone(in_channel=64, + out_channels=[64, 128, 256], + layer_nums=[3, 5, 5]) + self.neck = Neck(in_channels=[64, 128, 256], + upsample_strides=[1, 2, 4], + out_channels=[128, 128, 128]) + self.head = Head(in_channel=384, n_anchors=2*nclasses, n_classes=nclasses) + + # anchors + ranges = [[0, -39.68, -0.6, 69.12, 39.68, -0.6], + [0, -39.68, -0.6, 69.12, 39.68, -0.6], + [0, -39.68, -1.78, 69.12, 39.68, -1.78]] + sizes = [[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]] + rotations=[0, 1.57] + self.anchors_generator = Anchors(ranges=ranges, + sizes=sizes, + rotations=rotations) + + # train + self.assigners = [ + {'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_iou_thr': 0.35}, + {'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_iou_thr': 0.35}, + {'pos_iou_thr': 0.6, 'neg_iou_thr': 0.45, 'min_iou_thr': 0.45}, + ] + + # val and test + self.nms_pre = 100 + self.nms_thr = 0.01 + self.score_thr = 0.1 + self.max_num = 50 + + def get_predicted_bboxes_single(self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchors): + ''' + bbox_cls_pred: (n_anchors*3, 248, 216) + bbox_pred: (n_anchors*7, 248, 216) + bbox_dir_cls_pred: (n_anchors*2, 248, 216) + anchors: (y_l, x_l, 3, 2, 7) + return: + bboxes: (k, 7) + labels: (k, ) + scores: (k, ) + ''' + # 0. pre-process + bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(-1, self.nclasses) + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 7) + bbox_dir_cls_pred = bbox_dir_cls_pred.permute(1, 2, 0).reshape(-1, 2) + anchors = anchors.reshape(-1, 7) + + bbox_cls_pred = torch.sigmoid(bbox_cls_pred) + bbox_dir_cls_pred = torch.max(bbox_dir_cls_pred, dim=1)[1] + + # 1. obtain self.nms_pre bboxes based on scores + inds = bbox_cls_pred.max(1)[0].topk(self.nms_pre)[1] + bbox_cls_pred = bbox_cls_pred[inds] + bbox_pred = bbox_pred[inds] + bbox_dir_cls_pred = bbox_dir_cls_pred[inds] + anchors = anchors[inds] + + # 2. decode predicted offsets to bboxes + bbox_pred = anchors2bboxes(anchors, bbox_pred) + + # 3. nms + bbox_pred2d_xy = bbox_pred[:, [0, 1]] + bbox_pred2d_lw = bbox_pred[:, [3, 4]] + bbox_pred2d = torch.cat([bbox_pred2d_xy - bbox_pred2d_lw / 2, + bbox_pred2d_xy + bbox_pred2d_lw / 2, + bbox_pred[:, 6:]], dim=-1) # (n_anchors, 5) + + ret_bboxes, ret_labels, ret_scores = [], [], [] + for i in range(self.nclasses): + # 3.1 filter bboxes with scores below self.score_thr + cur_bbox_cls_pred = bbox_cls_pred[:, i] + score_inds = cur_bbox_cls_pred > self.score_thr + if score_inds.sum() == 0: + continue + + cur_bbox_cls_pred = cur_bbox_cls_pred[score_inds] + cur_bbox_pred2d = bbox_pred2d[score_inds] + cur_bbox_pred = bbox_pred[score_inds] + cur_bbox_dir_cls_pred = bbox_dir_cls_pred[score_inds] + + # 3.2 nms core + keep_inds = nms_cuda(boxes=cur_bbox_pred2d, + scores=cur_bbox_cls_pred, + thresh=self.nms_thr, + pre_maxsize=None, + post_max_size=None) + + cur_bbox_cls_pred = cur_bbox_cls_pred[keep_inds] + cur_bbox_pred = cur_bbox_pred[keep_inds] + cur_bbox_dir_cls_pred = cur_bbox_dir_cls_pred[keep_inds] + cur_bbox_pred[:, -1] = limit_period(cur_bbox_pred[:, -1].detach().cpu(), 1, np.pi).to(cur_bbox_pred) # [-pi, 0] + cur_bbox_pred[:, -1] += (1 - cur_bbox_dir_cls_pred) * np.pi + + ret_bboxes.append(cur_bbox_pred) + ret_labels.append(torch.zeros_like(cur_bbox_pred[:, 0], dtype=torch.long) + i) + ret_scores.append(cur_bbox_cls_pred) + + # 4. filter some bboxes if bboxes number is above self.max_num + if len(ret_bboxes) == 0: + return [], [], [] + ret_bboxes = torch.cat(ret_bboxes, 0) + ret_labels = torch.cat(ret_labels, 0) + ret_scores = torch.cat(ret_scores, 0) + if ret_bboxes.size(0) > self.max_num: + final_inds = ret_scores.topk(self.max_num)[1] + ret_bboxes = ret_bboxes[final_inds] + ret_labels = ret_labels[final_inds] + ret_scores = ret_scores[final_inds] + result = { + 'lidar_bboxes': ret_bboxes.detach().cpu().numpy(), + 'labels': ret_labels.detach().cpu().numpy(), + 'scores': ret_scores.detach().cpu().numpy() + } + return result + + + def get_predicted_bboxes(self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, batched_anchors): + ''' + bbox_cls_pred: (bs, n_anchors*3, 248, 216) + bbox_pred: (bs, n_anchors*7, 248, 216) + bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216) + batched_anchors: (bs, y_l, x_l, 3, 2, 7) + return: + bboxes: [(k1, 7), (k2, 7), ... ] + labels: [(k1, ), (k2, ), ... ] + scores: [(k1, ), (k2, ), ... ] + ''' + results = [] + bs = bbox_cls_pred.size(0) + for i in range(bs): + result = self.get_predicted_bboxes_single(bbox_cls_pred=bbox_cls_pred[i], + bbox_pred=bbox_pred[i], + bbox_dir_cls_pred=bbox_dir_cls_pred[i], + anchors=batched_anchors[i]) + results.append(result) + return results + + def forward(self, batched_pts, mode='test', batched_gt_bboxes=None, batched_gt_labels=None): + batch_size = len(batched_pts) + # batched_pts: list[tensor] -> pillars: (p1 + p2 + ... + pb, num_points, c), + # coors_batch: (p1 + p2 + ... + pb, 1 + 3), + # num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size) + pillars, coors_batch, npoints_per_pillar = self.pillar_layer(batched_pts) + + # pillars: (p1 + p2 + ... + pb, num_points, c), c = 4 + # coors_batch: (p1 + p2 + ... + pb, 1 + 3) + # npoints_per_pillar: (p1 + p2 + ... + pb, ) + # -> pillar_features: (bs, out_channel, y_l, x_l) + pillar_features = self.pillar_encoder(pillars, coors_batch, npoints_per_pillar) + + # xs: [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)] + xs = self.backbone(pillar_features) + + # x: (bs, 384, 248, 216) + x = self.neck(xs) + + # bbox_cls_pred: (bs, n_anchors*3, 248, 216) + # bbox_pred: (bs, n_anchors*7, 248, 216) + # bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216) + bbox_cls_pred, bbox_pred, bbox_dir_cls_pred = self.head(x) + + # anchors + device = bbox_cls_pred.device + feature_map_size = torch.tensor(list(bbox_cls_pred.size()[-2:]), device=device) + anchors = self.anchors_generator.get_multi_anchors(feature_map_size) + batched_anchors = [anchors for _ in range(batch_size)] + + if mode == 'train': + anchor_target_dict = anchor_target(batched_anchors=batched_anchors, + batched_gt_bboxes=batched_gt_bboxes, + batched_gt_labels=batched_gt_labels, + assigners=self.assigners, + nclasses=self.nclasses) + + return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict + elif mode == 'val': + results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred, + bbox_pred=bbox_pred, + bbox_dir_cls_pred=bbox_dir_cls_pred, + batched_anchors=batched_anchors) + return results + + elif mode == 'test': + results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred, + bbox_pred=bbox_pred, + bbox_dir_cls_pred=bbox_dir_cls_pred, + batched_anchors=batched_anchors) + return results + else: + raise ValueError diff --git a/cv/3d_detection/pointpillars/pytorch/ops/__init__.py b/cv/3d_detection/pointpillars/pytorch/ops/__init__.py new file mode 100755 index 000000000..e4469056b --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/__init__.py @@ -0,0 +1,2 @@ +from .voxel_module import Voxelization +from .iou3d_module import boxes_iou_bev, nms_cuda, boxes_overlap_bev \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/ops/iou3d/iou3d.cpp b/cv/3d_detection/pointpillars/pytorch/ops/iou3d/iou3d.cpp new file mode 100755 index 000000000..25a5cd98f --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/iou3d/iou3d.cpp @@ -0,0 +1,210 @@ +// Modified from +// https://github.com/open-mmlab/OpenPCDet/blob/master/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp + +/* +3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others) +Written by Shaoshuai Shi +All Rights Reserved 2019-2020. +*/ + +#include +#include +#include +#include + +#include +#include + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDAtensor ") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0)) + +#define CHECK_ERROR(ans) \ + { gpuAssert((ans), __FILE__, __LINE__); } +inline void gpuAssert(cudaError_t code, const char *file, int line, + bool abort = true) { + if (code != cudaSuccess) { + fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file, + line); + if (abort) exit(code); + } +} + +const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8; + +void boxesoverlapLauncher(const int num_a, const float *boxes_a, + const int num_b, const float *boxes_b, + float *ans_overlap); +void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, + const float *boxes_b, float *ans_iou); +void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num, + float nms_overlap_thresh); +void nmsNormalLauncher(const float *boxes, unsigned long long *mask, + int boxes_num, float nms_overlap_thresh); + +int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, + at::Tensor ans_overlap) { + // params boxes_a: (N, 5) [x1, y1, x2, y2, ry] + // params boxes_b: (M, 5) + // params ans_overlap: (N, M) + + CHECK_INPUT(boxes_a); + CHECK_INPUT(boxes_b); + CHECK_INPUT(ans_overlap); + + int num_a = boxes_a.size(0); + int num_b = boxes_b.size(0); + + const float *boxes_a_data = boxes_a.data_ptr(); + const float *boxes_b_data = boxes_b.data_ptr(); + float *ans_overlap_data = ans_overlap.data_ptr(); + + boxesoverlapLauncher(num_a, boxes_a_data, num_b, boxes_b_data, + ans_overlap_data); + + return 1; +} + +int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, + at::Tensor ans_iou) { + // params boxes_a: (N, 5) [x1, y1, x2, y2, ry] + // params boxes_b: (M, 5) + // params ans_overlap: (N, M) + + CHECK_INPUT(boxes_a); + CHECK_INPUT(boxes_b); + CHECK_INPUT(ans_iou); + + int num_a = boxes_a.size(0); + int num_b = boxes_b.size(0); + + const float *boxes_a_data = boxes_a.data_ptr(); + const float *boxes_b_data = boxes_b.data_ptr(); + float *ans_iou_data = ans_iou.data_ptr(); + + boxesioubevLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_iou_data); + + return 1; +} + +int nms_gpu(at::Tensor boxes, at::Tensor keep, + float nms_overlap_thresh, int device_id) { + // params boxes: (N, 5) [x1, y1, x2, y2, ry] + // params keep: (N) + + CHECK_INPUT(boxes); + CHECK_CONTIGUOUS(keep); + cudaSetDevice(device_id); + + int boxes_num = boxes.size(0); + const float *boxes_data = boxes.data_ptr(); + int64_t *keep_data = keep.data_ptr(); + + const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS); + + unsigned long long *mask_data = NULL; + CHECK_ERROR(cudaMalloc((void **)&mask_data, + boxes_num * col_blocks * sizeof(unsigned long long))); + nmsLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh); + + // unsigned long long mask_cpu[boxes_num * col_blocks]; + // unsigned long long *mask_cpu = new unsigned long long [boxes_num * + // col_blocks]; + std::vector mask_cpu(boxes_num * col_blocks); + + // printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks); + CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data, + boxes_num * col_blocks * sizeof(unsigned long long), + cudaMemcpyDeviceToHost)); + + cudaFree(mask_data); + + unsigned long long *remv_cpu = new unsigned long long[col_blocks](); + + int num_to_keep = 0; + + for (int i = 0; i < boxes_num; i++) { + int nblock = i / THREADS_PER_BLOCK_NMS; + int inblock = i % THREADS_PER_BLOCK_NMS; + + if (!(remv_cpu[nblock] & (1ULL << inblock))) { + keep_data[num_to_keep++] = i; + unsigned long long *p = &mask_cpu[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv_cpu[j] |= p[j]; + } + } + } + delete[] remv_cpu; + if (cudaSuccess != cudaGetLastError()) printf("Error!\n"); + + return num_to_keep; +} + +int nms_normal_gpu(at::Tensor boxes, at::Tensor keep, + float nms_overlap_thresh, int device_id) { + // params boxes: (N, 5) [x1, y1, x2, y2, ry] + // params keep: (N) + + CHECK_INPUT(boxes); + CHECK_CONTIGUOUS(keep); + cudaSetDevice(device_id); + + int boxes_num = boxes.size(0); + const float *boxes_data = boxes.data_ptr(); + int64_t *keep_data = keep.data_ptr(); + + const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS); + + unsigned long long *mask_data = NULL; + CHECK_ERROR(cudaMalloc((void **)&mask_data, + boxes_num * col_blocks * sizeof(unsigned long long))); + nmsNormalLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh); + + // unsigned long long mask_cpu[boxes_num * col_blocks]; + // unsigned long long *mask_cpu = new unsigned long long [boxes_num * + // col_blocks]; + std::vector mask_cpu(boxes_num * col_blocks); + + // printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks); + CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data, + boxes_num * col_blocks * sizeof(unsigned long long), + cudaMemcpyDeviceToHost)); + + cudaFree(mask_data); + + unsigned long long *remv_cpu = new unsigned long long[col_blocks](); + + int num_to_keep = 0; + + for (int i = 0; i < boxes_num; i++) { + int nblock = i / THREADS_PER_BLOCK_NMS; + int inblock = i % THREADS_PER_BLOCK_NMS; + + if (!(remv_cpu[nblock] & (1ULL << inblock))) { + keep_data[num_to_keep++] = i; + unsigned long long *p = &mask_cpu[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv_cpu[j] |= p[j]; + } + } + } + delete[] remv_cpu; + if (cudaSuccess != cudaGetLastError()) printf("Error!\n"); + + return num_to_keep; +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("boxes_overlap_bev_gpu", &boxes_overlap_bev_gpu, + "oriented boxes overlap"); + m.def("boxes_iou_bev_gpu", &boxes_iou_bev_gpu, "oriented boxes iou"); + m.def("nms_gpu", &nms_gpu, "oriented nms gpu"); + m.def("nms_normal_gpu", &nms_normal_gpu, "nms gpu"); +} diff --git a/cv/3d_detection/pointpillars/pytorch/ops/iou3d/iou3d_kernel.cu b/cv/3d_detection/pointpillars/pytorch/ops/iou3d/iou3d_kernel.cu new file mode 100755 index 000000000..861aea3c5 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/iou3d/iou3d_kernel.cu @@ -0,0 +1,439 @@ +// Modified from +// https://github.com/open-mmlab/OpenPCDet/blob/master/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu + +/* +3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others) +Written by Shaoshuai Shi +All Rights Reserved 2019-2020. +*/ + +#include +#define THREADS_PER_BLOCK 16 +#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0)) + +//#define DEBUG +const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8; +__device__ const float EPS = 1e-8; +struct Point { + float x, y; + __device__ Point() {} + __device__ Point(double _x, double _y) { x = _x, y = _y; } + + __device__ void set(float _x, float _y) { + x = _x; + y = _y; + } + + __device__ Point operator+(const Point &b) const { + return Point(x + b.x, y + b.y); + } + + __device__ Point operator-(const Point &b) const { + return Point(x - b.x, y - b.y); + } +}; + +__device__ inline float cross(const Point &a, const Point &b) { + return a.x * b.y - a.y * b.x; +} + +__device__ inline float cross(const Point &p1, const Point &p2, + const Point &p0) { + return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y); +} + +__device__ int check_rect_cross(const Point &p1, const Point &p2, + const Point &q1, const Point &q2) { + int ret = min(p1.x, p2.x) <= max(q1.x, q2.x) && + min(q1.x, q2.x) <= max(p1.x, p2.x) && + min(p1.y, p2.y) <= max(q1.y, q2.y) && + min(q1.y, q2.y) <= max(p1.y, p2.y); + return ret; +} + +__device__ inline int check_in_box2d(const float *box, const Point &p) { + // params: box (5) [x1, y1, x2, y2, angle] + const float MARGIN = 1e-5; + + float center_x = (box[0] + box[2]) / 2; + float center_y = (box[1] + box[3]) / 2; + float angle_cos = cos(-box[4]), + angle_sin = + sin(-box[4]); // rotate the point in the opposite direction of box + float rot_x = + (p.x - center_x) * angle_cos + (p.y - center_y) * angle_sin + center_x; + float rot_y = + -(p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos + center_y; +#ifdef DEBUG + printf("box: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", box[0], box[1], box[2], + box[3], box[4]); + printf( + "center: (%.3f, %.3f), cossin(%.3f, %.3f), src(%.3f, %.3f), rot(%.3f, " + "%.3f)\n", + center_x, center_y, angle_cos, angle_sin, p.x, p.y, rot_x, rot_y); +#endif + return (rot_x > box[0] - MARGIN && rot_x < box[2] + MARGIN && + rot_y > box[1] - MARGIN && rot_y < box[3] + MARGIN); +} + +__device__ inline int intersection(const Point &p1, const Point &p0, + const Point &q1, const Point &q0, + Point &ans) { + // fast exclusion + if (check_rect_cross(p0, p1, q0, q1) == 0) return 0; + + // check cross standing + float s1 = cross(q0, p1, p0); + float s2 = cross(p1, q1, p0); + float s3 = cross(p0, q1, q0); + float s4 = cross(q1, p1, q0); + + if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0; + + // calculate intersection of two lines + float s5 = cross(q1, p1, p0); + if (fabs(s5 - s1) > EPS) { + ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1); + ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1); + + } else { + float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y; + float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y; + float D = a0 * b1 - a1 * b0; + + ans.x = (b0 * c1 - b1 * c0) / D; + ans.y = (a1 * c0 - a0 * c1) / D; + } + + return 1; +} + +__device__ inline void rotate_around_center(const Point ¢er, + const float angle_cos, + const float angle_sin, Point &p) { + float new_x = + (p.x - center.x) * angle_cos + (p.y - center.y) * angle_sin + center.x; + float new_y = + -(p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y; + p.set(new_x, new_y); +} + +__device__ inline int point_cmp(const Point &a, const Point &b, + const Point ¢er) { + return atan2(a.y - center.y, a.x - center.x) > + atan2(b.y - center.y, b.x - center.x); +} + +__device__ inline float box_overlap(const float *box_a, const float *box_b) { + // params: box_a (5) [x1, y1, x2, y2, angle] + // params: box_b (5) [x1, y1, x2, y2, angle] + + float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3], + a_angle = box_a[4]; + float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3], + b_angle = box_b[4]; + + Point center_a((a_x1 + a_x2) / 2, (a_y1 + a_y2) / 2); + Point center_b((b_x1 + b_x2) / 2, (b_y1 + b_y2) / 2); +#ifdef DEBUG + printf( + "a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", + a_x1, a_y1, a_x2, a_y2, a_angle, b_x1, b_y1, b_x2, b_y2, b_angle); + printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y, + center_b.x, center_b.y); +#endif + + Point box_a_corners[5]; + box_a_corners[0].set(a_x1, a_y1); + box_a_corners[1].set(a_x2, a_y1); + box_a_corners[2].set(a_x2, a_y2); + box_a_corners[3].set(a_x1, a_y2); + + Point box_b_corners[5]; + box_b_corners[0].set(b_x1, b_y1); + box_b_corners[1].set(b_x2, b_y1); + box_b_corners[2].set(b_x2, b_y2); + box_b_corners[3].set(b_x1, b_y2); + + // get oriented corners + float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle); + float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle); + + for (int k = 0; k < 4; k++) { +#ifdef DEBUG + printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, + box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, + box_b_corners[k].y); +#endif + rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]); + rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]); +#ifdef DEBUG + printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, + box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y); +#endif + } + + box_a_corners[4] = box_a_corners[0]; + box_b_corners[4] = box_b_corners[0]; + + // get intersection of lines + Point cross_points[16]; + Point poly_center; + int cnt = 0, flag = 0; + + poly_center.set(0, 0); + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + flag = intersection(box_a_corners[i + 1], box_a_corners[i], + box_b_corners[j + 1], box_b_corners[j], + cross_points[cnt]); + if (flag) { + poly_center = poly_center + cross_points[cnt]; + cnt++; + } + } + } + + // check corners + for (int k = 0; k < 4; k++) { + if (check_in_box2d(box_a, box_b_corners[k])) { + poly_center = poly_center + box_b_corners[k]; + cross_points[cnt] = box_b_corners[k]; + cnt++; + } + if (check_in_box2d(box_b, box_a_corners[k])) { + poly_center = poly_center + box_a_corners[k]; + cross_points[cnt] = box_a_corners[k]; + cnt++; + } + } + + poly_center.x /= cnt; + poly_center.y /= cnt; + + // sort the points of polygon + Point temp; + for (int j = 0; j < cnt - 1; j++) { + for (int i = 0; i < cnt - j - 1; i++) { + if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)) { + temp = cross_points[i]; + cross_points[i] = cross_points[i + 1]; + cross_points[i + 1] = temp; + } + } + } + +#ifdef DEBUG + printf("cnt=%d\n", cnt); + for (int i = 0; i < cnt; i++) { + printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x, + cross_points[i].y); + } +#endif + + // get the overlap areas + float area = 0; + for (int k = 0; k < cnt - 1; k++) { + area += cross(cross_points[k] - cross_points[0], + cross_points[k + 1] - cross_points[0]); + } + + return fabs(area) / 2.0; +} + +__device__ inline float iou_bev(const float *box_a, const float *box_b) { + // params: box_a (5) [x1, y1, x2, y2, angle] + // params: box_b (5) [x1, y1, x2, y2, angle] + float sa = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1]); + float sb = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1]); + float s_overlap = box_overlap(box_a, box_b); + return s_overlap / fmaxf(sa + sb - s_overlap, EPS); +} + +__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a, + const int num_b, const float *boxes_b, + float *ans_overlap) { + const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y; + const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x; + + if (a_idx >= num_a || b_idx >= num_b) { + return; + } + const float *cur_box_a = boxes_a + a_idx * 5; + const float *cur_box_b = boxes_b + b_idx * 5; + float s_overlap = box_overlap(cur_box_a, cur_box_b); + ans_overlap[a_idx * num_b + b_idx] = s_overlap; +} + +__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a, + const int num_b, const float *boxes_b, + float *ans_iou) { + const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y; + const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x; + + if (a_idx >= num_a || b_idx >= num_b) { + return; + } + + const float *cur_box_a = boxes_a + a_idx * 5; + const float *cur_box_b = boxes_b + b_idx * 5; + float cur_iou_bev = iou_bev(cur_box_a, cur_box_b); + ans_iou[a_idx * num_b + b_idx] = cur_iou_bev; +} + +__global__ void nms_kernel(const int boxes_num, const float nms_overlap_thresh, + const float *boxes, unsigned long long *mask) { + // params: boxes (N, 5) [x1, y1, x2, y2, ry] + // params: mask (N, N/THREADS_PER_BLOCK_NMS) + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, + THREADS_PER_BLOCK_NMS); + const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, + THREADS_PER_BLOCK_NMS); + + __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5]; + + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x; + const float *cur_box = boxes + cur_box_idx * 5; + + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (iou_bev(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS); + mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +__device__ inline float iou_normal(float const *const a, float const *const b) { + float left = fmaxf(a[0], b[0]), right = fminf(a[2], b[2]); + float top = fmaxf(a[1], b[1]), bottom = fminf(a[3], b[3]); + float width = fmaxf(right - left, 0.f), height = fmaxf(bottom - top, 0.f); + float interS = width * height; + float Sa = (a[2] - a[0]) * (a[3] - a[1]); + float Sb = (b[2] - b[0]) * (b[3] - b[1]); + return interS / fmaxf(Sa + Sb - interS, EPS); +} + +__global__ void nms_normal_kernel(const int boxes_num, + const float nms_overlap_thresh, + const float *boxes, + unsigned long long *mask) { + // params: boxes (N, 5) [x1, y1, x2, y2, ry] + // params: mask (N, N/THREADS_PER_BLOCK_NMS) + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, + THREADS_PER_BLOCK_NMS); + const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, + THREADS_PER_BLOCK_NMS); + + __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5]; + + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x; + const float *cur_box = boxes + cur_box_idx * 5; + + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (iou_normal(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS); + mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +void boxesoverlapLauncher(const int num_a, const float *boxes_a, + const int num_b, const float *boxes_b, + float *ans_overlap) { + dim3 blocks( + DIVUP(num_b, THREADS_PER_BLOCK), + DIVUP(num_a, THREADS_PER_BLOCK)); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK); + + boxes_overlap_kernel<<>>(num_a, boxes_a, num_b, boxes_b, + ans_overlap); +#ifdef DEBUG + cudaDeviceSynchronize(); // for using printf in kernel function +#endif +} + +void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, + const float *boxes_b, float *ans_iou) { + dim3 blocks( + DIVUP(num_b, THREADS_PER_BLOCK), + DIVUP(num_a, THREADS_PER_BLOCK)); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK); + + boxes_iou_bev_kernel<<>>(num_a, boxes_a, num_b, boxes_b, + ans_iou); +} + +void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num, + float nms_overlap_thresh) { + dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS), + DIVUP(boxes_num, THREADS_PER_BLOCK_NMS)); + dim3 threads(THREADS_PER_BLOCK_NMS); + nms_kernel<<>>(boxes_num, nms_overlap_thresh, boxes, mask); +} + +void nmsNormalLauncher(const float *boxes, unsigned long long *mask, + int boxes_num, float nms_overlap_thresh) { + dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS), + DIVUP(boxes_num, THREADS_PER_BLOCK_NMS)); + dim3 threads(THREADS_PER_BLOCK_NMS); + nms_normal_kernel<<>>(boxes_num, nms_overlap_thresh, boxes, + mask); +} diff --git a/cv/3d_detection/pointpillars/pytorch/ops/iou3d_module.py b/cv/3d_detection/pointpillars/pytorch/ops/iou3d_module.py new file mode 100755 index 000000000..e3fcdb113 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/iou3d_module.py @@ -0,0 +1,89 @@ +# This file is modified from https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/ops/iou3d/iou3d_utils.py + +import torch +from .iou3d_op import boxes_overlap_bev_gpu, boxes_iou_bev_gpu, nms_gpu, nms_normal_gpu + + +def boxes_overlap_bev(boxes_a, boxes_b): + """Calculate boxes Overlap in the bird view. + + Args: + boxes_a (torch.Tensor): Input boxes a with shape (M, 5). + boxes_b (torch.Tensor): Input boxes b with shape (N, 5). + + Returns: + ans_overlap (torch.Tensor): Overlap result with shape (M, N). + """ + ans_overlap = boxes_a.new_zeros( + torch.Size((boxes_a.shape[0], boxes_b.shape[0]))) + + boxes_overlap_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_overlap) + + return ans_overlap + + +def boxes_iou_bev(boxes_a, boxes_b): + """Calculate boxes IoU in the bird view. + + Args: + boxes_a (torch.Tensor): Input boxes a with shape (M, 5). + boxes_b (torch.Tensor): Input boxes b with shape (N, 5). + + Returns: + ans_iou (torch.Tensor): IoU result with shape (M, N). + """ + ans_iou = boxes_a.new_zeros( + torch.Size((boxes_a.shape[0], boxes_b.shape[0]))) + + boxes_iou_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_iou) + + return ans_iou + + +def nms_cuda(boxes, scores, thresh, pre_maxsize=None, post_max_size=None): + """Nms function with gpu implementation. + + Args: + boxes (torch.Tensor): Input boxes with the shape of [N, 5] + ([x1, y1, x2, y2, ry]). + scores (torch.Tensor): Scores of boxes with the shape of [N]. + thresh (int): Threshold. + pre_maxsize (int): Max size of boxes before nms. Default: None. + post_maxsize (int): Max size of boxes after nms. Default: None. + + Returns: + torch.Tensor: Indexes after nms. + """ + order = scores.sort(0, descending=True)[1] + + if pre_maxsize is not None: + order = order[:pre_maxsize] + boxes = boxes[order].contiguous() + + keep = torch.zeros(boxes.size(0), dtype=torch.long) + num_out = nms_gpu(boxes, keep, thresh, boxes.device.index) + keep = order[keep[:num_out].cuda(boxes.device)].contiguous() + if post_max_size is not None: + keep = keep[:post_max_size] + return keep + + +def nms_normal_gpu(boxes, scores, thresh): + """Normal non maximum suppression on GPU. + + Args: + boxes (torch.Tensor): Input boxes with shape (N, 5). + scores (torch.Tensor): Scores of predicted boxes with shape (N). + thresh (torch.Tensor): Threshold of non maximum suppression. + + Returns: + torch.Tensor: Remaining indices with scores in descending order. + """ + order = scores.sort(0, descending=True)[1] + + boxes = boxes[order].contiguous() + + keep = torch.zeros(boxes.size(0), dtype=torch.long) + num_out = nms_normal_gpu(boxes, keep, thresh, + boxes.device.index) + return order[keep[:num_out].cuda(boxes.device)].contiguous() diff --git a/cv/3d_detection/pointpillars/pytorch/ops/setup.py b/cv/3d_detection/pointpillars/pytorch/ops/setup.py new file mode 100755 index 000000000..3d1e67986 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/setup.py @@ -0,0 +1,25 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +setup( + name='pointpillars', + ext_modules=[ + CUDAExtension( + name='voxel_op', + sources=['voxelization/voxelization.cpp', + 'voxelization/voxelization_cpu.cpp', + 'voxelization/voxelization_cuda.cu', + ], + define_macros=[('WITH_CUDA', None)] + ), + CUDAExtension( + name='iou3d_op', + sources=['iou3d/iou3d.cpp', + 'iou3d/iou3d_kernel.cu', + ], + define_macros=[('WITH_CUDA', None)] + ) + ], + cmdclass={ + 'build_ext': BuildExtension + }) \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/ops/voxel_module.py b/cv/3d_detection/pointpillars/pytorch/ops/voxel_module.py new file mode 100755 index 000000000..8f9cc90b6 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/voxel_module.py @@ -0,0 +1,133 @@ +# This file is modified from https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/ops/voxel/voxelize.py + +import torch +import torch.nn as nn +from .voxel_op import hard_voxelize + + +class _Voxelization(torch.autograd.Function): + + @staticmethod + def forward(ctx, + points, + voxel_size, + coors_range, + max_points=35, + max_voxels=20000, + deterministic=True): + """convert kitti points(N, >=3) to voxels. + Args: + points: [N, ndim] float tensor. points[:, :3] contain xyz points + and points[:, 3:] contain other information like reflectivity + voxel_size: [3] list/tuple or array, float. xyz, indicate voxel + size + coors_range: [6] list/tuple or array, float. indicate voxel + range. format: xyzxyz, minmax + max_points: int. indicate maximum points contained in a voxel. if + max_points=-1, it means using dynamic_voxelize + max_voxels: int. indicate maximum voxels this function create. + for second, 20000 is a good choice. Users should shuffle points + before call this function because max_voxels may drop points. + deterministic: bool. whether to invoke the non-deterministic + version of hard-voxelization implementations. non-deterministic + version is considerablly fast but is not deterministic. only + affects hard voxelization. default True. for more information + of this argument and the implementation insights, please refer + to the following links: + https://github.com/open-mmlab/mmdetection3d/issues/894 + https://github.com/open-mmlab/mmdetection3d/pull/904 + it is an experimental feature and we will appreciate it if + you could share with us the failing cases. + Returns: + voxels: [M, max_points, ndim] float tensor. only contain points + and returned when max_points != -1. + coordinates: [M, 3] int32 tensor, always returned. + num_points_per_voxel: [M] int32 tensor. Only returned when + max_points != -1. + """ + + voxels = points.new_zeros( + size=(max_voxels, max_points, points.size(1))) + coors = points.new_zeros(size=(max_voxels, 3), dtype=torch.int) + num_points_per_voxel = points.new_zeros( + size=(max_voxels, ), dtype=torch.int) + voxel_num = hard_voxelize(points, voxels, coors, + num_points_per_voxel, voxel_size, + coors_range, max_points, max_voxels, 3, + deterministic) + # select the valid voxels + voxels_out = voxels[:voxel_num] + coors_out = coors[:voxel_num].flip(-1) # (z, y, x) -> (x, y, z) + num_points_per_voxel_out = num_points_per_voxel[:voxel_num] + return voxels_out, coors_out, num_points_per_voxel_out + + +class Voxelization(nn.Module): + + def __init__(self, + voxel_size, + point_cloud_range, + max_num_points, + max_voxels, + deterministic=True): + super(Voxelization, self).__init__() + """ + Args: + voxel_size (list): list [x, y, z] size of three dimension + point_cloud_range (list): + [x_min, y_min, z_min, x_max, y_max, z_max] + max_num_points (int): max number of points per voxel + max_voxels (tuple): max number of voxels in + (training, testing) time + deterministic: bool. whether to invoke the non-deterministic + version of hard-voxelization implementations. non-deterministic + version is considerablly fast but is not deterministic. only + affects hard voxelization. default True. for more information + of this argument and the implementation insights, please refer + to the following links: + https://github.com/open-mmlab/mmdetection3d/issues/894 + https://github.com/open-mmlab/mmdetection3d/pull/904 + it is an experimental feature and we will appreciate it if + you could share with us the failing cases. + """ + self.voxel_size = voxel_size + self.point_cloud_range = point_cloud_range + self.max_num_points = max_num_points + self.max_voxels = max_voxels + self.deterministic = deterministic + + point_cloud_range = torch.tensor( + point_cloud_range, dtype=torch.float32) + + voxel_size = torch.tensor(voxel_size, dtype=torch.float32) + grid_size = (point_cloud_range[3:] - + point_cloud_range[:3]) / voxel_size + grid_size = torch.round(grid_size).long() + input_feat_shape = grid_size[:2] + self.grid_size = grid_size + # the origin shape is as [x-len, y-len, z-len] + # [w, h, d] -> [d, h, w] + self.pcd_shape = [*input_feat_shape, 1][::-1] + + def forward(self, input): + """ + input: shape=(N, c) + """ + if self.training: + max_voxels = self.max_voxels[0] + else: + max_voxels = self.max_voxels[1] + + return _Voxelization.apply(input, self.voxel_size, self.point_cloud_range, + self.max_num_points, max_voxels, + self.deterministic) + + def __repr__(self): + tmpstr = self.__class__.__name__ + '(' + tmpstr += 'voxel_size=' + str(self.voxel_size) + tmpstr += ', point_cloud_range=' + str(self.point_cloud_range) + tmpstr += ', max_num_points=' + str(self.max_num_points) + tmpstr += ', max_voxels=' + str(self.max_voxels) + tmpstr += ', deterministic=' + str(self.deterministic) + tmpstr += ')' + return tmpstr diff --git a/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization.cpp b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization.cpp new file mode 100755 index 000000000..2fc115547 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization.cpp @@ -0,0 +1,10 @@ +#include +#include "voxelization.h" + +namespace voxelization { + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("hard_voxelize", &hard_voxelize, "hard voxelize"); +} + +} // namespace voxelization diff --git a/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization.h b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization.h new file mode 100755 index 000000000..116bead49 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization.h @@ -0,0 +1,69 @@ +#pragma once +#include + +typedef enum { SUM = 0, MEAN = 1, MAX = 2 } reduce_t; + +namespace voxelization { + +int hard_voxelize_cpu(const at::Tensor &points, at::Tensor &voxels, + at::Tensor &coors, at::Tensor &num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3); + +#ifdef WITH_CUDA +int hard_voxelize_gpu(const at::Tensor &points, at::Tensor &voxels, + at::Tensor &coors, at::Tensor &num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3); + +int nondisterministic_hard_voxelize_gpu(const at::Tensor &points, at::Tensor &voxels, + at::Tensor &coors, at::Tensor &num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3); +#endif + +// Interface for Python +inline int hard_voxelize(const at::Tensor &points, at::Tensor &voxels, + at::Tensor &coors, at::Tensor &num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3, const bool deterministic = true) { + if (points.device().is_cuda()) { +#ifdef WITH_CUDA + if (deterministic) { + return hard_voxelize_gpu(points, voxels, coors, num_points_per_voxel, + voxel_size, coors_range, max_points, max_voxels, + NDim); + } + return nondisterministic_hard_voxelize_gpu(points, voxels, coors, num_points_per_voxel, + voxel_size, coors_range, max_points, max_voxels, + NDim); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return hard_voxelize_cpu(points, voxels, coors, num_points_per_voxel, + voxel_size, coors_range, max_points, max_voxels, + NDim); +} + + +inline reduce_t convert_reduce_type(const std::string &reduce_type) { + if (reduce_type == "max") + return reduce_t::MAX; + else if (reduce_type == "sum") + return reduce_t::SUM; + else if (reduce_type == "mean") + return reduce_t::MEAN; + else TORCH_CHECK(false, "do not support reduce type " + reduce_type) + return reduce_t::SUM; +} + +} // namespace voxelization diff --git a/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization_cpu.cpp b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization_cpu.cpp new file mode 100755 index 000000000..6bcec4019 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization_cpu.cpp @@ -0,0 +1,173 @@ +#include +#include +// #include "voxelization.h" + +namespace { + +template +void dynamic_voxelize_kernel(const torch::TensorAccessor points, + torch::TensorAccessor coors, + const std::vector voxel_size, + const std::vector coors_range, + const std::vector grid_size, + const int num_points, const int num_features, + const int NDim) { + const int ndim_minus_1 = NDim - 1; + bool failed = false; + // int coor[NDim]; + int* coor = new int[NDim](); + int c; + + for (int i = 0; i < num_points; ++i) { + failed = false; + for (int j = 0; j < NDim; ++j) { + c = floor((points[i][j] - coors_range[j]) / voxel_size[j]); + // necessary to rm points out of range + if ((c < 0 || c >= grid_size[j])) { + failed = true; + break; + } + coor[ndim_minus_1 - j] = c; + } + + for (int k = 0; k < NDim; ++k) { + if (failed) + coors[i][k] = -1; + else + coors[i][k] = coor[k]; + } + } + + delete[] coor; + return; +} + +template +void hard_voxelize_kernel(const torch::TensorAccessor points, + torch::TensorAccessor voxels, + torch::TensorAccessor coors, + torch::TensorAccessor num_points_per_voxel, + torch::TensorAccessor coor_to_voxelidx, + int& voxel_num, const std::vector voxel_size, + const std::vector coors_range, + const std::vector grid_size, + const int max_points, const int max_voxels, + const int num_points, const int num_features, + const int NDim) { + // declare a temp coors + at::Tensor temp_coors = at::zeros( + {num_points, NDim}, at::TensorOptions().dtype(at::kInt).device(at::kCPU)); + + // First use dynamic voxelization to get coors, + // then check max points/voxels constraints + dynamic_voxelize_kernel(points, temp_coors.accessor(), + voxel_size, coors_range, grid_size, + num_points, num_features, NDim); + + int voxelidx, num; + auto coor = temp_coors.accessor(); + + for (int i = 0; i < num_points; ++i) { + // T_int* coor = temp_coors.data_ptr() + i * NDim; + + if (coor[i][0] == -1) continue; + + voxelidx = coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]]; + + // record voxel + if (voxelidx == -1) { + voxelidx = voxel_num; + if (max_voxels != -1 && voxel_num >= max_voxels) continue; + voxel_num += 1; + + coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]] = voxelidx; + + for (int k = 0; k < NDim; ++k) { + coors[voxelidx][k] = coor[i][k]; + } + } + + // put points into voxel + num = num_points_per_voxel[voxelidx]; + if (max_points == -1 || num < max_points) { + for (int k = 0; k < num_features; ++k) { + voxels[voxelidx][num][k] = points[i][k]; + } + num_points_per_voxel[voxelidx] += 1; + } + } + + return; +} + +} // namespace + +namespace voxelization { + +int hard_voxelize_cpu(const at::Tensor& points, at::Tensor& voxels, + at::Tensor& coors, at::Tensor& num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3) { + // current version tooks about 0.02s_0.03s for one frame on cpu + // check device + AT_ASSERTM(points.device().is_cpu(), "points must be a CPU tensor"); + + std::vector grid_size(NDim); + const int num_points = points.size(0); + const int num_features = points.size(1); + + for (int i = 0; i < NDim; ++i) { + grid_size[i] = + round((coors_range[NDim + i] - coors_range[i]) / voxel_size[i]); + } + + // coors, num_points_per_voxel, coor_to_voxelidx are int Tensor + // printf("cpu coor_to_voxelidx size: [%d, %d, %d]\n", grid_size[2], + // grid_size[1], grid_size[0]); + at::Tensor coor_to_voxelidx = + -at::ones({grid_size[2], grid_size[1], grid_size[0]}, coors.options()); + + int voxel_num = 0; + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + points.scalar_type(), "hard_voxelize_forward", [&] { + hard_voxelize_kernel( + points.accessor(), voxels.accessor(), + coors.accessor(), num_points_per_voxel.accessor(), + coor_to_voxelidx.accessor(), voxel_num, voxel_size, + coors_range, grid_size, max_points, max_voxels, num_points, + num_features, NDim); + }); + + return voxel_num; +} + +void dynamic_voxelize_cpu(const at::Tensor& points, at::Tensor& coors, + const std::vector voxel_size, + const std::vector coors_range, + const int NDim = 3) { + // check device + AT_ASSERTM(points.device().is_cpu(), "points must be a CPU tensor"); + + std::vector grid_size(NDim); + const int num_points = points.size(0); + const int num_features = points.size(1); + + for (int i = 0; i < NDim; ++i) { + grid_size[i] = + round((coors_range[NDim + i] - coors_range[i]) / voxel_size[i]); + } + + // coors, num_points_per_voxel, coor_to_voxelidx are int Tensor + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + points.scalar_type(), "hard_voxelize_forward", [&] { + dynamic_voxelize_kernel( + points.accessor(), coors.accessor(), + voxel_size, coors_range, grid_size, num_points, num_features, NDim); + }); + + return; +} + +} // namespace voxelization diff --git a/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization_cuda.cu b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization_cuda.cu new file mode 100755 index 000000000..b4edcf3b1 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/ops/voxelization/voxelization_cuda.cu @@ -0,0 +1,530 @@ +#include +#include +#include +#include + +#include + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +namespace { +int const threadsPerBlock = sizeof(unsigned long long) * 8; +} + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +template +__global__ void dynamic_voxelize_kernel( + const T* points, T_int* coors, const float voxel_x, const float voxel_y, + const float voxel_z, const float coors_x_min, const float coors_y_min, + const float coors_z_min, const float coors_x_max, const float coors_y_max, + const float coors_z_max, const int grid_x, const int grid_y, + const int grid_z, const int num_points, const int num_features, + const int NDim) { + // const int index = blockIdx.x * threadsPerBlock + threadIdx.x; + CUDA_1D_KERNEL_LOOP(index, num_points) { + // To save some computation + auto points_offset = points + index * num_features; + auto coors_offset = coors + index * NDim; + int c_x = floor((points_offset[0] - coors_x_min) / voxel_x); + if (c_x < 0 || c_x >= grid_x) { + coors_offset[0] = -1; + return; + } + + int c_y = floor((points_offset[1] - coors_y_min) / voxel_y); + if (c_y < 0 || c_y >= grid_y) { + coors_offset[0] = -1; + coors_offset[1] = -1; + return; + } + + int c_z = floor((points_offset[2] - coors_z_min) / voxel_z); + if (c_z < 0 || c_z >= grid_z) { + coors_offset[0] = -1; + coors_offset[1] = -1; + coors_offset[2] = -1; + } else { + coors_offset[0] = c_z; + coors_offset[1] = c_y; + coors_offset[2] = c_x; + } + } +} + +template +__global__ void assign_point_to_voxel(const int nthreads, const T* points, + T_int* point_to_voxelidx, + T_int* coor_to_voxelidx, T* voxels, + const int max_points, + const int num_features, + const int num_points, const int NDim) { + CUDA_1D_KERNEL_LOOP(thread_idx, nthreads) { + // const int index = blockIdx.x * threadsPerBlock + threadIdx.x; + int index = thread_idx / num_features; + + int num = point_to_voxelidx[index]; + int voxelidx = coor_to_voxelidx[index]; + if (num > -1 && voxelidx > -1) { + auto voxels_offset = + voxels + voxelidx * max_points * num_features + num * num_features; + + int k = thread_idx % num_features; + voxels_offset[k] = points[thread_idx]; + } + } +} + +template +__global__ void assign_voxel_coors(const int nthreads, T_int* coor, + T_int* point_to_voxelidx, + T_int* coor_to_voxelidx, T_int* voxel_coors, + const int num_points, const int NDim) { + CUDA_1D_KERNEL_LOOP(thread_idx, nthreads) { + // const int index = blockIdx.x * threadsPerBlock + threadIdx.x; + // if (index >= num_points) return; + int index = thread_idx / NDim; + int num = point_to_voxelidx[index]; + int voxelidx = coor_to_voxelidx[index]; + if (num == 0 && voxelidx > -1) { + auto coors_offset = voxel_coors + voxelidx * NDim; + int k = thread_idx % NDim; + coors_offset[k] = coor[thread_idx]; + } + } +} + +template +__global__ void point_to_voxelidx_kernel(const T_int* coor, + T_int* point_to_voxelidx, + T_int* point_to_pointidx, + const int max_points, + const int max_voxels, + const int num_points, const int NDim) { + CUDA_1D_KERNEL_LOOP(index, num_points) { + auto coor_offset = coor + index * NDim; + // skip invalid points + if ((index >= num_points) || (coor_offset[0] == -1)) return; + + int num = 0; + int coor_x = coor_offset[0]; + int coor_y = coor_offset[1]; + int coor_z = coor_offset[2]; + // only calculate the coors before this coor[index] + for (int i = 0; i < index; ++i) { + auto prev_coor = coor + i * NDim; + if (prev_coor[0] == -1) continue; + + // Find all previous points that have the same coors + // if find the same coor, record it + if ((prev_coor[0] == coor_x) && (prev_coor[1] == coor_y) && + (prev_coor[2] == coor_z)) { + num++; + if (num == 1) { + // point to the same coor that first show up + point_to_pointidx[index] = i; + } else if (num >= max_points) { + // out of boundary + return; + } + } + } + if (num == 0) { + point_to_pointidx[index] = index; + } + if (num < max_points) { + point_to_voxelidx[index] = num; + } + } +} + +template +__global__ void determin_voxel_num( + // const T_int* coor, + T_int* num_points_per_voxel, T_int* point_to_voxelidx, + T_int* point_to_pointidx, T_int* coor_to_voxelidx, T_int* voxel_num, + const int max_points, const int max_voxels, const int num_points) { + // only calculate the coors before this coor[index] + for (int i = 0; i < num_points; ++i) { + // if (coor[i][0] == -1) + // continue; + int point_pos_in_voxel = point_to_voxelidx[i]; + // record voxel + if (point_pos_in_voxel == -1) { + // out of max_points or invalid point + continue; + } else if (point_pos_in_voxel == 0) { + // record new voxel + int voxelidx = voxel_num[0]; + if (voxel_num[0] >= max_voxels) continue; + voxel_num[0] += 1; + coor_to_voxelidx[i] = voxelidx; + num_points_per_voxel[voxelidx] = 1; + } else { + int point_idx = point_to_pointidx[i]; + int voxelidx = coor_to_voxelidx[point_idx]; + if (voxelidx != -1) { + coor_to_voxelidx[i] = voxelidx; + num_points_per_voxel[voxelidx] += 1; + } + } + } +} + +__global__ void nondisterministic_get_assign_pos( + const int nthreads, const int32_t *coors_map, int32_t *pts_id, + int32_t *coors_count, int32_t *reduce_count, int32_t *coors_order) { + CUDA_1D_KERNEL_LOOP(thread_idx, nthreads) { + int coors_idx = coors_map[thread_idx]; + if (coors_idx > -1) { + int32_t coors_pts_pos = atomicAdd(&reduce_count[coors_idx], 1); + pts_id[thread_idx] = coors_pts_pos; + if (coors_pts_pos == 0) { + coors_order[coors_idx] = atomicAdd(coors_count, 1); + } + } + } +} + +template +__global__ void nondisterministic_assign_point_voxel( + const int nthreads, const T *points, const int32_t *coors_map, + const int32_t *pts_id, const int32_t *coors_in, + const int32_t *reduce_count, const int32_t *coors_order, + T *voxels, int32_t *coors, int32_t *pts_count, const int max_voxels, + const int max_points, const int num_features, const int NDim) { + CUDA_1D_KERNEL_LOOP(thread_idx, nthreads) { + int coors_idx = coors_map[thread_idx]; + int coors_pts_pos = pts_id[thread_idx]; + if (coors_idx > -1) { + int coors_pos = coors_order[coors_idx]; + if (coors_pos < max_voxels && coors_pts_pos < max_points) { + auto voxels_offset = + voxels + (coors_pos * max_points + coors_pts_pos) * num_features; + auto points_offset = points + thread_idx * num_features; + for (int k = 0; k < num_features; k++) { + voxels_offset[k] = points_offset[k]; + } + if (coors_pts_pos == 0) { + pts_count[coors_pos] = min(reduce_count[coors_idx], max_points); + auto coors_offset = coors + coors_pos * NDim; + auto coors_in_offset = coors_in + coors_idx * NDim; + for (int k = 0; k < NDim; k++) { + coors_offset[k] = coors_in_offset[k]; + } + } + } + } + } +} + +namespace voxelization { + +int hard_voxelize_gpu(const at::Tensor& points, at::Tensor& voxels, + at::Tensor& coors, at::Tensor& num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3) { + // current version tooks about 0.04s for one frame on cpu + // check device + CHECK_INPUT(points); + + at::cuda::CUDAGuard device_guard(points.device()); + + const int num_points = points.size(0); + const int num_features = points.size(1); + + const float voxel_x = voxel_size[0]; + const float voxel_y = voxel_size[1]; + const float voxel_z = voxel_size[2]; + const float coors_x_min = coors_range[0]; + const float coors_y_min = coors_range[1]; + const float coors_z_min = coors_range[2]; + const float coors_x_max = coors_range[3]; + const float coors_y_max = coors_range[4]; + const float coors_z_max = coors_range[5]; + + const int grid_x = round((coors_x_max - coors_x_min) / voxel_x); + const int grid_y = round((coors_y_max - coors_y_min) / voxel_y); + const int grid_z = round((coors_z_max - coors_z_min) / voxel_z); + + // map points to voxel coors + at::Tensor temp_coors = + at::zeros({num_points, NDim}, points.options().dtype(at::kInt)); + + dim3 grid(std::min(at::cuda::ATenCeilDiv(num_points, 512), 4096)); + dim3 block(512); + + // 1. link point to corresponding voxel coors + AT_DISPATCH_ALL_TYPES( + points.scalar_type(), "hard_voxelize_kernel", ([&] { + dynamic_voxelize_kernel + <<>>( + points.contiguous().data_ptr(), + temp_coors.contiguous().data_ptr(), voxel_x, voxel_y, + voxel_z, coors_x_min, coors_y_min, coors_z_min, coors_x_max, + coors_y_max, coors_z_max, grid_x, grid_y, grid_z, num_points, + num_features, NDim); + })); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + + // 2. map point to the idx of the corresponding voxel, find duplicate coor + // create some temporary variables + auto point_to_pointidx = -at::ones( + { + num_points, + }, + points.options().dtype(at::kInt)); + auto point_to_voxelidx = -at::ones( + { + num_points, + }, + points.options().dtype(at::kInt)); + + dim3 map_grid(std::min(at::cuda::ATenCeilDiv(num_points, 512), 4096)); + dim3 map_block(512); + AT_DISPATCH_ALL_TYPES( + temp_coors.scalar_type(), "determin_duplicate", ([&] { + point_to_voxelidx_kernel + <<>>( + temp_coors.contiguous().data_ptr(), + point_to_voxelidx.contiguous().data_ptr(), + point_to_pointidx.contiguous().data_ptr(), max_points, + max_voxels, num_points, NDim); + })); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + + // 3. determin voxel num and voxel's coor index + // make the logic in the CUDA device could accelerate about 10 times + auto coor_to_voxelidx = -at::ones( + { + num_points, + }, + points.options().dtype(at::kInt)); + auto voxel_num = at::zeros( + { + 1, + }, + points.options().dtype(at::kInt)); // must be zero from the begining + + AT_DISPATCH_ALL_TYPES( + temp_coors.scalar_type(), "determin_duplicate", ([&] { + determin_voxel_num<<<1, 1, 0, at::cuda::getCurrentCUDAStream()>>>( + num_points_per_voxel.contiguous().data_ptr(), + point_to_voxelidx.contiguous().data_ptr(), + point_to_pointidx.contiguous().data_ptr(), + coor_to_voxelidx.contiguous().data_ptr(), + voxel_num.contiguous().data_ptr(), max_points, max_voxels, + num_points); + })); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + + // 4. copy point features to voxels + // Step 4 & 5 could be parallel + auto pts_output_size = num_points * num_features; + dim3 cp_grid(std::min(at::cuda::ATenCeilDiv(pts_output_size, 512), 4096)); + dim3 cp_block(512); + AT_DISPATCH_ALL_TYPES( + points.scalar_type(), "assign_point_to_voxel", ([&] { + assign_point_to_voxel + <<>>( + pts_output_size, points.contiguous().data_ptr(), + point_to_voxelidx.contiguous().data_ptr(), + coor_to_voxelidx.contiguous().data_ptr(), + voxels.contiguous().data_ptr(), max_points, num_features, + num_points, NDim); + })); + // cudaDeviceSynchronize(); + // AT_CUDA_CHECK(cudaGetLastError()); + + // 5. copy coors of each voxels + auto coors_output_size = num_points * NDim; + dim3 coors_cp_grid( + std::min(at::cuda::ATenCeilDiv(coors_output_size, 512), 4096)); + dim3 coors_cp_block(512); + AT_DISPATCH_ALL_TYPES( + points.scalar_type(), "assign_point_to_voxel", ([&] { + assign_voxel_coors<<>>( + coors_output_size, temp_coors.contiguous().data_ptr(), + point_to_voxelidx.contiguous().data_ptr(), + coor_to_voxelidx.contiguous().data_ptr(), + coors.contiguous().data_ptr(), num_points, NDim); + })); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + + auto voxel_num_cpu = voxel_num.to(at::kCPU); + int voxel_num_int = voxel_num_cpu.data_ptr()[0]; + + return voxel_num_int; +} + +int nondisterministic_hard_voxelize_gpu( + const at::Tensor &points, at::Tensor &voxels, + at::Tensor &coors, at::Tensor &num_points_per_voxel, + const std::vector voxel_size, + const std::vector coors_range, + const int max_points, const int max_voxels, + const int NDim = 3) { + + CHECK_INPUT(points); + + at::cuda::CUDAGuard device_guard(points.device()); + + const int num_points = points.size(0); + const int num_features = points.size(1); + + if (num_points == 0) + return 0; + + const float voxel_x = voxel_size[0]; + const float voxel_y = voxel_size[1]; + const float voxel_z = voxel_size[2]; + const float coors_x_min = coors_range[0]; + const float coors_y_min = coors_range[1]; + const float coors_z_min = coors_range[2]; + const float coors_x_max = coors_range[3]; + const float coors_y_max = coors_range[4]; + const float coors_z_max = coors_range[5]; + + const int grid_x = round((coors_x_max - coors_x_min) / voxel_x); + const int grid_y = round((coors_y_max - coors_y_min) / voxel_y); + const int grid_z = round((coors_z_max - coors_z_min) / voxel_z); + + // map points to voxel coors + at::Tensor temp_coors = + at::zeros({num_points, NDim}, points.options().dtype(torch::kInt32)); + + dim3 grid(std::min(at::cuda::ATenCeilDiv(num_points, 512), 4096)); + dim3 block(512); + + // 1. link point to corresponding voxel coors + AT_DISPATCH_ALL_TYPES( + points.scalar_type(), "hard_voxelize_kernel", ([&] { + dynamic_voxelize_kernel + <<>>( + points.contiguous().data_ptr(), + temp_coors.contiguous().data_ptr(), voxel_x, voxel_y, + voxel_z, coors_x_min, coors_y_min, coors_z_min, coors_x_max, + coors_y_max, coors_z_max, grid_x, grid_y, grid_z, num_points, + num_features, NDim); + })); + + at::Tensor coors_map; + at::Tensor coors_count; + at::Tensor coors_order; + at::Tensor reduce_count; + at::Tensor pts_id; + + auto coors_clean = temp_coors.masked_fill(temp_coors.lt(0).any(-1, true), -1); + + std::tie(temp_coors, coors_map, reduce_count) = + at::unique_dim(coors_clean, 0, true, true, false); + + if (temp_coors.index({0, 0}).lt(0).item()) { + // the first element of temp_coors is (-1,-1,-1) and should be removed + temp_coors = temp_coors.slice(0, 1); + coors_map = coors_map - 1; + } + + int num_coors = temp_coors.size(0); + temp_coors = temp_coors.to(torch::kInt32); + coors_map = coors_map.to(torch::kInt32); + + coors_count = coors_map.new_zeros(1); + coors_order = coors_map.new_empty(num_coors); + reduce_count = coors_map.new_zeros(num_coors); + pts_id = coors_map.new_zeros(num_points); + + dim3 cp_grid(std::min(at::cuda::ATenCeilDiv(num_points, 512), 4096)); + dim3 cp_block(512); + AT_DISPATCH_ALL_TYPES(points.scalar_type(), "get_assign_pos", ([&] { + nondisterministic_get_assign_pos<<>>( + num_points, + coors_map.contiguous().data_ptr(), + pts_id.contiguous().data_ptr(), + coors_count.contiguous().data_ptr(), + reduce_count.contiguous().data_ptr(), + coors_order.contiguous().data_ptr()); + })); + + AT_DISPATCH_ALL_TYPES( + points.scalar_type(), "assign_point_to_voxel", ([&] { + nondisterministic_assign_point_voxel + <<>>( + num_points, points.contiguous().data_ptr(), + coors_map.contiguous().data_ptr(), + pts_id.contiguous().data_ptr(), + temp_coors.contiguous().data_ptr(), + reduce_count.contiguous().data_ptr(), + coors_order.contiguous().data_ptr(), + voxels.contiguous().data_ptr(), + coors.contiguous().data_ptr(), + num_points_per_voxel.contiguous().data_ptr(), + max_voxels, max_points, + num_features, NDim); + })); + AT_CUDA_CHECK(cudaGetLastError()); + return max_voxels < num_coors ? max_voxels : num_coors; +} + +void dynamic_voxelize_gpu(const at::Tensor& points, at::Tensor& coors, + const std::vector voxel_size, + const std::vector coors_range, + const int NDim = 3) { + // current version tooks about 0.04s for one frame on cpu + // check device + CHECK_INPUT(points); + + at::cuda::CUDAGuard device_guard(points.device()); + + const int num_points = points.size(0); + const int num_features = points.size(1); + + const float voxel_x = voxel_size[0]; + const float voxel_y = voxel_size[1]; + const float voxel_z = voxel_size[2]; + const float coors_x_min = coors_range[0]; + const float coors_y_min = coors_range[1]; + const float coors_z_min = coors_range[2]; + const float coors_x_max = coors_range[3]; + const float coors_y_max = coors_range[4]; + const float coors_z_max = coors_range[5]; + + const int grid_x = round((coors_x_max - coors_x_min) / voxel_x); + const int grid_y = round((coors_y_max - coors_y_min) / voxel_y); + const int grid_z = round((coors_z_max - coors_z_min) / voxel_z); + + const int col_blocks = at::cuda::ATenCeilDiv(num_points, threadsPerBlock); + dim3 blocks(col_blocks); + dim3 threads(threadsPerBlock); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_ALL_TYPES(points.scalar_type(), "dynamic_voxelize_kernel", [&] { + dynamic_voxelize_kernel<<>>( + points.contiguous().data_ptr(), + coors.contiguous().data_ptr(), voxel_x, voxel_y, voxel_z, + coors_x_min, coors_y_min, coors_z_min, coors_x_max, coors_y_max, + coors_z_max, grid_x, grid_y, grid_z, num_points, num_features, NDim); + }); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + + return; +} + +} // namespace voxelization diff --git a/cv/3d_detection/pointpillars/pytorch/pre_process_kitti.py b/cv/3d_detection/pointpillars/pytorch/pre_process_kitti.py new file mode 100755 index 000000000..ad5067408 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/pre_process_kitti.py @@ -0,0 +1,160 @@ +import argparse +import pdb +import cv2 +import numpy as np +import os +from tqdm import tqdm +import sys +CUR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(CUR) + +from utils import read_points, write_points, read_calib, read_label, \ + write_pickle, remove_outside_points, get_points_num_in_bbox, \ + points_in_bboxes_v2 + + +def judge_difficulty(annotation_dict): + truncated = annotation_dict['truncated'] + occluded = annotation_dict['occluded'] + bbox = annotation_dict['bbox'] + height = bbox[:, 3] - bbox[:, 1] + + MIN_HEIGHTS = [40, 25, 25] + MAX_OCCLUSION = [0, 1, 2] + MAX_TRUNCATION = [0.15, 0.30, 0.50] + difficultys = [] + for h, o, t in zip(height, occluded, truncated): + difficulty = -1 + for i in range(2, -1, -1): + if h > MIN_HEIGHTS[i] and o <= MAX_OCCLUSION[i] and t <= MAX_TRUNCATION[i]: + difficulty = i + difficultys.append(difficulty) + return np.array(difficultys, dtype=np.int) + + +def create_data_info_pkl(data_root, data_type, prefix, label=True, db=False): + sep = os.path.sep + print(f"Processing {data_type} data..") + ids_file = os.path.join(CUR, 'dataset', 'ImageSets', f'{data_type}.txt') + with open(ids_file, 'r') as f: + ids = [id.strip() for id in f.readlines()] + + split = 'training' if label else 'testing' + + kitti_infos_dict = {} + if db: + kitti_dbinfos_train = {} + db_points_saved_path = os.path.join(data_root, f'{prefix}_gt_database') + os.makedirs(db_points_saved_path, exist_ok=True) + for id in tqdm(ids): + cur_info_dict={} + img_path = os.path.join(data_root, split, 'image_2', f'{id}.png') + lidar_path = os.path.join(data_root, split, 'velodyne', f'{id}.bin') + calib_path = os.path.join(data_root, split, 'calib', f'{id}.txt') + cur_info_dict['velodyne_path'] = sep.join(lidar_path.split(sep)[-3:]) + + img = cv2.imread(img_path) + image_shape = img.shape[:2] + cur_info_dict['image'] = { + 'image_shape': image_shape, + 'image_path': sep.join(img_path.split(sep)[-3:]), + 'image_idx': int(id), + } + + calib_dict = read_calib(calib_path) + cur_info_dict['calib'] = calib_dict + + lidar_points = read_points(lidar_path) + reduced_lidar_points = remove_outside_points( + points=lidar_points, + r0_rect=calib_dict['R0_rect'], + tr_velo_to_cam=calib_dict['Tr_velo_to_cam'], + P2=calib_dict['P2'], + image_shape=image_shape) + saved_reduced_path = os.path.join(data_root, split, 'velodyne_reduced') + os.makedirs(saved_reduced_path, exist_ok=True) + saved_reduced_points_name = os.path.join(saved_reduced_path, f'{id}.bin') + write_points(reduced_lidar_points, saved_reduced_points_name) + + if label: + label_path = os.path.join(data_root, split, 'label_2', f'{id}.txt') + annotation_dict = read_label(label_path) + annotation_dict['difficulty'] = judge_difficulty(annotation_dict) + annotation_dict['num_points_in_gt'] = get_points_num_in_bbox( + points=reduced_lidar_points, + r0_rect=calib_dict['R0_rect'], + tr_velo_to_cam=calib_dict['Tr_velo_to_cam'], + dimensions=annotation_dict['dimensions'], + location=annotation_dict['location'], + rotation_y=annotation_dict['rotation_y'], + name=annotation_dict['name']) + cur_info_dict['annos'] = annotation_dict + + if db: + indices, n_total_bbox, n_valid_bbox, bboxes_lidar, name = \ + points_in_bboxes_v2( + points=lidar_points, + r0_rect=calib_dict['R0_rect'].astype(np.float32), + tr_velo_to_cam=calib_dict['Tr_velo_to_cam'].astype(np.float32), + dimensions=annotation_dict['dimensions'].astype(np.float32), + location=annotation_dict['location'].astype(np.float32), + rotation_y=annotation_dict['rotation_y'].astype(np.float32), + name=annotation_dict['name'] + ) + for j in range(n_valid_bbox): + db_points = lidar_points[indices[:, j]] + db_points[:, :3] -= bboxes_lidar[j, :3] + db_points_saved_name = os.path.join(db_points_saved_path, f'{int(id)}_{name[j]}_{j}.bin') + write_points(db_points, db_points_saved_name) + + db_info={ + 'name': name[j], + 'path': os.path.join(os.path.basename(db_points_saved_path), f'{int(id)}_{name[j]}_{j}.bin'), + 'box3d_lidar': bboxes_lidar[j], + 'difficulty': annotation_dict['difficulty'][j], + 'num_points_in_gt': len(db_points), + } + if name[j] not in kitti_dbinfos_train: + kitti_dbinfos_train[name[j]] = [db_info] + else: + kitti_dbinfos_train[name[j]].append(db_info) + + kitti_infos_dict[int(id)] = cur_info_dict + + saved_path = os.path.join(data_root, f'{prefix}_infos_{data_type}.pkl') + write_pickle(kitti_infos_dict, saved_path) + if db: + saved_db_path = os.path.join(data_root, f'{prefix}_dbinfos_train.pkl') + write_pickle(kitti_dbinfos_train, saved_db_path) + return kitti_infos_dict + + +def main(args): + data_root = args.data_root + prefix = args.prefix + + ## 1. train: create data infomation pkl file && create reduced point clouds + ## && create database(points in gt bbox) for data aumentation + kitti_train_infos_dict = create_data_info_pkl(data_root, 'train', prefix, db=True) + + ## 2. val: create data infomation pkl file && create reduced point clouds + kitti_val_infos_dict = create_data_info_pkl(data_root, 'val', prefix) + + ## 3. trainval: create data infomation pkl file + kitti_trainval_infos_dict = {**kitti_train_infos_dict, **kitti_val_infos_dict} + saved_path = os.path.join(data_root, f'{prefix}_infos_trainval.pkl') + write_pickle(kitti_trainval_infos_dict, saved_path) + + ## 4. test: create data infomation pkl file && create reduced point clouds + kitti_test_infos_dict = create_data_info_pkl(data_root, 'test', prefix, label=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Dataset infomation') + parser.add_argument('--data_root', default='/mnt/ssd1/lifa_rdata/det/kitti', + help='your data root for kitti') + parser.add_argument('--prefix', default='kitti', + help='the prefix name for the saved .pkl file') + args = parser.parse_args() + + main(args) \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/requirements.txt b/cv/3d_detection/pointpillars/pytorch/requirements.txt new file mode 100755 index 000000000..7844bc5ef --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/requirements.txt @@ -0,0 +1,9 @@ +numba +numpy==1.19.5 +# open3d==0.13.0 +opencv_python==4.5.5.62 +# pointpillars.egg==info +PyYAML==6.0 +setuptools==58.0.4 +# torch==1.8.1+cu111 +tqdm==4.62.3 diff --git a/cv/3d_detection/pointpillars/pytorch/test.py b/cv/3d_detection/pointpillars/pytorch/test.py new file mode 100755 index 000000000..ad848d54b --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/test.py @@ -0,0 +1,138 @@ +import argparse +import cv2 +import numpy as np +import os +import torch +import pdb + +from utils import setup_seed, read_points, read_calib, read_label, \ + keep_bbox_from_image_range, keep_bbox_from_lidar_range, vis_pc, \ + vis_img_3d, bbox3d2corners_camera, points_camera2image, \ + bbox_camera2lidar +from model import PointPillars + + +def point_range_filter(pts, point_range=[0, -39.68, -3, 69.12, 39.68, 1]): + ''' + data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty) + point_range: [x1, y1, z1, x2, y2, z2] + ''' + flag_x_low = pts[:, 0] > point_range[0] + flag_y_low = pts[:, 1] > point_range[1] + flag_z_low = pts[:, 2] > point_range[2] + flag_x_high = pts[:, 0] < point_range[3] + flag_y_high = pts[:, 1] < point_range[4] + flag_z_high = pts[:, 2] < point_range[5] + keep_mask = flag_x_low & flag_y_low & flag_z_low & flag_x_high & flag_y_high & flag_z_high + pts = pts[keep_mask] + return pts + + +def main(args): + CLASSES = { + 'Pedestrian': 0, + 'Cyclist': 1, + 'Car': 2 + } + LABEL2CLASSES = {v:k for k, v in CLASSES.items()} + pcd_limit_range = np.array([0, -40, -3, 70.4, 40, 0.0], dtype=np.float32) + + if not args.no_cuda: + model = PointPillars(nclasses=len(CLASSES)).cuda() + model.load_state_dict(torch.load(args.ckpt)) + else: + model = PointPillars(nclasses=len(CLASSES)) + model.load_state_dict( + torch.load(args.ckpt, map_location=torch.device('cpu'))) + + if not os.path.exists(args.pc_path): + raise FileNotFoundError + pc = read_points(args.pc_path) + pc = point_range_filter(pc) + pc_torch = torch.from_numpy(pc) + if os.path.exists(args.calib_path): + calib_info = read_calib(args.calib_path) + else: + calib_info = None + + if os.path.exists(args.gt_path): + gt_label = read_label(args.gt_path) + else: + gt_label = None + + if os.path.exists(args.img_path): + img = cv2.imread(args.img_path, 1) + else: + img = None + + model.eval() + with torch.no_grad(): + if not args.no_cuda: + pc_torch = pc_torch.cuda() + + result_filter = model(batched_pts=[pc_torch], + mode='test')[0] + if calib_info is not None and img is not None: + tr_velo_to_cam = calib_info['Tr_velo_to_cam'].astype(np.float32) + r0_rect = calib_info['R0_rect'].astype(np.float32) + P2 = calib_info['P2'].astype(np.float32) + + image_shape = img.shape[:2] + result_filter = keep_bbox_from_image_range(result_filter, tr_velo_to_cam, r0_rect, P2, image_shape) + + result_filter = keep_bbox_from_lidar_range(result_filter, pcd_limit_range) + lidar_bboxes = result_filter['lidar_bboxes'] + labels, scores = result_filter['labels'], result_filter['scores'] + + vis_pc(pc, bboxes=lidar_bboxes, labels=labels) + + if calib_info is not None and img is not None: + bboxes2d, camera_bboxes = result_filter['bboxes2d'], result_filter['camera_bboxes'] + bboxes_corners = bbox3d2corners_camera(camera_bboxes) + image_points = points_camera2image(bboxes_corners, P2) + img = vis_img_3d(img, image_points, labels, rt=True) + + if calib_info is not None and gt_label is not None: + tr_velo_to_cam = calib_info['Tr_velo_to_cam'].astype(np.float32) + r0_rect = calib_info['R0_rect'].astype(np.float32) + + dimensions = gt_label['dimensions'] + location = gt_label['location'] + rotation_y = gt_label['rotation_y'] + gt_labels = np.array([CLASSES.get(item, -1) for item in gt_label['name']]) + sel = gt_labels != -1 + gt_labels = gt_labels[sel] + bboxes_camera = np.concatenate([location, dimensions, rotation_y[:, None]], axis=-1) + gt_lidar_bboxes = bbox_camera2lidar(bboxes_camera, tr_velo_to_cam, r0_rect) + bboxes_camera = bboxes_camera[sel] + gt_lidar_bboxes = gt_lidar_bboxes[sel] + + gt_labels = [-1] * len(gt_label['name']) # to distinguish between the ground truth and the predictions + + pred_gt_lidar_bboxes = np.concatenate([lidar_bboxes, gt_lidar_bboxes], axis=0) + pred_gt_labels = np.concatenate([labels, gt_labels]) + vis_pc(pc, pred_gt_lidar_bboxes, labels=pred_gt_labels) + + if img is not None: + bboxes_corners = bbox3d2corners_camera(bboxes_camera) + image_points = points_camera2image(bboxes_corners, P2) + gt_labels = [-1] * len(gt_label['name']) + img = vis_img_3d(img, image_points, gt_labels, rt=True) + + if calib_info is not None and img is not None: + cv2.imshow(f'{os.path.basename(args.img_path)}-3d bbox', img) + cv2.waitKey(0) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Configuration Parameters') + parser.add_argument('--ckpt', default='pretrained/epoch_160.pth', help='your checkpoint for kitti') + parser.add_argument('--pc_path', help='your point cloud path') + parser.add_argument('--calib_path', default='', help='your calib file path') + parser.add_argument('--gt_path', default='', help='your ground truth path') + parser.add_argument('--img_path', default='', help='your image path') + parser.add_argument('--no_cuda', action='store_true', + help='whether to use cuda') + args = parser.parse_args() + + main(args) diff --git a/cv/3d_detection/pointpillars/pytorch/train.py b/cv/3d_detection/pointpillars/pytorch/train.py new file mode 100755 index 000000000..533796313 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/train.py @@ -0,0 +1,217 @@ +import argparse +import os +import torch +from tqdm import tqdm +import pdb + +from utils import setup_seed +from dataset import Kitti, get_dataloader +from model import PointPillars +from loss import Loss +from torch.utils.tensorboard import SummaryWriter + + +def save_summary(writer, loss_dict, global_step, tag, lr=None, momentum=None): + for k, v in loss_dict.items(): + writer.add_scalar(f'{tag}/{k}', v, global_step) + if lr is not None: + writer.add_scalar('lr', lr, global_step) + if momentum is not None: + writer.add_scalar('momentum', momentum, global_step) + + +def main(args): + setup_seed() + train_dataset = Kitti(data_root=args.data_root, + split='train') + print("train data lens {}" .format(len(train_dataset))) + val_dataset = Kitti(data_root=args.data_root, + split='val') + print("val data lens {}" .format(len(val_dataset))) + train_dataloader = get_dataloader(dataset=train_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=True) + val_dataloader = get_dataloader(dataset=val_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + shuffle=False) + + if not args.no_cuda: + pointpillars = PointPillars(nclasses=args.nclasses).cuda() + else: + pointpillars = PointPillars(nclasses=args.nclasses) + loss_func = Loss() + + max_iters = len(train_dataloader) * args.max_epoch + init_lr = args.init_lr + optimizer = torch.optim.AdamW(params=pointpillars.parameters(), + lr=init_lr, + betas=(0.95, 0.99), + weight_decay=0.01) + scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, + max_lr=init_lr*10, + total_steps=max_iters, + pct_start=0.4, + anneal_strategy='cos', + cycle_momentum=True, + base_momentum=0.95*0.895, + max_momentum=0.95, + div_factor=10) + saved_logs_path = os.path.join(args.saved_path, 'summary') + os.makedirs(saved_logs_path, exist_ok=True) + writer = SummaryWriter(saved_logs_path) + saved_ckpt_path = os.path.join(args.saved_path, 'checkpoints') + os.makedirs(saved_ckpt_path, exist_ok=True) + + for epoch in range(args.max_epoch): + print('=' * 20, epoch, '=' * 20) + train_step, val_step = 0, 0 + for i, data_dict in enumerate(tqdm(train_dataloader)): + if not args.no_cuda: + # move the tensors to the cuda + for key in data_dict: + for j, item in enumerate(data_dict[key]): + if torch.is_tensor(item): + data_dict[key][j] = data_dict[key][j].cuda() + + optimizer.zero_grad() + + batched_pts = data_dict['batched_pts'] + batched_gt_bboxes = data_dict['batched_gt_bboxes'] + batched_labels = data_dict['batched_labels'] + batched_difficulty = data_dict['batched_difficulty'] + bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict = \ + pointpillars(batched_pts=batched_pts, + mode='train', + batched_gt_bboxes=batched_gt_bboxes, + batched_gt_labels=batched_labels) + + bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(-1, args.nclasses) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 7) + bbox_dir_cls_pred = bbox_dir_cls_pred.permute(0, 2, 3, 1).reshape(-1, 2) + + batched_bbox_labels = anchor_target_dict['batched_labels'].reshape(-1) + batched_label_weights = anchor_target_dict['batched_label_weights'].reshape(-1) + batched_bbox_reg = anchor_target_dict['batched_bbox_reg'].reshape(-1, 7) + # batched_bbox_reg_weights = anchor_target_dict['batched_bbox_reg_weights'].reshape(-1) + batched_dir_labels = anchor_target_dict['batched_dir_labels'].reshape(-1) + # batched_dir_labels_weights = anchor_target_dict['batched_dir_labels_weights'].reshape(-1) + + pos_idx = (batched_bbox_labels >= 0) & (batched_bbox_labels < args.nclasses) + bbox_pred = bbox_pred[pos_idx] + batched_bbox_reg = batched_bbox_reg[pos_idx] + # sin(a - b) = sin(a)*cos(b) - cos(a)*sin(b) + bbox_pred[:, -1] = torch.sin(bbox_pred[:, -1].clone()) * torch.cos(batched_bbox_reg[:, -1].clone()) + batched_bbox_reg[:, -1] = torch.cos(bbox_pred[:, -1].clone()) * torch.sin(batched_bbox_reg[:, -1].clone()) + bbox_dir_cls_pred = bbox_dir_cls_pred[pos_idx] + batched_dir_labels = batched_dir_labels[pos_idx] + + num_cls_pos = (batched_bbox_labels < args.nclasses).sum() + bbox_cls_pred = bbox_cls_pred[batched_label_weights > 0] + batched_bbox_labels[batched_bbox_labels < 0] = args.nclasses + batched_bbox_labels = batched_bbox_labels[batched_label_weights > 0] + + loss_dict = loss_func(bbox_cls_pred=bbox_cls_pred, + bbox_pred=bbox_pred, + bbox_dir_cls_pred=bbox_dir_cls_pred, + batched_labels=batched_bbox_labels, + num_cls_pos=num_cls_pos, + batched_bbox_reg=batched_bbox_reg, + batched_dir_labels=batched_dir_labels) + + loss = loss_dict['total_loss'] + loss.backward() + # torch.nn.utils.clip_grad_norm_(pointpillars.parameters(), max_norm=35) + optimizer.step() + scheduler.step() + + global_step = epoch * len(train_dataloader) + train_step + 1 + + if global_step % args.log_freq == 0: + save_summary(writer, loss_dict, global_step, 'train', + lr=optimizer.param_groups[0]['lr'], + momentum=optimizer.param_groups[0]['betas'][0]) + train_step += 1 + if (epoch + 1) % args.ckpt_freq_epoch == 0: + torch.save(pointpillars.state_dict(), os.path.join(saved_ckpt_path, f'epoch_{epoch+1}.pth')) + + if epoch % 2 == 0: + continue + pointpillars.eval() + with torch.no_grad(): + for i, data_dict in enumerate(tqdm(val_dataloader)): + if not args.no_cuda: + # move the tensors to the cuda + for key in data_dict: + for j, item in enumerate(data_dict[key]): + if torch.is_tensor(item): + data_dict[key][j] = data_dict[key][j].cuda() + + batched_pts = data_dict['batched_pts'] + batched_gt_bboxes = data_dict['batched_gt_bboxes'] + batched_labels = data_dict['batched_labels'] + batched_difficulty = data_dict['batched_difficulty'] + bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict = \ + pointpillars(batched_pts=batched_pts, + mode='train', + batched_gt_bboxes=batched_gt_bboxes, + batched_gt_labels=batched_labels) + + bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(-1, args.nclasses) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 7) + bbox_dir_cls_pred = bbox_dir_cls_pred.permute(0, 2, 3, 1).reshape(-1, 2) + + batched_bbox_labels = anchor_target_dict['batched_labels'].reshape(-1) + batched_label_weights = anchor_target_dict['batched_label_weights'].reshape(-1) + batched_bbox_reg = anchor_target_dict['batched_bbox_reg'].reshape(-1, 7) + # batched_bbox_reg_weights = anchor_target_dict['batched_bbox_reg_weights'].reshape(-1) + batched_dir_labels = anchor_target_dict['batched_dir_labels'].reshape(-1) + # batched_dir_labels_weights = anchor_target_dict['batched_dir_labels_weights'].reshape(-1) + + pos_idx = (batched_bbox_labels >= 0) & (batched_bbox_labels < args.nclasses) + bbox_pred = bbox_pred[pos_idx] + batched_bbox_reg = batched_bbox_reg[pos_idx] + # sin(a - b) = sin(a)*cos(b) - cos(a)*sin(b) + bbox_pred[:, -1] = torch.sin(bbox_pred[:, -1]) * torch.cos(batched_bbox_reg[:, -1]) + batched_bbox_reg[:, -1] = torch.cos(bbox_pred[:, -1]) * torch.sin(batched_bbox_reg[:, -1]) + bbox_dir_cls_pred = bbox_dir_cls_pred[pos_idx] + batched_dir_labels = batched_dir_labels[pos_idx] + + num_cls_pos = (batched_bbox_labels < args.nclasses).sum() + bbox_cls_pred = bbox_cls_pred[batched_label_weights > 0] + batched_bbox_labels[batched_bbox_labels < 0] = args.nclasses + batched_bbox_labels = batched_bbox_labels[batched_label_weights > 0] + + loss_dict = loss_func(bbox_cls_pred=bbox_cls_pred, + bbox_pred=bbox_pred, + bbox_dir_cls_pred=bbox_dir_cls_pred, + batched_labels=batched_bbox_labels, + num_cls_pos=num_cls_pos, + batched_bbox_reg=batched_bbox_reg, + batched_dir_labels=batched_dir_labels) + + global_step = epoch * len(val_dataloader) + val_step + 1 + if global_step % args.log_freq == 0: + save_summary(writer, loss_dict, global_step, 'val') + val_step += 1 + pointpillars.train() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Configuration Parameters') + parser.add_argument('--data_root', default='/mnt/ssd1/lifa_rdata/det/kitti', + help='your data root for kitti') + parser.add_argument('--saved_path', default='pillar_logs') + parser.add_argument('--batch_size', type=int, default=6) + parser.add_argument('--num_workers', type=int, default=4) + parser.add_argument('--nclasses', type=int, default=3) + parser.add_argument('--init_lr', type=float, default=0.00025) + parser.add_argument('--max_epoch', type=int, default=160) + parser.add_argument('--log_freq', type=int, default=8) + parser.add_argument('--ckpt_freq_epoch', type=int, default=20) + parser.add_argument('--no_cuda', action='store_true', + help='whether to use cuda') + args = parser.parse_args() + + main(args) diff --git a/cv/3d_detection/pointpillars/pytorch/train_dist.py b/cv/3d_detection/pointpillars/pytorch/train_dist.py new file mode 100644 index 000000000..48cd54710 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/train_dist.py @@ -0,0 +1,255 @@ +# Copyright (c) 2023, Shanghai Iluvatar CoreX Semiconductor Co., Ltd. +# All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. You may obtain +# a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations +# under the License. +import argparse +import os +import torch +from tqdm import tqdm +import pdb + +from utils import setup_seed +from dataset import Kitti, get_dataloader_dist +from model import PointPillars +from loss import Loss +from torch.utils.tensorboard import SummaryWriter + +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP + +def save_summary(writer, loss_dict, global_step, tag, lr=None, momentum=None): + for k, v in loss_dict.items(): + writer.add_scalar(f'{tag}/{k}', v, global_step) + if lr is not None: + writer.add_scalar('lr', lr, global_step) + if momentum is not None: + writer.add_scalar('momentum', momentum, global_step) + + +def main(args): + setup_seed() + local_rank = args.local_rank + + # DDP：DDP backend初始化 + torch.cuda.set_device(local_rank) + dist.init_process_group(backend='nccl') # nccl是GPU设备上最快、最推荐的后端 + + train_dataset = Kitti(data_root=args.data_root, + split='train') + print("train data lens {}" .format(len(train_dataset))) + val_dataset = Kitti(data_root=args.data_root, + split='val') + print("val data lens {}" .format(len(val_dataset))) + + # DDP：使用DistributedSampler，DDP帮我们把细节都封装起来了。 + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) + # DDP：需要注意的是，这里的batch_size指的是每个进程下的batch_size。 + # 也就是说，总batch_size是这里的batch_size再乘以并行数(world_size)。 + val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset) + + train_dataloader = get_dataloader_dist(dataset=train_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + # shuffle=True, + sampler=train_sampler) + val_dataloader = get_dataloader_dist(dataset=val_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + # shuffle=False, + sampler=val_sampler) + + pointpillars = PointPillars(nclasses=args.nclasses) + pointpillars.to(local_rank) + + # DDP: 构造DDP model + pointpillars = DDP(pointpillars, device_ids=[local_rank], output_device=local_rank) + + loss_func = Loss() + + max_iters = len(train_dataloader) * args.max_epoch + init_lr = args.init_lr + optimizer = torch.optim.AdamW(params=pointpillars.parameters(), + lr=init_lr, + betas=(0.95, 0.99), + weight_decay=0.01) + scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, + max_lr=init_lr*10, + total_steps=max_iters, + pct_start=0.4, + anneal_strategy='cos', + cycle_momentum=True, + base_momentum=0.95*0.895, + max_momentum=0.95, + div_factor=10) + saved_logs_path = os.path.join(args.saved_path, 'summary') + os.makedirs(saved_logs_path, exist_ok=True) + writer = SummaryWriter(saved_logs_path) + saved_ckpt_path = os.path.join(args.saved_path, 'checkpoints') + os.makedirs(saved_ckpt_path, exist_ok=True) + + for epoch in range(args.max_epoch): + # DDP：设置sampler的epoch， + # DistributedSampler需要这个来指定shuffle方式， + # 通过维持各个进程之间的相同随机数种子使不同进程能获得同样的shuffle效果。 + train_dataloader.sampler.set_epoch(epoch) + # 后面这部分，则与原来完全一致了。 + print('=' * 20, epoch, '=' * 20) + train_step, val_step = 0, 0 + for i, data_dict in enumerate(tqdm(train_dataloader)): + # move the tensors to the cuda + for key in data_dict: + for j, item in enumerate(data_dict[key]): + if torch.is_tensor(item): + data_dict[key][j] = data_dict[key][j] + + optimizer.zero_grad() + + batched_pts = data_dict['batched_pts'] + batched_gt_bboxes = data_dict['batched_gt_bboxes'] + batched_labels = data_dict['batched_labels'] + batched_difficulty = data_dict['batched_difficulty'] + bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict = \ + pointpillars(batched_pts=batched_pts, + mode='train', + batched_gt_bboxes=batched_gt_bboxes, + batched_gt_labels=batched_labels) + + bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(-1, args.nclasses) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 7) + bbox_dir_cls_pred = bbox_dir_cls_pred.permute(0, 2, 3, 1).reshape(-1, 2) + + batched_bbox_labels = anchor_target_dict['batched_labels'].reshape(-1) + batched_label_weights = anchor_target_dict['batched_label_weights'].reshape(-1) + batched_bbox_reg = anchor_target_dict['batched_bbox_reg'].reshape(-1, 7) + # batched_bbox_reg_weights = anchor_target_dict['batched_bbox_reg_weights'].reshape(-1) + batched_dir_labels = anchor_target_dict['batched_dir_labels'].reshape(-1) + # batched_dir_labels_weights = anchor_target_dict['batched_dir_labels_weights'].reshape(-1) + + pos_idx = (batched_bbox_labels >= 0) & (batched_bbox_labels < args.nclasses) + bbox_pred = bbox_pred[pos_idx] + batched_bbox_reg = batched_bbox_reg[pos_idx] + # sin(a - b) = sin(a)*cos(b) - cos(a)*sin(b) + bbox_pred[:, -1] = torch.sin(bbox_pred[:, -1].clone()) * torch.cos(batched_bbox_reg[:, -1].clone()) + batched_bbox_reg[:, -1] = torch.cos(bbox_pred[:, -1].clone()) * torch.sin(batched_bbox_reg[:, -1].clone()) + bbox_dir_cls_pred = bbox_dir_cls_pred[pos_idx] + batched_dir_labels = batched_dir_labels[pos_idx] + + num_cls_pos = (batched_bbox_labels < args.nclasses).sum() + bbox_cls_pred = bbox_cls_pred[batched_label_weights > 0] + batched_bbox_labels[batched_bbox_labels < 0] = args.nclasses + batched_bbox_labels = batched_bbox_labels[batched_label_weights > 0] + + loss_dict = loss_func(bbox_cls_pred=bbox_cls_pred, + bbox_pred=bbox_pred, + bbox_dir_cls_pred=bbox_dir_cls_pred, + batched_labels=batched_bbox_labels, + num_cls_pos=num_cls_pos, + batched_bbox_reg=batched_bbox_reg, + batched_dir_labels=batched_dir_labels) + + loss = loss_dict['total_loss'] + loss.backward() + # torch.nn.utils.clip_grad_norm_(pointpillars.parameters(), max_norm=35) + optimizer.step() + scheduler.step() + + if dist.get_rank() == 0: + global_step = epoch * len(train_dataloader) + train_step + 1 + + if global_step % args.log_freq == 0: + save_summary(writer, loss_dict, global_step, 'train', + lr=optimizer.param_groups[0]['lr'], + momentum=optimizer.param_groups[0]['betas'][0]) + train_step += 1 + if dist.get_rank() == 0: + if (epoch + 1) % args.ckpt_freq_epoch == 0: + torch.save(pointpillars.state_dict(), os.path.join(saved_ckpt_path, f'epoch_{epoch+1}.pth')) + + if epoch % 2 == 0: + continue + pointpillars.eval() + with torch.no_grad(): + for i, data_dict in enumerate(tqdm(val_dataloader)): + # move the tensors to the cuda + for key in data_dict: + for j, item in enumerate(data_dict[key]): + if torch.is_tensor(item): + data_dict[key][j] = data_dict[key][j] + + batched_pts = data_dict['batched_pts'] + batched_gt_bboxes = data_dict['batched_gt_bboxes'] + batched_labels = data_dict['batched_labels'] + batched_difficulty = data_dict['batched_difficulty'] + bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict = \ + pointpillars(batched_pts=batched_pts, + mode='train', + batched_gt_bboxes=batched_gt_bboxes, + batched_gt_labels=batched_labels) + + bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(-1, args.nclasses) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 7) + bbox_dir_cls_pred = bbox_dir_cls_pred.permute(0, 2, 3, 1).reshape(-1, 2) + + batched_bbox_labels = anchor_target_dict['batched_labels'].reshape(-1) + batched_label_weights = anchor_target_dict['batched_label_weights'].reshape(-1) + batched_bbox_reg = anchor_target_dict['batched_bbox_reg'].reshape(-1, 7) + # batched_bbox_reg_weights = anchor_target_dict['batched_bbox_reg_weights'].reshape(-1) + batched_dir_labels = anchor_target_dict['batched_dir_labels'].reshape(-1) + # batched_dir_labels_weights = anchor_target_dict['batched_dir_labels_weights'].reshape(-1) + + pos_idx = (batched_bbox_labels >= 0) & (batched_bbox_labels < args.nclasses) + bbox_pred = bbox_pred[pos_idx] + batched_bbox_reg = batched_bbox_reg[pos_idx] + # sin(a - b) = sin(a)*cos(b) - cos(a)*sin(b) + bbox_pred[:, -1] = torch.sin(bbox_pred[:, -1]) * torch.cos(batched_bbox_reg[:, -1]) + batched_bbox_reg[:, -1] = torch.cos(bbox_pred[:, -1]) * torch.sin(batched_bbox_reg[:, -1]) + bbox_dir_cls_pred = bbox_dir_cls_pred[pos_idx] + batched_dir_labels = batched_dir_labels[pos_idx] + + num_cls_pos = (batched_bbox_labels < args.nclasses).sum() + bbox_cls_pred = bbox_cls_pred[batched_label_weights > 0] + batched_bbox_labels[batched_bbox_labels < 0] = args.nclasses + batched_bbox_labels = batched_bbox_labels[batched_label_weights > 0] + + loss_dict = loss_func(bbox_cls_pred=bbox_cls_pred, + bbox_pred=bbox_pred, + bbox_dir_cls_pred=bbox_dir_cls_pred, + batched_labels=batched_bbox_labels, + num_cls_pos=num_cls_pos, + batched_bbox_reg=batched_bbox_reg, + batched_dir_labels=batched_dir_labels) + + global_step = epoch * len(val_dataloader) + val_step + 1 + if global_step % args.log_freq == 0: + save_summary(writer, loss_dict, global_step, 'val') + val_step += 1 + pointpillars.train() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Configuration Parameters') + parser.add_argument('--data_root', default='/mnt/ssd1/lifa_rdata/det/kitti', + help='your data root for kitti') + parser.add_argument('--saved_path', default='pillar_logs') + parser.add_argument('--batch_size', type=int, default=6) + parser.add_argument('--num_workers', type=int, default=4) + parser.add_argument('--nclasses', type=int, default=3) + parser.add_argument('--init_lr', type=float, default=0.00025) + parser.add_argument('--max_epoch', type=int, default=160) + parser.add_argument('--log_freq', type=int, default=8) + parser.add_argument('--ckpt_freq_epoch', type=int, default=20) + parser.add_argument("--local_rank", default=-1, type=int) + + args = parser.parse_args() + + main(args) diff --git a/cv/3d_detection/pointpillars/pytorch/utils/__init__.py b/cv/3d_detection/pointpillars/pytorch/utils/__init__.py new file mode 100755 index 000000000..ba0ebf4a2 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/utils/__init__.py @@ -0,0 +1,9 @@ +from .io import read_pickle, write_pickle, read_points, write_points, read_calib, \ + read_label, write_label +from .process import bbox_camera2lidar, bbox3d2bevcorners, box_collision_test, \ + remove_pts_in_bboxes, limit_period, bbox3d2corners, points_lidar2image, \ + keep_bbox_from_image_range, keep_bbox_from_lidar_range, \ + points_camera2lidar, setup_seed, remove_outside_points, points_in_bboxes_v2, \ + get_points_num_in_bbox, iou2d_nearest, iou2d, iou3d, iou3d_camera, iou_bev, \ + bbox3d2corners_camera, points_camera2image +# from .vis_o3d import vis_pc, vis_img_3d diff --git a/cv/3d_detection/pointpillars/pytorch/utils/io.py b/cv/3d_detection/pointpillars/pytorch/utils/io.py new file mode 100755 index 000000000..6c6e53809 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/utils/io.py @@ -0,0 +1,109 @@ +import numpy as np +import os +import pickle + + +def read_pickle(file_path, suffix='.pkl'): + assert os.path.splitext(file_path)[1] == suffix + with open(file_path, 'rb') as f: + data = pickle.load(f) + return data + + +def write_pickle(results, file_path): + with open(file_path, 'wb') as f: + pickle.dump(results, f) + + +def read_points(file_path, dim=4): + suffix = os.path.splitext(file_path)[1] + assert suffix in ['.bin', '.ply'] + if suffix == '.bin': + return np.fromfile(file_path, dtype=np.float32).reshape(-1, dim) + else: + raise NotImplementedError + + +def write_points(lidar_points, file_path): + suffix = os.path.splitext(file_path)[1] + assert suffix in ['.bin', '.ply'] + if suffix == '.bin': + with open(file_path, 'w') as f: + lidar_points.tofile(f) + else: + raise NotImplementedError + + +def read_calib(file_path, extend_matrix=True): + with open(file_path, 'r') as f: + lines = f.readlines() + lines = [line.strip() for line in lines] + P0 = np.array([item for item in lines[0].split(' ')[1:]], dtype=np.float).reshape(3, 4) + P1 = np.array([item for item in lines[1].split(' ')[1:]], dtype=np.float).reshape(3, 4) + P2 = np.array([item for item in lines[2].split(' ')[1:]], dtype=np.float).reshape(3, 4) + P3 = np.array([item for item in lines[3].split(' ')[1:]], dtype=np.float).reshape(3, 4) + + R0_rect = np.array([item for item in lines[4].split(' ')[1:]], dtype=np.float).reshape(3, 3) + Tr_velo_to_cam = np.array([item for item in lines[5].split(' ')[1:]], dtype=np.float).reshape(3, 4) + Tr_imu_to_velo = np.array([item for item in lines[6].split(' ')[1:]], dtype=np.float).reshape(3, 4) + + if extend_matrix: + P0 = np.concatenate([P0, np.array([[0, 0, 0, 1]])], axis=0) + P1 = np.concatenate([P1, np.array([[0, 0, 0, 1]])], axis=0) + P2 = np.concatenate([P2, np.array([[0, 0, 0, 1]])], axis=0) + P3 = np.concatenate([P3, np.array([[0, 0, 0, 1]])], axis=0) + + R0_rect_extend = np.eye(4, dtype=R0_rect.dtype) + R0_rect_extend[:3, :3] = R0_rect + R0_rect = R0_rect_extend + + Tr_velo_to_cam = np.concatenate([Tr_velo_to_cam, np.array([[0, 0, 0, 1]])], axis=0) + Tr_imu_to_velo = np.concatenate([Tr_imu_to_velo, np.array([[0, 0, 0, 1]])], axis=0) + + calib_dict=dict( + P0=P0, + P1=P1, + P2=P2, + P3=P3, + R0_rect=R0_rect, + Tr_velo_to_cam=Tr_velo_to_cam, + Tr_imu_to_velo=Tr_imu_to_velo + ) + return calib_dict + + +def read_label(file_path): + with open(file_path, 'r') as f: + lines = f.readlines() + lines = [line.strip().split(' ') for line in lines] + annotation = {} + annotation['name'] = np.array([line[0] for line in lines]) + annotation['truncated'] = np.array([line[1] for line in lines], dtype=np.float) + annotation['occluded'] = np.array([line[2] for line in lines], dtype=np.int) + annotation['alpha'] = np.array([line[3] for line in lines], dtype=np.float) + annotation['bbox'] = np.array([line[4:8] for line in lines], dtype=np.float) + annotation['dimensions'] = np.array([line[8:11] for line in lines], dtype=np.float)[:, [2, 0, 1]] # hwl -> camera coordinates (lhw) + annotation['location'] = np.array([line[11:14] for line in lines], dtype=np.float) + annotation['rotation_y'] = np.array([line[14] for line in lines], dtype=np.float) + + return annotation + + +def write_label(result, file_path, suffix='.txt'): + ''' + result: dict, + file_path: str + ''' + assert os.path.splitext(file_path)[1] == suffix + name, truncated, occluded, alpha, bbox, dimensions, location, rotation_y, score = \ + result['name'], result['truncated'], result['occluded'], result['alpha'], \ + result['bbox'], result['dimensions'], result['location'], result['rotation_y'], \ + result['score'] + + with open(file_path, 'w') as f: + for i in range(len(name)): + bbox_str = ' '.join(map(str, bbox[i])) + hwl = ' '.join(map(str, dimensions[i])) + xyz = ' '.join(map(str, location[i])) + line = f'{name[i]} {truncated[i]} {occluded[i]} {alpha[i]} {bbox_str} {hwl} {xyz} {rotation_y[i]} {score[i]}\n' + f.writelines(line) diff --git a/cv/3d_detection/pointpillars/pytorch/utils/process.py b/cv/3d_detection/pointpillars/pytorch/utils/process.py new file mode 100755 index 000000000..9f64b4d9a --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/utils/process.py @@ -0,0 +1,745 @@ +import copy +import numba +import numpy as np +import random +import torch +import pdb +from ops.iou3d_module import boxes_overlap_bev, boxes_iou_bev + + +def setup_seed(seed=0, deterministic = True): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if deterministic: + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def bbox_camera2lidar(bboxes, tr_velo_to_cam, r0_rect): + ''' + bboxes: shape=(N, 7) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + return: shape=(N, 7) + ''' + x_size, y_size, z_size = bboxes[:, 3:4], bboxes[:, 4:5], bboxes[:, 5:6] + xyz_size = np.concatenate([z_size, x_size, y_size], axis=1) + extended_xyz = np.pad(bboxes[:, :3], ((0, 0), (0, 1)), 'constant', constant_values=1.0) + rt_mat = np.linalg.inv(r0_rect @ tr_velo_to_cam) + xyz = extended_xyz @ rt_mat.T + bboxes_lidar = np.concatenate([xyz[:, :3], xyz_size, bboxes[:, 6:]], axis=1) + return np.array(bboxes_lidar, dtype=np.float32) + + +def bbox_lidar2camera(bboxes, tr_velo_to_cam, r0_rect): + ''' + bboxes: shape=(N, 7) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + return: shape=(N, 7) + ''' + x_size, y_size, z_size = bboxes[:, 3:4], bboxes[:, 4:5], bboxes[:, 5:6] + xyz_size = np.concatenate([y_size, z_size, x_size], axis=1) + extended_xyz = np.pad(bboxes[:, :3], ((0, 0), (0, 1)), 'constant', constant_values=1.0) + rt_mat = r0_rect @ tr_velo_to_cam + xyz = extended_xyz @ rt_mat.T + bboxes_camera = np.concatenate([xyz[:, :3], xyz_size, bboxes[:, 6:]], axis=1) + return bboxes_camera + + +def points_camera2image(points, P2): + ''' + points: shape=(N, 8, 3) + P2: shape=(4, 4) + return: shape=(N, 8, 2) + ''' + extended_points = np.pad(points, ((0, 0), (0, 0), (0, 1)), 'constant', constant_values=1.0) # (n, 8, 4) + image_points = extended_points @ P2.T # (N, 8, 4) + image_points = image_points[:, :, :2] / image_points[:, :, 2:3] + return image_points + + +def points_lidar2image(points, tr_velo_to_cam, r0_rect, P2): + ''' + points: shape=(N, 8, 3) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + P2: shape=(4, 4) + return: shape=(N, 8, 2) + ''' + # points = points[:, :, [1, 2, 0]] + extended_points = np.pad(points, ((0, 0), (0, 0), (0, 1)), 'constant', constant_values=1.0) # (N, 8, 4) + rt_mat = r0_rect @ tr_velo_to_cam + camera_points = extended_points @ rt_mat.T # (N, 8, 4) + # camera_points = camera_points[:, :, [1, 2, 0, 3]] + image_points = camera_points @ P2.T # (N, 8, 4) + image_points = image_points[:, :, :2] / image_points[:, :, 2:3] + + return image_points + + +def points_camera2lidar(points, tr_velo_to_cam, r0_rect): + ''' + points: shape=(N, 8, 3) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + return: shape=(N, 8, 3) + ''' + extended_xyz = np.pad(points, ((0, 0), (0, 0), (0, 1)), 'constant', constant_values=1.0) + rt_mat = np.linalg.inv(r0_rect @ tr_velo_to_cam) + xyz = extended_xyz @ rt_mat.T + return xyz[..., :3] + + +def bbox3d2bevcorners(bboxes): + ''' + bboxes: shape=(n, 7) + + ^ x (-0.5 * pi) + | + | (bird's eye view) + (-pi) o | + y <-------------- (0) + \ / (ag) + \ + \ + + return: shape=(n, 4, 2) + ''' + centers, dims, angles = bboxes[:, :2], bboxes[:, 3:5], bboxes[:, 6] + + # 1.generate bbox corner coordinates, clockwise from minimal point + bev_corners = np.array([[-0.5, -0.5], [-0.5, 0.5], [0.5, 0.5], [0.5, -0.5]], dtype=np.float32) + bev_corners = bev_corners[None, ...] * dims[:, None, :] # (1, 4, 2) * (n, 1, 2) -> (n, 4, 2) + + # 2. rotate + rot_sin, rot_cos = np.sin(angles), np.cos(angles) + # in fact, -angle + rot_mat = np.array([[rot_cos, rot_sin], + [-rot_sin, rot_cos]]) # (2, 2, n) + rot_mat = np.transpose(rot_mat, (2, 1, 0)) # (N, 2, 2) + bev_corners = bev_corners @ rot_mat # (n, 4, 2) + + # 3. translate to centers + bev_corners += centers[:, None, :] + return bev_corners.astype(np.float32) + + +def bbox3d2corners(bboxes): + ''' + bboxes: shape=(n, 7) + return: shape=(n, 8, 3) + ^ z x 6 ------ 5 + | / / | / | + | / 2 -|---- 1 | + y | / | | | | + <------|o | 7 -----| 4 + |/ o |/ + 3 ------ 0 + x: front, y: left, z: top + ''' + centers, dims, angles = bboxes[:, :3], bboxes[:, 3:6], bboxes[:, 6] + + # 1.generate bbox corner coordinates, clockwise from minimal point + bboxes_corners = np.array([[-0.5, -0.5, 0], [-0.5, -0.5, 1.0], [-0.5, 0.5, 1.0], [-0.5, 0.5, 0.0], + [0.5, -0.5, 0], [0.5, -0.5, 1.0], [0.5, 0.5, 1.0], [0.5, 0.5, 0.0]], + dtype=np.float32) + bboxes_corners = bboxes_corners[None, :, :] * dims[:, None, :] # (1, 8, 3) * (n, 1, 3) -> (n, 8, 3) + + # 2. rotate around z axis + rot_sin, rot_cos = np.sin(angles), np.cos(angles) + # in fact, -angle + rot_mat = np.array([[rot_cos, rot_sin, np.zeros_like(rot_cos)], + [-rot_sin, rot_cos, np.zeros_like(rot_cos)], + [np.zeros_like(rot_cos), np.zeros_like(rot_cos), np.ones_like(rot_cos)]], + dtype=np.float32) # (3, 3, n) + rot_mat = np.transpose(rot_mat, (2, 1, 0)) # (n, 3, 3) + bboxes_corners = bboxes_corners @ rot_mat # (n, 8, 3) + + # 3. translate to centers + bboxes_corners += centers[:, None, :] + return bboxes_corners + + +def bbox3d2corners_camera(bboxes): + ''' + bboxes: shape=(n, 7) + return: shape=(n, 8, 3) + z (front) 6 ------ 5 + / / | / | + / 2 -|---- 1 | + / | | | | + |o ------> x(right) | 7 -----| 4 + | |/ o |/ + | 3 ------ 0 + | + v y(down) + ''' + centers, dims, angles = bboxes[:, :3], bboxes[:, 3:6], bboxes[:, 6] + + # 1.generate bbox corner coordinates, clockwise from minimal point + bboxes_corners = np.array([[0.5, 0.0, -0.5], [0.5, -1.0, -0.5], [-0.5, -1.0, -0.5], [-0.5, 0.0, -0.5], + [0.5, 0.0, 0.5], [0.5, -1.0, 0.5], [-0.5, -1.0, 0.5], [-0.5, 0.0, 0.5]], + dtype=np.float32) + bboxes_corners = bboxes_corners[None, :, :] * dims[:, None, :] # (1, 8, 3) * (n, 1, 3) -> (n, 8, 3) + + # 2. rotate around y axis + rot_sin, rot_cos = np.sin(angles), np.cos(angles) + # in fact, angle + rot_mat = np.array([[rot_cos, np.zeros_like(rot_cos), rot_sin], + [np.zeros_like(rot_cos), np.ones_like(rot_cos), np.zeros_like(rot_cos)], + [-rot_sin, np.zeros_like(rot_cos), rot_cos]], + dtype=np.float32) # (3, 3, n) + rot_mat = np.transpose(rot_mat, (2, 1, 0)) # (n, 3, 3) + bboxes_corners = bboxes_corners @ rot_mat # (n, 8, 3) + + # 3. translate to centers + bboxes_corners += centers[:, None, :] + return bboxes_corners + + +def group_rectangle_vertexs(bboxes_corners): + ''' + bboxes_corners: shape=(n, 8, 3) + return: shape=(n, 6, 4, 3) + ''' + rec1 = np.stack([bboxes_corners[:, 0], bboxes_corners[:, 1], bboxes_corners[:, 3], bboxes_corners[:, 2]], axis=1) # (n, 4, 3) + rec2 = np.stack([bboxes_corners[:, 4], bboxes_corners[:, 7], bboxes_corners[:, 6], bboxes_corners[:, 5]], axis=1) # (n, 4, 3) + rec3 = np.stack([bboxes_corners[:, 0], bboxes_corners[:, 4], bboxes_corners[:, 5], bboxes_corners[:, 1]], axis=1) # (n, 4, 3) + rec4 = np.stack([bboxes_corners[:, 2], bboxes_corners[:, 6], bboxes_corners[:, 7], bboxes_corners[:, 3]], axis=1) # (n, 4, 3) + rec5 = np.stack([bboxes_corners[:, 1], bboxes_corners[:, 5], bboxes_corners[:, 6], bboxes_corners[:, 2]], axis=1) # (n, 4, 3) + rec6 = np.stack([bboxes_corners[:, 0], bboxes_corners[:, 3], bboxes_corners[:, 7], bboxes_corners[:, 4]], axis=1) # (n, 4, 3) + group_rectangle_vertexs = np.stack([rec1, rec2, rec3, rec4, rec5, rec6], axis=1) + return group_rectangle_vertexs + + +@numba.jit(nopython=True) +def bevcorner2alignedbbox(bev_corners): + ''' + bev_corners: shape=(N, 4, 2) + return: shape=(N, 4) + ''' + # xmin, xmax = np.min(bev_corners[:, :, 0], axis=-1), np.max(bev_corners[:, :, 0], axis=-1) + # ymin, ymax = np.min(bev_corners[:, :, 1], axis=-1), np.max(bev_corners[:, :, 1], axis=-1) + + # why we don't implement like the above ? please see + # https://numba.pydata.org/numba-doc/latest/reference/numpysupported.html#calculation + n = len(bev_corners) + alignedbbox = np.zeros((n, 4), dtype=np.float32) + for i in range(n): + cur_bev = bev_corners[i] + alignedbbox[i, 0] = np.min(cur_bev[:, 0]) + alignedbbox[i, 2] = np.max(cur_bev[:, 0]) + alignedbbox[i, 1] = np.min(cur_bev[:, 1]) + alignedbbox[i, 3] = np.max(cur_bev[:, 1]) + return alignedbbox + + +# modified from https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/data_augment_utils.py#L31 +@numba.jit(nopython=True) +def box_collision_test(boxes, qboxes, clockwise=True): + """Box collision test. + Args: + boxes (np.ndarray): Corners of current boxes. # (n1, 4, 2) + qboxes (np.ndarray): Boxes to be avoid colliding. # (n2, 4, 2) + clockwise (bool, optional): Whether the corners are in + clockwise order. Default: True. + return: shape=(n1, n2) + """ + N = boxes.shape[0] + K = qboxes.shape[0] + ret = np.zeros((N, K), dtype=np.bool_) + slices = np.array([1, 2, 3, 0]) + lines_boxes = np.stack((boxes, boxes[:, slices, :]), + axis=2) # [N, 4, 2(line), 2(xy)] + lines_qboxes = np.stack((qboxes, qboxes[:, slices, :]), axis=2) + # vec = np.zeros((2,), dtype=boxes.dtype) + boxes_standup = bevcorner2alignedbbox(boxes) + qboxes_standup = bevcorner2alignedbbox(qboxes) + for i in range(N): + for j in range(K): + # calculate standup first + iw = ( + min(boxes_standup[i, 2], qboxes_standup[j, 2]) - + max(boxes_standup[i, 0], qboxes_standup[j, 0])) + if iw > 0: + ih = ( + min(boxes_standup[i, 3], qboxes_standup[j, 3]) - + max(boxes_standup[i, 1], qboxes_standup[j, 1])) + if ih > 0: + for k in range(4): + for box_l in range(4): + A = lines_boxes[i, k, 0] + B = lines_boxes[i, k, 1] + C = lines_qboxes[j, box_l, 0] + D = lines_qboxes[j, box_l, 1] + acd = (D[1] - A[1]) * (C[0] - + A[0]) > (C[1] - A[1]) * ( + D[0] - A[0]) + bcd = (D[1] - B[1]) * (C[0] - + B[0]) > (C[1] - B[1]) * ( + D[0] - B[0]) + if acd != bcd: + abc = (C[1] - A[1]) * (B[0] - A[0]) > ( + B[1] - A[1]) * ( + C[0] - A[0]) + abd = (D[1] - A[1]) * (B[0] - A[0]) > ( + B[1] - A[1]) * ( + D[0] - A[0]) + if abc != abd: + ret[i, j] = True # collision. + break + if ret[i, j] is True: + break + if ret[i, j] is False: + # now check complete overlap. + # box overlap qbox: + box_overlap_qbox = True + for box_l in range(4): # point l in qboxes + for k in range(4): # corner k in boxes + vec = boxes[i, k] - boxes[i, (k + 1) % 4] + if clockwise: + vec = -vec + cross = vec[1] * ( + boxes[i, k, 0] - qboxes[j, box_l, 0]) + cross -= vec[0] * ( + boxes[i, k, 1] - qboxes[j, box_l, 1]) + if cross >= 0: + box_overlap_qbox = False + break + if box_overlap_qbox is False: + break + + if box_overlap_qbox is False: + qbox_overlap_box = True + for box_l in range(4): # point box_l in boxes + for k in range(4): # corner k in qboxes + vec = qboxes[j, k] - qboxes[j, (k + 1) % 4] + if clockwise: + vec = -vec + cross = vec[1] * ( + qboxes[j, k, 0] - boxes[i, box_l, 0]) + cross -= vec[0] * ( + qboxes[j, k, 1] - boxes[i, box_l, 1]) + if cross >= 0: # + qbox_overlap_box = False + break + if qbox_overlap_box is False: + break + if qbox_overlap_box: + ret[i, j] = True # collision. + else: + ret[i, j] = True # collision. + return ret + + +def group_plane_equation(bbox_group_rectangle_vertexs): + ''' + bbox_group_rectangle_vertexs: shape=(n, 6, 4, 3) + return: shape=(n, 6, 4) + ''' + # 1. generate vectors for a x b + vectors = bbox_group_rectangle_vertexs[:, :, :2] - bbox_group_rectangle_vertexs[:, :, 1:3] + normal_vectors = np.cross(vectors[:, :, 0], vectors[:, :, 1]) # (n, 6, 3) + normal_d = np.einsum('ijk,ijk->ij', bbox_group_rectangle_vertexs[:, :, 0], normal_vectors) # (n, 6) + plane_equation_params = np.concatenate([normal_vectors, -normal_d[:, :, None]], axis=-1) + return plane_equation_params + + +@numba.jit(nopython=True) +def points_in_bboxes(points, plane_equation_params): + ''' + points: shape=(N, 3) + plane_equation_params: shape=(n, 6, 4) + return: shape=(N, n), bool + ''' + N, n = len(points), len(plane_equation_params) + m = plane_equation_params.shape[1] + masks = np.ones((N, n), dtype=np.bool_) + for i in range(N): + x, y, z = points[i, :3] + for j in range(n): + bbox_plane_equation_params = plane_equation_params[j] + for k in range(m): + a, b, c, d = bbox_plane_equation_params[k] + if a * x + b * y + c * z + d >= 0: + masks[i][j] = False + break + return masks + + +def remove_pts_in_bboxes(points, bboxes, rm=True): + ''' + points: shape=(N, 3) + bboxes: shape=(n, 7) + return: shape=(N, n), bool + ''' + # 1. get 6 groups of rectangle vertexs + bboxes_corners = bbox3d2corners(bboxes) # (n, 8, 3) + bbox_group_rectangle_vertexs = group_rectangle_vertexs(bboxes_corners) # (n, 6, 4, 3) + + # 2. calculate plane equation: ax + by + cd + d = 0 + group_plane_equation_params = group_plane_equation(bbox_group_rectangle_vertexs) + + # 3. Judge each point inside or outside the bboxes + # if point (x0, y0, z0) lies on the direction of normal vector(a, b, c), then ax0 + by0 + cz0 + d > 0. + masks = points_in_bboxes(points, group_plane_equation_params) # (N, n) + + if not rm: + return masks + + # 4. remove point insider the bboxes + masks = np.any(masks, axis=-1) + + return points[~masks] + + +# modified from https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/utils.py#L11 +def limit_period(val, offset=0.5, period=np.pi): + """ + val: array or float + offset: float + period: float + return: Value in the range of [-offset * period, (1-offset) * period] + """ + limited_val = val - np.floor(val / period + offset) * period + return limited_val + + +def nearest_bev(bboxes): + ''' + bboxes: (n, 7), (x, y, z, w, l, h, theta) + return: (n, 4), (x1, y1, x2, y2) + ''' + bboxes_bev = copy.deepcopy(bboxes[:, [0, 1, 3, 4]]) + bboxes_angle = limit_period(bboxes[:, 6].cpu(), offset=0.5, period=np.pi).to(bboxes_bev) + bboxes_bev = torch.where(torch.abs(bboxes_angle[:, None]) > np.pi / 4, bboxes_bev[:, [0, 1, 3, 2]], bboxes_bev) + + bboxes_xy = bboxes_bev[:, :2] + bboxes_wl = bboxes_bev[:, 2:] + bboxes_bev_x1y1x2y2 = torch.cat([bboxes_xy - bboxes_wl / 2, bboxes_xy + bboxes_wl / 2], dim=-1) + return bboxes_bev_x1y1x2y2 + + +def iou2d(bboxes1, bboxes2, metric=0): + ''' + bboxes1: (n, 4), (x1, y1, x2, y2) + bboxes2: (m, 4), (x1, y1, x2, y2) + return: (n, m) + ''' + bboxes_x1 = torch.maximum(bboxes1[:, 0][:, None], bboxes2[:, 0][None, :]) # (n, m) + bboxes_y1 = torch.maximum(bboxes1[:, 1][:, None], bboxes2[:, 1][None, :]) # (n, m) + bboxes_x2 = torch.minimum(bboxes1[:, 2][:, None], bboxes2[:, 2][None, :]) + bboxes_y2 = torch.minimum(bboxes1[:, 3][:, None], bboxes2[:, 3][None, :]) + + bboxes_w = torch.clamp(bboxes_x2 - bboxes_x1, min=0) + bboxes_h = torch.clamp(bboxes_y2 - bboxes_y1, min=0) + + iou_area = bboxes_w * bboxes_h # (n, m) + + bboxes1_wh = bboxes1[:, 2:] - bboxes1[:, :2] + area1 = bboxes1_wh[:, 0] * bboxes1_wh[:, 1] # (n, ) + bboxes2_wh = bboxes2[:, 2:] - bboxes2[:, :2] + area2 = bboxes2_wh[:, 0] * bboxes2_wh[:, 1] # (m, ) + if metric == 0: + iou = iou_area / (area1[:, None] + area2[None, :] - iou_area + 1e-8) + elif metric == 1: + iou = iou_area / (area1[:, None] + 1e-8) + return iou + + +def iou2d_nearest(bboxes1, bboxes2): + ''' + bboxes1: (n, 7), (x, y, z, w, l, h, theta) + bboxes2: (m, 7), + return: (n, m) + ''' + bboxes1_bev = nearest_bev(bboxes1) + bboxes2_bev = nearest_bev(bboxes2) + iou = iou2d(bboxes1_bev, bboxes2_bev) + return iou + + +def iou3d(bboxes1, bboxes2): + ''' + bboxes1: (n, 7), (x, y, z, w, l, h, theta) + bboxes2: (m, 7) + return: (n, m) + ''' + # 1. height overlap + bboxes1_bottom, bboxes2_bottom = bboxes1[:, 2], bboxes2[:, 2] # (n, ), (m, ) + bboxes1_top, bboxes2_top = bboxes1[:, 2] + bboxes1[:, 5], bboxes2[:, 2] + bboxes2[:, 5] # (n, ), (m, ) + bboxes_bottom = torch.maximum(bboxes1_bottom[:, None], bboxes2_bottom[None, :]) # (n, m) + bboxes_top = torch.minimum(bboxes1_top[:, None], bboxes2_top[None, :]) + height_overlap = torch.clamp(bboxes_top - bboxes_bottom, min=0) + + # 2. bev overlap + bboxes1_x1y1 = bboxes1[:, :2] - bboxes1[:, 3:5] / 2 + bboxes1_x2y2 = bboxes1[:, :2] + bboxes1[:, 3:5] / 2 + bboxes2_x1y1 = bboxes2[:, :2] - bboxes2[:, 3:5] / 2 + bboxes2_x2y2 = bboxes2[:, :2] + bboxes2[:, 3:5] / 2 + bboxes1_bev = torch.cat([bboxes1_x1y1, bboxes1_x2y2, bboxes1[:, 6:]], dim=-1) + bboxes2_bev = torch.cat([bboxes2_x1y1, bboxes2_x2y2, bboxes2[:, 6:]], dim=-1) + bev_overlap = boxes_overlap_bev(bboxes1_bev, bboxes2_bev) # (n, m) + + # 3. overlap and volume + overlap = height_overlap * bev_overlap + volume1 = bboxes1[:, 3] * bboxes1[:, 4] * bboxes1[:, 5] + volume2 = bboxes2[:, 3] * bboxes2[:, 4] * bboxes2[:, 5] + volume = volume1[:, None] + volume2[None, :] # (n, m) + + # 4. iou + iou = overlap / (volume - overlap + 1e-8) + + return iou + + +def iou3d_camera(bboxes1, bboxes2): + ''' + bboxes1: (n, 7), (x, y, z, w, l, h, theta) + bboxes2: (m, 7) + return: (n, m) + ''' + # 1. height overlap + bboxes1_bottom, bboxes2_bottom = bboxes1[:, 1] - bboxes1[:, 4], bboxes2[:, 1] - bboxes2[:, 4] # (n, ), (m, ) + bboxes1_top, bboxes2_top = bboxes1[:, 1], bboxes2[:, 1] # (n, ), (m, ) + bboxes_bottom = torch.maximum(bboxes1_bottom[:, None], bboxes2_bottom[None, :]) # (n, m) + bboxes_top = torch.minimum(bboxes1_top[:, None], bboxes2_top[None, :]) + height_overlap = torch.clamp(bboxes_top - bboxes_bottom, min=0) + + # 2. bev overlap + bboxes1_x1y1 = bboxes1[:, [0, 2]] - bboxes1[:, [3, 5]] / 2 + bboxes1_x2y2 = bboxes1[:, [0, 2]] + bboxes1[:, [3, 5]] / 2 + bboxes2_x1y1 = bboxes2[:, [0, 2]] - bboxes2[:, [3, 5]] / 2 + bboxes2_x2y2 = bboxes2[:, [0, 2]] + bboxes2[:, [3, 5]] / 2 + bboxes1_bev = torch.cat([bboxes1_x1y1, bboxes1_x2y2, bboxes1[:, 6:]], dim=-1) + bboxes2_bev = torch.cat([bboxes2_x1y1, bboxes2_x2y2, bboxes2[:, 6:]], dim=-1) + bev_overlap = boxes_overlap_bev(bboxes1_bev, bboxes2_bev) # (n, m) + + # 3. overlap and volume + overlap = height_overlap * bev_overlap + volume1 = bboxes1[:, 3] * bboxes1[:, 4] * bboxes1[:, 5] + volume2 = bboxes2[:, 3] * bboxes2[:, 4] * bboxes2[:, 5] + volume = volume1[:, None] + volume2[None, :] # (n, m) + + # 4. iou + iou = overlap / (volume - overlap + 1e-8) + + return iou + + +def iou_bev(bboxes1, bboxes2): + ''' + bboxes1: (n, 5), (x, z, w, h, theta) + bboxes2: (m, 5) + return: (n, m) + ''' + bboxes1_x1y1 = bboxes1[:, :2] - bboxes1[:, 2:4] / 2 + bboxes1_x2y2 = bboxes1[:, :2] + bboxes1[:, 2:4] / 2 + bboxes2_x1y1 = bboxes2[:, :2] - bboxes2[:, 2:4] / 2 + bboxes2_x2y2 = bboxes2[:, :2] + bboxes2[:, 2:4] / 2 + bboxes1_bev = torch.cat([bboxes1_x1y1, bboxes1_x2y2, bboxes1[:, 4:]], dim=-1) + bboxes2_bev = torch.cat([bboxes2_x1y1, bboxes2_x2y2, bboxes2[:, 4:]], dim=-1) + bev_overlap = boxes_iou_bev(bboxes1_bev, bboxes2_bev) # (n, m) + + return bev_overlap + + +def keep_bbox_from_image_range(result, tr_velo_to_cam, r0_rect, P2, image_shape): + ''' + result: dict(lidar_bboxes, labels, scores) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + P2: shape=(4, 4) + image_shape: (h, w) + return: dict(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes) + ''' + h, w = image_shape + + lidar_bboxes = result['lidar_bboxes'] + labels = result['labels'] + scores = result['scores'] + camera_bboxes = bbox_lidar2camera(lidar_bboxes, tr_velo_to_cam, r0_rect) # (n, 7) + bboxes_points = bbox3d2corners_camera(camera_bboxes) # (n, 8, 3) + image_points = points_camera2image(bboxes_points, P2) # (n, 8, 2) + image_x1y1 = np.min(image_points, axis=1) # (n, 2) + image_x1y1 = np.maximum(image_x1y1, 0) + image_x2y2 = np.max(image_points, axis=1) # (n, 2) + image_x2y2 = np.minimum(image_x2y2, [w, h]) + bboxes2d = np.concatenate([image_x1y1, image_x2y2], axis=-1) + + keep_flag = (image_x1y1[:, 0] < w) & (image_x1y1[:, 1] < h) & (image_x2y2[:, 0] > 0) & (image_x2y2[:, 1] > 0) + + result = { + 'lidar_bboxes': lidar_bboxes[keep_flag], + 'labels': labels[keep_flag], + 'scores': scores[keep_flag], + 'bboxes2d': bboxes2d[keep_flag], + 'camera_bboxes': camera_bboxes[keep_flag] + } + return result + + +def keep_bbox_from_lidar_range(result, pcd_limit_range): + ''' + result: dict(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes) + pcd_limit_range: [] + return: dict(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes) + ''' + lidar_bboxes, labels, scores = result['lidar_bboxes'], result['labels'], result['scores'] + if 'bboxes2d' not in result: + result['bboxes2d'] = np.zeros_like(lidar_bboxes[:, :4]) + if 'camera_bboxes' not in result: + result['camera_bboxes'] = np.zeros_like(lidar_bboxes) + bboxes2d, camera_bboxes = result['bboxes2d'], result['camera_bboxes'] + flag1 = lidar_bboxes[:, :3] > pcd_limit_range[:3][None, :] # (n, 3) + flag2 = lidar_bboxes[:, :3] < pcd_limit_range[3:][None, :] # (n, 3) + keep_flag = np.all(flag1, axis=-1) & np.all(flag2, axis=-1) + + result = { + 'lidar_bboxes': lidar_bboxes[keep_flag], + 'labels': labels[keep_flag], + 'scores': scores[keep_flag], + 'bboxes2d': bboxes2d[keep_flag], + 'camera_bboxes': camera_bboxes[keep_flag] + } + return result + + +def points_in_bboxes_v2(points, r0_rect, tr_velo_to_cam, dimensions, location, rotation_y, name): + ''' + points: shape=(N, 4) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + dimensions: shape=(n, 3) + location: shape=(n, 3) + rotation_y: shape=(n, ) + name: shape=(n, ) + return: + indices: shape=(N, n_valid_bbox), indices[i, j] denotes whether point i is in bbox j. + n_total_bbox: int. + n_valid_bbox: int, not including 'DontCare' + bboxes_lidar: shape=(n_valid_bbox, 7) + name: shape=(n_valid_bbox, ) + ''' + n_total_bbox = len(dimensions) + n_valid_bbox = len([item for item in name if item != 'DontCare']) + location, dimensions = location[:n_valid_bbox], dimensions[:n_valid_bbox] + rotation_y, name = rotation_y[:n_valid_bbox], name[:n_valid_bbox] + bboxes_camera = np.concatenate([location, dimensions, rotation_y[:, None]], axis=1) + bboxes_lidar = bbox_camera2lidar(bboxes_camera, tr_velo_to_cam, r0_rect) + bboxes_corners = bbox3d2corners(bboxes_lidar) + group_rectangle_vertexs_v = group_rectangle_vertexs(bboxes_corners) + frustum_surfaces = group_plane_equation(group_rectangle_vertexs_v) + indices = points_in_bboxes(points[:, :3], frustum_surfaces) # (N, n), N is points num, n is bboxes number + return indices, n_total_bbox, n_valid_bbox, bboxes_lidar, name + + +def get_points_num_in_bbox(points, r0_rect, tr_velo_to_cam, dimensions, location, rotation_y, name): + ''' + points: shape=(N, 4) + tr_velo_to_cam: shape=(4, 4) + r0_rect: shape=(4, 4) + dimensions: shape=(n, 3) + location: shape=(n, 3) + rotation_y: shape=(n, ) + name: shape=(n, ) + return: shape=(n, ) + ''' + indices, n_total_bbox, n_valid_bbox, bboxes_lidar, name = \ + points_in_bboxes_v2( + points=points, + r0_rect=r0_rect, + tr_velo_to_cam=tr_velo_to_cam, + dimensions=dimensions, + location=location, + rotation_y=rotation_y, + name=name) + points_num = np.sum(indices, axis=0) + non_valid_points_num = [-1] * (n_total_bbox - n_valid_bbox) + points_num = np.concatenate([points_num, non_valid_points_num], axis=0) + return np.array(points_num, dtype=np.int) + + +# Modified from https://github.com/open-mmlab/mmdetection3d/blob/f45977008a52baaf97640a0e9b2bbe5ea1c4be34/mmdet3d/core/bbox/box_np_ops.py#L609 +def remove_outside_points(points, r0_rect, tr_velo_to_cam, P2, image_shape): + """Remove points which are outside of image. + Args: + points (np.ndarray, shape=[N, 3+dims]): Total points. + rect (np.ndarray, shape=[4, 4]): Matrix to project points in + specific camera coordinate (e.g. CAM2) to CAM0. + Trv2c (np.ndarray, shape=[4, 4]): Matrix to project points in + camera coordinate to lidar coordinate. + P2 (p.array, shape=[4, 4]): Intrinsics of Camera2. + image_shape (list[int]): Shape of image. + Returns: + np.ndarray, shape=[N, 3+dims]: Filtered points. + """ + # 5x faster than remove_outside_points_v1(2ms vs 10ms) + C, R, T = projection_matrix_to_CRT_kitti(P2) + image_bbox = [0, 0, image_shape[1], image_shape[0]] + frustum = get_frustum(image_bbox, C) + frustum -= T + frustum = np.linalg.inv(R) @ frustum.T + frustum = points_camera2lidar(frustum.T[None, ...], tr_velo_to_cam, r0_rect) # (1, 8, 3) + group_rectangle_vertexs_v = group_rectangle_vertexs(frustum) + frustum_surfaces = group_plane_equation(group_rectangle_vertexs_v) + indices = points_in_bboxes(points[:, :3], frustum_surfaces) # (N, 1) + points = points[indices.reshape([-1])] + return points + + +# Copied from https://github.com/open-mmlab/mmdetection3d/blob/f45977008a52baaf97640a0e9b2bbe5ea1c4be34/mmdet3d/core/bbox/box_np_ops.py#L609 +def projection_matrix_to_CRT_kitti(proj): + """Split projection matrix of kitti. + P = C @ [R|T] + C is upper triangular matrix, so we need to inverse CR and use QR + stable for all kitti camera projection matrix. + Args: + proj (p.array, shape=[4, 4]): Intrinsics of camera. + Returns: + tuple[np.ndarray]: Splited matrix of C, R and T. + """ + + CR = proj[0:3, 0:3] + CT = proj[0:3, 3] + RinvCinv = np.linalg.inv(CR) + Rinv, Cinv = np.linalg.qr(RinvCinv) + C = np.linalg.inv(Cinv) + R = np.linalg.inv(Rinv) + T = Cinv @ CT + return C, R, T + + +# Copied from https://github.com/open-mmlab/mmdetection3d/blob/f45977008a52baaf97640a0e9b2bbe5ea1c4be34/mmdet3d/core/bbox/box_np_ops.py#L661 +def get_frustum(bbox_image, C, near_clip=0.001, far_clip=100): + """Get frustum corners in camera coordinates. + Args: + bbox_image (list[int]): box in image coordinates. + C (np.ndarray): Intrinsics. + near_clip (float, optional): Nearest distance of frustum. + Defaults to 0.001. + far_clip (float, optional): Farthest distance of frustum. + Defaults to 100. + Returns: + np.ndarray, shape=[8, 3]: coordinates of frustum corners. + """ + fku = C[0, 0] + fkv = -C[1, 1] + u0v0 = C[0:2, 2] + z_points = np.array( + [near_clip] * 4 + [far_clip] * 4, dtype=C.dtype)[:, np.newaxis] + b = bbox_image + box_corners = np.array( + [[b[0], b[1]], [b[0], b[3]], [b[2], b[3]], [b[2], b[1]]], + dtype=C.dtype) + near_box_corners = (box_corners - u0v0) / np.array( + [fku / near_clip, -fkv / near_clip], dtype=C.dtype) + far_box_corners = (box_corners - u0v0) / np.array( + [fku / far_clip, -fkv / far_clip], dtype=C.dtype) + ret_xy = np.concatenate([near_box_corners, far_box_corners], + axis=0) # [8, 2] + ret_xyz = np.concatenate([ret_xy, z_points], axis=1) + return ret_xyz diff --git a/cv/3d_detection/pointpillars/pytorch/utils/viewpoint.json b/cv/3d_detection/pointpillars/pytorch/utils/viewpoint.json new file mode 100755 index 000000000..aebb9e11b --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/utils/viewpoint.json @@ -0,0 +1,30 @@ +{ + "class_name" : "PinholeCameraParameters", + "extrinsic" : + [ + 0.013862749108655318, + -0.99931910700250171, + 0.034192785304405081, + 0, + -0.99751226840734264, + -0.011457761025003947, + 0.069555690558944339, + 0, + -0.069116557813509449, + -0.035071955919460274, + -0.99699190535530191, + 0, + 7.0392596000563916, + 14.768969974020909, + 101.08753655485596, + 1 + ], + "intrinsic" : + { + "height" : 1056, + "intrinsic_matrix" : [ 914.52282639636724, 0, 0, 0, 914.52282639636724, 0, 927, 527.5, 1 ], + "width" : 1855 + }, + "version_major" : 1, + "version_minor" : 0 +} \ No newline at end of file diff --git a/cv/3d_detection/pointpillars/pytorch/utils/vis_o3d.py b/cv/3d_detection/pointpillars/pytorch/utils/vis_o3d.py new file mode 100755 index 000000000..41ca093d6 --- /dev/null +++ b/cv/3d_detection/pointpillars/pytorch/utils/vis_o3d.py @@ -0,0 +1,122 @@ +import cv2 +import numpy as np +import open3d as o3d +import os +from utils import bbox3d2corners + + +COLORS = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0]] +COLORS_IMG = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [0, 255, 255]] + +LINES = [ + [0, 1], + [1, 2], + [2, 3], + [3, 0], + [4, 5], + [5, 6], + [6, 7], + [7, 4], + [2, 6], + [7, 3], + [1, 5], + [4, 0] + ] + + +def npy2ply(npy): + ply = o3d.geometry.PointCloud() + ply.points = o3d.utility.Vector3dVector(npy[:, :3]) + density = npy[:, 3] + colors = [[item, item, item] for item in density] + ply.colors = o3d.utility.Vector3dVector(colors) + return ply + + +def ply2npy(ply): + return np.array(ply.points) + + +def bbox_obj(points, color=[1, 0, 0]): + colors = [color for i in range(len(LINES))] + line_set = o3d.geometry.LineSet( + points=o3d.utility.Vector3dVector(points), + lines=o3d.utility.Vector2iVector(LINES), + ) + line_set.colors = o3d.utility.Vector3dVector(colors) + return line_set + + +def vis_core(plys): + vis = o3d.visualization.Visualizer() + vis.create_window() + + PAR = os.path.dirname(os.path.abspath(__file__)) + ctr = vis.get_view_control() + param = o3d.io.read_pinhole_camera_parameters(os.path.join(PAR, 'viewpoint.json')) + for ply in plys: + vis.add_geometry(ply) + ctr.convert_from_pinhole_camera_parameters(param) + + vis.run() + # param = vis.get_view_control().convert_to_pinhole_camera_parameters() + # o3d.io.write_pinhole_camera_parameters(os.path.join(PAR, 'viewpoint.json'), param) + vis.destroy_window() + + +def vis_pc(pc, bboxes=None, labels=None): + ''' + pc: ply or np.ndarray (N, 4) + bboxes: np.ndarray, (n, 7) or (n, 8, 3) + labels: (n, ) + ''' + if isinstance(pc, np.ndarray): + pc = npy2ply(pc) + + mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame( + size=10, origin=[0, 0, 0]) + + if bboxes is None: + vis_core([pc, mesh_frame]) + return + + if len(bboxes.shape) == 2: + bboxes = bbox3d2corners(bboxes) + + vis_objs = [pc, mesh_frame] + for i in range(len(bboxes)): + bbox = bboxes[i] + if labels is None: + color = [1, 1, 0] + else: + if labels[i] >= 0 and labels[i] < 3: + color = COLORS[labels[i]] + else: + color = COLORS[-1] + vis_objs.append(bbox_obj(bbox, color=color)) + vis_core(vis_objs) + + +def vis_img_3d(img, image_points, labels, rt=True): + ''' + img: (h, w, 3) + image_points: (n, 8, 2) + labels: (n, ) + ''' + + for i in range(len(image_points)): + label = labels[i] + bbox_points = image_points[i] # (8, 2) + if label >= 0 and label < 3: + color = COLORS_IMG[label] + else: + color = COLORS_IMG[-1] + for line_id in LINES: + x1, y1 = bbox_points[line_id[0]] + x2, y2 = bbox_points[line_id[1]] + x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) + cv2.line(img, (x1, y1), (x2, y2), color, 1) + if rt: + return img + cv2.imshow('bbox', img) + cv2.waitKey(0) diff --git a/cv/detection/yolov7/pytorch/.gitignore b/cv/detection/yolov7/pytorch/.gitignore new file mode 100644 index 000000000..d1bbbbe39 --- /dev/null +++ b/cv/detection/yolov7/pytorch/.gitignore @@ -0,0 +1,263 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json +*.cfg +!setup.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* +data/* +data/images/* +!data/*.yaml +!data/hyps +!data/scripts +!data/images +!data/images/zidane.jpg +!data/images/bus.jpg +!data/*.sh + +results*.csv + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + +coco2017labels-segments.zip +test2017.zip +train2017.zip +val2017.zip + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*_saved_model/ +*_web_model/ +*_openvino_model/ +darknet53.conv.74 +yolov3-tiny.conv.15 +*.ptl +*.trt + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +/wandb/ +.installed.cfg +*.egg + + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # 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But first, please read +. diff --git a/cv/detection/yolov7/pytorch/README.md b/cv/detection/yolov7/pytorch/README.md new file mode 100644 index 000000000..0a86f09d3 --- /dev/null +++ b/cv/detection/yolov7/pytorch/README.md @@ -0,0 +1,55 @@ +# Official YOLOv7 + +Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696) + +## Step 1: Installing packages +``` +pip3 install -r requirements.txt +``` + +## Step 2: Preparing datasets +Download the [COCO Dataset](https://cocodataset.org/#home) + +Modify the configuration file(data/coco.yaml) +``` +$ vim data/coco.yaml +$ # path: the root of coco data +$ # train: the relative path of train images +$ # val: the relative path of valid images +``` + +## Training + +Train the yolov5 model as follows, the train log is saved in ./runs/train/exp + +### Single GPU training +``` +python3 train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml +``` +### Multiple GPU training +``` +python3 -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml +``` + +## Transfer learning + +[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt) + +``` +python3 train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml +``` + +## Inference + +On video: +``` +python3 detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4 +``` + +On image: +``` +python3 detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg +``` + +## Reference +https://github.com/WongKinYiu/yolov7 \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/r50-csp.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/r50-csp.yaml new file mode 100644 index 000000000..94559f7d0 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/r50-csp.yaml @@ -0,0 +1,49 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-ResNet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Stem, [128]], # 0-P1/2 + [-1, 3, ResCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 2-P3/8 + [-1, 4, ResCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 4-P3/8 + [-1, 6, ResCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 6-P3/8 + [-1, 3, ResCSPC, [1024]], # 7 + ] + +# CSP-Res-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 8 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [5, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResCSPB, [256]], # 13 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [3, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResCSPB, [128]], # 18 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 13], 1, Concat, [1]], # cat + [-1, 2, ResCSPB, [256]], # 22 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 8], 1, Concat, [1]], # cat + [-1, 2, ResCSPB, [512]], # 26 + [-1, 1, Conv, [1024, 3, 1]], + + [[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/x50-csp.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/x50-csp.yaml new file mode 100644 index 000000000..8de14f81a --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/x50-csp.yaml @@ -0,0 +1,49 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-ResNeXt backbone +backbone: + # [from, number, module, args] + [[-1, 1, Stem, [128]], # 0-P1/2 + [-1, 3, ResXCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 2-P3/8 + [-1, 4, ResXCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 4-P3/8 + [-1, 6, ResXCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 6-P3/8 + [-1, 3, ResXCSPC, [1024]], # 7 + ] + +# CSP-ResX-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 8 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [5, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResXCSPB, [256]], # 13 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [3, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResXCSPB, [128]], # 18 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 13], 1, Concat, [1]], # cat + [-1, 2, ResXCSPB, [256]], # 22 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 8], 1, Concat, [1]], # cat + [-1, 2, ResXCSPB, [512]], # 26 + [-1, 1, Conv, [1024, 3, 1]], + + [[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolor-csp-x.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-csp-x.yaml new file mode 100644 index 000000000..6e234c5c2 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-csp-x.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, BottleneckCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, BottleneckCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, BottleneckCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, BottleneckCSPC, [1024]], # 10 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 11 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [8, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [256]], # 16 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [6, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [128]], # 21 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [256]], # 25 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [512]], # 29 + [-1, 1, Conv, [1024, 3, 1]], + + [[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolor-csp.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-csp.yaml new file mode 100644 index 000000000..3beecf3dd --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-csp.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, BottleneckCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, BottleneckCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, BottleneckCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, BottleneckCSPC, [1024]], # 10 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 11 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [8, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [256]], # 16 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [6, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [128]], # 21 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [256]], # 25 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [512]], # 29 + [-1, 1, Conv, [1024, 3, 1]], + + [[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolor-d6.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-d6.yaml new file mode 100644 index 000000000..297b0d1c2 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-d6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.25 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, DownC, [128]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, DownC, [256]], # 4-P3/8 + [-1, 15, BottleneckCSPA, [256]], + [-1, 1, DownC, [512]], # 6-P4/16 + [-1, 15, BottleneckCSPA, [512]], + [-1, 1, DownC, [768]], # 8-P5/32 + [-1, 7, BottleneckCSPA, [768]], + [-1, 1, DownC, [1024]], # 10-P6/64 + [-1, 7, BottleneckCSPA, [1024]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 12 + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [384]], # 17 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, DownC, [256]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 31 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, DownC, [384]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [384]], # 35 + [-1, 1, Conv, [768, 3, 1]], + [-2, 1, DownC, [512]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [512]], # 39 + [-1, 1, Conv, [1024, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolor-e6.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-e6.yaml new file mode 100644 index 000000000..58afc5ba1 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-e6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.25 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, DownC, [128]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, DownC, [256]], # 4-P3/8 + [-1, 7, BottleneckCSPA, [256]], + [-1, 1, DownC, [512]], # 6-P4/16 + [-1, 7, BottleneckCSPA, [512]], + [-1, 1, DownC, [768]], # 8-P5/32 + [-1, 3, BottleneckCSPA, [768]], + [-1, 1, DownC, [1024]], # 10-P6/64 + [-1, 3, BottleneckCSPA, [1024]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 12 + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [384]], # 17 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, DownC, [256]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 31 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, DownC, [384]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [384]], # 35 + [-1, 1, Conv, [768, 3, 1]], + [-2, 1, DownC, [512]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [512]], # 39 + [-1, 1, Conv, [1024, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolor-p6.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-p6.yaml new file mode 100644 index 000000000..924cf5cf4 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-p6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.0 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, Conv, [256, 3, 2]], # 4-P3/8 + [-1, 7, BottleneckCSPA, [256]], + [-1, 1, Conv, [384, 3, 2]], # 6-P4/16 + [-1, 7, BottleneckCSPA, [384]], + [-1, 1, Conv, [512, 3, 2]], # 8-P5/32 + [-1, 3, BottleneckCSPA, [512]], + [-1, 1, Conv, [640, 3, 2]], # 10-P6/64 + [-1, 3, BottleneckCSPA, [640]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [320]], # 12 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [256, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 17 + [-1, 1, Conv, [192, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [192, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [192]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [192, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [192]], # 31 + [-1, 1, Conv, [384, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 35 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [320, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [320]], # 39 + [-1, 1, Conv, [640, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolor-w6.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-w6.yaml new file mode 100644 index 000000000..a2fc96969 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolor-w6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.0 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, Conv, [256, 3, 2]], # 4-P3/8 + [-1, 7, BottleneckCSPA, [256]], + [-1, 1, Conv, [512, 3, 2]], # 6-P4/16 + [-1, 7, BottleneckCSPA, [512]], + [-1, 1, Conv, [768, 3, 2]], # 8-P5/32 + [-1, 3, BottleneckCSPA, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 10-P6/64 + [-1, 3, BottleneckCSPA, [1024]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 12 + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [384]], # 17 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 31 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [384, 3, 2]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [384]], # 35 + [-1, 1, Conv, [768, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [512]], # 39 + [-1, 1, Conv, [1024, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolov3-spp.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolov3-spp.yaml new file mode 100644 index 000000000..38dcc449f --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolov3-spp.yaml @@ -0,0 +1,51 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolov3.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolov3.yaml new file mode 100644 index 000000000..f2e761355 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolov3.yaml @@ -0,0 +1,51 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, [1, 1]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/baseline/yolov4-csp.yaml b/cv/detection/yolov7/pytorch/cfg/baseline/yolov4-csp.yaml new file mode 100644 index 000000000..3c908c786 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/baseline/yolov4-csp.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, BottleneckCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, BottleneckCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, BottleneckCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, BottleneckCSPC, [1024]], # 10 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 11 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [8, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [256]], # 16 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [6, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [128]], # 21 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [256]], # 25 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [512]], # 29 + [-1, 1, Conv, [1024, 3, 1]], + + [[22,26,30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-d6.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-d6.yaml new file mode 100644 index 000000000..75a8cf58b --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-d6.yaml @@ -0,0 +1,202 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-d6 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [96, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [192]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 14 + + [-1, 1, DownC, [384]], # 15-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 27 + + [-1, 1, DownC, [768]], # 28-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 40 + + [-1, 1, DownC, [1152]], # 41-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1152, 1, 1]], # 53 + + [-1, 1, DownC, [1536]], # 54-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1536, 1, 1]], # 66 + ] + +# yolov7-d6 head +head: + [[-1, 1, SPPCSPC, [768]], # 67 + + [-1, 1, Conv, [576, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [53, 1, Conv, [576, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 83 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [40, 1, Conv, [384, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 99 + + [-1, 1, Conv, [192, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [27, 1, Conv, [192, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 115 + + [-1, 1, DownC, [384]], + [[-1, 99], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 129 + + [-1, 1, DownC, [576]], + [[-1, 83], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 143 + + [-1, 1, DownC, [768]], + [[-1, 67], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 157 + + [115, 1, Conv, [384, 3, 1]], + [129, 1, Conv, [768, 3, 1]], + [143, 1, Conv, [1152, 3, 1]], + [157, 1, Conv, [1536, 3, 1]], + + [[158,159,160,161], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-e6.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-e6.yaml new file mode 100644 index 000000000..e68040695 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-e6.yaml @@ -0,0 +1,180 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-e6 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + + [-1, 1, DownC, [320]], # 13-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 23 + + [-1, 1, DownC, [640]], # 24-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 34 + + [-1, 1, DownC, [960]], # 35-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 45 + + [-1, 1, DownC, [1280]], # 46-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 56 + ] + +# yolov7-e6 head +head: + [[-1, 1, SPPCSPC, [640]], # 57 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 71 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [34, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 85 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [23, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 99 + + [-1, 1, DownC, [320]], + [[-1, 85], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 111 + + [-1, 1, DownC, [480]], + [[-1, 71], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 123 + + [-1, 1, DownC, [640]], + [[-1, 57], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 135 + + [99, 1, Conv, [320, 3, 1]], + [111, 1, Conv, [640, 3, 1]], + [123, 1, Conv, [960, 3, 1]], + [135, 1, Conv, [1280, 3, 1]], + + [[136,137,138,139], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-e6e.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-e6e.yaml new file mode 100644 index 000000000..135990d86 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-e6e.yaml @@ -0,0 +1,301 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-e6e backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + [-11, 1, Conv, [64, 1, 1]], + [-12, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 22 + [[-1, -11], 1, Shortcut, [1]], # 23 + + [-1, 1, DownC, [320]], # 24-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 34 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 44 + [[-1, -11], 1, Shortcut, [1]], # 45 + + [-1, 1, DownC, [640]], # 46-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 56 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 66 + [[-1, -11], 1, Shortcut, [1]], # 67 + + [-1, 1, DownC, [960]], # 68-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 78 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 88 + [[-1, -11], 1, Shortcut, [1]], # 89 + + [-1, 1, DownC, [1280]], # 90-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 100 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 110 + [[-1, -11], 1, Shortcut, [1]], # 111 + ] + +# yolov7-e6e head +head: + [[-1, 1, SPPCSPC, [640]], # 112 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [89, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 126 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 136 + [[-1, -11], 1, Shortcut, [1]], # 137 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [67, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 151 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 161 + [[-1, -11], 1, Shortcut, [1]], # 162 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 176 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 186 + [[-1, -11], 1, Shortcut, [1]], # 187 + + [-1, 1, DownC, [320]], + [[-1, 162], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 199 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 209 + [[-1, -11], 1, Shortcut, [1]], # 210 + + [-1, 1, DownC, [480]], + [[-1, 137], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 222 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 232 + [[-1, -11], 1, Shortcut, [1]], # 233 + + [-1, 1, DownC, [640]], + [[-1, 112], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 245 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 255 + [[-1, -11], 1, Shortcut, [1]], # 256 + + [187, 1, Conv, [320, 3, 1]], + [210, 1, Conv, [640, 3, 1]], + [233, 1, Conv, [960, 3, 1]], + [256, 1, Conv, [1280, 3, 1]], + + [[257,258,259,260], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-tiny-silu.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-tiny-silu.yaml new file mode 100644 index 000000000..9250573ac --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-tiny-silu.yaml @@ -0,0 +1,112 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv7-tiny backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 2]], # 0-P1/2 + + [-1, 1, Conv, [64, 3, 2]], # 1-P2/4 + + [-1, 1, Conv, [32, 1, 1]], + [-2, 1, Conv, [32, 1, 1]], + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, Conv, [32, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1]], # 7 + + [-1, 1, MP, []], # 8-P3/8 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 14 + + [-1, 1, MP, []], # 15-P4/16 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 21 + + [-1, 1, MP, []], # 22-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 28 + ] + +# YOLOv7-tiny head +head: + [[-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, SP, [5]], + [-2, 1, SP, [9]], + [-3, 1, SP, [13]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], + [[-1, -7], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 37 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [21, 1, Conv, [128, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 47 + + [-1, 1, Conv, [64, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [14, 1, Conv, [64, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [32, 1, 1]], + [-2, 1, Conv, [32, 1, 1]], + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, Conv, [32, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1]], # 57 + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 47], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 65 + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 37], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 73 + + [57, 1, Conv, [128, 3, 1]], + [65, 1, Conv, [256, 3, 1]], + [73, 1, Conv, [512, 3, 1]], + + [[74,75,76], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-tiny.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-tiny.yaml new file mode 100644 index 000000000..b09f130b8 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-tiny.yaml @@ -0,0 +1,112 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# yolov7-tiny backbone +backbone: + # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True + [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2 + + [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4 + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 + + [-1, 1, MP, []], # 8-P3/8 + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14 + + [-1, 1, MP, []], # 15-P4/16 + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21 + + [-1, 1, MP, []], # 22-P5/32 + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28 + ] + +# yolov7-tiny head +head: + [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, SP, [5]], + [-2, 1, SP, [9]], + [-3, 1, SP, [13]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -7], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37 + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47 + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57 + + [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 47], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65 + + [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 37], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 73 + + [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + + [[74,75,76], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-w6.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-w6.yaml new file mode 100644 index 000000000..5637a6152 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7-w6.yaml @@ -0,0 +1,158 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-w6 backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 10 + + [-1, 1, Conv, [256, 3, 2]], # 11-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 19 + + [-1, 1, Conv, [512, 3, 2]], # 20-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 28 + + [-1, 1, Conv, [768, 3, 2]], # 29-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 37 + + [-1, 1, Conv, [1024, 3, 2]], # 38-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 46 + ] + +# yolov7-w6 head +head: + [[-1, 1, SPPCSPC, [512]], # 47 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 59 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 71 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [19, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 83 + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 71], 1, Concat, [1]], # cat + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 93 + + [-1, 1, Conv, [384, 3, 2]], + [[-1, 59], 1, Concat, [1]], # cat + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 103 + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 47], 1, Concat, [1]], # cat + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 113 + + [83, 1, Conv, [256, 3, 1]], + [93, 1, Conv, [512, 3, 1]], + [103, 1, Conv, [768, 3, 1]], + [113, 1, Conv, [1024, 3, 1]], + + [[114,115,116,117], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7.yaml new file mode 100644 index 000000000..201f98da6 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7.yaml @@ -0,0 +1,140 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [64, 3, 1]], + + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 11 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 16-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 24 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 29-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 37 + + [-1, 1, MP, []], + [-1, 1, Conv, [512, 1, 1]], + [-3, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 42-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 50 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 51 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 63 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [24, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 75 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3, 63], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 88 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3, 51], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 101 + + [75, 1, RepConv, [256, 3, 1]], + [88, 1, RepConv, [512, 3, 1]], + [101, 1, RepConv, [1024, 3, 1]], + + [[102,103,104], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/deploy/yolov7x.yaml b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7x.yaml new file mode 100644 index 000000000..c1b4acce4 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/deploy/yolov7x.yaml @@ -0,0 +1,156 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7x backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [40, 3, 1]], # 0 + + [-1, 1, Conv, [80, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [80, 3, 1]], + + [-1, 1, Conv, [160, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 13 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 18-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 28 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 33-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 43 + + [-1, 1, MP, []], + [-1, 1, Conv, [640, 1, 1]], + [-3, 1, Conv, [640, 1, 1]], + [-1, 1, Conv, [640, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 48-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 58 + ] + +# yolov7x head +head: + [[-1, 1, SPPCSPC, [640]], # 59 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [43, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 73 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 87 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3, 73], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 102 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3, 59], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 117 + + [87, 1, Conv, [320, 3, 1]], + [102, 1, Conv, [640, 3, 1]], + [117, 1, Conv, [1280, 3, 1]], + + [[118,119,120], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7-d6.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7-d6.yaml new file mode 100644 index 000000000..4faedda45 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7-d6.yaml @@ -0,0 +1,207 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [96, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [192]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 14 + + [-1, 1, DownC, [384]], # 15-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 27 + + [-1, 1, DownC, [768]], # 28-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 40 + + [-1, 1, DownC, [1152]], # 41-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1152, 1, 1]], # 53 + + [-1, 1, DownC, [1536]], # 54-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1536, 1, 1]], # 66 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [768]], # 67 + + [-1, 1, Conv, [576, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [53, 1, Conv, [576, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 83 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [40, 1, Conv, [384, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 99 + + [-1, 1, Conv, [192, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [27, 1, Conv, [192, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 115 + + [-1, 1, DownC, [384]], + [[-1, 99], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 129 + + [-1, 1, DownC, [576]], + [[-1, 83], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 143 + + [-1, 1, DownC, [768]], + [[-1, 67], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 157 + + [115, 1, Conv, [384, 3, 1]], + [129, 1, Conv, [768, 3, 1]], + [143, 1, Conv, [1152, 3, 1]], + [157, 1, Conv, [1536, 3, 1]], + + [115, 1, Conv, [384, 3, 1]], + [99, 1, Conv, [768, 3, 1]], + [83, 1, Conv, [1152, 3, 1]], + [67, 1, Conv, [1536, 3, 1]], + + [[158,159,160,161,162,163,164,165], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7-e6.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7-e6.yaml new file mode 100644 index 000000000..58b27f097 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7-e6.yaml @@ -0,0 +1,185 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + + [-1, 1, DownC, [320]], # 13-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 23 + + [-1, 1, DownC, [640]], # 24-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 34 + + [-1, 1, DownC, [960]], # 35-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 45 + + [-1, 1, DownC, [1280]], # 46-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 56 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 57 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 71 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [34, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 85 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [23, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 99 + + [-1, 1, DownC, [320]], + [[-1, 85], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 111 + + [-1, 1, DownC, [480]], + [[-1, 71], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 123 + + [-1, 1, DownC, [640]], + [[-1, 57], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 135 + + [99, 1, Conv, [320, 3, 1]], + [111, 1, Conv, [640, 3, 1]], + [123, 1, Conv, [960, 3, 1]], + [135, 1, Conv, [1280, 3, 1]], + + [99, 1, Conv, [320, 3, 1]], + [85, 1, Conv, [640, 3, 1]], + [71, 1, Conv, [960, 3, 1]], + [57, 1, Conv, [1280, 3, 1]], + + [[136,137,138,139,140,141,142,143], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7-e6e.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7-e6e.yaml new file mode 100644 index 000000000..3c836619e --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7-e6e.yaml @@ -0,0 +1,306 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + [-11, 1, Conv, [64, 1, 1]], + [-12, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 22 + [[-1, -11], 1, Shortcut, [1]], # 23 + + [-1, 1, DownC, [320]], # 24-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 34 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 44 + [[-1, -11], 1, Shortcut, [1]], # 45 + + [-1, 1, DownC, [640]], # 46-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 56 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 66 + [[-1, -11], 1, Shortcut, [1]], # 67 + + [-1, 1, DownC, [960]], # 68-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 78 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 88 + [[-1, -11], 1, Shortcut, [1]], # 89 + + [-1, 1, DownC, [1280]], # 90-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 100 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 110 + [[-1, -11], 1, Shortcut, [1]], # 111 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 112 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [89, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 126 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 136 + [[-1, -11], 1, Shortcut, [1]], # 137 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [67, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 151 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 161 + [[-1, -11], 1, Shortcut, [1]], # 162 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 176 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 186 + [[-1, -11], 1, Shortcut, [1]], # 187 + + [-1, 1, DownC, [320]], + [[-1, 162], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 199 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 209 + [[-1, -11], 1, Shortcut, [1]], # 210 + + [-1, 1, DownC, [480]], + [[-1, 137], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 222 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 232 + [[-1, -11], 1, Shortcut, [1]], # 233 + + [-1, 1, DownC, [640]], + [[-1, 112], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 245 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 255 + [[-1, -11], 1, Shortcut, [1]], # 256 + + [187, 1, Conv, [320, 3, 1]], + [210, 1, Conv, [640, 3, 1]], + [233, 1, Conv, [960, 3, 1]], + [256, 1, Conv, [1280, 3, 1]], + + [186, 1, Conv, [320, 3, 1]], + [161, 1, Conv, [640, 3, 1]], + [136, 1, Conv, [960, 3, 1]], + [112, 1, Conv, [1280, 3, 1]], + + [[257,258,259,260,261,262,263,264], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7-tiny.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7-tiny.yaml new file mode 100644 index 000000000..3679b0d55 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7-tiny.yaml @@ -0,0 +1,112 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# yolov7-tiny backbone +backbone: + # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True + [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2 + + [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4 + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 + + [-1, 1, MP, []], # 8-P3/8 + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14 + + [-1, 1, MP, []], # 15-P4/16 + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21 + + [-1, 1, MP, []], # 22-P5/32 + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28 + ] + +# yolov7-tiny head +head: + [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, SP, [5]], + [-2, 1, SP, [9]], + [-3, 1, SP, [13]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -7], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37 + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47 + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57 + + [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 47], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65 + + [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 37], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 73 + + [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + + [[74,75,76], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7-w6.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7-w6.yaml new file mode 100644 index 000000000..4b9c0131a --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7-w6.yaml @@ -0,0 +1,163 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 10 + + [-1, 1, Conv, [256, 3, 2]], # 11-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 19 + + [-1, 1, Conv, [512, 3, 2]], # 20-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 28 + + [-1, 1, Conv, [768, 3, 2]], # 29-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 37 + + [-1, 1, Conv, [1024, 3, 2]], # 38-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 46 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 47 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 59 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 71 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [19, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 83 + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 71], 1, Concat, [1]], # cat + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 93 + + [-1, 1, Conv, [384, 3, 2]], + [[-1, 59], 1, Concat, [1]], # cat + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 103 + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 47], 1, Concat, [1]], # cat + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 113 + + [83, 1, Conv, [256, 3, 1]], + [93, 1, Conv, [512, 3, 1]], + [103, 1, Conv, [768, 3, 1]], + [113, 1, Conv, [1024, 3, 1]], + + [83, 1, Conv, [320, 3, 1]], + [71, 1, Conv, [640, 3, 1]], + [59, 1, Conv, [960, 3, 1]], + [47, 1, Conv, [1280, 3, 1]], + + [[114,115,116,117,118,119,120,121], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7.yaml new file mode 100644 index 000000000..9a807e58f --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7.yaml @@ -0,0 +1,140 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [64, 3, 1]], + + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 11 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 16-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 24 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 29-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 37 + + [-1, 1, MP, []], + [-1, 1, Conv, [512, 1, 1]], + [-3, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 42-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 50 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 51 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 63 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [24, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 75 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3, 63], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 88 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3, 51], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 101 + + [75, 1, RepConv, [256, 3, 1]], + [88, 1, RepConv, [512, 3, 1]], + [101, 1, RepConv, [1024, 3, 1]], + + [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/cfg/training/yolov7x.yaml b/cv/detection/yolov7/pytorch/cfg/training/yolov7x.yaml new file mode 100644 index 000000000..207be8863 --- /dev/null +++ b/cv/detection/yolov7/pytorch/cfg/training/yolov7x.yaml @@ -0,0 +1,156 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [40, 3, 1]], # 0 + + [-1, 1, Conv, [80, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [80, 3, 1]], + + [-1, 1, Conv, [160, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 13 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 18-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 28 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 33-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 43 + + [-1, 1, MP, []], + [-1, 1, Conv, [640, 1, 1]], + [-3, 1, Conv, [640, 1, 1]], + [-1, 1, Conv, [640, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 48-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 58 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 59 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [43, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 73 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 87 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3, 73], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 102 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3, 59], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 117 + + [87, 1, Conv, [320, 3, 1]], + [102, 1, Conv, [640, 3, 1]], + [117, 1, Conv, [1280, 3, 1]], + + [[118,119,120], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/cv/detection/yolov7/pytorch/data/coco.yaml b/cv/detection/yolov7/pytorch/data/coco.yaml new file mode 100644 index 000000000..447c792fe --- /dev/null +++ b/cv/detection/yolov7/pytorch/data/coco.yaml @@ -0,0 +1,23 @@ +# COCO 2017 dataset http://cocodataset.org + +# download command/URL (optional) +download: bash ./scripts/get_coco.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: /home/datasets/cv/coco/train2017.txt # 118287 images +val: /home/datasets/cv/coco/val2017.txt # 5000 images +test: /home/datasets/cv/coco/val2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# number of classes +nc: 80 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] diff --git a/cv/detection/yolov7/pytorch/data/hyp.scratch.custom.yaml b/cv/detection/yolov7/pytorch/data/hyp.scratch.custom.yaml new file mode 100644 index 000000000..8570d7301 --- /dev/null +++ b/cv/detection/yolov7/pytorch/data/hyp.scratch.custom.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.0 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/data/hyp.scratch.p5.yaml b/cv/detection/yolov7/pytorch/data/hyp.scratch.p5.yaml new file mode 100644 index 000000000..a409bac3d --- /dev/null +++ b/cv/detection/yolov7/pytorch/data/hyp.scratch.p5.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.15 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.15 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/data/hyp.scratch.p6.yaml b/cv/detection/yolov7/pytorch/data/hyp.scratch.p6.yaml new file mode 100644 index 000000000..192d0d5dd --- /dev/null +++ b/cv/detection/yolov7/pytorch/data/hyp.scratch.p6.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.15 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.15 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/data/hyp.scratch.tiny.yaml b/cv/detection/yolov7/pytorch/data/hyp.scratch.tiny.yaml new file mode 100644 index 000000000..b0dc14ae1 --- /dev/null +++ b/cv/detection/yolov7/pytorch/data/hyp.scratch.tiny.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.05 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.05 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training diff --git a/cv/detection/yolov7/pytorch/deploy/triton-inference-server/README.md b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/README.md new file mode 100644 index 000000000..13af4daa9 --- /dev/null +++ b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/README.md @@ -0,0 +1,164 @@ +# YOLOv7 on Triton Inference Server + +Instructions to deploy YOLOv7 as TensorRT engine to [Triton Inference Server](https://github.com/NVIDIA/triton-inference-server). + +Triton Inference Server takes care of model deployment with many out-of-the-box benefits, like a GRPC and HTTP interface, automatic scheduling on multiple GPUs, shared memory (even on GPU), dynamic server-side batching, health metrics and memory resource management. + +There are no additional dependencies needed to run this deployment, except a working docker daemon with GPU support. + +## Export TensorRT + +See https://github.com/WongKinYiu/yolov7#export for more info. + +```bash +#install onnx-simplifier not listed in general yolov7 requirements.txt +pip3 install onnx-simplifier + +# Pytorch Yolov7 -> ONNX with grid, EfficientNMS plugin and dynamic batch size +python export.py --weights ./yolov7.pt --grid --end2end --dynamic-batch --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 +# ONNX -> TensorRT with trtexec and docker +docker run -it --rm --gpus=all nvcr.io/nvidia/tensorrt:22.06-py3 +# Copy onnx -> container: docker cp yolov7.onnx :/workspace/ +# Export with FP16 precision, min batch 1, opt batch 8 and max batch 8 +./tensorrt/bin/trtexec --onnx=yolov7.onnx --minShapes=images:1x3x640x640 --optShapes=images:8x3x640x640 --maxShapes=images:8x3x640x640 --fp16 --workspace=4096 --saveEngine=yolov7-fp16-1x8x8.engine --timingCacheFile=timing.cache +# Test engine +./tensorrt/bin/trtexec --loadEngine=yolov7-fp16-1x8x8.engine +# Copy engine -> host: docker cp :/workspace/yolov7-fp16-1x8x8.engine . +``` + +Example output of test with RTX 3090. + +``` +[I] === Performance summary === +[I] Throughput: 73.4985 qps +[I] Latency: min = 14.8578 ms, max = 15.8344 ms, mean = 15.07 ms, median = 15.0422 ms, percentile(99%) = 15.7443 ms +[I] End-to-End Host Latency: min = 25.8715 ms, max = 28.4102 ms, mean = 26.672 ms, median = 26.6082 ms, percentile(99%) = 27.8314 ms +[I] Enqueue Time: min = 0.793701 ms, max = 1.47144 ms, mean = 1.2008 ms, median = 1.28644 ms, percentile(99%) = 1.38965 ms +[I] H2D Latency: min = 1.50073 ms, max = 1.52454 ms, mean = 1.51225 ms, median = 1.51404 ms, percentile(99%) = 1.51941 ms +[I] GPU Compute Time: min = 13.3386 ms, max = 14.3186 ms, mean = 13.5448 ms, median = 13.5178 ms, percentile(99%) = 14.2151 ms +[I] D2H Latency: min = 0.00878906 ms, max = 0.0172729 ms, mean = 0.0128844 ms, median = 0.0125732 ms, percentile(99%) = 0.0166016 ms +[I] Total Host Walltime: 3.04768 s +[I] Total GPU Compute Time: 3.03404 s +[I] Explanations of the performance metrics are printed in the verbose logs. +``` +Note: 73.5 qps x batch 8 = 588 fps @ ~15ms latency. + +## Model Repository + +See [Triton Model Repository Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_repository.md#model-repository) for more info. + +```bash +# Create folder structure +mkdir -p triton-deploy/models/yolov7/1/ +touch triton-deploy/models/yolov7/config.pbtxt +# Place model +mv yolov7-fp16-1x8x8.engine triton-deploy/models/yolov7/1/model.plan +``` + +## Model Configuration + +See [Triton Model Configuration Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#model-configuration) for more info. + +Minimal configuration for `triton-deploy/models/yolov7/config.pbtxt`: + +``` +name: "yolov7" +platform: "tensorrt_plan" +max_batch_size: 8 +dynamic_batching { } +``` + +Example repository: + +```bash +$ tree triton-deploy/ +triton-deploy/ +└── models + └── yolov7 + ├── 1 + │   └── model.plan + └── config.pbtxt + +3 directories, 2 files +``` + +## Start Triton Inference Server + +``` +docker run --gpus all --rm --ipc=host --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8000:8000 -p8001:8001 -p8002:8002 -v$(pwd)/triton-deploy/models:/models nvcr.io/nvidia/tritonserver:22.06-py3 tritonserver --model-repository=/models --strict-model-config=false --log-verbose 1 +``` + +In the log you should see: + +``` ++--------+---------+--------+ +| Model | Version | Status | ++--------+---------+--------+ +| yolov7 | 1 | READY | ++--------+---------+--------+ +``` + +## Performance with Model Analyzer + +See [Triton Model Analyzer Documentation](https://github.com/triton-inference-server/server/blob/main/docs/model_analyzer.md#model-analyzer) for more info. + +Performance numbers @ RTX 3090 + AMD Ryzen 9 5950X + +Example test for 16 concurrent clients using shared memory, each with batch size 1 requests: + +```bash +docker run -it --ipc=host --net=host nvcr.io/nvidia/tritonserver:22.06-py3-sdk /bin/bash + +./install/bin/perf_analyzer -m yolov7 -u 127.0.0.1:8001 -i grpc --shared-memory system --concurrency-range 16 + +# Result (truncated) +Concurrency: 16, throughput: 590.119 infer/sec, latency 27080 usec +``` + +Throughput for 16 clients with batch size 1 is the same as for a single thread running the engine at 16 batch size locally thanks to Triton [Dynamic Batching Strategy](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#dynamic-batcher). Result without dynamic batching (disable in model configuration) considerably worse: + +```bash +# Result (truncated) +Concurrency: 16, throughput: 335.587 infer/sec, latency 47616 usec +``` + +## How to run model in your code + +Example client can be found in client.py. It can run dummy input, images and videos. + +```bash +pip3 install tritonclient[all] opencv-python +python3 client.py image data/dog.jpg +``` + +![exemplary output result](data/dog_result.jpg) + +``` +$ python3 client.py --help +usage: client.py [-h] [-m MODEL] [--width WIDTH] [--height HEIGHT] [-u URL] [-o OUT] [-f FPS] [-i] [-v] [-t CLIENT_TIMEOUT] [-s] [-r ROOT_CERTIFICATES] [-p PRIVATE_KEY] [-x CERTIFICATE_CHAIN] {dummy,image,video} [input] + +positional arguments: + {dummy,image,video} Run mode. 'dummy' will send an emtpy buffer to the server to test if inference works. 'image' will process an image. 'video' will process a video. + input Input file to load from in image or video mode + +optional arguments: + -h, --help show this help message and exit + -m MODEL, --model MODEL + Inference model name, default yolov7 + --width WIDTH Inference model input width, default 640 + --height HEIGHT Inference model input height, default 640 + -u URL, --url URL Inference server URL, default localhost:8001 + -o OUT, --out OUT Write output into file instead of displaying it + -f FPS, --fps FPS Video output fps, default 24.0 FPS + -i, --model-info Print model status, configuration and statistics + -v, --verbose Enable verbose client output + -t CLIENT_TIMEOUT, --client-timeout CLIENT_TIMEOUT + Client timeout in seconds, default no timeout + -s, --ssl Enable SSL encrypted channel to the server + -r ROOT_CERTIFICATES, --root-certificates ROOT_CERTIFICATES + File holding PEM-encoded root certificates, default none + -p PRIVATE_KEY, --private-key PRIVATE_KEY + File holding PEM-encoded private key, default is none + -x CERTIFICATE_CHAIN, --certificate-chain CERTIFICATE_CHAIN + File holding PEM-encoded certicate chain default is none +``` diff --git a/cv/detection/yolov7/pytorch/deploy/triton-inference-server/boundingbox.py b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/boundingbox.py new file mode 100644 index 000000000..8b95330b8 --- /dev/null +++ b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/boundingbox.py @@ -0,0 +1,33 @@ +class BoundingBox: + def __init__(self, classID, confidence, x1, x2, y1, y2, image_width, image_height): + self.classID = classID + self.confidence = confidence + self.x1 = x1 + self.x2 = x2 + self.y1 = y1 + self.y2 = y2 + self.u1 = x1 / image_width + self.u2 = x2 / image_width + self.v1 = y1 / image_height + self.v2 = y2 / image_height + + def box(self): + return (self.x1, self.y1, self.x2, self.y2) + + def width(self): + return self.x2 - self.x1 + + def height(self): + return self.y2 - self.y1 + + def center_absolute(self): + return (0.5 * (self.x1 + self.x2), 0.5 * (self.y1 + self.y2)) + + def center_normalized(self): + return (0.5 * (self.u1 + self.u2), 0.5 * (self.v1 + self.v2)) + + def size_absolute(self): + return (self.x2 - self.x1, self.y2 - self.y1) + + def size_normalized(self): + return (self.u2 - self.u1, self.v2 - self.v1) diff --git a/cv/detection/yolov7/pytorch/deploy/triton-inference-server/client.py b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/client.py new file mode 100644 index 000000000..aedca11c7 --- /dev/null +++ b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/client.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python + +import argparse +import numpy as np +import sys +import cv2 + +import tritonclient.grpc as grpcclient +from tritonclient.utils import InferenceServerException + +from processing import preprocess, postprocess +from render import render_box, render_filled_box, get_text_size, render_text, RAND_COLORS +from labels import COCOLabels + +INPUT_NAMES = ["images"] +OUTPUT_NAMES = ["num_dets", "det_boxes", "det_scores", "det_classes"] + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('mode', + choices=['dummy', 'image', 'video'], + default='dummy', + help='Run mode. \'dummy\' will send an emtpy buffer to the server to test if inference works. \'image\' will process an image. \'video\' will process a video.') + parser.add_argument('input', + type=str, + nargs='?', + help='Input file to load from in image or video mode') + parser.add_argument('-m', + '--model', + type=str, + required=False, + default='yolov7', + help='Inference model name, default yolov7') + parser.add_argument('--width', + type=int, + required=False, + default=640, + help='Inference model input width, default 640') + parser.add_argument('--height', + type=int, + required=False, + default=640, + help='Inference model input height, default 640') + parser.add_argument('-u', + '--url', + type=str, + required=False, + default='localhost:8001', + help='Inference server URL, default localhost:8001') + parser.add_argument('-o', + '--out', + type=str, + required=False, + default='', + help='Write output into file instead of displaying it') + parser.add_argument('-f', + '--fps', + type=float, + required=False, + default=24.0, + help='Video output fps, default 24.0 FPS') + parser.add_argument('-i', + '--model-info', + action="store_true", + required=False, + default=False, + help='Print model status, configuration and statistics') + parser.add_argument('-v', + '--verbose', + action="store_true", + required=False, + default=False, + help='Enable verbose client output') + parser.add_argument('-t', + '--client-timeout', + type=float, + required=False, + default=None, + help='Client timeout in seconds, default no timeout') + parser.add_argument('-s', + '--ssl', + action="store_true", + required=False, + default=False, + help='Enable SSL encrypted channel to the server') + parser.add_argument('-r', + '--root-certificates', + type=str, + required=False, + default=None, + help='File holding PEM-encoded root certificates, default none') + parser.add_argument('-p', + '--private-key', + type=str, + required=False, + default=None, + help='File holding PEM-encoded private key, default is none') + parser.add_argument('-x', + '--certificate-chain', + type=str, + required=False, + default=None, + help='File holding PEM-encoded certicate chain default is none') + + FLAGS = parser.parse_args() + + # Create server context + try: + triton_client = grpcclient.InferenceServerClient( + url=FLAGS.url, + verbose=FLAGS.verbose, + ssl=FLAGS.ssl, + root_certificates=FLAGS.root_certificates, + private_key=FLAGS.private_key, + certificate_chain=FLAGS.certificate_chain) + except Exception as e: + print("context creation failed: " + str(e)) + sys.exit() + + # Health check + if not triton_client.is_server_live(): + print("FAILED : is_server_live") + sys.exit(1) + + if not triton_client.is_server_ready(): + print("FAILED : is_server_ready") + sys.exit(1) + + if not triton_client.is_model_ready(FLAGS.model): + print("FAILED : is_model_ready") + sys.exit(1) + + if FLAGS.model_info: + # Model metadata + try: + metadata = triton_client.get_model_metadata(FLAGS.model) + print(metadata) + except InferenceServerException as ex: + if "Request for unknown model" not in ex.message(): + print("FAILED : get_model_metadata") + print("Got: {}".format(ex.message())) + sys.exit(1) + else: + print("FAILED : get_model_metadata") + sys.exit(1) + + # Model configuration + try: + config = triton_client.get_model_config(FLAGS.model) + if not (config.config.name == FLAGS.model): + print("FAILED: get_model_config") + sys.exit(1) + print(config) + except InferenceServerException as ex: + print("FAILED : get_model_config") + print("Got: {}".format(ex.message())) + sys.exit(1) + + # DUMMY MODE + if FLAGS.mode == 'dummy': + print("Running in 'dummy' mode") + print("Creating emtpy buffer filled with ones...") + inputs = [] + outputs = [] + inputs.append(grpcclient.InferInput(INPUT_NAMES[0], [1, 3, FLAGS.width, FLAGS.height], "FP32")) + inputs[0].set_data_from_numpy(np.ones(shape=(1, 3, FLAGS.width, FLAGS.height), dtype=np.float32)) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[0])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[1])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[2])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[3])) + + print("Invoking inference...") + results = triton_client.infer(model_name=FLAGS.model, + inputs=inputs, + outputs=outputs, + client_timeout=FLAGS.client_timeout) + if FLAGS.model_info: + statistics = triton_client.get_inference_statistics(model_name=FLAGS.model) + if len(statistics.model_stats) != 1: + print("FAILED: get_inference_statistics") + sys.exit(1) + print(statistics) + print("Done") + + for output in OUTPUT_NAMES: + result = results.as_numpy(output) + print(f"Received result buffer \"{output}\" of size {result.shape}") + print(f"Naive buffer sum: {np.sum(result)}") + + # IMAGE MODE + if FLAGS.mode == 'image': + print("Running in 'image' mode") + if not FLAGS.input: + print("FAILED: no input image") + sys.exit(1) + + inputs = [] + outputs = [] + inputs.append(grpcclient.InferInput(INPUT_NAMES[0], [1, 3, FLAGS.width, FLAGS.height], "FP32")) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[0])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[1])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[2])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[3])) + + print("Creating buffer from image file...") + input_image = cv2.imread(str(FLAGS.input)) + if input_image is None: + print(f"FAILED: could not load input image {str(FLAGS.input)}") + sys.exit(1) + input_image_buffer = preprocess(input_image, [FLAGS.width, FLAGS.height]) + input_image_buffer = np.expand_dims(input_image_buffer, axis=0) + + inputs[0].set_data_from_numpy(input_image_buffer) + + print("Invoking inference...") + results = triton_client.infer(model_name=FLAGS.model, + inputs=inputs, + outputs=outputs, + client_timeout=FLAGS.client_timeout) + if FLAGS.model_info: + statistics = triton_client.get_inference_statistics(model_name=FLAGS.model) + if len(statistics.model_stats) != 1: + print("FAILED: get_inference_statistics") + sys.exit(1) + print(statistics) + print("Done") + + for output in OUTPUT_NAMES: + result = results.as_numpy(output) + print(f"Received result buffer \"{output}\" of size {result.shape}") + print(f"Naive buffer sum: {np.sum(result)}") + + num_dets = results.as_numpy(OUTPUT_NAMES[0]) + det_boxes = results.as_numpy(OUTPUT_NAMES[1]) + det_scores = results.as_numpy(OUTPUT_NAMES[2]) + det_classes = results.as_numpy(OUTPUT_NAMES[3]) + detected_objects = postprocess(num_dets, det_boxes, det_scores, det_classes, input_image.shape[1], input_image.shape[0], [FLAGS.width, FLAGS.height]) + print(f"Detected objects: {len(detected_objects)}") + + for box in detected_objects: + print(f"{COCOLabels(box.classID).name}: {box.confidence}") + input_image = render_box(input_image, box.box(), color=tuple(RAND_COLORS[box.classID % 64].tolist())) + size = get_text_size(input_image, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", normalised_scaling=0.6) + input_image = render_filled_box(input_image, (box.x1 - 3, box.y1 - 3, box.x1 + size[0], box.y1 + size[1]), color=(220, 220, 220)) + input_image = render_text(input_image, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", (box.x1, box.y1), color=(30, 30, 30), normalised_scaling=0.5) + + if FLAGS.out: + cv2.imwrite(FLAGS.out, input_image) + print(f"Saved result to {FLAGS.out}") + else: + cv2.imshow('image', input_image) + cv2.waitKey(0) + cv2.destroyAllWindows() + + # VIDEO MODE + if FLAGS.mode == 'video': + print("Running in 'video' mode") + if not FLAGS.input: + print("FAILED: no input video") + sys.exit(1) + + inputs = [] + outputs = [] + inputs.append(grpcclient.InferInput(INPUT_NAMES[0], [1, 3, FLAGS.width, FLAGS.height], "FP32")) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[0])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[1])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[2])) + outputs.append(grpcclient.InferRequestedOutput(OUTPUT_NAMES[3])) + + print("Opening input video stream...") + cap = cv2.VideoCapture(FLAGS.input) + if not cap.isOpened(): + print(f"FAILED: cannot open video {FLAGS.input}") + sys.exit(1) + + counter = 0 + out = None + print("Invoking inference...") + while True: + ret, frame = cap.read() + if not ret: + print("failed to fetch next frame") + break + + if counter == 0 and FLAGS.out: + print("Opening output video stream...") + fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V') + out = cv2.VideoWriter(FLAGS.out, fourcc, FLAGS.fps, (frame.shape[1], frame.shape[0])) + + input_image_buffer = preprocess(frame, [FLAGS.width, FLAGS.height]) + input_image_buffer = np.expand_dims(input_image_buffer, axis=0) + + inputs[0].set_data_from_numpy(input_image_buffer) + + results = triton_client.infer(model_name=FLAGS.model, + inputs=inputs, + outputs=outputs, + client_timeout=FLAGS.client_timeout) + + num_dets = results.as_numpy("num_dets") + det_boxes = results.as_numpy("det_boxes") + det_scores = results.as_numpy("det_scores") + det_classes = results.as_numpy("det_classes") + detected_objects = postprocess(num_dets, det_boxes, det_scores, det_classes, frame.shape[1], frame.shape[0], [FLAGS.width, FLAGS.height]) + print(f"Frame {counter}: {len(detected_objects)} objects") + counter += 1 + + for box in detected_objects: + print(f"{COCOLabels(box.classID).name}: {box.confidence}") + frame = render_box(frame, box.box(), color=tuple(RAND_COLORS[box.classID % 64].tolist())) + size = get_text_size(frame, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", normalised_scaling=0.6) + frame = render_filled_box(frame, (box.x1 - 3, box.y1 - 3, box.x1 + size[0], box.y1 + size[1]), color=(220, 220, 220)) + frame = render_text(frame, f"{COCOLabels(box.classID).name}: {box.confidence:.2f}", (box.x1, box.y1), color=(30, 30, 30), normalised_scaling=0.5) + + if FLAGS.out: + out.write(frame) + else: + cv2.imshow('image', frame) + if cv2.waitKey(1) == ord('q'): + break + + if FLAGS.model_info: + statistics = triton_client.get_inference_statistics(model_name=FLAGS.model) + if len(statistics.model_stats) != 1: + print("FAILED: get_inference_statistics") + sys.exit(1) + print(statistics) + print("Done") + + cap.release() + if FLAGS.out: + out.release() + else: + cv2.destroyAllWindows() diff --git a/cv/detection/yolov7/pytorch/deploy/triton-inference-server/labels.py b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/labels.py new file mode 100644 index 000000000..ba6c5c516 --- /dev/null +++ b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/labels.py @@ -0,0 +1,83 @@ +from enum import Enum + +class COCOLabels(Enum): + PERSON = 0 + BICYCLE = 1 + CAR = 2 + MOTORBIKE = 3 + AEROPLANE = 4 + BUS = 5 + TRAIN = 6 + TRUCK = 7 + BOAT = 8 + TRAFFIC_LIGHT = 9 + FIRE_HYDRANT = 10 + STOP_SIGN = 11 + PARKING_METER = 12 + BENCH = 13 + BIRD = 14 + CAT = 15 + DOG = 16 + HORSE = 17 + SHEEP = 18 + COW = 19 + ELEPHANT = 20 + BEAR = 21 + ZEBRA = 22 + GIRAFFE = 23 + BACKPACK = 24 + UMBRELLA = 25 + HANDBAG = 26 + TIE = 27 + SUITCASE = 28 + FRISBEE = 29 + SKIS = 30 + SNOWBOARD = 31 + SPORTS_BALL = 32 + KITE = 33 + BASEBALL_BAT = 34 + BASEBALL_GLOVE = 35 + SKATEBOARD = 36 + SURFBOARD = 37 + TENNIS_RACKET = 38 + BOTTLE = 39 + WINE_GLASS = 40 + CUP = 41 + FORK = 42 + KNIFE = 43 + SPOON = 44 + BOWL = 45 + BANANA = 46 + APPLE = 47 + SANDWICH = 48 + ORANGE = 49 + BROCCOLI = 50 + CARROT = 51 + HOT_DOG = 52 + PIZZA = 53 + DONUT = 54 + CAKE = 55 + CHAIR = 56 + SOFA = 57 + POTTEDPLANT = 58 + BED = 59 + DININGTABLE = 60 + TOILET = 61 + TVMONITOR = 62 + LAPTOP = 63 + MOUSE = 64 + REMOTE = 65 + KEYBOARD = 66 + CELL_PHONE = 67 + MICROWAVE = 68 + OVEN = 69 + TOASTER = 70 + SINK = 71 + REFRIGERATOR = 72 + BOOK = 73 + CLOCK = 74 + VASE = 75 + SCISSORS = 76 + TEDDY_BEAR = 77 + HAIR_DRIER = 78 + TOOTHBRUSH = 79 diff --git a/cv/detection/yolov7/pytorch/deploy/triton-inference-server/processing.py b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/processing.py new file mode 100644 index 000000000..3d51c50a3 --- /dev/null +++ b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/processing.py @@ -0,0 +1,51 @@ +from boundingbox import BoundingBox + +import cv2 +import numpy as np + +def preprocess(img, input_shape, letter_box=True): + if letter_box: + img_h, img_w, _ = img.shape + new_h, new_w = input_shape[0], input_shape[1] + offset_h, offset_w = 0, 0 + if (new_w / img_w) <= (new_h / img_h): + new_h = int(img_h * new_w / img_w) + offset_h = (input_shape[0] - new_h) // 2 + else: + new_w = int(img_w * new_h / img_h) + offset_w = (input_shape[1] - new_w) // 2 + resized = cv2.resize(img, (new_w, new_h)) + img = np.full((input_shape[0], input_shape[1], 3), 127, dtype=np.uint8) + img[offset_h:(offset_h + new_h), offset_w:(offset_w + new_w), :] = resized + else: + img = cv2.resize(img, (input_shape[1], input_shape[0])) + + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = img.transpose((2, 0, 1)).astype(np.float32) + img /= 255.0 + return img + +def postprocess(num_dets, det_boxes, det_scores, det_classes, img_w, img_h, input_shape, letter_box=True): + boxes = det_boxes[0, :num_dets[0][0]] / np.array([input_shape[0], input_shape[1], input_shape[0], input_shape[1]], dtype=np.float32) + scores = det_scores[0, :num_dets[0][0]] + classes = det_classes[0, :num_dets[0][0]].astype(np.int) + + old_h, old_w = img_h, img_w + offset_h, offset_w = 0, 0 + if letter_box: + if (img_w / input_shape[1]) >= (img_h / input_shape[0]): + old_h = int(input_shape[0] * img_w / input_shape[1]) + offset_h = (old_h - img_h) // 2 + else: + old_w = int(input_shape[1] * img_h / input_shape[0]) + offset_w = (old_w - img_w) // 2 + + boxes = boxes * np.array([old_w, old_h, old_w, old_h], dtype=np.float32) + if letter_box: + boxes -= np.array([offset_w, offset_h, offset_w, offset_h], dtype=np.float32) + boxes = boxes.astype(np.int) + + detected_objects = [] + for box, score, label in zip(boxes, scores, classes): + detected_objects.append(BoundingBox(label, score, box[0], box[2], box[1], box[3], img_w, img_h)) + return detected_objects diff --git a/cv/detection/yolov7/pytorch/deploy/triton-inference-server/render.py b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/render.py new file mode 100644 index 000000000..dea040156 --- /dev/null +++ b/cv/detection/yolov7/pytorch/deploy/triton-inference-server/render.py @@ -0,0 +1,110 @@ +import numpy as np + +import cv2 + +from math import sqrt + +_LINE_THICKNESS_SCALING = 500.0 + +np.random.seed(0) +RAND_COLORS = np.random.randint(50, 255, (64, 3), "int") # used for class visu +RAND_COLORS[0] = [220, 220, 220] + +def render_box(img, box, color=(200, 200, 200)): + """ + Render a box. Calculates scaling and thickness automatically. + :param img: image to render into + :param box: (x1, y1, x2, y2) - box coordinates + :param color: (b, g, r) - box color + :return: updated image + """ + x1, y1, x2, y2 = box + thickness = int( + round( + (img.shape[0] * img.shape[1]) + / (_LINE_THICKNESS_SCALING * _LINE_THICKNESS_SCALING) + ) + ) + thickness = max(1, thickness) + img = cv2.rectangle( + img, + (int(x1), int(y1)), + (int(x2), int(y2)), + color, + thickness=thickness + ) + return img + +def render_filled_box(img, box, color=(200, 200, 200)): + """ + Render a box. Calculates scaling and thickness automatically. + :param img: image to render into + :param box: (x1, y1, x2, y2) - box coordinates + :param color: (b, g, r) - box color + :return: updated image + """ + x1, y1, x2, y2 = box + img = cv2.rectangle( + img, + (int(x1), int(y1)), + (int(x2), int(y2)), + color, + thickness=cv2.FILLED + ) + return img + +_TEXT_THICKNESS_SCALING = 700.0 +_TEXT_SCALING = 520.0 + + +def get_text_size(img, text, normalised_scaling=1.0): + """ + Get calculated text size (as box width and height) + :param img: image reference, used to determine appropriate text scaling + :param text: text to display + :param normalised_scaling: additional normalised scaling. Default 1.0. + :return: (width, height) - width and height of text box + """ + thickness = int( + round( + (img.shape[0] * img.shape[1]) + / (_TEXT_THICKNESS_SCALING * _TEXT_THICKNESS_SCALING) + ) + * normalised_scaling + ) + thickness = max(1, thickness) + scaling = img.shape[0] / _TEXT_SCALING * normalised_scaling + return cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, scaling, thickness)[0] + + +def render_text(img, text, pos, color=(200, 200, 200), normalised_scaling=1.0): + """ + Render a text into the image. Calculates scaling and thickness automatically. + :param img: image to render into + :param text: text to display + :param pos: (x, y) - upper left coordinates of render position + :param color: (b, g, r) - text color + :param normalised_scaling: additional normalised scaling. Default 1.0. + :return: updated image + """ + x, y = pos + thickness = int( + round( + (img.shape[0] * img.shape[1]) + / (_TEXT_THICKNESS_SCALING * _TEXT_THICKNESS_SCALING) + ) + * normalised_scaling + ) + thickness = max(1, thickness) + scaling = img.shape[0] / _TEXT_SCALING * normalised_scaling + size = get_text_size(img, text, normalised_scaling) + cv2.putText( + img, + text, + (int(x), int(y + size[1])), + cv2.FONT_HERSHEY_SIMPLEX, + scaling, + color, + thickness=thickness, + ) + return img diff --git a/cv/detection/yolov7/pytorch/detect.py b/cv/detection/yolov7/pytorch/detect.py new file mode 100644 index 000000000..5e0c4416a --- /dev/null +++ b/cv/detection/yolov7/pytorch/detect.py @@ -0,0 +1,196 @@ +import argparse +import time +from pathlib import Path + +import cv2 +import torch +import torch.backends.cudnn as cudnn +from numpy import random + +from models.experimental import attempt_load +from utils.datasets import LoadStreams, LoadImages +from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ + scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path +from utils.plots import plot_one_box +from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel + + +def detect(save_img=False): + source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace + save_img = not opt.nosave and not source.endswith('.txt') # save inference images + webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( + ('rtsp://', 'rtmp://', 'http://', 'https://')) + + # Directories + save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Initialize + set_logging() + device = select_device(opt.device) + half = device.type != 'cpu' # half precision only supported on CUDA + + # Load model + model = attempt_load(weights, map_location=device) # load FP32 model + stride = int(model.stride.max()) # model stride + imgsz = check_img_size(imgsz, s=stride) # check img_size + + if trace: + model = TracedModel(model, device, opt.img_size) + + if half: + model.half() # to FP16 + + # Second-stage classifier + classify = False + if classify: + modelc = load_classifier(name='resnet101', n=2) # initialize + modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() + + # Set Dataloader + vid_path, vid_writer = None, None + if webcam: + view_img = check_imshow() + cudnn.benchmark = True # set True to speed up constant image size inference + dataset = LoadStreams(source, img_size=imgsz, stride=stride) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride) + + # Get names and colors + names = model.module.names if hasattr(model, 'module') else model.names + colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] + + # Run inference + if device.type != 'cpu': + model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once + old_img_w = old_img_h = imgsz + old_img_b = 1 + + t0 = time.time() + for path, img, im0s, vid_cap in dataset: + img = torch.from_numpy(img).to(device) + img = img.half() if half else img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + + # Warmup + if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): + old_img_b = img.shape[0] + old_img_h = img.shape[2] + old_img_w = img.shape[3] + for i in range(3): + model(img, augment=opt.augment)[0] + + # Inference + t1 = time_synchronized() + with torch.no_grad(): # Calculating gradients would cause a GPU memory leak + pred = model(img, augment=opt.augment)[0] + t2 = time_synchronized() + + # Apply NMS + pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + t3 = time_synchronized() + + # Apply Classifier + if classify: + pred = apply_classifier(pred, modelc, img, im0s) + + # Process detections + for i, det in enumerate(pred): # detections per image + if webcam: # batch_size >= 1 + p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count + else: + p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # img.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Write results + for *xyxy, conf, cls in reversed(det): + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format + with open(txt_path + '.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or view_img: # Add bbox to image + label = f'{names[int(cls)]} {conf:.2f}' + plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) + + # Print time (inference + NMS) + print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') + + # Stream results + if view_img: + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + print(f" The image with the result is saved in: {save_path}") + else: # 'video' or 'stream' + if vid_path != save_path: # new video + vid_path = save_path + if isinstance(vid_writer, cv2.VideoWriter): + vid_writer.release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path += '.mp4' + vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer.write(im0) + + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + #print(f"Results saved to {save_dir}{s}") + + print(f'Done. ({time.time() - t0:.3f}s)') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)') + parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam + parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='display results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default='runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--no-trace', action='store_true', help='don`t trace model') + opt = parser.parse_args() + print(opt) + #check_requirements(exclude=('pycocotools', 'thop')) + + with torch.no_grad(): + if opt.update: # update all models (to fix SourceChangeWarning) + for opt.weights in ['yolov7.pt']: + detect() + strip_optimizer(opt.weights) + else: + detect() diff --git a/cv/detection/yolov7/pytorch/hubconf.py b/cv/detection/yolov7/pytorch/hubconf.py new file mode 100644 index 000000000..50ff257e2 --- /dev/null +++ b/cv/detection/yolov7/pytorch/hubconf.py @@ -0,0 +1,97 @@ +"""PyTorch Hub models + +Usage: + import torch + model = torch.hub.load('repo', 'model') +""" + +from pathlib import Path + +import torch + +from models.yolo import Model +from utils.general import check_requirements, set_logging +from utils.google_utils import attempt_download +from utils.torch_utils import select_device + +dependencies = ['torch', 'yaml'] +check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop')) +set_logging() + + +def create(name, pretrained, channels, classes, autoshape): + """Creates a specified model + + Arguments: + name (str): name of model, i.e. 'yolov7' + pretrained (bool): load pretrained weights into the model + channels (int): number of input channels + classes (int): number of model classes + + Returns: + pytorch model + """ + try: + cfg = list((Path(__file__).parent / 'cfg').rglob(f'{name}.yaml'))[0] # model.yaml path + model = Model(cfg, channels, classes) + if pretrained: + fname = f'{name}.pt' # checkpoint filename + attempt_download(fname) # download if not found locally + ckpt = torch.load(fname, map_location=torch.device('cpu')) # load + msd = model.state_dict() # model state_dict + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter + model.load_state_dict(csd, strict=False) # load + if len(ckpt['model'].names) == classes: + model.names = ckpt['model'].names # set class names attribute + if autoshape: + model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return model.to(device) + + except Exception as e: + s = 'Cache maybe be out of date, try force_reload=True.' + raise Exception(s) from e + + +def custom(path_or_model='path/to/model.pt', autoshape=True): + """custom mode + + Arguments (3 options): + path_or_model (str): 'path/to/model.pt' + path_or_model (dict): torch.load('path/to/model.pt') + path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] + + Returns: + pytorch model + """ + model = torch.load(path_or_model, map_location=torch.device('cpu')) if isinstance(path_or_model, str) else path_or_model # load checkpoint + if isinstance(model, dict): + model = model['ema' if model.get('ema') else 'model'] # load model + + hub_model = Model(model.yaml).to(next(model.parameters()).device) # create + hub_model.load_state_dict(model.float().state_dict()) # load state_dict + hub_model.names = model.names # class names + if autoshape: + hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return hub_model.to(device) + + +def yolov7(pretrained=True, channels=3, classes=80, autoshape=True): + return create('yolov7', pretrained, channels, classes, autoshape) + + +if __name__ == '__main__': + model = custom(path_or_model='yolov7.pt') # custom example + # model = create(name='yolov7', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example + + # Verify inference + import numpy as np + from PIL import Image + + imgs = [np.zeros((640, 480, 3))] + + results = model(imgs) # batched inference + results.print() + results.save() diff --git a/cv/detection/yolov7/pytorch/models/__init__.py b/cv/detection/yolov7/pytorch/models/__init__.py new file mode 100644 index 000000000..84952a816 --- /dev/null +++ b/cv/detection/yolov7/pytorch/models/__init__.py @@ -0,0 +1 @@ +# init \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/models/common.py b/cv/detection/yolov7/pytorch/models/common.py new file mode 100644 index 000000000..edb5edc9f --- /dev/null +++ b/cv/detection/yolov7/pytorch/models/common.py @@ -0,0 +1,2019 @@ +import math +from copy import copy +from pathlib import Path + +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.ops import DeformConv2d +from PIL import Image +from torch.cuda import amp + +from utils.datasets import letterbox +from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh +from utils.plots import color_list, plot_one_box +from utils.torch_utils import time_synchronized + + +##### basic #### + +def autopad(k, p=None): # kernel, padding + # Pad to 'same' + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +class MP(nn.Module): + def __init__(self, k=2): + super(MP, self).__init__() + self.m = nn.MaxPool2d(kernel_size=k, stride=k) + + def forward(self, x): + return self.m(x) + + +class SP(nn.Module): + def __init__(self, k=3, s=1): + super(SP, self).__init__() + self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2) + + def forward(self, x): + return self.m(x) + + +class ReOrg(nn.Module): + def __init__(self): + super(ReOrg, self).__init__() + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1) + + +class Concat(nn.Module): + def __init__(self, dimension=1): + super(Concat, self).__init__() + self.d = dimension + + def forward(self, x): + return torch.cat(x, self.d) + + +class Chuncat(nn.Module): + def __init__(self, dimension=1): + super(Chuncat, self).__init__() + self.d = dimension + + def forward(self, x): + x1 = [] + x2 = [] + for xi in x: + xi1, xi2 = xi.chunk(2, self.d) + x1.append(xi1) + x2.append(xi2) + return torch.cat(x1+x2, self.d) + + +class Shortcut(nn.Module): + def __init__(self, dimension=0): + super(Shortcut, self).__init__() + self.d = dimension + + def forward(self, x): + return x[0]+x[1] + + +class Foldcut(nn.Module): + def __init__(self, dimension=0): + super(Foldcut, self).__init__() + self.d = dimension + + def forward(self, x): + x1, x2 = x.chunk(2, self.d) + return x1+x2 + + +class Conv(nn.Module): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super(Conv, self).__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def fuseforward(self, x): + return self.act(self.conv(x)) + + +class RobustConv(nn.Module): + # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs. + def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups + super(RobustConv, self).__init__() + self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) + self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True) + self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None + + def forward(self, x): + x = x.to(memory_format=torch.channels_last) + x = self.conv1x1(self.conv_dw(x)) + if self.gamma is not None: + x = x.mul(self.gamma.reshape(1, -1, 1, 1)) + return x + + +class RobustConv2(nn.Module): + # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP). + def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups + super(RobustConv2, self).__init__() + self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) + self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s, + padding=0, bias=True, dilation=1, groups=1 + ) + self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None + + def forward(self, x): + x = self.conv_deconv(self.conv_strided(x)) + if self.gamma is not None: + x = x.mul(self.gamma.reshape(1, -1, 1, 1)) + return x + + +def DWConv(c1, c2, k=1, s=1, act=True): + # Depthwise convolution + return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups + super(GhostConv, self).__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) + + def forward(self, x): + y = self.cv1(x) + return torch.cat([y, self.cv2(y)], 1) + + +class Stem(nn.Module): + # Stem + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super(Stem, self).__init__() + c_ = int(c2/2) # hidden channels + self.cv1 = Conv(c1, c_, 3, 2) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(c_, c_, 3, 2) + self.pool = torch.nn.MaxPool2d(2, stride=2) + self.cv4 = Conv(2 * c_, c2, 1, 1) + + def forward(self, x): + x = self.cv1(x) + return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1)) + + +class DownC(nn.Module): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, n=1, k=2): + super(DownC, self).__init__() + c_ = int(c1) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2//2, 3, k) + self.cv3 = Conv(c1, c2//2, 1, 1) + self.mp = nn.MaxPool2d(kernel_size=k, stride=k) + + def forward(self, x): + return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1) + + +class SPP(nn.Module): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13)): + super(SPP, self).__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + x = self.cv1(x) + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class Bottleneck(nn.Module): + # Darknet bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super(Bottleneck, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class Res(nn.Module): + # ResNet bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super(Res, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 3, 1, g=g) + self.cv3 = Conv(c_, c2, 1, 1) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x))) + + +class ResX(Res): + # ResNet bottleneck + def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + + +class Ghost(nn.Module): + # Ghost Bottleneck https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride + super(Ghost, self).__init__() + c_ = c2 // 2 + self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False)) # pw-linear + self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), + Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() + + def forward(self, x): + return self.conv(x) + self.shortcut(x) + +##### end of basic ##### + + +##### cspnet ##### + +class SPPCSPC(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): + super(SPPCSPC, self).__init__() + c_ = int(2 * c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 3, 1) + self.cv4 = Conv(c_, c_, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + self.cv5 = Conv(4 * c_, c_, 1, 1) + self.cv6 = Conv(c_, c_, 3, 1) + self.cv7 = Conv(2 * c_, c2, 1, 1) + + def forward(self, x): + x1 = self.cv4(self.cv3(self.cv1(x))) + y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) + y2 = self.cv2(x) + return self.cv7(torch.cat((y1, y2), dim=1)) + +class GhostSPPCSPC(SPPCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): + super().__init__(c1, c2, n, shortcut, g, e, k) + c_ = int(2 * c2 * e) # hidden channels + self.cv1 = GhostConv(c1, c_, 1, 1) + self.cv2 = GhostConv(c1, c_, 1, 1) + self.cv3 = GhostConv(c_, c_, 3, 1) + self.cv4 = GhostConv(c_, c_, 1, 1) + self.cv5 = GhostConv(4 * c_, c_, 1, 1) + self.cv6 = GhostConv(c_, c_, 3, 1) + self.cv7 = GhostConv(2 * c_, c2, 1, 1) + + +class GhostStem(Stem): + # Stem + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__(c1, c2, k, s, p, g, act) + c_ = int(c2/2) # hidden channels + self.cv1 = GhostConv(c1, c_, 3, 2) + self.cv2 = GhostConv(c_, c_, 1, 1) + self.cv3 = GhostConv(c_, c_, 3, 2) + self.cv4 = GhostConv(2 * c_, c2, 1, 1) + + +class BottleneckCSPA(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(BottleneckCSPA, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.m(self.cv1(x)) + y2 = self.cv2(x) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class BottleneckCSPB(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(BottleneckCSPB, self).__init__() + c_ = int(c2) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + x1 = self.cv1(x) + y1 = self.m(x1) + y2 = self.cv2(x1) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class BottleneckCSPC(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(BottleneckCSPC, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 1, 1) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(torch.cat((y1, y2), dim=1)) + + +class ResCSPA(BottleneckCSPA): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class ResCSPB(BottleneckCSPB): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class ResCSPC(BottleneckCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class ResXCSPA(ResCSPA): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class ResXCSPB(ResCSPB): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class ResXCSPC(ResCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class GhostCSPA(BottleneckCSPA): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) + + +class GhostCSPB(BottleneckCSPB): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) + + +class GhostCSPC(BottleneckCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) + +##### end of cspnet ##### + + +##### yolor ##### + +class ImplicitA(nn.Module): + def __init__(self, channel, mean=0., std=.02): + super(ImplicitA, self).__init__() + self.channel = channel + self.mean = mean + self.std = std + self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) + nn.init.normal_(self.implicit, mean=self.mean, std=self.std) + + def forward(self, x): + return self.implicit + x + + +class ImplicitM(nn.Module): + def __init__(self, channel, mean=1., std=.02): + super(ImplicitM, self).__init__() + self.channel = channel + self.mean = mean + self.std = std + self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1)) + nn.init.normal_(self.implicit, mean=self.mean, std=self.std) + + def forward(self, x): + return self.implicit * x + +##### end of yolor ##### + + +##### repvgg ##### + +class RepConv(nn.Module): + # Represented convolution + # https://arxiv.org/abs/2101.03697 + + def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False): + super(RepConv, self).__init__() + + self.deploy = deploy + self.groups = g + self.in_channels = c1 + self.out_channels = c2 + + assert k == 3 + assert autopad(k, p) == 1 + + padding_11 = autopad(k, p) - k // 2 + + self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + + if deploy: + self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True) + + else: + self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None) + + self.rbr_dense = nn.Sequential( + nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False), + nn.BatchNorm2d(num_features=c2), + ) + + self.rbr_1x1 = nn.Sequential( + nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False), + nn.BatchNorm2d(num_features=c2), + ) + + def forward(self, inputs): + if hasattr(self, "rbr_reparam"): + return self.act(self.rbr_reparam(inputs)) + + if self.rbr_identity is None: + id_out = 0 + else: + id_out = self.rbr_identity(inputs) + + return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out) + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) + kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) + kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) + return ( + kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, + bias3x3 + bias1x1 + biasid, + ) + + def _pad_1x1_to_3x3_tensor(self, kernel1x1): + if kernel1x1 is None: + return 0 + else: + return nn.functional.pad(kernel1x1, [1, 1, 1, 1]) + + def _fuse_bn_tensor(self, branch): + if branch is None: + return 0, 0 + if isinstance(branch, nn.Sequential): + kernel = branch[0].weight + running_mean = branch[1].running_mean + running_var = branch[1].running_var + gamma = branch[1].weight + beta = branch[1].bias + eps = branch[1].eps + else: + assert isinstance(branch, nn.BatchNorm2d) + if not hasattr(self, "id_tensor"): + input_dim = self.in_channels // self.groups + kernel_value = np.zeros( + (self.in_channels, input_dim, 3, 3), dtype=np.float32 + ) + for i in range(self.in_channels): + kernel_value[i, i % input_dim, 1, 1] = 1 + self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) + kernel = self.id_tensor + running_mean = branch.running_mean + running_var = branch.running_var + gamma = branch.weight + beta = branch.bias + eps = branch.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + def repvgg_convert(self): + kernel, bias = self.get_equivalent_kernel_bias() + return ( + kernel.detach().cpu().numpy(), + bias.detach().cpu().numpy(), + ) + + def fuse_conv_bn(self, conv, bn): + + std = (bn.running_var + bn.eps).sqrt() + bias = bn.bias - bn.running_mean * bn.weight / std + + t = (bn.weight / std).reshape(-1, 1, 1, 1) + weights = conv.weight * t + + bn = nn.Identity() + conv = nn.Conv2d(in_channels = conv.in_channels, + out_channels = conv.out_channels, + kernel_size = conv.kernel_size, + stride=conv.stride, + padding = conv.padding, + dilation = conv.dilation, + groups = conv.groups, + bias = True, + padding_mode = conv.padding_mode) + + conv.weight = torch.nn.Parameter(weights) + conv.bias = torch.nn.Parameter(bias) + return conv + + def fuse_repvgg_block(self): + if self.deploy: + return + print(f"RepConv.fuse_repvgg_block") + + self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1]) + + self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1]) + rbr_1x1_bias = self.rbr_1x1.bias + weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1]) + + # Fuse self.rbr_identity + if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)): + # print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm") + identity_conv_1x1 = nn.Conv2d( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=1, + stride=1, + padding=0, + groups=self.groups, + bias=False) + identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device) + identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze() + # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") + identity_conv_1x1.weight.data.fill_(0.0) + identity_conv_1x1.weight.data.fill_diagonal_(1.0) + identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3) + # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") + + identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity) + bias_identity_expanded = identity_conv_1x1.bias + weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1]) + else: + # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}") + bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) ) + weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) ) + + + #print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ") + #print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ") + #print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ") + + self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded) + self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded) + + self.rbr_reparam = self.rbr_dense + self.deploy = True + + if self.rbr_identity is not None: + del self.rbr_identity + self.rbr_identity = None + + if self.rbr_1x1 is not None: + del self.rbr_1x1 + self.rbr_1x1 = None + + if self.rbr_dense is not None: + del self.rbr_dense + self.rbr_dense = None + + +class RepBottleneck(Bottleneck): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut=True, g=1, e=0.5) + c_ = int(c2 * e) # hidden channels + self.cv2 = RepConv(c_, c2, 3, 1, g=g) + + +class RepBottleneckCSPA(BottleneckCSPA): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class RepBottleneckCSPB(BottleneckCSPB): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class RepBottleneckCSPC(BottleneckCSPC): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class RepRes(Res): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.cv2 = RepConv(c_, c_, 3, 1, g=g) + + +class RepResCSPA(ResCSPA): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResCSPB(ResCSPB): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResCSPC(ResCSPC): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResX(ResX): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.cv2 = RepConv(c_, c_, 3, 1, g=g) + + +class RepResXCSPA(ResXCSPA): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResXCSPB(ResXCSPB): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResXCSPC(ResXCSPC): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + +##### end of repvgg ##### + + +##### transformer ##### + +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2) + p = p.unsqueeze(0) + p = p.transpose(0, 3) + p = p.squeeze(3) + e = self.linear(p) + x = p + e + + x = self.tr(x) + x = x.unsqueeze(3) + x = x.transpose(0, 3) + x = x.reshape(b, self.c2, w, h) + return x + +##### end of transformer ##### + + +##### yolov5 ##### + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super(Focus, self).__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act) + # self.contract = Contract(gain=2) + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) + # return self.conv(self.contract(x)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.cv1(x) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) + + +class Contract(nn.Module): + # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) + + +class Expand(nn.Module): + # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160) + + +class NMS(nn.Module): + # Non-Maximum Suppression (NMS) module + conf = 0.25 # confidence threshold + iou = 0.45 # IoU threshold + classes = None # (optional list) filter by class + + def __init__(self): + super(NMS, self).__init__() + + def forward(self, x): + return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) + + +class autoShape(nn.Module): + # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + classes = None # (optional list) filter by class + + def __init__(self, model): + super(autoShape, self).__init__() + self.model = model.eval() + + def autoshape(self): + print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() + return self + + @torch.no_grad() + def forward(self, imgs, size=640, augment=False, profile=False): + # Inference from various sources. For height=640, width=1280, RGB images example inputs are: + # filename: imgs = 'data/samples/zidane.jpg' + # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + t = [time_synchronized()] + p = next(self.model.parameters()) # for device and type + if isinstance(imgs, torch.Tensor): # torch + with amp.autocast(enabled=p.device.type != 'cpu'): + return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference + + # Pre-process + n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(imgs): + f = f'image{i}' # filename + if isinstance(im, str): # filename or uri + im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(im), getattr(im, 'filename', f) or f + files.append(Path(f).with_suffix('.jpg').name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = (size / max(s)) # gain + shape1.append([y * g for y in s]) + imgs[i] = im # update + shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape + x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad + x = np.stack(x, 0) if n > 1 else x[0][None] # stack + x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 + t.append(time_synchronized()) + + with amp.autocast(enabled=p.device.type != 'cpu'): + # Inference + y = self.model(x, augment, profile)[0] # forward + t.append(time_synchronized()) + + # Post-process + y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) + + t.append(time_synchronized()) + return Detections(imgs, y, files, t, self.names, x.shape) + + +class Detections: + # detections class for YOLOv5 inference results + def __init__(self, imgs, pred, files, times=None, names=None, shape=None): + super(Detections, self).__init__() + d = pred[0].device # device + gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations + self.imgs = imgs # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) + self.s = shape # inference BCHW shape + + def display(self, pprint=False, show=False, save=False, render=False, save_dir=''): + colors = color_list() + for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): + str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' + if pred is not None: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + if show or save or render: + for *box, conf, cls in pred: # xyxy, confidence, class + label = f'{self.names[int(cls)]} {conf:.2f}' + plot_one_box(box, img, label=label, color=colors[int(cls) % 10]) + img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np + if pprint: + print(str.rstrip(', ')) + if show: + img.show(self.files[i]) # show + if save: + f = self.files[i] + img.save(Path(save_dir) / f) # save + print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') + if render: + self.imgs[i] = np.asarray(img) + + def print(self): + self.display(pprint=True) # print results + print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) + + def show(self): + self.display(show=True) # show results + + def save(self, save_dir='runs/hub/exp'): + save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir + Path(save_dir).mkdir(parents=True, exist_ok=True) + self.display(save=True, save_dir=save_dir) # save results + + def render(self): + self.display(render=True) # render results + return self.imgs + + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns + cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns + for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + # return a list of Detections objects, i.e. 'for result in results.tolist():' + x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] + for d in x: + for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: + setattr(d, k, getattr(d, k)[0]) # pop out of list + return x + + def __len__(self): + return self.n + + +class Classify(nn.Module): + # Classification head, i.e. x(b,c1,20,20) to x(b,c2) + def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups + super(Classify, self).__init__() + self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) + self.flat = nn.Flatten() + + def forward(self, x): + z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list + return self.flat(self.conv(z)) # flatten to x(b,c2) + +##### end of yolov5 ###### + + +##### orepa ##### + +def transI_fusebn(kernel, bn): + gamma = bn.weight + std = (bn.running_var + bn.eps).sqrt() + return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std + + +class ConvBN(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, + stride=1, padding=0, dilation=1, groups=1, deploy=False, nonlinear=None): + super().__init__() + if nonlinear is None: + self.nonlinear = nn.Identity() + else: + self.nonlinear = nonlinear + if deploy: + self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, + stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True) + else: + self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, + stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False) + self.bn = nn.BatchNorm2d(num_features=out_channels) + + def forward(self, x): + if hasattr(self, 'bn'): + return self.nonlinear(self.bn(self.conv(x))) + else: + return self.nonlinear(self.conv(x)) + + def switch_to_deploy(self): + kernel, bias = transI_fusebn(self.conv.weight, self.bn) + conv = nn.Conv2d(in_channels=self.conv.in_channels, out_channels=self.conv.out_channels, kernel_size=self.conv.kernel_size, + stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups, bias=True) + conv.weight.data = kernel + conv.bias.data = bias + for para in self.parameters(): + para.detach_() + self.__delattr__('conv') + self.__delattr__('bn') + self.conv = conv + +class OREPA_3x3_RepConv(nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size, + stride=1, padding=0, dilation=1, groups=1, + internal_channels_1x1_3x3=None, + deploy=False, nonlinear=None, single_init=False): + super(OREPA_3x3_RepConv, self).__init__() + self.deploy = deploy + + if nonlinear is None: + self.nonlinear = nn.Identity() + else: + self.nonlinear = nonlinear + + self.kernel_size = kernel_size + self.in_channels = in_channels + self.out_channels = out_channels + self.groups = groups + assert padding == kernel_size // 2 + + self.stride = stride + self.padding = padding + self.dilation = dilation + + self.branch_counter = 0 + + self.weight_rbr_origin = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), kernel_size, kernel_size)) + nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0)) + self.branch_counter += 1 + + + if groups < out_channels: + self.weight_rbr_avg_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1)) + self.weight_rbr_pfir_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1)) + nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0) + nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0) + self.weight_rbr_avg_conv.data + self.weight_rbr_pfir_conv.data + self.register_buffer('weight_rbr_avg_avg', torch.ones(kernel_size, kernel_size).mul(1.0/kernel_size/kernel_size)) + self.branch_counter += 1 + + else: + raise NotImplementedError + self.branch_counter += 1 + + if internal_channels_1x1_3x3 is None: + internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels + + if internal_channels_1x1_3x3 == in_channels: + self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter(torch.zeros(in_channels, int(in_channels/self.groups), 1, 1)) + id_value = np.zeros((in_channels, int(in_channels/self.groups), 1, 1)) + for i in range(in_channels): + id_value[i, i % int(in_channels/self.groups), 0, 0] = 1 + id_tensor = torch.from_numpy(id_value).type_as(self.weight_rbr_1x1_kxk_idconv1) + self.register_buffer('id_tensor', id_tensor) + + else: + self.weight_rbr_1x1_kxk_conv1 = nn.Parameter(torch.Tensor(internal_channels_1x1_3x3, int(in_channels/self.groups), 1, 1)) + nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0)) + self.weight_rbr_1x1_kxk_conv2 = nn.Parameter(torch.Tensor(out_channels, int(internal_channels_1x1_3x3/self.groups), kernel_size, kernel_size)) + nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0)) + self.branch_counter += 1 + + expand_ratio = 8 + self.weight_rbr_gconv_dw = nn.Parameter(torch.Tensor(in_channels*expand_ratio, 1, kernel_size, kernel_size)) + self.weight_rbr_gconv_pw = nn.Parameter(torch.Tensor(out_channels, in_channels*expand_ratio, 1, 1)) + nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0)) + nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0)) + self.branch_counter += 1 + + if out_channels == in_channels and stride == 1: + self.branch_counter += 1 + + self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels)) + self.bn = nn.BatchNorm2d(out_channels) + + self.fre_init() + + nn.init.constant_(self.vector[0, :], 0.25) #origin + nn.init.constant_(self.vector[1, :], 0.25) #avg + nn.init.constant_(self.vector[2, :], 0.0) #prior + nn.init.constant_(self.vector[3, :], 0.5) #1x1_kxk + nn.init.constant_(self.vector[4, :], 0.5) #dws_conv + + + def fre_init(self): + prior_tensor = torch.Tensor(self.out_channels, self.kernel_size, self.kernel_size) + half_fg = self.out_channels/2 + for i in range(self.out_channels): + for h in range(3): + for w in range(3): + if i < half_fg: + prior_tensor[i, h, w] = math.cos(math.pi*(h+0.5)*(i+1)/3) + else: + prior_tensor[i, h, w] = math.cos(math.pi*(w+0.5)*(i+1-half_fg)/3) + + self.register_buffer('weight_rbr_prior', prior_tensor) + + def weight_gen(self): + + weight_rbr_origin = torch.einsum('oihw,o->oihw', self.weight_rbr_origin, self.vector[0, :]) + + weight_rbr_avg = torch.einsum('oihw,o->oihw', torch.einsum('oihw,hw->oihw', self.weight_rbr_avg_conv, self.weight_rbr_avg_avg), self.vector[1, :]) + + weight_rbr_pfir = torch.einsum('oihw,o->oihw', torch.einsum('oihw,ohw->oihw', self.weight_rbr_pfir_conv, self.weight_rbr_prior), self.vector[2, :]) + + weight_rbr_1x1_kxk_conv1 = None + if hasattr(self, 'weight_rbr_1x1_kxk_idconv1'): + weight_rbr_1x1_kxk_conv1 = (self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor).squeeze() + elif hasattr(self, 'weight_rbr_1x1_kxk_conv1'): + weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze() + else: + raise NotImplementedError + weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2 + + if self.groups > 1: + g = self.groups + t, ig = weight_rbr_1x1_kxk_conv1.size() + o, tg, h, w = weight_rbr_1x1_kxk_conv2.size() + weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t/g), ig) + weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view(g, int(o/g), tg, h, w) + weight_rbr_1x1_kxk = torch.einsum('gti,gothw->goihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2).view(o, ig, h, w) + else: + weight_rbr_1x1_kxk = torch.einsum('ti,othw->oihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2) + + weight_rbr_1x1_kxk = torch.einsum('oihw,o->oihw', weight_rbr_1x1_kxk, self.vector[3, :]) + + weight_rbr_gconv = self.dwsc2full(self.weight_rbr_gconv_dw, self.weight_rbr_gconv_pw, self.in_channels) + weight_rbr_gconv = torch.einsum('oihw,o->oihw', weight_rbr_gconv, self.vector[4, :]) + + weight = weight_rbr_origin + weight_rbr_avg + weight_rbr_1x1_kxk + weight_rbr_pfir + weight_rbr_gconv + + return weight + + def dwsc2full(self, weight_dw, weight_pw, groups): + + t, ig, h, w = weight_dw.size() + o, _, _, _ = weight_pw.size() + tg = int(t/groups) + i = int(ig*groups) + weight_dw = weight_dw.view(groups, tg, ig, h, w) + weight_pw = weight_pw.squeeze().view(o, groups, tg) + + weight_dsc = torch.einsum('gtihw,ogt->ogihw', weight_dw, weight_pw) + return weight_dsc.view(o, i, h, w) + + def forward(self, inputs): + weight = self.weight_gen() + out = F.conv2d(inputs, weight, bias=None, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) + + return self.nonlinear(self.bn(out)) + +class RepConv_OREPA(nn.Module): + + def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False, nonlinear=nn.SiLU()): + super(RepConv_OREPA, self).__init__() + self.deploy = deploy + self.groups = groups + self.in_channels = c1 + self.out_channels = c2 + + self.padding = padding + self.dilation = dilation + self.groups = groups + + assert k == 3 + assert padding == 1 + + padding_11 = padding - k // 2 + + if nonlinear is None: + self.nonlinearity = nn.Identity() + else: + self.nonlinearity = nonlinear + + if use_se: + self.se = SEBlock(self.out_channels, internal_neurons=self.out_channels // 16) + else: + self.se = nn.Identity() + + if deploy: + self.rbr_reparam = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, + padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode) + + else: + self.rbr_identity = nn.BatchNorm2d(num_features=self.in_channels) if self.out_channels == self.in_channels and s == 1 else None + self.rbr_dense = OREPA_3x3_RepConv(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, padding=padding, groups=groups, dilation=1) + self.rbr_1x1 = ConvBN(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=1, stride=s, padding=padding_11, groups=groups, dilation=1) + print('RepVGG Block, identity = ', self.rbr_identity) + + + def forward(self, inputs): + if hasattr(self, 'rbr_reparam'): + return self.nonlinearity(self.se(self.rbr_reparam(inputs))) + + if self.rbr_identity is None: + id_out = 0 + else: + id_out = self.rbr_identity(inputs) + + out1 = self.rbr_dense(inputs) + out2 = self.rbr_1x1(inputs) + out3 = id_out + out = out1 + out2 + out3 + + return self.nonlinearity(self.se(out)) + + + # Optional. This improves the accuracy and facilitates quantization. + # 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight. + # 2. Use like this. + # loss = criterion(....) + # for every RepVGGBlock blk: + # loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2() + # optimizer.zero_grad() + # loss.backward() + + # Not used for OREPA + def get_custom_L2(self): + K3 = self.rbr_dense.weight_gen() + K1 = self.rbr_1x1.conv.weight + t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach() + t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach() + + l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them. + eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel. + l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2. + return l2_loss_eq_kernel + l2_loss_circle + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) + kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) + kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) + return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid + + def _pad_1x1_to_3x3_tensor(self, kernel1x1): + if kernel1x1 is None: + return 0 + else: + return torch.nn.functional.pad(kernel1x1, [1,1,1,1]) + + def _fuse_bn_tensor(self, branch): + if branch is None: + return 0, 0 + if not isinstance(branch, nn.BatchNorm2d): + if isinstance(branch, OREPA_3x3_RepConv): + kernel = branch.weight_gen() + elif isinstance(branch, ConvBN): + kernel = branch.conv.weight + else: + raise NotImplementedError + running_mean = branch.bn.running_mean + running_var = branch.bn.running_var + gamma = branch.bn.weight + beta = branch.bn.bias + eps = branch.bn.eps + else: + if not hasattr(self, 'id_tensor'): + input_dim = self.in_channels // self.groups + kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32) + for i in range(self.in_channels): + kernel_value[i, i % input_dim, 1, 1] = 1 + self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) + kernel = self.id_tensor + running_mean = branch.running_mean + running_var = branch.running_var + gamma = branch.weight + beta = branch.bias + eps = branch.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + def switch_to_deploy(self): + if hasattr(self, 'rbr_reparam'): + return + print(f"RepConv_OREPA.switch_to_deploy") + kernel, bias = self.get_equivalent_kernel_bias() + self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.in_channels, out_channels=self.rbr_dense.out_channels, + kernel_size=self.rbr_dense.kernel_size, stride=self.rbr_dense.stride, + padding=self.rbr_dense.padding, dilation=self.rbr_dense.dilation, groups=self.rbr_dense.groups, bias=True) + self.rbr_reparam.weight.data = kernel + self.rbr_reparam.bias.data = bias + for para in self.parameters(): + para.detach_() + self.__delattr__('rbr_dense') + self.__delattr__('rbr_1x1') + if hasattr(self, 'rbr_identity'): + self.__delattr__('rbr_identity') + +##### end of orepa ##### + + +##### swin transformer ##### + +class WindowAttention(nn.Module): + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + nn.init.normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + # print(attn.dtype, v.dtype) + try: + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + except: + #print(attn.dtype, v.dtype) + x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + +class Mlp(nn.Module): + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + +def window_partition(x, window_size): + + B, H, W, C = x.shape + assert H % window_size == 0, 'feature map h and w can not divide by window size' + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + +def window_reverse(windows, window_size, H, W): + + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class SwinTransformerLayer(nn.Module): + + def __init__(self, dim, num_heads, window_size=8, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.SiLU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + # if min(self.input_resolution) <= self.window_size: + # # if window size is larger than input resolution, we don't partition windows + # self.shift_size = 0 + # self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def create_mask(self, H, W): + # calculate attention mask for SW-MSA + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x): + # reshape x[b c h w] to x[b l c] + _, _, H_, W_ = x.shape + + Padding = False + if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0: + Padding = True + # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') + pad_r = (self.window_size - W_ % self.window_size) % self.window_size + pad_b = (self.window_size - H_ % self.window_size) % self.window_size + x = F.pad(x, (0, pad_r, 0, pad_b)) + + # print('2', x.shape) + B, C, H, W = x.shape + L = H * W + x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c + + # create mask from init to forward + if self.shift_size > 0: + attn_mask = self.create_mask(H, W).to(x.device) + else: + attn_mask = None + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w + + if Padding: + x = x[:, :, :H_, :W_] # reverse padding + + return x + + +class SwinTransformerBlock(nn.Module): + def __init__(self, c1, c2, num_heads, num_layers, window_size=8): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + + # remove input_resolution + self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + x = self.blocks(x) + return x + + +class STCSPA(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(STCSPA, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformerBlock(c_, c_, num_heads, n) + #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.m(self.cv1(x)) + y2 = self.cv2(x) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class STCSPB(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(STCSPB, self).__init__() + c_ = int(c2) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformerBlock(c_, c_, num_heads, n) + #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + x1 = self.cv1(x) + y1 = self.m(x1) + y2 = self.cv2(x1) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class STCSPC(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(STCSPC, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 1, 1) + self.cv4 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformerBlock(c_, c_, num_heads, n) + #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(torch.cat((y1, y2), dim=1)) + +##### end of swin transformer ##### + + +##### swin transformer v2 ##### + +class WindowAttention_v2(nn.Module): + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., + pretrained_window_size=[0, 0]): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False)) + + # get relative_coords_table + relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) + relative_coords_table = torch.stack( + torch.meshgrid([relative_coords_h, + relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) + else: + relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + torch.abs(relative_coords_table) + 1.0) / np.log2(8) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) + self.v_bias = nn.Parameter(torch.zeros(dim)) + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + try: + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + except: + x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, ' \ + f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + +class Mlp_v2(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition_v2(x, window_size): + + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse_v2(windows, window_size, H, W): + + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class SwinTransformerLayer_v2(nn.Module): + + def __init__(self, dim, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0): + super().__init__() + self.dim = dim + #self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + #if min(self.input_resolution) <= self.window_size: + # # if window size is larger than input resolution, we don't partition windows + # self.shift_size = 0 + # self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention_v2( + dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, + pretrained_window_size=(pretrained_window_size, pretrained_window_size)) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def create_mask(self, H, W): + # calculate attention mask for SW-MSA + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x): + # reshape x[b c h w] to x[b l c] + _, _, H_, W_ = x.shape + + Padding = False + if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0: + Padding = True + # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') + pad_r = (self.window_size - W_ % self.window_size) % self.window_size + pad_b = (self.window_size - H_ % self.window_size) % self.window_size + x = F.pad(x, (0, pad_r, 0, pad_b)) + + # print('2', x.shape) + B, C, H, W = x.shape + L = H * W + x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c + + # create mask from init to forward + if self.shift_size > 0: + attn_mask = self.create_mask(H, W).to(x.device) + else: + attn_mask = None + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition_v2(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse_v2(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w + + if Padding: + x = x[:, :, :H_, :W_] # reverse padding + + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class SwinTransformer2Block(nn.Module): + def __init__(self, c1, c2, num_heads, num_layers, window_size=7): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + + # remove input_resolution + self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + x = self.blocks(x) + return x + + +class ST2CSPA(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(ST2CSPA, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformer2Block(c_, c_, num_heads, n) + #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.m(self.cv1(x)) + y2 = self.cv2(x) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class ST2CSPB(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(ST2CSPB, self).__init__() + c_ = int(c2) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformer2Block(c_, c_, num_heads, n) + #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + x1 = self.cv1(x) + y1 = self.m(x1) + y2 = self.cv2(x1) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class ST2CSPC(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(ST2CSPC, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 1, 1) + self.cv4 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformer2Block(c_, c_, num_heads, n) + #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(torch.cat((y1, y2), dim=1)) + +##### end of swin transformer v2 ##### diff --git a/cv/detection/yolov7/pytorch/models/experimental.py b/cv/detection/yolov7/pytorch/models/experimental.py new file mode 100644 index 000000000..735d7aa0e --- /dev/null +++ b/cv/detection/yolov7/pytorch/models/experimental.py @@ -0,0 +1,272 @@ +import numpy as np +import random +import torch +import torch.nn as nn + +from models.common import Conv, DWConv +from utils.google_utils import attempt_download + + +class CrossConv(nn.Module): + # Cross Convolution Downsample + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + # ch_in, ch_out, kernel, stride, groups, expansion, shortcut + super(CrossConv, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class Sum(nn.Module): + # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 + def __init__(self, n, weight=False): # n: number of inputs + super(Sum, self).__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights + + def forward(self, x): + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class MixConv2d(nn.Module): + # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): + super(MixConv2d, self).__init__() + groups = len(k) + if equal_ch: # equal c_ per group + i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(groups)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * groups + a = np.eye(groups + 1, groups, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.LeakyReLU(0.1, inplace=True) + + def forward(self, x): + return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + # Ensemble of models + def __init__(self): + super(Ensemble, self).__init__() + + def forward(self, x, augment=False): + y = [] + for module in self: + y.append(module(x, augment)[0]) + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.stack(y).mean(0) # mean ensemble + y = torch.cat(y, 1) # nms ensemble + return y, None # inference, train output + + + + + +class ORT_NMS(torch.autograd.Function): + '''ONNX-Runtime NMS operation''' + @staticmethod + def forward(ctx, + boxes, + scores, + max_output_boxes_per_class=torch.tensor([100]), + iou_threshold=torch.tensor([0.45]), + score_threshold=torch.tensor([0.25])): + device = boxes.device + batch = scores.shape[0] + num_det = random.randint(0, 100) + batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device) + idxs = torch.arange(100, 100 + num_det).to(device) + zeros = torch.zeros((num_det,), dtype=torch.int64).to(device) + selected_indices = torch.cat([batches[None], zeros[None], idxs[None]], 0).T.contiguous() + selected_indices = selected_indices.to(torch.int64) + return selected_indices + + @staticmethod + def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold): + return g.op("NonMaxSuppression", boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold) + + +class TRT_NMS(torch.autograd.Function): + '''TensorRT NMS operation''' + @staticmethod + def forward( + ctx, + boxes, + scores, + background_class=-1, + box_coding=1, + iou_threshold=0.45, + max_output_boxes=100, + plugin_version="1", + score_activation=0, + score_threshold=0.25, + ): + batch_size, num_boxes, num_classes = scores.shape + num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32) + det_boxes = torch.randn(batch_size, max_output_boxes, 4) + det_scores = torch.randn(batch_size, max_output_boxes) + det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32) + return num_det, det_boxes, det_scores, det_classes + + @staticmethod + def symbolic(g, + boxes, + scores, + background_class=-1, + box_coding=1, + iou_threshold=0.45, + max_output_boxes=100, + plugin_version="1", + score_activation=0, + score_threshold=0.25): + out = g.op("TRT::EfficientNMS_TRT", + boxes, + scores, + background_class_i=background_class, + box_coding_i=box_coding, + iou_threshold_f=iou_threshold, + max_output_boxes_i=max_output_boxes, + plugin_version_s=plugin_version, + score_activation_i=score_activation, + score_threshold_f=score_threshold, + outputs=4) + nums, boxes, scores, classes = out + return nums, boxes, scores, classes + + +class ONNX_ORT(nn.Module): + '''onnx module with ONNX-Runtime NMS operation.''' + def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None, n_classes=80): + super().__init__() + self.device = device if device else torch.device("cpu") + self.max_obj = torch.tensor([max_obj]).to(device) + self.iou_threshold = torch.tensor([iou_thres]).to(device) + self.score_threshold = torch.tensor([score_thres]).to(device) + self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic + self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=self.device) + self.n_classes=n_classes + + def forward(self, x): + boxes = x[:, :, :4] + conf = x[:, :, 4:5] + scores = x[:, :, 5:] + if self.n_classes == 1: + scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + scores *= conf # conf = obj_conf * cls_conf + boxes @= self.convert_matrix + max_score, category_id = scores.max(2, keepdim=True) + dis = category_id.float() * self.max_wh + nmsbox = boxes + dis + max_score_tp = max_score.transpose(1, 2).contiguous() + selected_indices = ORT_NMS.apply(nmsbox, max_score_tp, self.max_obj, self.iou_threshold, self.score_threshold) + X, Y = selected_indices[:, 0], selected_indices[:, 2] + selected_boxes = boxes[X, Y, :] + selected_categories = category_id[X, Y, :].float() + selected_scores = max_score[X, Y, :] + X = X.unsqueeze(1).float() + return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1) + +class ONNX_TRT(nn.Module): + '''onnx module with TensorRT NMS operation.''' + def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None, n_classes=80): + super().__init__() + assert max_wh is None + self.device = device if device else torch.device('cpu') + self.background_class = -1, + self.box_coding = 1, + self.iou_threshold = iou_thres + self.max_obj = max_obj + self.plugin_version = '1' + self.score_activation = 0 + self.score_threshold = score_thres + self.n_classes=n_classes + + def forward(self, x): + boxes = x[:, :, :4] + conf = x[:, :, 4:5] + scores = x[:, :, 5:] + if self.n_classes == 1: + scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + scores *= conf # conf = obj_conf * cls_conf + num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(boxes, scores, self.background_class, self.box_coding, + self.iou_threshold, self.max_obj, + self.plugin_version, self.score_activation, + self.score_threshold) + return num_det, det_boxes, det_scores, det_classes + + +class End2End(nn.Module): + '''export onnx or tensorrt model with NMS operation.''' + def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None, n_classes=80): + super().__init__() + device = device if device else torch.device('cpu') + assert isinstance(max_wh,(int)) or max_wh is None + self.model = model.to(device) + self.model.model[-1].end2end = True + self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT + self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device, n_classes) + self.end2end.eval() + + def forward(self, x): + x = self.model(x) + x = self.end2end(x) + return x + + + + + +def attempt_load(weights, map_location=None): + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + attempt_download(w) + ckpt = torch.load(w, map_location=map_location) # load + model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model + + # Compatibility updates + for m in model.modules(): + if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: + m.inplace = True # pytorch 1.7.0 compatibility + elif type(m) is nn.Upsample: + m.recompute_scale_factor = None # torch 1.11.0 compatibility + elif type(m) is Conv: + m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility + + if len(model) == 1: + return model[-1] # return model + else: + print('Ensemble created with %s\n' % weights) + for k in ['names', 'stride']: + setattr(model, k, getattr(model[-1], k)) + return model # return ensemble + + diff --git a/cv/detection/yolov7/pytorch/models/yolo.py b/cv/detection/yolov7/pytorch/models/yolo.py new file mode 100644 index 000000000..95a019c6a --- /dev/null +++ b/cv/detection/yolov7/pytorch/models/yolo.py @@ -0,0 +1,843 @@ +import argparse +import logging +import sys +from copy import deepcopy + +sys.path.append('./') # to run '$ python *.py' files in subdirectories +logger = logging.getLogger(__name__) +import torch +from models.common import * +from models.experimental import * +from utils.autoanchor import check_anchor_order +from utils.general import make_divisible, check_file, set_logging +from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ + select_device, copy_attr +from utils.loss import SigmoidBin + +try: + import thop # for FLOPS computation +except ImportError: + thop = None + + +class Detect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + end2end = False + include_nms = False + concat = False + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super(Detect, self).__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy, wh, conf = y.split((2, 2, self.nc + 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 + xy = xy * (2. * self.stride[i]) + (self.stride[i] * (self.grid[i] - 0.5)) # new xy + wh = wh ** 2 * (4 * self.anchor_grid[i].data) # new wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, -1, self.no)) + + if self.training: + out = x + elif self.end2end: + out = torch.cat(z, 1) + elif self.include_nms: + z = self.convert(z) + out = (z, ) + elif self.concat: + out = torch.cat(z, 1) + else: + out = (torch.cat(z, 1), x) + + return out + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def convert(self, z): + z = torch.cat(z, 1) + box = z[:, :, :4] + conf = z[:, :, 4:5] + score = z[:, :, 5:] + score *= conf + convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=z.device) + box @= convert_matrix + return (box, score) + + +class IDetect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + end2end = False + include_nms = False + concat = False + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super(IDetect, self).__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch) + self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch) + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](self.ia[i](x[i])) # conv + x[i] = self.im[i](x[i]) + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + def fuseforward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy, wh, conf = y.split((2, 2, self.nc + 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 + xy = xy * (2. * self.stride[i]) + (self.stride[i] * (self.grid[i] - 0.5)) # new xy + wh = wh ** 2 * (4 * self.anchor_grid[i].data) # new wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, -1, self.no)) + + if self.training: + out = x + elif self.end2end: + out = torch.cat(z, 1) + elif self.include_nms: + z = self.convert(z) + out = (z, ) + elif self.concat: + out = torch.cat(z, 1) + else: + out = (torch.cat(z, 1), x) + + return out + + def fuse(self): + print("IDetect.fuse") + # fuse ImplicitA and Convolution + for i in range(len(self.m)): + c1,c2,_,_ = self.m[i].weight.shape + c1_,c2_, _,_ = self.ia[i].implicit.shape + self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1,c2),self.ia[i].implicit.reshape(c2_,c1_)).squeeze(1) + + # fuse ImplicitM and Convolution + for i in range(len(self.m)): + c1,c2, _,_ = self.im[i].implicit.shape + self.m[i].bias *= self.im[i].implicit.reshape(c2) + self.m[i].weight *= self.im[i].implicit.transpose(0,1) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def convert(self, z): + z = torch.cat(z, 1) + box = z[:, :, :4] + conf = z[:, :, 4:5] + score = z[:, :, 5:] + score *= conf + convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=z.device) + box @= convert_matrix + return (box, score) + + +class IKeypoint(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__(self, nc=80, anchors=(), nkpt=17, ch=(), inplace=True, dw_conv_kpt=False): # detection layer + super(IKeypoint, self).__init__() + self.nc = nc # number of classes + self.nkpt = nkpt + self.dw_conv_kpt = dw_conv_kpt + self.no_det=(nc + 5) # number of outputs per anchor for box and class + self.no_kpt = 3*self.nkpt ## number of outputs per anchor for keypoints + self.no = self.no_det+self.no_kpt + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + self.flip_test = False + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no_det * self.na, 1) for x in ch) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch) + self.im = nn.ModuleList(ImplicitM(self.no_det * self.na) for _ in ch) + + if self.nkpt is not None: + if self.dw_conv_kpt: #keypoint head is slightly more complex + self.m_kpt = nn.ModuleList( + nn.Sequential(DWConv(x, x, k=3), Conv(x,x), + DWConv(x, x, k=3), Conv(x, x), + DWConv(x, x, k=3), Conv(x,x), + DWConv(x, x, k=3), Conv(x, x), + DWConv(x, x, k=3), Conv(x, x), + DWConv(x, x, k=3), nn.Conv2d(x, self.no_kpt * self.na, 1)) for x in ch) + else: #keypoint head is a single convolution + self.m_kpt = nn.ModuleList(nn.Conv2d(x, self.no_kpt * self.na, 1) for x in ch) + + self.inplace = inplace # use in-place ops (e.g. slice assignment) + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + if self.nkpt is None or self.nkpt==0: + x[i] = self.im[i](self.m[i](self.ia[i](x[i]))) # conv + else : + x[i] = torch.cat((self.im[i](self.m[i](self.ia[i](x[i]))), self.m_kpt[i](x[i])), axis=1) + + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + x_det = x[i][..., :6] + x_kpt = x[i][..., 6:] + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + kpt_grid_x = self.grid[i][..., 0:1] + kpt_grid_y = self.grid[i][..., 1:2] + + if self.nkpt == 0: + y = x[i].sigmoid() + else: + y = x_det.sigmoid() + + if self.inplace: + xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh + if self.nkpt != 0: + x_kpt[..., 0::3] = (x_kpt[..., ::3] * 2. - 0.5 + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i] # xy + x_kpt[..., 1::3] = (x_kpt[..., 1::3] * 2. - 0.5 + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i] # xy + #x_kpt[..., 0::3] = (x_kpt[..., ::3] + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i] # xy + #x_kpt[..., 1::3] = (x_kpt[..., 1::3] + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i] # xy + #print('=============') + #print(self.anchor_grid[i].shape) + #print(self.anchor_grid[i][...,0].unsqueeze(4).shape) + #print(x_kpt[..., 0::3].shape) + #x_kpt[..., 0::3] = ((x_kpt[..., 0::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i] # xy + #x_kpt[..., 1::3] = ((x_kpt[..., 1::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i] # xy + #x_kpt[..., 0::3] = (((x_kpt[..., 0::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i] # xy + #x_kpt[..., 1::3] = (((x_kpt[..., 1::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i] # xy + x_kpt[..., 2::3] = x_kpt[..., 2::3].sigmoid() + + y = torch.cat((xy, wh, y[..., 4:], x_kpt), dim = -1) + + else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 + xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + if self.nkpt != 0: + y[..., 6:] = (y[..., 6:] * 2. - 0.5 + self.grid[i].repeat((1,1,1,1,self.nkpt))) * self.stride[i] # xy + y = torch.cat((xy, wh, y[..., 4:]), -1) + + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + +class IAuxDetect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + end2end = False + include_nms = False + concat = False + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super(IAuxDetect, self).__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch[:self.nl]) # output conv + self.m2 = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch[self.nl:]) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch[:self.nl]) + self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch[:self.nl]) + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](self.ia[i](x[i])) # conv + x[i] = self.im[i](x[i]) + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + x[i+self.nl] = self.m2[i](x[i+self.nl]) + x[i+self.nl] = x[i+self.nl].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy, wh, conf = y.split((2, 2, self.nc + 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 + xy = xy * (2. * self.stride[i]) + (self.stride[i] * (self.grid[i] - 0.5)) # new xy + wh = wh ** 2 * (4 * self.anchor_grid[i].data) # new wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x[:self.nl]) + + def fuseforward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].data # wh + y = torch.cat((xy, wh, y[..., 4:]), -1) + z.append(y.view(bs, -1, self.no)) + + if self.training: + out = x + elif self.end2end: + out = torch.cat(z, 1) + elif self.include_nms: + z = self.convert(z) + out = (z, ) + elif self.concat: + out = torch.cat(z, 1) + else: + out = (torch.cat(z, 1), x) + + return out + + def fuse(self): + print("IAuxDetect.fuse") + # fuse ImplicitA and Convolution + for i in range(len(self.m)): + c1,c2,_,_ = self.m[i].weight.shape + c1_,c2_, _,_ = self.ia[i].implicit.shape + self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1,c2),self.ia[i].implicit.reshape(c2_,c1_)).squeeze(1) + + # fuse ImplicitM and Convolution + for i in range(len(self.m)): + c1,c2, _,_ = self.im[i].implicit.shape + self.m[i].bias *= self.im[i].implicit.reshape(c2) + self.m[i].weight *= self.im[i].implicit.transpose(0,1) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def convert(self, z): + z = torch.cat(z, 1) + box = z[:, :, :4] + conf = z[:, :, 4:5] + score = z[:, :, 5:] + score *= conf + convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=z.device) + box @= convert_matrix + return (box, score) + + +class IBin(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__(self, nc=80, anchors=(), ch=(), bin_count=21): # detection layer + super(IBin, self).__init__() + self.nc = nc # number of classes + self.bin_count = bin_count + + self.w_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0) + self.h_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0) + # classes, x,y,obj + self.no = nc + 3 + \ + self.w_bin_sigmoid.get_length() + self.h_bin_sigmoid.get_length() # w-bce, h-bce + # + self.x_bin_sigmoid.get_length() + self.y_bin_sigmoid.get_length() + + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch) + self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch) + + def forward(self, x): + + #self.x_bin_sigmoid.use_fw_regression = True + #self.y_bin_sigmoid.use_fw_regression = True + self.w_bin_sigmoid.use_fw_regression = True + self.h_bin_sigmoid.use_fw_regression = True + + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](self.ia[i](x[i])) # conv + x[i] = self.im[i](x[i]) + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + #y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + + + #px = (self.x_bin_sigmoid.forward(y[..., 0:12]) + self.grid[i][..., 0]) * self.stride[i] + #py = (self.y_bin_sigmoid.forward(y[..., 12:24]) + self.grid[i][..., 1]) * self.stride[i] + + pw = self.w_bin_sigmoid.forward(y[..., 2:24]) * self.anchor_grid[i][..., 0] + ph = self.h_bin_sigmoid.forward(y[..., 24:46]) * self.anchor_grid[i][..., 1] + + #y[..., 0] = px + #y[..., 1] = py + y[..., 2] = pw + y[..., 3] = ph + + y = torch.cat((y[..., 0:4], y[..., 46:]), dim=-1) + + z.append(y.view(bs, -1, y.shape[-1])) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + +class Model(nn.Module): + def __init__(self, cfg='yolor-csp-c.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes + super(Model, self).__init__() + self.traced = False + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict + + # Define model + ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels + if nc and nc != self.yaml['nc']: + logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") + self.yaml['nc'] = nc # override yaml value + if anchors: + logger.info(f'Overriding model.yaml anchors with anchors={anchors}') + self.yaml['anchors'] = round(anchors) # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist + self.names = [str(i) for i in range(self.yaml['nc'])] # default names + # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, Detect): + s = 256 # 2x min stride + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IDetect): + s = 256 # 2x min stride + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IAuxDetect): + s = 256 # 2x min stride + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))[:4]]) # forward + #print(m.stride) + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_aux_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IBin): + s = 256 # 2x min stride + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases_bin() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IKeypoint): + s = 256 # 2x min stride + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases_kpt() # only run once + # print('Strides: %s' % m.stride.tolist()) + + # Init weights, biases + initialize_weights(self) + self.info() + logger.info('') + + def forward(self, x, augment=False, profile=False): + if augment: + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) + yi = self.forward_once(xi)[0] # forward + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi[..., :4] /= si # de-scale + if fi == 2: + yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud + elif fi == 3: + yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr + y.append(yi) + return torch.cat(y, 1), None # augmented inference, train + else: + return self.forward_once(x, profile) # single-scale inference, train + + def forward_once(self, x, profile=False): + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + if not hasattr(self, 'traced'): + self.traced=False + + if self.traced: + if isinstance(m, Detect) or isinstance(m, IDetect) or isinstance(m, IAuxDetect) or isinstance(m, IKeypoint): + break + + if profile: + c = isinstance(m, (Detect, IDetect, IAuxDetect, IBin)) + o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS + for _ in range(10): + m(x.copy() if c else x) + t = time_synchronized() + for _ in range(10): + m(x.copy() if c else x) + dt.append((time_synchronized() - t) * 100) + print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) + + x = m(x) # run + + y.append(x if m.i in self.save else None) # save output + + if profile: + print('%.1fms total' % sum(dt)) + return x + + def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _initialize_aux_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, mi2, s in zip(m.m, m.m2, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + b2 = mi2.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b2.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b2.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + mi2.bias = torch.nn.Parameter(b2.view(-1), requires_grad=True) + + def _initialize_biases_bin(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Bin() module + bc = m.bin_count + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + old = b[:, (0,1,2,bc+3)].data + obj_idx = 2*bc+4 + b[:, :obj_idx].data += math.log(0.6 / (bc + 1 - 0.99)) + b[:, obj_idx].data += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b[:, (obj_idx+1):].data += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + b[:, (0,1,2,bc+3)].data = old + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _initialize_biases_kpt(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _print_biases(self): + m = self.model[-1] # Detect() module + for mi in m.m: # from + b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) + print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) + + # def _print_weights(self): + # for m in self.model.modules(): + # if type(m) is Bottleneck: + # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights + + def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers + print('Fusing layers... ') + for m in self.model.modules(): + if isinstance(m, RepConv): + #print(f" fuse_repvgg_block") + m.fuse_repvgg_block() + elif isinstance(m, RepConv_OREPA): + #print(f" switch_to_deploy") + m.switch_to_deploy() + elif type(m) is Conv and hasattr(m, 'bn'): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, 'bn') # remove batchnorm + m.forward = m.fuseforward # update forward + elif isinstance(m, (IDetect, IAuxDetect)): + m.fuse() + m.forward = m.fuseforward + self.info() + return self + + def nms(self, mode=True): # add or remove NMS module + present = type(self.model[-1]) is NMS # last layer is NMS + if mode and not present: + print('Adding NMS... ') + m = NMS() # module + m.f = -1 # from + m.i = self.model[-1].i + 1 # index + self.model.add_module(name='%s' % m.i, module=m) # add + self.eval() + elif not mode and present: + print('Removing NMS... ') + self.model = self.model[:-1] # remove + return self + + def autoshape(self): # add autoShape module + print('Adding autoShape... ') + m = autoShape(self) # wrap model + copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes + return m + + def info(self, verbose=False, img_size=640): # print model information + model_info(self, verbose, img_size) + + +def parse_model(d, ch): # model_dict, input_channels(3) + logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, DWConv, GhostConv, RepConv, RepConv_OREPA, DownC, + SPP, SPPF, SPPCSPC, GhostSPPCSPC, MixConv2d, Focus, Stem, GhostStem, CrossConv, + Bottleneck, BottleneckCSPA, BottleneckCSPB, BottleneckCSPC, + RepBottleneck, RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC, + Res, ResCSPA, ResCSPB, ResCSPC, + RepRes, RepResCSPA, RepResCSPB, RepResCSPC, + ResX, ResXCSPA, ResXCSPB, ResXCSPC, + RepResX, RepResXCSPA, RepResXCSPB, RepResXCSPC, + Ghost, GhostCSPA, GhostCSPB, GhostCSPC, + SwinTransformerBlock, STCSPA, STCSPB, STCSPC, + SwinTransformer2Block, ST2CSPA, ST2CSPB, ST2CSPC]: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) + + args = [c1, c2, *args[1:]] + if m in [DownC, SPPCSPC, GhostSPPCSPC, + BottleneckCSPA, BottleneckCSPB, BottleneckCSPC, + RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC, + ResCSPA, ResCSPB, ResCSPC, + RepResCSPA, RepResCSPB, RepResCSPC, + ResXCSPA, ResXCSPB, ResXCSPC, + RepResXCSPA, RepResXCSPB, RepResXCSPC, + GhostCSPA, GhostCSPB, GhostCSPC, + STCSPA, STCSPB, STCSPC, + ST2CSPA, ST2CSPB, ST2CSPC]: + args.insert(2, n) # number of repeats + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum([ch[x] for x in f]) + elif m is Chuncat: + c2 = sum([ch[x] for x in f]) + elif m is Shortcut: + c2 = ch[f[0]] + elif m is Foldcut: + c2 = ch[f] // 2 + elif m in [Detect, IDetect, IAuxDetect, IBin, IKeypoint]: + args.append([ch[x] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + elif m is ReOrg: + c2 = ch[f] * 4 + elif m is Contract: + c2 = ch[f] * args[0] ** 2 + elif m is Expand: + c2 = ch[f] // args[0] ** 2 + else: + c2 = ch[f] + + m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum([x.numel() for x in m_.parameters()]) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + if i == 0: + ch = [] + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='yolor-csp-c.yaml', help='model.yaml') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--profile', action='store_true', help='profile model speed') + opt = parser.parse_args() + opt.cfg = check_file(opt.cfg) # check file + set_logging() + device = select_device(opt.device) + + # Create model + model = Model(opt.cfg).to(device) + model.train() + + if opt.profile: + img = torch.rand(1, 3, 640, 640).to(device) + y = model(img, profile=True) + + # Profile + # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) + # y = model(img, profile=True) + + # Tensorboard + # from torch.utils.tensorboard import SummaryWriter + # tb_writer = SummaryWriter() + # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/") + # tb_writer.add_graph(model.model, img) # add model to tensorboard + # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/cv/detection/yolov7/pytorch/requirements.txt b/cv/detection/yolov7/pytorch/requirements.txt new file mode 100644 index 000000000..f4d218218 --- /dev/null +++ b/cv/detection/yolov7/pytorch/requirements.txt @@ -0,0 +1,39 @@ +# Usage: pip install -r requirements.txt + +# Base ---------------------------------------- +matplotlib>=3.2.2 +numpy>=1.18.5,<1.24.0 +opencv-python>=4.1.1 +Pillow>=7.1.2 +PyYAML>=5.3.1 +requests>=2.23.0 +scipy>=1.4.1 +torch>=1.7.0,!=1.12.0 +torchvision>=0.8.1,!=0.13.0 +tqdm>=4.41.0 +protobuf<4.21.3 + +# Logging ------------------------------------- +tensorboard>=2.4.1 +# wandb + +# Plotting ------------------------------------ +pandas>=1.1.4 +seaborn>=0.11.0 + +# Export -------------------------------------- +# coremltools>=4.1 # CoreML export +# onnx>=1.9.0 # ONNX export +# onnx-simplifier>=0.3.6 # ONNX simplifier +# scikit-learn==0.19.2 # CoreML quantization +# tensorflow>=2.4.1 # TFLite export +# tensorflowjs>=3.9.0 # TF.js export +# openvino-dev # OpenVINO export + +# Extras -------------------------------------- +ipython # interactive notebook +psutil # system utilization +thop # FLOPs computation +# albumentations>=1.0.3 +# pycocotools>=2.0 # COCO mAP +# roboflow diff --git a/cv/detection/yolov7/pytorch/test.py b/cv/detection/yolov7/pytorch/test.py new file mode 100644 index 000000000..17b48060b --- /dev/null +++ b/cv/detection/yolov7/pytorch/test.py @@ -0,0 +1,353 @@ +import argparse +import json +import os +from pathlib import Path +from threading import Thread + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from models.experimental import attempt_load +from utils.datasets import create_dataloader +from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \ + box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr +from utils.metrics import ap_per_class, ConfusionMatrix +from utils.plots import plot_images, output_to_target, plot_study_txt +from utils.torch_utils import select_device, time_synchronized, TracedModel + + +def test(data, + weights=None, + batch_size=32, + imgsz=640, + conf_thres=0.001, + iou_thres=0.6, # for NMS + save_json=False, + single_cls=False, + augment=False, + verbose=False, + model=None, + dataloader=None, + save_dir=Path(''), # for saving images + save_txt=False, # for auto-labelling + save_hybrid=False, # for hybrid auto-labelling + save_conf=False, # save auto-label confidences + plots=True, + wandb_logger=None, + compute_loss=None, + half_precision=True, + trace=False, + is_coco=False, + v5_metric=False): + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device = next(model.parameters()).device # get model device + + else: # called directly + set_logging() + device = select_device(opt.device, batch_size=batch_size) + + # Directories + save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = attempt_load(weights, map_location=device) # load FP32 model + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(imgsz, s=gs) # check img_size + + if trace: + model = TracedModel(model, device, imgsz) + + # Half + half = device.type != 'cpu' and half_precision # half precision only supported on CUDA + if half: + model.half() + + # Configure + model.eval() + if isinstance(data, str): + is_coco = data.endswith('coco.yaml') + with open(data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) + check_dataset(data) # check + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Logging + log_imgs = 0 + if wandb_logger and wandb_logger.wandb: + log_imgs = min(wandb_logger.log_imgs, 100) + # Dataloader + if not training: + if device.type != 'cpu': + model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once + task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, + prefix=colorstr(f'{task}: '))[0] + + if v5_metric: + print("Testing with YOLOv5 AP metric...") + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} + coco91class = coco80_to_coco91_class() + s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') + p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. + loss = torch.zeros(3, device=device) + jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] + for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): + img = img.to(device, non_blocking=True) + img = img.half() if half else img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + targets = targets.to(device) + nb, _, height, width = img.shape # batch size, channels, height, width + + with torch.no_grad(): + # Run model + t = time_synchronized() + out, train_out = model(img, augment=augment) # inference and training outputs + t0 += time_synchronized() - t + + # Compute loss + if compute_loss: + loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls + + # Run NMS + targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + t = time_synchronized() + out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True) + t1 += time_synchronized() - t + + # Statistics per image + for si, pred in enumerate(out): + labels = targets[targets[:, 0] == si, 1:] + nl = len(labels) + tcls = labels[:, 0].tolist() if nl else [] # target class + path = Path(paths[si]) + seen += 1 + + if len(pred) == 0: + if nl: + stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) + continue + + # Predictions + predn = pred.clone() + scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred + + # Append to text file + if save_txt: + gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + # W&B logging - Media Panel Plots + if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation + if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: + box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name)) + wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None + + # Append to pycocotools JSON dictionary + if save_json: + # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + for p, b in zip(pred.tolist(), box.tolist()): + jdict.append({'image_id': image_id, + 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]), + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5)}) + + # Assign all predictions as incorrect + correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) + if nl: + detected = [] # target indices + tcls_tensor = labels[:, 0] + + # target boxes + tbox = xywh2xyxy(labels[:, 1:5]) + scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels + if plots: + confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) + + # Per target class + for cls in torch.unique(tcls_tensor): + ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices + pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices + + # Search for detections + if pi.shape[0]: + # Prediction to target ious + ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices + + # Append detections + detected_set = set() + for j in (ious > iouv[0]).nonzero(as_tuple=False): + d = ti[i[j]] # detected target + if d.item() not in detected_set: + detected_set.add(d.item()) + detected.append(d) + correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn + if len(detected) == nl: # all targets already located in image + break + + # Append statistics (correct, conf, pcls, tcls) + stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) + + # Plot images + if plots and batch_i < 3: + f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels + Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() + f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions + Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start() + + # Compute statistics + stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names) + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 + mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() + nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class + else: + nt = torch.zeros(1) + + # Print results + pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format + print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(ap_class): + print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) + + # Print speeds + t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple + if not training: + print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + if wandb_logger and wandb_logger.wandb: + val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))] + wandb_logger.log({"Validation": val_batches}) + if wandb_images: + wandb_logger.log({"Bounding Box Debugger/Images": wandb_images}) + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights + anno_json = './coco/annotations/instances_val2017.json' # annotations json + pred_json = str(save_dir / f"{w}_predictions.json") # predictions json + print('\nEvaluating pycocotools mAP... saving %s...' % pred_json) + with open(pred_json, 'w') as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + eval = COCOeval(anno, pred, 'bbox') + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) + except Exception as e: + print(f'pycocotools unable to run: {e}') + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + print(f"Results saved to {save_dir}{s}") + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(prog='test.py') + parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)') + parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path') + parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') + parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS') + parser.add_argument('--task', default='val', help='train, val, test, speed or study') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--verbose', action='store_true', help='report mAP by class') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') + parser.add_argument('--project', default='runs/test', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--no-trace', action='store_true', help='don`t trace model') + parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation') + opt = parser.parse_args() + opt.save_json |= opt.data.endswith('coco.yaml') + opt.data = check_file(opt.data) # check file + print(opt) + #check_requirements() + + if opt.task in ('train', 'val', 'test'): # run normally + test(opt.data, + opt.weights, + opt.batch_size, + opt.img_size, + opt.conf_thres, + opt.iou_thres, + opt.save_json, + opt.single_cls, + opt.augment, + opt.verbose, + save_txt=opt.save_txt | opt.save_hybrid, + save_hybrid=opt.save_hybrid, + save_conf=opt.save_conf, + trace=not opt.no_trace, + v5_metric=opt.v5_metric + ) + + elif opt.task == 'speed': # speed benchmarks + for w in opt.weights: + test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, v5_metric=opt.v5_metric) + + elif opt.task == 'study': # run over a range of settings and save/plot + # python test.py --task study --data coco.yaml --iou 0.65 --weights yolov7.pt + x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) + for w in opt.weights: + f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to + y = [] # y axis + for i in x: # img-size + print(f'\nRunning {f} point {i}...') + r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, + plots=False, v5_metric=opt.v5_metric) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + os.system('zip -r study.zip study_*.txt') + plot_study_txt(x=x) # plot diff --git a/cv/detection/yolov7/pytorch/train.py b/cv/detection/yolov7/pytorch/train.py new file mode 100644 index 000000000..86c7e48d5 --- /dev/null +++ b/cv/detection/yolov7/pytorch/train.py @@ -0,0 +1,705 @@ +import argparse +import logging +import math +import os +import random +import time +from copy import deepcopy +from pathlib import Path +from threading import Thread + +import numpy as np +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler +import torch.utils.data +import yaml +from torch.cuda import amp +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +import test # import test.py to get mAP after each epoch +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.datasets import create_dataloader +from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ + fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ + check_requirements, print_mutation, set_logging, one_cycle, colorstr +from utils.google_utils import attempt_download +from utils.loss import ComputeLoss, ComputeLossOTA +from utils.plots import plot_images, plot_labels, plot_results, plot_evolution +from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel +from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume + +logger = logging.getLogger(__name__) + + +def train(hyp, opt, device, tb_writer=None): + logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze + + # Directories + wdir = save_dir / 'weights' + wdir.mkdir(parents=True, exist_ok=True) # make dir + last = wdir / 'last.pt' + best = wdir / 'best.pt' + results_file = save_dir / 'results.txt' + + # Save run settings + with open(save_dir / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(save_dir / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + + # Configure + plots = not opt.evolve # create plots + cuda = device.type != 'cpu' + init_seeds(2 + rank) + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + is_coco = opt.data.endswith('coco.yaml') + + # Logging- Doing this before checking the dataset. Might update data_dict + loggers = {'wandb': None} # loggers dict + if rank in [-1, 0]: + opt.hyp = hyp # add hyperparameters + run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None + wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict) + loggers['wandb'] = wandb_logger.wandb + data_dict = wandb_logger.data_dict + if wandb_logger.wandb: + weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming + + nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes + names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check + + # Model + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(rank): + attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location=device) # load checkpoint + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys + state_dict = ckpt['model'].float().state_dict() # to FP32 + state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(state_dict, strict=False) # load + logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report + else: + model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + train_path = data_dict['train'] + test_path = data_dict['val'] + + # Freeze + freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # parameter names to freeze (full or partial) + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + if any(x in k for x in freeze): + print('freezing %s' % k) + v.requires_grad = False + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay + logger.info(f"Scaled weight_decay = {hyp['weight_decay']}") + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + for k, v in model.named_modules(): + if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): + pg2.append(v.bias) # biases + if isinstance(v, nn.BatchNorm2d): + pg0.append(v.weight) # no decay + elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): + pg1.append(v.weight) # apply decay + if hasattr(v, 'im'): + if hasattr(v.im, 'implicit'): + pg0.append(v.im.implicit) + else: + for iv in v.im: + pg0.append(iv.implicit) + if hasattr(v, 'imc'): + if hasattr(v.imc, 'implicit'): + pg0.append(v.imc.implicit) + else: + for iv in v.imc: + pg0.append(iv.implicit) + if hasattr(v, 'imb'): + if hasattr(v.imb, 'implicit'): + pg0.append(v.imb.implicit) + else: + for iv in v.imb: + pg0.append(iv.implicit) + if hasattr(v, 'imo'): + if hasattr(v.imo, 'implicit'): + pg0.append(v.imo.implicit) + else: + for iv in v.imo: + pg0.append(iv.implicit) + if hasattr(v, 'ia'): + if hasattr(v.ia, 'implicit'): + pg0.append(v.ia.implicit) + else: + for iv in v.ia: + pg0.append(iv.implicit) + if hasattr(v, 'attn'): + if hasattr(v.attn, 'logit_scale'): + pg0.append(v.attn.logit_scale) + if hasattr(v.attn, 'q_bias'): + pg0.append(v.attn.q_bias) + if hasattr(v.attn, 'v_bias'): + pg0.append(v.attn.v_bias) + if hasattr(v.attn, 'relative_position_bias_table'): + pg0.append(v.attn.relative_position_bias_table) + if hasattr(v, 'rbr_dense'): + if hasattr(v.rbr_dense, 'weight_rbr_origin'): + pg0.append(v.rbr_dense.weight_rbr_origin) + if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'): + pg0.append(v.rbr_dense.weight_rbr_avg_conv) + if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'): + pg0.append(v.rbr_dense.weight_rbr_pfir_conv) + if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'): + pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1) + if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'): + pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2) + if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'): + pg0.append(v.rbr_dense.weight_rbr_gconv_dw) + if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'): + pg0.append(v.rbr_dense.weight_rbr_gconv_pw) + if hasattr(v.rbr_dense, 'vector'): + pg0.append(v.rbr_dense.vector) + + if opt.adam: + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + else: + optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay + optimizer.add_param_group({'params': pg2}) # add pg2 (biases) + logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) + del pg0, pg1, pg2 + + # Scheduler https://arxiv.org/pdf/1812.01187.pdf + # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + if opt.linear_lr: + lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + else: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if rank in [-1, 0] else None + + # Resume + start_epoch, best_fitness = 0, 0.0 + if pretrained: + # Optimizer + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) + best_fitness = ckpt['best_fitness'] + + # EMA + if ema and ckpt.get('ema'): + ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) + ema.updates = ckpt['updates'] + + # Results + if ckpt.get('training_results') is not None: + results_file.write_text(ckpt['training_results']) # write results.txt + + # Epochs + start_epoch = ckpt['epoch'] + 1 + if opt.resume: + assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) + if epochs < start_epoch: + logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % + (weights, ckpt['epoch'], epochs)) + epochs += ckpt['epoch'] # finetune additional epochs + + del ckpt, state_dict + + # Image sizes + gs = max(int(model.stride.max()), 32) # grid size (max stride) + nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) + imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + + # DP mode + if cuda and rank == -1 and torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and rank != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + logger.info('Using SyncBatchNorm()') + + # Trainloader + dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, + hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, + world_size=opt.world_size, workers=opt.workers, + image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: ')) + mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class + nb = len(dataloader) # number of batches + assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) + + # Process 0 + if rank in [-1, 0]: + testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader + hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, + world_size=opt.world_size, workers=opt.workers, + pad=0.5, prefix=colorstr('val: '))[0] + + if not opt.resume: + labels = np.concatenate(dataset.labels, 0) + c = torch.tensor(labels[:, 0]) # classes + # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency + # model._initialize_biases(cf.to(device)) + if plots: + #plot_labels(labels, names, save_dir, loggers) + if tb_writer: + tb_writer.add_histogram('classes', c, 0) + + # Anchors + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + model.half().float() # pre-reduce anchor precision + + # DDP mode + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, + # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 + find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) + + # Model parameters + hyp['box'] *= 3. / nl # scale to layers + hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers + hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = amp.GradScaler(enabled=cuda) + compute_loss_ota = ComputeLossOTA(model) # init loss class + compute_loss = ComputeLoss(model) # init loss class + logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' + f'Using {dataloader.num_workers} dataloader workers\n' + f'Logging results to {save_dir}\n' + f'Starting training for {epochs} epochs...') + torch.save(model, wdir / 'init.pt') + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + model.train() + + # Update image weights (optional) + if opt.image_weights: + # Generate indices + if rank in [-1, 0]: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + # Broadcast if DDP + if rank != -1: + indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() + dist.broadcast(indices, 0) + if rank != 0: + dataset.indices = indices.cpu().numpy() + + # Update mosaic border + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(4, device=device) # mean losses + if rank != -1: + dataloader.sampler.set_epoch(epoch) + pbar = enumerate(dataloader) + logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) + if rank in [-1, 0]: + pbar = tqdm(pbar, total=nb) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with amp.autocast(enabled=cuda): + pred = model(imgs) # forward + if 'loss_ota' not in hyp or hyp['loss_ota'] == 1: + loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size + else: + loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size + if rank != -1: + loss *= opt.world_size # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4. + + # Backward + scaler.scale(loss).backward() + + # Optimize + if ni % accumulate == 0: + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + # Print + if rank in [-1, 0]: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) + s = ('%10s' * 2 + '%10.4g' * 6) % ( + '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) + pbar.set_description(s) + + # Plot + if plots and ni < 10: + f = save_dir / f'train_batch{ni}.jpg' # filename + Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() + # if tb_writer: + # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) + # tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph + elif plots and ni == 10 and wandb_logger.wandb: + wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in + save_dir.glob('train*.jpg') if x.exists()]}) + + # end batch ------------------------------------------------------------------------------------------------ + # end epoch ---------------------------------------------------------------------------------------------------- + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard + scheduler.step() + + # DDP process 0 or single-GPU + if rank in [-1, 0]: + # mAP + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) + final_epoch = epoch + 1 == epochs + if not opt.notest or final_epoch: # Calculate mAP + wandb_logger.current_epoch = epoch + 1 + results, maps, times = test.test(data_dict, + batch_size=batch_size * 2, + imgsz=imgsz_test, + model=ema.ema, + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + verbose=nc < 50 and final_epoch, + plots=plots and final_epoch, + wandb_logger=wandb_logger, + compute_loss=compute_loss, + is_coco=is_coco, + v5_metric=opt.v5_metric) + + # Write + with open(results_file, 'a') as f: + f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss + if len(opt.name) and opt.bucket: + os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) + + # Log + tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss + 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', + 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss + 'x/lr0', 'x/lr1', 'x/lr2'] # params + for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): + if tb_writer: + tb_writer.add_scalar(tag, x, epoch) # tensorboard + if wandb_logger.wandb: + wandb_logger.log({tag: x}) # W&B + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + if fi > best_fitness: + best_fitness = fi + wandb_logger.end_epoch(best_result=best_fitness == fi) + + # Save model + if (not opt.nosave) or (final_epoch and not opt.evolve): # if save + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': results_file.read_text(), + 'model': deepcopy(model.module if is_parallel(model) else model).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if (best_fitness == fi) and (epoch >= 200): + torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch)) + if epoch == 0: + torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) + elif ((epoch+1) % 25) == 0: + torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) + elif epoch >= (epochs-5): + torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) + if wandb_logger.wandb: + if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: + wandb_logger.log_model( + last.parent, opt, epoch, fi, best_model=best_fitness == fi) + del ckpt + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training + if rank in [-1, 0]: + # Plots + if plots: + plot_results(save_dir=save_dir) # save as results.png + if wandb_logger.wandb: + files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] + wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files + if (save_dir / f).exists()]}) + # Test best.pt + logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + if opt.data.endswith('coco.yaml') and nc == 80: # if COCO + for m in (last, best) if best.exists() else (last): # speed, mAP tests + results, _, _ = test.test(opt.data, + batch_size=batch_size * 2, + imgsz=imgsz_test, + conf_thres=0.001, + iou_thres=0.7, + model=attempt_load(m, device).half(), + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + save_json=True, + plots=False, + is_coco=is_coco, + v5_metric=opt.v5_metric) + + # Strip optimizers + final = best if best.exists() else last # final model + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if opt.bucket: + os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + if wandb_logger.wandb and not opt.evolve: # Log the stripped model + wandb_logger.wandb.log_artifact(str(final), type='model', + name='run_' + wandb_logger.wandb_run.id + '_model', + aliases=['last', 'best', 'stripped']) + wandb_logger.finish_run() + else: + dist.destroy_process_group() + torch.cuda.empty_cache() + return results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='yolo7.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=300) + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--notest', action='store_true', help='only test final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') + parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') + parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') + parser.add_argument('--project', default='runs/train', help='save to project/name') + parser.add_argument('--entity', default=None, help='W&B entity') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--linear-lr', action='store_true', help='linear LR') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') + parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') + parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') + parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone of yolov7=50, first3=0 1 2') + parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation') + opt = parser.parse_args() + + # Set DDP variables + opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 + set_logging(opt.global_rank) + #if opt.global_rank in [-1, 0]: + # check_git_status() + # check_requirements() + + # Resume + wandb_run = check_wandb_resume(opt) + if opt.resume and not wandb_run: # resume an interrupted run + ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path + assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' + apriori = opt.global_rank, opt.local_rank + with open(Path(ckpt).parent.parent / 'opt.yaml') as f: + opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace + opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate + logger.info('Resuming training from %s' % ckpt) + else: + # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') + opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + opt.name = 'evolve' if opt.evolve else opt.name + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run + + # DDP mode + opt.total_batch_size = opt.batch_size + device = select_device(opt.device, batch_size=opt.batch_size) + if opt.local_rank != -1: + assert torch.cuda.device_count() > opt.local_rank + torch.cuda.set_device(opt.local_rank) + device = torch.device('cuda', opt.local_rank) + dist.init_process_group(backend='nccl', init_method='env://') # distributed backend + assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' + opt.batch_size = opt.total_batch_size // opt.world_size + + # Hyperparameters + with open(opt.hyp) as f: + hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps + + # Train + logger.info(opt) + if not opt.evolve: + tb_writer = None # init loggers + if opt.global_rank in [-1, 0]: + prefix = colorstr('tensorboard: ') + logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") + tb_writer = SummaryWriter(opt.save_dir) # Tensorboard + train(hyp, opt, device, tb_writer) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0), # image mixup (probability) + 'copy_paste': (1, 0.0, 1.0), # segment copy-paste (probability) + 'paste_in': (1, 0.0, 1.0)} # segment copy-paste (probability) + + with open(opt.hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + if 'anchors' not in hyp: # anchors commented in hyp.yaml + hyp['anchors'] = 3 + + assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' + opt.notest, opt.nosave = True, True # only test/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here + if opt.bucket: + os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists + + for _ in range(300): # generations to evolve + if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt('evolve.txt', ndmin=2) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() # weights + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([x[0] for x in meta.values()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device) + + # Write mutation results + print_mutation(hyp.copy(), results, yaml_file, opt.bucket) + + # Plot results + plot_evolution(yaml_file) + print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' + f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') diff --git a/cv/detection/yolov7/pytorch/train_aux.py b/cv/detection/yolov7/pytorch/train_aux.py new file mode 100644 index 000000000..0e8053f85 --- /dev/null +++ b/cv/detection/yolov7/pytorch/train_aux.py @@ -0,0 +1,699 @@ +import argparse +import logging +import math +import os +import random +import time +from copy import deepcopy +from pathlib import Path +from threading import Thread + +import numpy as np +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler +import torch.utils.data +import yaml +from torch.cuda import amp +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +import test # import test.py to get mAP after each epoch +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.datasets import create_dataloader +from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ + fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ + check_requirements, print_mutation, set_logging, one_cycle, colorstr +from utils.google_utils import attempt_download +from utils.loss import ComputeLoss, ComputeLossAuxOTA +from utils.plots import plot_images, plot_labels, plot_results, plot_evolution +from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel +from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume + +logger = logging.getLogger(__name__) + + +def train(hyp, opt, device, tb_writer=None): + logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + save_dir, epochs, batch_size, total_batch_size, weights, rank = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank + + # Directories + wdir = save_dir / 'weights' + wdir.mkdir(parents=True, exist_ok=True) # make dir + last = wdir / 'last.pt' + best = wdir / 'best.pt' + results_file = save_dir / 'results.txt' + + # Save run settings + with open(save_dir / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(save_dir / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + + # Configure + plots = not opt.evolve # create plots + cuda = device.type != 'cpu' + init_seeds(2 + rank) + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + is_coco = opt.data.endswith('coco.yaml') + + # Logging- Doing this before checking the dataset. Might update data_dict + loggers = {'wandb': None} # loggers dict + if rank in [-1, 0]: + opt.hyp = hyp # add hyperparameters + run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None + wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict) + loggers['wandb'] = wandb_logger.wandb + data_dict = wandb_logger.data_dict + if wandb_logger.wandb: + weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming + + nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes + names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check + + # Model + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(rank): + attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location=device) # load checkpoint + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys + state_dict = ckpt['model'].float().state_dict() # to FP32 + state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(state_dict, strict=False) # load + logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report + else: + model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + train_path = data_dict['train'] + test_path = data_dict['val'] + + # Freeze + freeze = [] # parameter names to freeze (full or partial) + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + if any(x in k for x in freeze): + print('freezing %s' % k) + v.requires_grad = False + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay + logger.info(f"Scaled weight_decay = {hyp['weight_decay']}") + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + for k, v in model.named_modules(): + if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): + pg2.append(v.bias) # biases + if isinstance(v, nn.BatchNorm2d): + pg0.append(v.weight) # no decay + elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): + pg1.append(v.weight) # apply decay + if hasattr(v, 'im'): + if hasattr(v.im, 'implicit'): + pg0.append(v.im.implicit) + else: + for iv in v.im: + pg0.append(iv.implicit) + if hasattr(v, 'imc'): + if hasattr(v.imc, 'implicit'): + pg0.append(v.imc.implicit) + else: + for iv in v.imc: + pg0.append(iv.implicit) + if hasattr(v, 'imb'): + if hasattr(v.imb, 'implicit'): + pg0.append(v.imb.implicit) + else: + for iv in v.imb: + pg0.append(iv.implicit) + if hasattr(v, 'imo'): + if hasattr(v.imo, 'implicit'): + pg0.append(v.imo.implicit) + else: + for iv in v.imo: + pg0.append(iv.implicit) + if hasattr(v, 'ia'): + if hasattr(v.ia, 'implicit'): + pg0.append(v.ia.implicit) + else: + for iv in v.ia: + pg0.append(iv.implicit) + if hasattr(v, 'attn'): + if hasattr(v.attn, 'logit_scale'): + pg0.append(v.attn.logit_scale) + if hasattr(v.attn, 'q_bias'): + pg0.append(v.attn.q_bias) + if hasattr(v.attn, 'v_bias'): + pg0.append(v.attn.v_bias) + if hasattr(v.attn, 'relative_position_bias_table'): + pg0.append(v.attn.relative_position_bias_table) + if hasattr(v, 'rbr_dense'): + if hasattr(v.rbr_dense, 'weight_rbr_origin'): + pg0.append(v.rbr_dense.weight_rbr_origin) + if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'): + pg0.append(v.rbr_dense.weight_rbr_avg_conv) + if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'): + pg0.append(v.rbr_dense.weight_rbr_pfir_conv) + if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'): + pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1) + if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'): + pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2) + if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'): + pg0.append(v.rbr_dense.weight_rbr_gconv_dw) + if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'): + pg0.append(v.rbr_dense.weight_rbr_gconv_pw) + if hasattr(v.rbr_dense, 'vector'): + pg0.append(v.rbr_dense.vector) + + if opt.adam: + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + else: + optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay + optimizer.add_param_group({'params': pg2}) # add pg2 (biases) + logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) + del pg0, pg1, pg2 + + # Scheduler https://arxiv.org/pdf/1812.01187.pdf + # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + if opt.linear_lr: + lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + else: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if rank in [-1, 0] else None + + # Resume + start_epoch, best_fitness = 0, 0.0 + if pretrained: + # Optimizer + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) + best_fitness = ckpt['best_fitness'] + + # EMA + if ema and ckpt.get('ema'): + ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) + ema.updates = ckpt['updates'] + + # Results + if ckpt.get('training_results') is not None: + results_file.write_text(ckpt['training_results']) # write results.txt + + # Epochs + start_epoch = ckpt['epoch'] + 1 + if opt.resume: + assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) + if epochs < start_epoch: + logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % + (weights, ckpt['epoch'], epochs)) + epochs += ckpt['epoch'] # finetune additional epochs + + del ckpt, state_dict + + # Image sizes + gs = max(int(model.stride.max()), 32) # grid size (max stride) + nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) + imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + + # DP mode + if cuda and rank == -1 and torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and rank != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + logger.info('Using SyncBatchNorm()') + + # Trainloader + dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, + hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, + world_size=opt.world_size, workers=opt.workers, + image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: ')) + mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class + nb = len(dataloader) # number of batches + assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) + + # Process 0 + if rank in [-1, 0]: + testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader + hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, + world_size=opt.world_size, workers=opt.workers, + pad=0.5, prefix=colorstr('val: '))[0] + + if not opt.resume: + labels = np.concatenate(dataset.labels, 0) + c = torch.tensor(labels[:, 0]) # classes + # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency + # model._initialize_biases(cf.to(device)) + if plots: + #plot_labels(labels, names, save_dir, loggers) + if tb_writer: + tb_writer.add_histogram('classes', c, 0) + + # Anchors + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + model.half().float() # pre-reduce anchor precision + + # DDP mode + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, + # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 + find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) + + # Model parameters + hyp['box'] *= 3. / nl # scale to layers + hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers + hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = amp.GradScaler(enabled=cuda) + compute_loss_ota = ComputeLossAuxOTA(model) # init loss class + compute_loss = ComputeLoss(model) # init loss class + logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' + f'Using {dataloader.num_workers} dataloader workers\n' + f'Logging results to {save_dir}\n' + f'Starting training for {epochs} epochs...') + torch.save(model, wdir / 'init.pt') + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + model.train() + + # Update image weights (optional) + if opt.image_weights: + # Generate indices + if rank in [-1, 0]: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + # Broadcast if DDP + if rank != -1: + indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() + dist.broadcast(indices, 0) + if rank != 0: + dataset.indices = indices.cpu().numpy() + + # Update mosaic border + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(4, device=device) # mean losses + if rank != -1: + dataloader.sampler.set_epoch(epoch) + pbar = enumerate(dataloader) + logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) + if rank in [-1, 0]: + pbar = tqdm(pbar, total=nb) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with amp.autocast(enabled=cuda): + pred = model(imgs) # forward + loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size + if rank != -1: + loss *= opt.world_size # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4. + + # Backward + scaler.scale(loss).backward() + + # Optimize + if ni % accumulate == 0: + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + # Print + if rank in [-1, 0]: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) + s = ('%10s' * 2 + '%10.4g' * 6) % ( + '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) + pbar.set_description(s) + + # Plot + if plots and ni < 10: + f = save_dir / f'train_batch{ni}.jpg' # filename + Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() + # if tb_writer: + # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) + # tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph + elif plots and ni == 10 and wandb_logger.wandb: + wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in + save_dir.glob('train*.jpg') if x.exists()]}) + + # end batch ------------------------------------------------------------------------------------------------ + # end epoch ---------------------------------------------------------------------------------------------------- + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard + scheduler.step() + + # DDP process 0 or single-GPU + if rank in [-1, 0]: + # mAP + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) + final_epoch = epoch + 1 == epochs + if not opt.notest or final_epoch: # Calculate mAP + wandb_logger.current_epoch = epoch + 1 + results, maps, times = test.test(data_dict, + batch_size=batch_size * 2, + imgsz=imgsz_test, + model=ema.ema, + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + verbose=nc < 50 and final_epoch, + plots=plots and final_epoch, + wandb_logger=wandb_logger, + compute_loss=compute_loss, + is_coco=is_coco, + v5_metric=opt.v5_metric) + + # Write + with open(results_file, 'a') as f: + f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss + if len(opt.name) and opt.bucket: + os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) + + # Log + tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss + 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', + 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss + 'x/lr0', 'x/lr1', 'x/lr2'] # params + for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): + if tb_writer: + tb_writer.add_scalar(tag, x, epoch) # tensorboard + if wandb_logger.wandb: + wandb_logger.log({tag: x}) # W&B + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + if fi > best_fitness: + best_fitness = fi + wandb_logger.end_epoch(best_result=best_fitness == fi) + + # Save model + if (not opt.nosave) or (final_epoch and not opt.evolve): # if save + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': results_file.read_text(), + 'model': deepcopy(model.module if is_parallel(model) else model).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if (best_fitness == fi) and (epoch >= 200): + torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch)) + if epoch == 0: + torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) + elif ((epoch+1) % 25) == 0: + torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) + elif epoch >= (epochs-5): + torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) + if wandb_logger.wandb: + if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: + wandb_logger.log_model( + last.parent, opt, epoch, fi, best_model=best_fitness == fi) + del ckpt + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training + if rank in [-1, 0]: + # Plots + if plots: + plot_results(save_dir=save_dir) # save as results.png + if wandb_logger.wandb: + files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] + wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files + if (save_dir / f).exists()]}) + # Test best.pt + logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + if opt.data.endswith('coco.yaml') and nc == 80: # if COCO + for m in (last, best) if best.exists() else (last): # speed, mAP tests + results, _, _ = test.test(opt.data, + batch_size=batch_size * 2, + imgsz=imgsz_test, + conf_thres=0.001, + iou_thres=0.7, + model=attempt_load(m, device).half(), + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + save_json=True, + plots=False, + is_coco=is_coco, + v5_metric=opt.v5_metric) + + # Strip optimizers + final = best if best.exists() else last # final model + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if opt.bucket: + os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + if wandb_logger.wandb and not opt.evolve: # Log the stripped model + wandb_logger.wandb.log_artifact(str(final), type='model', + name='run_' + wandb_logger.wandb_run.id + '_model', + aliases=['last', 'best', 'stripped']) + wandb_logger.finish_run() + else: + dist.destroy_process_group() + torch.cuda.empty_cache() + return results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='yolo7.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=300) + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--notest', action='store_true', help='only test final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') + parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') + parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') + parser.add_argument('--project', default='runs/train', help='save to project/name') + parser.add_argument('--entity', default=None, help='W&B entity') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--linear-lr', action='store_true', help='linear LR') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') + parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') + parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') + parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') + parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation') + opt = parser.parse_args() + + # Set DDP variables + opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 + set_logging(opt.global_rank) + #if opt.global_rank in [-1, 0]: + # check_git_status() + # check_requirements() + + # Resume + wandb_run = check_wandb_resume(opt) + if opt.resume and not wandb_run: # resume an interrupted run + ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path + assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' + apriori = opt.global_rank, opt.local_rank + with open(Path(ckpt).parent.parent / 'opt.yaml') as f: + opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace + opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate + logger.info('Resuming training from %s' % ckpt) + else: + # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') + opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + opt.name = 'evolve' if opt.evolve else opt.name + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run + + # DDP mode + opt.total_batch_size = opt.batch_size + device = select_device(opt.device, batch_size=opt.batch_size) + if opt.local_rank != -1: + assert torch.cuda.device_count() > opt.local_rank + torch.cuda.set_device(opt.local_rank) + device = torch.device('cuda', opt.local_rank) + dist.init_process_group(backend='nccl', init_method='env://') # distributed backend + assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' + opt.batch_size = opt.total_batch_size // opt.world_size + + # Hyperparameters + with open(opt.hyp) as f: + hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps + + # Train + logger.info(opt) + if not opt.evolve: + tb_writer = None # init loggers + if opt.global_rank in [-1, 0]: + prefix = colorstr('tensorboard: ') + logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") + tb_writer = SummaryWriter(opt.save_dir) # Tensorboard + train(hyp, opt, device, tb_writer) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0)} # image mixup (probability) + + with open(opt.hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + if 'anchors' not in hyp: # anchors commented in hyp.yaml + hyp['anchors'] = 3 + + assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' + opt.notest, opt.nosave = True, True # only test/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here + if opt.bucket: + os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists + + for _ in range(300): # generations to evolve + if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt('evolve.txt', ndmin=2) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() # weights + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([x[0] for x in meta.values()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device) + + # Write mutation results + print_mutation(hyp.copy(), results, yaml_file, opt.bucket) + + # Plot results + plot_evolution(yaml_file) + print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' + f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') diff --git a/cv/detection/yolov7/pytorch/utils/__init__.py b/cv/detection/yolov7/pytorch/utils/__init__.py new file mode 100644 index 000000000..84952a816 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/__init__.py @@ -0,0 +1 @@ +# init \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/utils/activations.py b/cv/detection/yolov7/pytorch/utils/activations.py new file mode 100644 index 000000000..aa3ddf071 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/activations.py @@ -0,0 +1,72 @@ +# Activation functions + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +# SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- +class SiLU(nn.Module): # export-friendly version of nn.SiLU() + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() + @staticmethod + def forward(x): + # return x * F.hardsigmoid(x) # for torchscript and CoreML + return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX + + +class MemoryEfficientSwish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x * torch.sigmoid(x) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + return grad_output * (sx * (1 + x * (1 - sx))) + + def forward(self, x): + return self.F.apply(x) + + +# Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- +class Mish(nn.Module): + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +# FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- +class FReLU(nn.Module): + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) diff --git a/cv/detection/yolov7/pytorch/utils/add_nms.py b/cv/detection/yolov7/pytorch/utils/add_nms.py new file mode 100644 index 000000000..0a1f7976a --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/add_nms.py @@ -0,0 +1,155 @@ +import numpy as np +import onnx +from onnx import shape_inference +try: + import onnx_graphsurgeon as gs +except Exception as e: + print('Import onnx_graphsurgeon failure: %s' % e) + +import logging + +LOGGER = logging.getLogger(__name__) + +class RegisterNMS(object): + def __init__( + self, + onnx_model_path: str, + precision: str = "fp32", + ): + + self.graph = gs.import_onnx(onnx.load(onnx_model_path)) + assert self.graph + LOGGER.info("ONNX graph created successfully") + # Fold constants via ONNX-GS that PyTorch2ONNX may have missed + self.graph.fold_constants() + self.precision = precision + self.batch_size = 1 + def infer(self): + """ + Sanitize the graph by cleaning any unconnected nodes, do a topological resort, + and fold constant inputs values. When possible, run shape inference on the + ONNX graph to determine tensor shapes. + """ + for _ in range(3): + count_before = len(self.graph.nodes) + + self.graph.cleanup().toposort() + try: + for node in self.graph.nodes: + for o in node.outputs: + o.shape = None + model = gs.export_onnx(self.graph) + model = shape_inference.infer_shapes(model) + self.graph = gs.import_onnx(model) + except Exception as e: + LOGGER.info(f"Shape inference could not be performed at this time:\n{e}") + try: + self.graph.fold_constants(fold_shapes=True) + except TypeError as e: + LOGGER.error( + "This version of ONNX GraphSurgeon does not support folding shapes, " + f"please upgrade your onnx_graphsurgeon module. Error:\n{e}" + ) + raise + + count_after = len(self.graph.nodes) + if count_before == count_after: + # No new folding occurred in this iteration, so we can stop for now. + break + + def save(self, output_path): + """ + Save the ONNX model to the given location. + Args: + output_path: Path pointing to the location where to write + out the updated ONNX model. + """ + self.graph.cleanup().toposort() + model = gs.export_onnx(self.graph) + onnx.save(model, output_path) + LOGGER.info(f"Saved ONNX model to {output_path}") + + def register_nms( + self, + *, + score_thresh: float = 0.25, + nms_thresh: float = 0.45, + detections_per_img: int = 100, + ): + """ + Register the ``EfficientNMS_TRT`` plugin node. + NMS expects these shapes for its input tensors: + - box_net: [batch_size, number_boxes, 4] + - class_net: [batch_size, number_boxes, number_labels] + Args: + score_thresh (float): The scalar threshold for score (low scoring boxes are removed). + nms_thresh (float): The scalar threshold for IOU (new boxes that have high IOU + overlap with previously selected boxes are removed). + detections_per_img (int): Number of best detections to keep after NMS. + """ + + self.infer() + # Find the concat node at the end of the network + op_inputs = self.graph.outputs + op = "EfficientNMS_TRT" + attrs = { + "plugin_version": "1", + "background_class": -1, # no background class + "max_output_boxes": detections_per_img, + "score_threshold": score_thresh, + "iou_threshold": nms_thresh, + "score_activation": False, + "box_coding": 0, + } + + if self.precision == "fp32": + dtype_output = np.float32 + elif self.precision == "fp16": + dtype_output = np.float16 + else: + raise NotImplementedError(f"Currently not supports precision: {self.precision}") + + # NMS Outputs + output_num_detections = gs.Variable( + name="num_dets", + dtype=np.int32, + shape=[self.batch_size, 1], + ) # A scalar indicating the number of valid detections per batch image. + output_boxes = gs.Variable( + name="det_boxes", + dtype=dtype_output, + shape=[self.batch_size, detections_per_img, 4], + ) + output_scores = gs.Variable( + name="det_scores", + dtype=dtype_output, + shape=[self.batch_size, detections_per_img], + ) + output_labels = gs.Variable( + name="det_classes", + dtype=np.int32, + shape=[self.batch_size, detections_per_img], + ) + + op_outputs = [output_num_detections, output_boxes, output_scores, output_labels] + + # Create the NMS Plugin node with the selected inputs. The outputs of the node will also + # become the final outputs of the graph. + self.graph.layer(op=op, name="batched_nms", inputs=op_inputs, outputs=op_outputs, attrs=attrs) + LOGGER.info(f"Created NMS plugin '{op}' with attributes: {attrs}") + + self.graph.outputs = op_outputs + + self.infer() + + def save(self, output_path): + """ + Save the ONNX model to the given location. + Args: + output_path: Path pointing to the location where to write + out the updated ONNX model. + """ + self.graph.cleanup().toposort() + model = gs.export_onnx(self.graph) + onnx.save(model, output_path) + LOGGER.info(f"Saved ONNX model to {output_path}") diff --git a/cv/detection/yolov7/pytorch/utils/autoanchor.py b/cv/detection/yolov7/pytorch/utils/autoanchor.py new file mode 100644 index 000000000..f491032e5 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/autoanchor.py @@ -0,0 +1,160 @@ +# Auto-anchor utils + +import numpy as np +import torch +import yaml +from scipy.cluster.vq import kmeans +from tqdm import tqdm + +from utils.general import colorstr + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary + a = m.anchor_grid.prod(-1).view(-1) # anchor area + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da.sign() != ds.sign(): # same order + print('Reversing anchor order') + m.anchors[:] = m.anchors.flip(0) + m.anchor_grid[:] = m.anchor_grid.flip(0) + + +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + prefix = colorstr('autoanchor: ') + print(f'\n{prefix}Analyzing anchors... ', end='') + m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1. / thr).float().mean() # best possible recall + return bpr, aat + + anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors + bpr, aat = metric(anchors) + print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') + if bpr < 0.98: # threshold to recompute + print('. Attempting to improve anchors, please wait...') + na = m.anchor_grid.numel() // 2 # number of anchors + try: + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + except Exception as e: + print(f'{prefix}ERROR: {e}') + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference + check_anchor_order(m) + m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss + print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') + else: + print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') + print('') # newline + + +def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ Creates kmeans-evolved anchors from training dataset + + Arguments: + path: path to dataset *.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + thr = 1. / thr + prefix = colorstr('autoanchor: ') + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') + print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' + f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') + for i, x in enumerate(k): + print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg + return k + + if isinstance(path, str): # *.yaml file + with open(path) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict + from utils.datasets import LoadImagesAndLabels + dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + else: + dataset = path # dataset + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') + wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels + # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans calculation + print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') + s = wh.std(0) # sigmas for whitening + k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') + k *= s + wh = torch.tensor(wh, dtype=torch.float32) # filtered + wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered + k = print_results(k) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + npr = np.random + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' + if verbose: + print_results(k) + + return print_results(k) diff --git a/cv/detection/yolov7/pytorch/utils/aws/__init__.py b/cv/detection/yolov7/pytorch/utils/aws/__init__.py new file mode 100644 index 000000000..e9691f241 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/aws/__init__.py @@ -0,0 +1 @@ +#init \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/utils/aws/mime.sh b/cv/detection/yolov7/pytorch/utils/aws/mime.sh new file mode 100644 index 000000000..c319a83cf --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/cv/detection/yolov7/pytorch/utils/aws/resume.py b/cv/detection/yolov7/pytorch/utils/aws/resume.py new file mode 100644 index 000000000..338685b19 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/aws/resume.py @@ -0,0 +1,37 @@ +# Resume all interrupted trainings in yolor/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml') as f: + opt = yaml.load(f, Loader=yaml.SafeLoader) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/cv/detection/yolov7/pytorch/utils/aws/userdata.sh b/cv/detection/yolov7/pytorch/utils/aws/userdata.sh new file mode 100644 index 000000000..5a99d4bec --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolor ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone -b main https://github.com/WongKinYiu/yolov7 && sudo chmod -R 777 yolov7 + cd yolov7 + bash data/scripts/get_coco.sh && echo "Data done." & + sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/cv/detection/yolov7/pytorch/utils/datasets.py b/cv/detection/yolov7/pytorch/utils/datasets.py new file mode 100644 index 000000000..5fe4f7bcc --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/datasets.py @@ -0,0 +1,1320 @@ +# Dataset utils and dataloaders + +import glob +import logging +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from threading import Thread + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image, ExifTags +from torch.utils.data import Dataset +from tqdm import tqdm + +import pickle +from copy import deepcopy +#from pycocotools import mask as maskUtils +from torchvision.utils import save_image +from torchvision.ops import roi_pool, roi_align, ps_roi_pool, ps_roi_align + +from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \ + resample_segments, clean_str +from utils.torch_utils import torch_distributed_zero_first + +# Parameters +help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes +vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes +logger = logging.getLogger(__name__) + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + +def get_hash(files): + # Returns a single hash value of a list of files + return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + try: + rotation = dict(img._getexif().items())[orientation] + if rotation == 6: # rotation 270 + s = (s[1], s[0]) + elif rotation == 8: # rotation 90 + s = (s[1], s[0]) + except: + pass + + return s + + +def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, + rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''): + # Make sure only the first process in DDP process the dataset first, and the following others can use the cache + with torch_distributed_zero_first(rank): + dataset = LoadImagesAndLabels(path, imgsz, batch_size, + augment=augment, # augment images + hyp=hyp, # augmentation hyperparameters + rect=rect, # rectangular training + cache_images=cache, + single_cls=opt.single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix) + + batch_size = min(batch_size, len(dataset)) + nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None + loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader + # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() + dataloader = loader(dataset, + batch_size=batch_size, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn) + return dataloader, dataset + + +class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): + """ Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for i in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler(object): + """ Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadImages: # for inference + def __init__(self, path, img_size=640, stride=32): + p = str(Path(path).absolute()) # os-agnostic absolute path + if '*' in p: + files = sorted(glob.glob(p, recursive=True)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception(f'ERROR: {p} does not exist') + + images = [x for x in files if x.split('.')[-1].lower() in img_formats] + videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + if any(videos): + self.new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, f'No images or videos found in {p}. ' \ + f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}' + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + ret_val, img0 = self.cap.read() + if not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + else: + path = self.files[self.count] + self.new_video(path) + ret_val, img0 = self.cap.read() + + self.frame += 1 + print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='') + + else: + # Read image + self.count += 1 + img0 = cv2.imread(path) # BGR + assert img0 is not None, 'Image Not Found ' + path + #print(f'image {self.count}/{self.nf} {path}: ', end='') + + # Padded resize + img = letterbox(img0, self.img_size, stride=self.stride)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return path, img, img0, self.cap + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadWebcam: # for inference + def __init__(self, pipe='0', img_size=640, stride=32): + self.img_size = img_size + self.stride = stride + + if pipe.isnumeric(): + pipe = eval(pipe) # local camera + # pipe = 'rtsp://192.168.1.64/1' # IP camera + # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login + # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera + + self.pipe = pipe + self.cap = cv2.VideoCapture(pipe) # video capture object + self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if cv2.waitKey(1) == ord('q'): # q to quit + self.cap.release() + cv2.destroyAllWindows() + raise StopIteration + + # Read frame + if self.pipe == 0: # local camera + ret_val, img0 = self.cap.read() + img0 = cv2.flip(img0, 1) # flip left-right + else: # IP camera + n = 0 + while True: + n += 1 + self.cap.grab() + if n % 30 == 0: # skip frames + ret_val, img0 = self.cap.retrieve() + if ret_val: + break + + # Print + assert ret_val, f'Camera Error {self.pipe}' + img_path = 'webcam.jpg' + print(f'webcam {self.count}: ', end='') + + # Padded resize + img = letterbox(img0, self.img_size, stride=self.stride)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return img_path, img, img0, None + + def __len__(self): + return 0 + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources='streams.txt', img_size=640, stride=32): + self.mode = 'stream' + self.img_size = img_size + self.stride = stride + + if os.path.isfile(sources): + with open(sources, 'r') as f: + sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = [clean_str(x) for x in sources] # clean source names for later + for i, s in enumerate(sources): + # Start the thread to read frames from the video stream + print(f'{i + 1}/{n}: {s}... ', end='') + url = eval(s) if s.isnumeric() else s + if 'youtube.com/' in str(url) or 'youtu.be/' in str(url): # if source is YouTube video + check_requirements(('pafy', 'youtube_dl')) + import pafy + url = pafy.new(url).getbest(preftype="mp4").url + cap = cv2.VideoCapture(url) + assert cap.isOpened(), f'Failed to open {s}' + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + self.fps = cap.get(cv2.CAP_PROP_FPS) % 100 + + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(f' success ({w}x{h} at {self.fps:.2f} FPS).') + thread.start() + print('') # newline + + # check for common shapes + s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + if not self.rect: + print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + success, im = cap.retrieve() + self.imgs[index] = im if success else self.imgs[index] * 0 + n = 0 + time.sleep(1 / self.fps) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + if cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + # Letterbox + img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0] + + # Stack + img = np.stack(img, 0) + + # Convert + img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 + img = np.ascontiguousarray(img) + + return self.sources, img, img0, None + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings + return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths] + + +class LoadImagesAndLabels(Dataset): # for training/testing + def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, + cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + #self.albumentations = Albumentations() if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / '**' / '*.*'), recursive=True) + # f = list(p.rglob('**/*.*')) # pathlib + elif p.is_file(): # file + with open(p, 'r') as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) + else: + raise Exception(f'{prefix}{p} does not exist') + self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib + assert self.img_files, f'{prefix}No images found' + except Exception as e: + raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') + + # Check cache + self.label_files = img2label_paths(self.img_files) # labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels + if cache_path.is_file(): + cache, exists = torch.load(cache_path), True # load + #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed + # cache, exists = self.cache_labels(cache_path, prefix), False # re-cache + else: + cache, exists = self.cache_labels(cache_path, prefix), False # cache + + # Display cache + nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total + if exists: + d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results + assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' + + # Read cache + cache.pop('hash') # remove hash + cache.pop('version') # remove version + labels, shapes, self.segments = zip(*cache.values()) + self.labels = list(labels) + self.shapes = np.array(shapes, dtype=np.float64) + self.img_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + if single_cls: + for x in self.labels: + x[:, 0] = 0 + + n = len(shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.img_files = [self.img_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride + + # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) + self.imgs = [None] * n + if cache_images: + if cache_images == 'disk': + self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy') + self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files] + self.im_cache_dir.mkdir(parents=True, exist_ok=True) + gb = 0 # Gigabytes of cached images + self.img_hw0, self.img_hw = [None] * n, [None] * n + results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) + pbar = tqdm(enumerate(results), total=n) + for i, x in pbar: + if cache_images == 'disk': + if not self.img_npy[i].exists(): + np.save(self.img_npy[i].as_posix(), x[0]) + gb += self.img_npy[i].stat().st_size + else: + self.imgs[i], self.img_hw0[i], self.img_hw[i] = x + gb += self.imgs[i].nbytes + pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' + pbar.close() + + def cache_labels(self, path=Path('./labels.cache'), prefix=''): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate + pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) + for i, (im_file, lb_file) in enumerate(pbar): + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + segments = [] # instance segments + assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' + assert im.format.lower() in img_formats, f'invalid image format {im.format}' + + # verify labels + if os.path.isfile(lb_file): + nf += 1 # label found + with open(lb_file, 'r') as f: + l = [x.split() for x in f.read().strip().splitlines()] + if any([len(x) > 8 for x in l]): # is segment + classes = np.array([x[0] for x in l], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) + l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + l = np.array(l, dtype=np.float32) + if len(l): + assert l.shape[1] == 5, 'labels require 5 columns each' + assert (l >= 0).all(), 'negative labels' + assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' + assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' + else: + ne += 1 # label empty + l = np.zeros((0, 5), dtype=np.float32) + else: + nm += 1 # label missing + l = np.zeros((0, 5), dtype=np.float32) + x[im_file] = [l, shape, segments] + except Exception as e: + nc += 1 + print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') + + pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ + f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + pbar.close() + + if nf == 0: + print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') + + x['hash'] = get_hash(self.label_files + self.img_files) + x['results'] = nf, nm, ne, nc, i + 1 + x['version'] = 0.1 # cache version + torch.save(x, path) # save for next time + logging.info(f'{prefix}New cache created: {path}') + return x + + def __len__(self): + return len(self.img_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + if random.random() < 0.8: + img, labels = load_mosaic(self, index) + else: + img, labels = load_mosaic9(self, index) + shapes = None + + # MixUp https://arxiv.org/pdf/1710.09412.pdf + if random.random() < hyp['mixup']: + if random.random() < 0.8: + img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1)) + else: + img2, labels2 = load_mosaic9(self, random.randint(0, len(self.labels) - 1)) + r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 + img = (img * r + img2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + + else: + # Load image + img, (h0, w0), (h, w) = load_image(self, index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + # Augment imagespace + if not mosaic: + img, labels = random_perspective(img, labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + + #img, labels = self.albumentations(img, labels) + + # Augment colorspace + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Apply cutouts + # if random.random() < 0.9: + # labels = cutout(img, labels) + + if random.random() < hyp['paste_in']: + sample_labels, sample_images, sample_masks = [], [], [] + while len(sample_labels) < 30: + sample_labels_, sample_images_, sample_masks_ = load_samples(self, random.randint(0, len(self.labels) - 1)) + sample_labels += sample_labels_ + sample_images += sample_images_ + sample_masks += sample_masks_ + #print(len(sample_labels)) + if len(sample_labels) == 0: + break + labels = pastein(img, labels, sample_labels, sample_images, sample_masks) + + nL = len(labels) # number of labels + if nL: + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh + labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 + labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 + + if self.augment: + # flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nL: + labels[:, 2] = 1 - labels[:, 2] + + # flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nL: + labels[:, 1] = 1 - labels[:, 1] + + labels_out = torch.zeros((nL, 6)) + if nL: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + + @staticmethod + def collate_fn(batch): + img, label, path, shapes = zip(*batch) # transposed + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + img, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0., 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0., 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[ + 0].type(img[i].type()) + l = label[i] + else: + im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) + l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + img4.append(im) + label4.append(l) + + for i, l in enumerate(label4): + l[:, 0] = i # add target image index for build_targets() + + return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def load_image(self, index): + # loads 1 image from dataset, returns img, original hw, resized hw + img = self.imgs[index] + if img is None: # not cached + path = self.img_files[index] + img = cv2.imread(path) # BGR + assert img is not None, 'Image Not Found ' + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + else: + return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=np.int16) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + +def hist_equalize(img, clahe=True, bgr=False): + # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB + + +def load_mosaic(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + #img4, labels4, segments4 = remove_background(img4, labels4, segments4) + #sample_segments(img4, labels4, segments4, probability=self.hyp['copy_paste']) + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=self.hyp['copy_paste']) + img4, labels4 = random_perspective(img4, labels4, segments4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img4, labels4 + + +def load_mosaic9(self, index): + # loads images in a 9-mosaic + + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y + img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + #img9, labels9, segments9 = remove_background(img9, labels9, segments9) + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, probability=self.hyp['copy_paste']) + img9, labels9 = random_perspective(img9, labels9, segments9, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img9, labels9 + + +def load_samples(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + #img4, labels4, segments4 = remove_background(img4, labels4, segments4) + sample_labels, sample_images, sample_masks = sample_segments(img4, labels4, segments4, probability=0.5) + + return sample_labels, sample_images, sample_masks + + +def copy_paste(img, labels, segments, probability=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + if probability and n: + h, w, c = img.shape # height, width, channels + im_new = np.zeros(img.shape, np.uint8) + for j in random.sample(range(n), k=round(probability * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) + + result = cv2.bitwise_and(src1=img, src2=im_new) + result = cv2.flip(result, 1) # augment segments (flip left-right) + i = result > 0 # pixels to replace + # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch + img[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + + return img, labels, segments + + +def remove_background(img, labels, segments): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + h, w, c = img.shape # height, width, channels + im_new = np.zeros(img.shape, np.uint8) + img_new = np.ones(img.shape, np.uint8) * 114 + for j in range(n): + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) + + result = cv2.bitwise_and(src1=img, src2=im_new) + + i = result > 0 # pixels to replace + img_new[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + + return img_new, labels, segments + + +def sample_segments(img, labels, segments, probability=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + sample_labels = [] + sample_images = [] + sample_masks = [] + if probability and n: + h, w, c = img.shape # height, width, channels + for j in random.sample(range(n), k=round(probability * n)): + l, s = labels[j], segments[j] + box = l[1].astype(int).clip(0,w-1), l[2].astype(int).clip(0,h-1), l[3].astype(int).clip(0,w-1), l[4].astype(int).clip(0,h-1) + + #print(box) + if (box[2] <= box[0]) or (box[3] <= box[1]): + continue + + sample_labels.append(l[0]) + + mask = np.zeros(img.shape, np.uint8) + + cv2.drawContours(mask, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) + sample_masks.append(mask[box[1]:box[3],box[0]:box[2],:]) + + result = cv2.bitwise_and(src1=img, src2=mask) + i = result > 0 # pixels to replace + mask[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + #print(box) + sample_images.append(mask[box[1]:box[3],box[0]:box[2],:]) + + return sample_labels, sample_images, sample_masks + + +def replicate(img, labels): + # Replicate labels + h, w = img.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return img, labels + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = img.shape[0] + border[0] * 2 # shape(h,w,c) + width = img.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -img.shape[1] / 2 # x translation (pixels) + C[1, 2] = -img.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1.1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(img[:, :, ::-1]) # base + # ax[1].imshow(img2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + use_segments = any(x.any() for x in segments) + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return img, targets + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def bbox_ioa(box1, box2): + # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 + box2 = box2.transpose() + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 + + # Intersection over box2 area + return inter_area / box2_area + + +def cutout(image, labels): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + # create random masks + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def pastein(image, labels, sample_labels, sample_images, sample_masks): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + # create random masks + scales = [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6 # image size fraction + for s in scales: + if random.random() < 0.2: + continue + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + if len(labels): + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + else: + ioa = np.zeros(1) + + if (ioa < 0.30).all() and len(sample_labels) and (xmax > xmin+20) and (ymax > ymin+20): # allow 30% obscuration of existing labels + sel_ind = random.randint(0, len(sample_labels)-1) + #print(len(sample_labels)) + #print(sel_ind) + #print((xmax-xmin, ymax-ymin)) + #print(image[ymin:ymax, xmin:xmax].shape) + #print([[sample_labels[sel_ind], *box]]) + #print(labels.shape) + hs, ws, cs = sample_images[sel_ind].shape + r_scale = min((ymax-ymin)/hs, (xmax-xmin)/ws) + r_w = int(ws*r_scale) + r_h = int(hs*r_scale) + + if (r_w > 10) and (r_h > 10): + r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h)) + r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h)) + temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w] + m_ind = r_mask > 0 + if m_ind.astype(np.int32).sum() > 60: + temp_crop[m_ind] = r_image[m_ind] + #print(sample_labels[sel_ind]) + #print(sample_images[sel_ind].shape) + #print(temp_crop.shape) + box = np.array([xmin, ymin, xmin+r_w, ymin+r_h], dtype=np.float32) + if len(labels): + labels = np.concatenate((labels, [[sample_labels[sel_ind], *box]]), 0) + else: + labels = np.array([[sample_labels[sel_ind], *box]]) + + image[ymin:ymin+r_h, xmin:xmin+r_w] = temp_crop + + return labels + +class Albumentations: + # YOLOv5 Albumentations class (optional, only used if package is installed) + def __init__(self): + self.transform = None + import albumentations as A + + self.transform = A.Compose([ + A.CLAHE(p=0.01), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.01), + A.RandomGamma(gamma_limit=[80, 120], p=0.01), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.ImageCompression(quality_lower=75, p=0.01),], + bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels'])) + + #logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p)) + + def __call__(self, im, labels, p=1.0): + if self.transform and random.random() < p: + new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed + im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) + return im, labels + + +def create_folder(path='./new'): + # Create folder + if os.path.exists(path): + shutil.rmtree(path) # delete output folder + os.makedirs(path) # make new output folder + + +def flatten_recursive(path='../coco'): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(path + '_flat') + create_folder(new_path) + for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path='../coco/'): # from utils.datasets import *; extract_boxes('../coco128') + # Convert detection dataset into classification dataset, with one directory per class + + path = Path(path) # images dir + shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing + files = list(path.rglob('*.*')) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in img_formats: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file, 'r') as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + + +def autosplit(path='../coco', weights=(0.9, 0.1, 0.0), annotated_only=False): + """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.datasets import *; autosplit('../coco') + Arguments + path: Path to images directory + weights: Train, val, test weights (list) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only + n = len(files) # number of files + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + + print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) + for i, img in tqdm(zip(indices, files), total=n): + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label + with open(path / txt[i], 'a') as f: + f.write(str(img) + '\n') # add image to txt file + + +def load_segmentations(self, index): + key = '/work/handsomejw66/coco17/' + self.img_files[index] + #print(key) + # /work/handsomejw66/coco17/ + return self.segs[key] diff --git a/cv/detection/yolov7/pytorch/utils/general.py b/cv/detection/yolov7/pytorch/utils/general.py new file mode 100644 index 000000000..decdcc64e --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/general.py @@ -0,0 +1,892 @@ +# YOLOR general utils + +import glob +import logging +import math +import os +import platform +import random +import re +import subprocess +import time +from pathlib import Path + +import cv2 +import numpy as np +import pandas as pd +import torch +import torchvision +import yaml + +from utils.google_utils import gsutil_getsize +from utils.metrics import fitness +from utils.torch_utils import init_torch_seeds + +# Settings +torch.set_printoptions(linewidth=320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads + + +def set_logging(rank=-1): + logging.basicConfig( + format="%(message)s", + level=logging.INFO if rank in [-1, 0] else logging.WARN) + + +def init_seeds(seed=0): + # Initialize random number generator (RNG) seeds + random.seed(seed) + np.random.seed(seed) + init_torch_seeds(seed) + + +def get_latest_run(search_dir='.'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) if last_list else '' + + +def isdocker(): + # Is environment a Docker container + return Path('/workspace').exists() # or Path('/.dockerenv').exists() + + +def emojis(str=''): + # Return platform-dependent emoji-safe version of string + return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + + +def check_online(): + # Check internet connectivity + import socket + try: + socket.create_connection(("1.1.1.1", 443), 5) # check host accesability + return True + except OSError: + return False + + +def check_git_status(): + # Recommend 'git pull' if code is out of date + print(colorstr('github: '), end='') + try: + assert Path('.git').exists(), 'skipping check (not a git repository)' + assert not isdocker(), 'skipping check (Docker image)' + assert check_online(), 'skipping check (offline)' + + cmd = 'git fetch && git config --get remote.origin.url' + url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url + branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out + n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind + if n > 0: + s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ + f"Use 'git pull' to update or 'git clone {url}' to download latest." + else: + s = f'up to date with {url} ✅' + print(emojis(s)) # emoji-safe + except Exception as e: + print(e) + + +def check_requirements(requirements='requirements.txt', exclude=()): + # Check installed dependencies meet requirements (pass *.txt file or list of packages) + import pkg_resources as pkg + prefix = colorstr('red', 'bold', 'requirements:') + if isinstance(requirements, (str, Path)): # requirements.txt file + file = Path(requirements) + if not file.exists(): + print(f"{prefix} {file.resolve()} not found, check failed.") + return + requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] + else: # list or tuple of packages + requirements = [x for x in requirements if x not in exclude] + + n = 0 # number of packages updates + for r in requirements: + try: + pkg.require(r) + except Exception as e: # DistributionNotFound or VersionConflict if requirements not met + n += 1 + print(f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...") + print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode()) + + if n: # if packages updated + source = file.resolve() if 'file' in locals() else requirements + s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + print(emojis(s)) # emoji-safe + + +def check_img_size(img_size, s=32): + # Verify img_size is a multiple of stride s + new_size = make_divisible(img_size, int(s)) # ceil gs-multiple + if new_size != img_size: + print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) + return new_size + + +def check_imshow(): + # Check if environment supports image displays + try: + assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' + cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') + return False + + +def check_file(file): + # Search for file if not found + if Path(file).is_file() or file == '': + return file + else: + files = glob.glob('./**/' + file, recursive=True) # find file + assert len(files), f'File Not Found: {file}' # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_dataset(dict): + # Download dataset if not found locally + val, s = dict.get('val'), dict.get('download') + if val and len(val): + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) + if s and len(s): # download script + print('Downloading %s ...' % s) + if s.startswith('http') and s.endswith('.zip'): # URL + f = Path(s).name # filename + torch.hub.download_url_to_file(s, f) + r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip + else: # bash script + r = os.system(s) + print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value + else: + raise Exception('Dataset not found.') + + +def make_divisible(x, divisor): + # Returns x evenly divisible by divisor + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') + *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string + colors = {'black': '\033[30m', # basic colors + 'red': '\033[31m', + 'green': '\033[32m', + 'yellow': '\033[33m', + 'blue': '\033[34m', + 'magenta': '\033[35m', + 'cyan': '\033[36m', + 'white': '\033[37m', + 'bright_black': '\033[90m', # bright colors + 'bright_red': '\033[91m', + 'bright_green': '\033[92m', + 'bright_yellow': '\033[93m', + 'bright_blue': '\033[94m', + 'bright_magenta': '\033[95m', + 'bright_cyan': '\033[96m', + 'bright_white': '\033[97m', + 'end': '\033[0m', # misc + 'bold': '\033[1m', + 'underline': '\033[4m'} + return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(np.int32) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights) + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + class_counts = np.array([np.bincount(x[:, 0].astype(np.int32), minlength=nc) for x in labels]) + image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) + # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample + return image_weights + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + return x + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center + y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center + y[:, 2] = x[:, 2] - x[:, 0] # width + y[:, 3] = x[:, 3] - x[:, 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * x[:, 0] + padw # top left x + y[:, 1] = h * x[:, 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + x, y, = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + s = np.concatenate((s, s[0:1, :]), axis=0) + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ + (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + + + + +def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9): + # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ + (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + # change iou into pow(iou+eps) + # iou = inter / union + iou = torch.pow(inter/union + eps, alpha) + # beta = 2 * alpha + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = (cw ** 2 + ch ** 2) ** alpha + eps # convex diagonal + rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2) + rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2) + rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha # center distance + if DIoU: + return iou - rho2 / c2 # DIoU + elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) + with torch.no_grad(): + alpha_ciou = v / ((1 + eps) - inter / union + v) + # return iou - (rho2 / c2 + v * alpha_ciou) # CIoU + return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha)) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + # c_area = cw * ch + eps # convex area + # return iou - (c_area - union) / c_area # GIoU + c_area = torch.max(cw * ch + eps, union) # convex area + return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU + else: + return iou # torch.log(iou+eps) or iou + + +def box_iou(box1, box2): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) + + +def wh_iou(wh1, wh2): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) + + +def box_giou(box1, box2): + """ + Return generalized intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + union = (area1[:, None] + area2 - inter) + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + areai = whi[:, :, 0] * whi[:, :, 1] + + return iou - (areai - union) / areai + + +def box_ciou(box1, box2, eps: float = 1e-7): + """ + Return complete intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise complete IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + union = (area1[:, None] + area2 - inter) + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + + # centers of boxes + x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 + y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 + x_g = (box2[:, 0] + box2[:, 2]) / 2 + y_g = (box2[:, 1] + box2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 + + w_pred = box1[:, None, 2] - box1[:, None, 0] + h_pred = box1[:, None, 3] - box1[:, None, 1] + + w_gt = box2[:, 2] - box2[:, 0] + h_gt = box2[:, 3] - box2[:, 1] + + v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v + + +def box_diou(box1, box2, eps: float = 1e-7): + """ + Return distance intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise distance IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + union = (area1[:, None] + area2 - inter) + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + + # centers of boxes + x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 + y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 + x_g = (box2[:, 0] + box2[:, 2]) / 2 + y_g = (box2[:, 1] + box2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 + + # The distance IoU is the IoU penalized by a normalized + # distance between boxes' centers squared. + return iou - (centers_distance_squared / diagonal_distance_squared) + + +def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=()): + """Runs Non-Maximum Suppression (NMS) on inference results + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + nc = prediction.shape[2] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + if nc == 1: + x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + + +def non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=(), kpt_label=False, nc=None, nkpt=None): + """Runs Non-Maximum Suppression (NMS) on inference results + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + if nc is None: + nc = prediction.shape[2] - 5 if not kpt_label else prediction.shape[2] - 56 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0,6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:5+nc] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + if not kpt_label: + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + else: + kpts = x[:, 6:] + conf, j = x[:, 5:6].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), kpts), 1)[conf.view(-1) > conf_thres] + + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + + +def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device('cpu')) + if x.get('ema'): + x['model'] = x['ema'] # replace model with ema + for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys + x[k] = None + x['epoch'] = -1 + x['model'].half() # to FP16 + for p in x['model'].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1E6 # filesize + print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") + + +def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): + # Print mutation results to evolve.txt (for use with train.py --evolve) + a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys + b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) + + if bucket: + url = 'gs://%s/evolve.txt' % bucket + if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): + os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local + + with open('evolve.txt', 'a') as f: # append result + f.write(c + b + '\n') + x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows + x = x[np.argsort(-fitness(x))] # sort + np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness + + # Save yaml + for i, k in enumerate(hyp.keys()): + hyp[k] = float(x[0, i + 7]) + with open(yaml_file, 'w') as f: + results = tuple(x[0, :7]) + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') + yaml.dump(hyp, f, sort_keys=False) + + if bucket: + os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload + + +def apply_classifier(x, model, img, im0): + # applies a second stage classifier to yolo outputs + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_coords(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for j, a in enumerate(d): # per item + cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + # cv2.imwrite('test%i.jpg' % j, cutout) + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255.0 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=True, sep=''): + # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. + path = Path(path) # os-agnostic + if (path.exists() and exist_ok) or (not path.exists()): + return str(path) + else: + dirs = glob.glob(f"{path}{sep}*") # similar paths + matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] + i = [int(m.groups()[0]) for m in matches if m] # indices + n = max(i) + 1 if i else 2 # increment number + return f"{path}{sep}{n}" # update path diff --git a/cv/detection/yolov7/pytorch/utils/google_app_engine/Dockerfile b/cv/detection/yolov7/pytorch/utils/google_app_engine/Dockerfile new file mode 100644 index 000000000..0155618f4 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/cv/detection/yolov7/pytorch/utils/google_app_engine/additional_requirements.txt b/cv/detection/yolov7/pytorch/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 000000000..5fcc30524 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,4 @@ +# add these requirements in your app on top of the existing ones +pip==18.1 +Flask==1.0.2 +gunicorn==19.9.0 diff --git a/cv/detection/yolov7/pytorch/utils/google_app_engine/app.yaml b/cv/detection/yolov7/pytorch/utils/google_app_engine/app.yaml new file mode 100644 index 000000000..69b8f68b3 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolorapp + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/utils/google_utils.py b/cv/detection/yolov7/pytorch/utils/google_utils.py new file mode 100644 index 000000000..f363408e6 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/google_utils.py @@ -0,0 +1,123 @@ +# Google utils: https://cloud.google.com/storage/docs/reference/libraries + +import os +import platform +import subprocess +import time +from pathlib import Path + +import requests +import torch + + +def gsutil_getsize(url=''): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') + return eval(s.split(' ')[0]) if len(s) else 0 # bytes + + +def attempt_download(file, repo='WongKinYiu/yolov7'): + # Attempt file download if does not exist + file = Path(str(file).strip().replace("'", '').lower()) + + if not file.exists(): + try: + response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api + assets = [x['name'] for x in response['assets']] # release assets + tag = response['tag_name'] # i.e. 'v1.0' + except: # fallback plan + assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt', + 'yolov7-e6e.pt', 'yolov7-w6.pt'] + tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] + + name = file.name + if name in assets: + msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' + redundant = False # second download option + try: # GitHub + url = f'https://github.com/{repo}/releases/download/{tag}/{name}' + print(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, file) + assert file.exists() and file.stat().st_size > 1E6 # check + except Exception as e: # GCP + print(f'Download error: {e}') + assert redundant, 'No secondary mirror' + url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' + print(f'Downloading {url} to {file}...') + os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) + finally: + if not file.exists() or file.stat().st_size < 1E6: # check + file.unlink(missing_ok=True) # remove partial downloads + print(f'ERROR: Download failure: {msg}') + print('') + return + + +def gdrive_download(id='', file='tmp.zip'): + # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download() + t = time.time() + file = Path(file) + cookie = Path('cookie') # gdrive cookie + print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') + file.unlink(missing_ok=True) # remove existing file + cookie.unlink(missing_ok=True) # remove existing cookie + + # Attempt file download + out = "NUL" if platform.system() == "Windows" else "/dev/null" + os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') + if os.path.exists('cookie'): # large file + s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' + else: # small file + s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' + r = os.system(s) # execute, capture return + cookie.unlink(missing_ok=True) # remove existing cookie + + # Error check + if r != 0: + file.unlink(missing_ok=True) # remove partial + print('Download error ') # raise Exception('Download error') + return r + + # Unzip if archive + if file.suffix == '.zip': + print('unzipping... ', end='') + os.system(f'unzip -q {file}') # unzip + file.unlink() # remove zip to free space + + print(f'Done ({time.time() - t:.1f}s)') + return r + + +def get_token(cookie="./cookie"): + with open(cookie) as f: + for line in f: + if "download" in line: + return line.split()[-1] + return "" + +# def upload_blob(bucket_name, source_file_name, destination_blob_name): +# # Uploads a file to a bucket +# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python +# +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(destination_blob_name) +# +# blob.upload_from_filename(source_file_name) +# +# print('File {} uploaded to {}.'.format( +# source_file_name, +# destination_blob_name)) +# +# +# def download_blob(bucket_name, source_blob_name, destination_file_name): +# # Uploads a blob from a bucket +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(source_blob_name) +# +# blob.download_to_filename(destination_file_name) +# +# print('Blob {} downloaded to {}.'.format( +# source_blob_name, +# destination_file_name)) diff --git a/cv/detection/yolov7/pytorch/utils/loss.py b/cv/detection/yolov7/pytorch/utils/loss.py new file mode 100644 index 000000000..2b1d968f8 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/loss.py @@ -0,0 +1,1697 @@ +# Loss functions + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy +from utils.torch_utils import is_parallel + + +def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super(BCEBlurWithLogitsLoss, self).__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class SigmoidBin(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0): + super(SigmoidBin, self).__init__() + + self.bin_count = bin_count + self.length = bin_count + 1 + self.min = min + self.max = max + self.scale = float(max - min) + self.shift = self.scale / 2.0 + + self.use_loss_regression = use_loss_regression + self.use_fw_regression = use_fw_regression + self.reg_scale = reg_scale + self.BCE_weight = BCE_weight + + start = min + (self.scale/2.0) / self.bin_count + end = max - (self.scale/2.0) / self.bin_count + step = self.scale / self.bin_count + self.step = step + #print(f" start = {start}, end = {end}, step = {step} ") + + bins = torch.range(start, end + 0.0001, step).float() + self.register_buffer('bins', bins) + + + self.cp = 1.0 - 0.5 * smooth_eps + self.cn = 0.5 * smooth_eps + + self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight])) + self.MSELoss = nn.MSELoss() + + def get_length(self): + return self.length + + def forward(self, pred): + assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) + + pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step + pred_bin = pred[..., 1:(1+self.bin_count)] + + _, bin_idx = torch.max(pred_bin, dim=-1) + bin_bias = self.bins[bin_idx] + + if self.use_fw_regression: + result = pred_reg + bin_bias + else: + result = bin_bias + result = result.clamp(min=self.min, max=self.max) + + return result + + + def training_loss(self, pred, target): + assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) + assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0]) + device = pred.device + + pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step + pred_bin = pred[..., 1:(1+self.bin_count)] + + diff_bin_target = torch.abs(target[..., None] - self.bins) + _, bin_idx = torch.min(diff_bin_target, dim=-1) + + bin_bias = self.bins[bin_idx] + bin_bias.requires_grad = False + result = pred_reg + bin_bias + + target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets + n = pred.shape[0] + target_bins[range(n), bin_idx] = self.cp + + loss_bin = self.BCEbins(pred_bin, target_bins) # BCE + + if self.use_loss_regression: + loss_regression = self.MSELoss(result, target) # MSE + loss = loss_bin + loss_regression + else: + loss = loss_bin + + out_result = result.clamp(min=self.min, max=self.max) + + return loss, out_result + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(FocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(QFocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + +class RankSort(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): + + classification_grads=torch.zeros(logits.shape).cuda() + + #Filter fg logits + fg_labels = (targets > 0.) + fg_logits = logits[fg_labels] + fg_targets = targets[fg_labels] + fg_num = len(fg_logits) + + #Do not use bg with scores less than minimum fg logit + #since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits)-delta_RS + relevant_bg_labels=((targets==0) & (logits>=threshold_logit)) + + relevant_bg_logits = logits[relevant_bg_labels] + relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() + sorting_error=torch.zeros(fg_num).cuda() + ranking_error=torch.zeros(fg_num).cuda() + fg_grad=torch.zeros(fg_num).cuda() + + #sort the fg logits + order=torch.argsort(fg_logits) + #Loops over each positive following the order + for ii in order: + # Difference Transforms (x_ij) + fg_relations=fg_logits-fg_logits[ii] + bg_relations=relevant_bg_logits-fg_logits[ii] + + if delta_RS > 0: + fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1) + bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1) + else: + fg_relations = (fg_relations >= 0).float() + bg_relations = (bg_relations >= 0).float() + + # Rank of ii among pos and false positive number (bg with larger scores) + rank_pos=torch.sum(fg_relations) + FP_num=torch.sum(bg_relations) + + # Rank of ii among all examples + rank=rank_pos+FP_num + + # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) + ranking_error[ii]=FP_num/rank + + # Current sorting error of example ii. (Eq. 7) + current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos + + #Find examples in the target sorted order for example ii + iou_relations = (fg_targets >= fg_targets[ii]) + target_sorted_order = iou_relations * fg_relations + + #The rank of ii among positives in sorted order + rank_pos_target = torch.sum(target_sorted_order) + + #Compute target sorting error. (Eq. 8) + #Since target ranking error is 0, this is also total target error + target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target + + #Compute sorting error on example ii + sorting_error[ii] = current_sorting_error - target_sorting_error + + #Identity Update for Ranking Error + if FP_num > eps: + #For ii the update is the ranking error + fg_grad[ii] -= ranking_error[ii] + #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) + relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num)) + + #Find the positives that are misranked (the cause of the error) + #These are the ones with smaller IoU but larger logits + missorted_examples = (~ iou_relations) * fg_relations + + #Denominotor of sorting pmf + sorting_pmf_denom = torch.sum(missorted_examples) + + #Identity Update for Sorting Error + if sorting_pmf_denom > eps: + #For ii the update is the sorting error + fg_grad[ii] -= sorting_error[ii] + #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) + fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom)) + + #Normalize gradients by number of positives + classification_grads[fg_labels]= (fg_grad/fg_num) + classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num) + + ctx.save_for_backward(classification_grads) + + return ranking_error.mean(), sorting_error.mean() + + @staticmethod + def backward(ctx, out_grad1, out_grad2): + g1, =ctx.saved_tensors + return g1*out_grad1, None, None, None + +class aLRPLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): + classification_grads=torch.zeros(logits.shape).cuda() + + #Filter fg logits + fg_labels = (targets == 1) + fg_logits = logits[fg_labels] + fg_num = len(fg_logits) + + #Do not use bg with scores less than minimum fg logit + #since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits)-delta + + #Get valid bg logits + relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) + relevant_bg_logits=logits[relevant_bg_labels] + relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() + rank=torch.zeros(fg_num).cuda() + prec=torch.zeros(fg_num).cuda() + fg_grad=torch.zeros(fg_num).cuda() + + max_prec=0 + #sort the fg logits + order=torch.argsort(fg_logits) + #Loops over each positive following the order + for ii in order: + #x_ij s as score differences with fgs + fg_relations=fg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with fgs + fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) + #Discard i=j in the summation in rank_pos + fg_relations[ii]=0 + + #x_ij s as score differences with bgs + bg_relations=relevant_bg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with bgs + bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) + + #Compute the rank of the example within fgs and number of bgs with larger scores + rank_pos=1+torch.sum(fg_relations) + FP_num=torch.sum(bg_relations) + #Store the total since it is normalizer also for aLRP Regression error + rank[ii]=rank_pos+FP_num + + #Compute precision for this example to compute classification loss + prec[ii]=rank_pos/rank[ii] + #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads + if FP_num > eps: + fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii] + relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) + + #aLRP with grad formulation fg gradient + classification_grads[fg_labels]= fg_grad + #aLRP with grad formulation bg gradient + classification_grads[relevant_bg_labels]= relevant_bg_grad + + classification_grads /= (fg_num) + + cls_loss=1-prec.mean() + ctx.save_for_backward(classification_grads) + + return cls_loss, rank, order + + @staticmethod + def backward(ctx, out_grad1, out_grad2, out_grad3): + g1, =ctx.saved_tensors + return g1*out_grad1, None, None, None, None + + +class APLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, delta=1.): + classification_grads=torch.zeros(logits.shape).cuda() + + #Filter fg logits + fg_labels = (targets == 1) + fg_logits = logits[fg_labels] + fg_num = len(fg_logits) + + #Do not use bg with scores less than minimum fg logit + #since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits)-delta + + #Get valid bg logits + relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) + relevant_bg_logits=logits[relevant_bg_labels] + relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() + rank=torch.zeros(fg_num).cuda() + prec=torch.zeros(fg_num).cuda() + fg_grad=torch.zeros(fg_num).cuda() + + max_prec=0 + #sort the fg logits + order=torch.argsort(fg_logits) + #Loops over each positive following the order + for ii in order: + #x_ij s as score differences with fgs + fg_relations=fg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with fgs + fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) + #Discard i=j in the summation in rank_pos + fg_relations[ii]=0 + + #x_ij s as score differences with bgs + bg_relations=relevant_bg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with bgs + bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) + + #Compute the rank of the example within fgs and number of bgs with larger scores + rank_pos=1+torch.sum(fg_relations) + FP_num=torch.sum(bg_relations) + #Store the total since it is normalizer also for aLRP Regression error + rank[ii]=rank_pos+FP_num + + #Compute precision for this example + current_prec=rank_pos/rank[ii] + + #Compute interpolated AP and store gradients for relevant bg examples + if (max_prec<=current_prec): + max_prec=current_prec + relevant_bg_grad += (bg_relations/rank[ii]) + else: + relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec))) + + #Store fg gradients + fg_grad[ii]=-(1-max_prec) + prec[ii]=max_prec + + #aLRP with grad formulation fg gradient + classification_grads[fg_labels]= fg_grad + #aLRP with grad formulation bg gradient + classification_grads[relevant_bg_labels]= relevant_bg_grad + + classification_grads /= fg_num + + cls_loss=1-prec.mean() + ctx.save_for_backward(classification_grads) + + return cls_loss + + @staticmethod + def backward(ctx, out_grad1): + g1, =ctx.saved_tensors + return g1*out_grad1, None, None + + +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLoss, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7 + #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), tcls[i]] = self.cp + #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype) + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch + + +class ComputeLossOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLossOTA, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors', 'stride': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) + pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] + + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + #pxy = ps[:, :2].sigmoid() * 3. - 1. + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + + #indices, anch = self.find_positive(p, targets) + indices, anch = self.find_3_positive(p, targets) + #indices, anch = self.find_4_positive(p, targets) + #indices, anch = self.find_5_positive(p, targets) + #indices, anch = self.find_9_positive(p, targets) + device = torch.device(targets.device) + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append((torch.ones(size=(len(b),)) * i).to(device)) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost, device=device) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = (matching_matrix.sum(0) > 0.0).to(device) + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + +class ComputeLossBinOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLossBinOTA, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + #MSEangle = nn.MSELoss().to(device) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count': + setattr(self, k, getattr(det, k)) + + #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device) + wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device) + #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device) + self.wh_bin_sigmoid = wh_bin_sigmoid + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) + pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] + + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2 + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + + #pxy = ps[:, :2].sigmoid() * 2. - 0.5 + ##pxy = ps[:, :2].sigmoid() * 3. - 1. + #pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + #pbox = torch.cat((pxy, pwh), 1) # predicted box + + #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0]) + #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1]) + w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0]) + h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1]) + + pw *= anchors[i][..., 0] + ph *= anchors[i][..., 1] + + px = ps[:, 0].sigmoid() * 2. - 0.5 + py = ps[:, 1].sigmoid() * 2. - 0.5 + + lbox += w_loss + h_loss # + x_loss + y_loss + + #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n") + + pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device) # predicted box + + + + + iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, (1+obj_idx):], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., obj_idx], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + + #indices, anch = self.find_positive(p, targets) + indices, anch = self.find_3_positive(p, targets) + #indices, anch = self.find_4_positive(p, targets) + #indices, anch = self.find_5_positive(p, targets) + #indices, anch = self.find_9_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)]) + p_cls.append(fg_pred[:, (obj_idx+1):]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i] + ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i] + + pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + +class ComputeLossAuxOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLossAuxOTA, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors', 'stride': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux = self.build_targets2(p[:self.nl], targets, imgs) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p[:self.nl], targets, imgs) + pre_gen_gains_aux = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] + pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] + + + # Losses + for i in range(self.nl): # layer index, layer predictions + pi = p[i] + pi_aux = p[i+self.nl] + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + b_aux, a_aux, gj_aux, gi_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + n_aux = b_aux.shape[0] # number of targets + if n_aux: + ps_aux = pi_aux[b_aux, a_aux, gj_aux, gi_aux] # prediction subset corresponding to targets + grid_aux = torch.stack([gi_aux, gj_aux], dim=1) + pxy_aux = ps_aux[:, :2].sigmoid() * 2. - 0.5 + #pxy_aux = ps_aux[:, :2].sigmoid() * 3. - 1. + pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i] + pbox_aux = torch.cat((pxy_aux, pwh_aux), 1) # predicted box + selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i] + selected_tbox_aux[:, :2] -= grid_aux + iou_aux = bbox_iou(pbox_aux.T, selected_tbox_aux, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += 0.25 * (1.0 - iou_aux).mean() # iou loss + + # Objectness + tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = (1.0 - self.gr) + self.gr * iou_aux.detach().clamp(0).type(tobj_aux.dtype) # iou ratio + + # Classification + selected_tcls_aux = targets_aux[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t_aux = torch.full_like(ps_aux[:, 5:], self.cn, device=device) # targets + t_aux[range(n_aux), selected_tcls_aux] = self.cp + lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux) # BCE + + obji = self.BCEobj(pi[..., 4], tobj) + obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux) + lobj += obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + + indices, anch = self.find_3_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def build_targets2(self, p, targets, imgs): + + indices, anch = self.find_5_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def find_5_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 1.0 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch diff --git a/cv/detection/yolov7/pytorch/utils/metrics.py b/cv/detection/yolov7/pytorch/utils/metrics.py new file mode 100644 index 000000000..6d2f53647 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/metrics.py @@ -0,0 +1,227 @@ +# Model validation metrics + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from . import general + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def ap_per_class(tp, conf, pred_cls, target_cls, v5_metric=False, plot=False, save_dir='.', names=()): + """ Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes = np.unique(target_cls) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = (target_cls == c).sum() # number of labels + n_p = i.sum() # number of predictions + + if n_p == 0 or n_l == 0: + continue + else: + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + 1e-16) # recall curve + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j], v5_metric=v5_metric) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + 1e-16) + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) + plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') + plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') + plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') + + i = f1.mean(0).argmax() # max F1 index + return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') + + +def compute_ap(recall, precision, v5_metric=False): + """ Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + v5_metric: Assume maximum recall to be 1.0, as in YOLOv5, MMDetetion etc. + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + if v5_metric: # New YOLOv5 metric, same as MMDetection and Detectron2 repositories + mrec = np.concatenate(([0.], recall, [1.0])) + else: # Old YOLOv5 metric, i.e. default YOLOv7 metric + mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) + mpre = np.concatenate(([1.], precision, [0.])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = 'interp' # methods: 'continuous', 'interp' + if method == 'interp': + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = general.box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(np.int16) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[gc, detection_classes[m1[j]]] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # background FP + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # background FN + + def matrix(self): + return self.matrix + + def plot(self, save_dir='', names=()): + try: + import seaborn as sn + + array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig = plt.figure(figsize=(12, 9), tight_layout=True) + sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size + labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels + sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, + xticklabels=names + ['background FP'] if labels else "auto", + yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) + fig.axes[0].set_xlabel('True') + fig.axes[0].set_ylabel('Predicted') + fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + except Exception as e: + pass + + def print(self): + for i in range(self.nc + 1): + print(' '.join(map(str, self.matrix[i]))) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + +def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): + # Precision-recall curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) + + +def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + + y = py.mean(0) + ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) diff --git a/cv/detection/yolov7/pytorch/utils/plots.py b/cv/detection/yolov7/pytorch/utils/plots.py new file mode 100644 index 000000000..fdd8d0e85 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/plots.py @@ -0,0 +1,489 @@ +# Plotting utils + +import glob +import math +import os +import random +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import torch +import yaml +from PIL import Image, ImageDraw, ImageFont +from scipy.signal import butter, filtfilt + +from utils.general import xywh2xyxy, xyxy2xywh +from utils.metrics import fitness + +# Settings +matplotlib.rc('font', **{'size': 11}) +matplotlib.use('Agg') # for writing to files only + + +def color_list(): + # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb + def hex2rgb(h): + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + + return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()] # or BASE_ (8), CSS4_ (148), XKCD_ (949) + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype='low', analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def plot_one_box(x, img, color=None, label=None, line_thickness=3): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + +def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None): + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + line_thickness = line_thickness or max(int(min(img.size) / 200), 2) + draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot + if label: + fontsize = max(round(max(img.size) / 40), 12) + font = ImageFont.truetype("Arial.ttf", fontsize) + txt_width, txt_height = font.getsize(label) + draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) + draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) + return np.asarray(img) + + +def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() + # Compares the two methods for width-height anchor multiplication + # https://github.com/ultralytics/yolov3/issues/168 + x = np.arange(-4.0, 4.0, .1) + ya = np.exp(x) + yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 + + fig = plt.figure(figsize=(6, 3), tight_layout=True) + plt.plot(x, ya, '.-', label='YOLOv3') + plt.plot(x, yb ** 2, '.-', label='YOLOR ^2') + plt.plot(x, yb ** 1.6, '.-', label='YOLOR ^1.6') + plt.xlim(left=-4, right=4) + plt.ylim(bottom=0, top=6) + plt.xlabel('input') + plt.ylabel('output') + plt.grid() + plt.legend() + fig.savefig('comparison.png', dpi=200) + + +def output_to_target(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + for *box, conf, cls in o.cpu().numpy(): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) + return np.array(targets) + + +def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): + # Plot image grid with labels + + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + # un-normalise + if np.max(images[0]) <= 1: + images *= 255 + + tl = 3 # line thickness + tf = max(tl - 1, 1) # font thickness + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + + # Check if we should resize + scale_factor = max_size / max(h, w) + if scale_factor < 1: + h = math.ceil(scale_factor * h) + w = math.ceil(scale_factor * w) + + colors = color_list() # list of colors + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, img in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + + block_x = int(w * (i // ns)) + block_y = int(h * (i % ns)) + + img = img.transpose(1, 2, 0) + if scale_factor < 1: + img = cv2.resize(img, (w, h)) + + mosaic[block_y:block_y + h, block_x:block_x + w, :] = img + if len(targets) > 0: + image_targets = targets[targets[:, 0] == i] + boxes = xywh2xyxy(image_targets[:, 2:6]).T + classes = image_targets[:, 1].astype('int') + labels = image_targets.shape[1] == 6 # labels if no conf column + conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale_factor < 1: # absolute coords need scale if image scales + boxes *= scale_factor + boxes[[0, 2]] += block_x + boxes[[1, 3]] += block_y + for j, box in enumerate(boxes.T): + cls = int(classes[j]) + color = colors[cls % len(colors)] + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) + plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) + + # Draw image filename labels + if paths: + label = Path(paths[i]).name[:40] # trim to 40 char + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, + lineType=cv2.LINE_AA) + + # Image border + cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) + + if fname: + r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size + mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) + # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save + Image.fromarray(mosaic).save(fname) # PIL save + return mosaic + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]['lr']) + plt.plot(y, '.-', label='LR') + plt.xlabel('epoch') + plt.ylabel('LR') + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + plt.close() + + +def plot_test_txt(): # from utils.plots import *; plot_test() + # Plot test.txt histograms + x = np.loadtxt('test.txt', dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect('equal') + plt.savefig('hist2d.png', dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig('hist1d.png', dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt('targets.txt', dtype=np.float32).T + s = ['x targets', 'y targets', 'width targets', 'height targets'] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig('targets.jpg', dpi=200) + + +def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() + # Plot study.txt generated by test.py + fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) + # ax = ax.ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolor-p6', 'yolor-w6', 'yolor-e6', 'yolor-d6']]: + for f in sorted(Path(path).glob('study*.txt')): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] + # for i in range(7): + # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + # ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, + label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) + + ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(30, 55) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + plt.savefig(str(Path(path).name) + '.png', dpi=300) + + +def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): + # plot dataset labels + print('Plotting labels... ') + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + colors = color_list() + x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + + # seaborn correlogram + sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use('svg') # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + ax[0].set_ylabel('instances') + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(names, rotation=90, fontsize=10) + else: + ax[0].set_xlabel('classes') + sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) + sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot + ax[1].imshow(img) + ax[1].axis('off') + + for a in [0, 1, 2, 3]: + for s in ['top', 'right', 'left', 'bottom']: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / 'labels.jpg', dpi=200) + matplotlib.use('Agg') + plt.close() + + # loggers + for k, v in loggers.items() or {}: + if k == 'wandb' and v: + v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) + + +def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() + # Plot hyperparameter evolution results in evolve.txt + with open(yaml_file) as f: + hyp = yaml.load(f, Loader=yaml.SafeLoader) + x = np.loadtxt('evolve.txt', ndmin=2) + f = fitness(x) + # weights = (f - f.min()) ** 2 # for weighted results + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc('font', **{'size': 8}) + for i, (k, v) in enumerate(hyp.items()): + y = x[:, i + 7] + # mu = (y * weights).sum() / weights.sum() # best weighted result + mu = y[f.argmax()] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') + plt.plot(mu, f.max(), 'k+', markersize=15) + plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print('%15s: %.3g' % (k, mu)) + plt.savefig('evolve.png', dpi=200) + print('\nPlot saved as evolve.png') + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=''): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] + files = list(Path(save_dir).glob('frames*.txt')) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = (results[0] - results[0].min()) # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace('frames_', '') + a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel('time (s)') + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ['top', 'right']: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + + +def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() + # Plot training 'results*.txt', overlaying train and val losses + s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends + t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles + for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) + ax = ax.ravel() + for i in range(5): + for j in [i, i + 5]: + y = results[j, x] + ax[i].plot(x, y, marker='.', label=s[j]) + # y_smooth = butter_lowpass_filtfilt(y) + # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) + + ax[i].set_title(t[i]) + ax[i].legend() + ax[i].set_ylabel(f) if i == 0 else None # add filename + fig.savefig(f.replace('.txt', '.png'), dpi=200) + + +def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): + # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', + 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] + if bucket: + # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] + files = ['results%g.txt' % x for x in id] + c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) + os.system(c) + else: + files = list(Path(save_dir).glob('results*.txt')) + assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + for i in range(10): + y = results[i, x] + if i in [0, 1, 2, 5, 6, 7]: + y[y == 0] = np.nan # don't show zero loss values + # y /= y[0] # normalize + label = labels[fi] if len(labels) else f.stem + ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) + ax[i].set_title(s[i]) + # if i in [5, 6, 7]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + fig.savefig(Path(save_dir) / 'results.png', dpi=200) + + +def output_to_keypoint(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + kpts = o[:,6:] + o = o[:,:6] + for index, (*box, conf, cls) in enumerate(o.detach().cpu().numpy()): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf, *list(kpts.detach().cpu().numpy()[index])]) + return np.array(targets) + + +def plot_skeleton_kpts(im, kpts, steps, orig_shape=None): + #Plot the skeleton and keypointsfor coco datatset + palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], + [230, 230, 0], [255, 153, 255], [153, 204, 255], + [255, 102, 255], [255, 51, 255], [102, 178, 255], + [51, 153, 255], [255, 153, 153], [255, 102, 102], + [255, 51, 51], [153, 255, 153], [102, 255, 102], + [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], + [255, 255, 255]]) + + skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], + [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3], + [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]] + + pose_limb_color = palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]] + pose_kpt_color = palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]] + radius = 5 + num_kpts = len(kpts) // steps + + for kid in range(num_kpts): + r, g, b = pose_kpt_color[kid] + x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1] + if not (x_coord % 640 == 0 or y_coord % 640 == 0): + if steps == 3: + conf = kpts[steps * kid + 2] + if conf < 0.5: + continue + cv2.circle(im, (int(x_coord), int(y_coord)), radius, (int(r), int(g), int(b)), -1) + + for sk_id, sk in enumerate(skeleton): + r, g, b = pose_limb_color[sk_id] + pos1 = (int(kpts[(sk[0]-1)*steps]), int(kpts[(sk[0]-1)*steps+1])) + pos2 = (int(kpts[(sk[1]-1)*steps]), int(kpts[(sk[1]-1)*steps+1])) + if steps == 3: + conf1 = kpts[(sk[0]-1)*steps+2] + conf2 = kpts[(sk[1]-1)*steps+2] + if conf1<0.5 or conf2<0.5: + continue + if pos1[0]%640 == 0 or pos1[1]%640==0 or pos1[0]<0 or pos1[1]<0: + continue + if pos2[0] % 640 == 0 or pos2[1] % 640 == 0 or pos2[0]<0 or pos2[1]<0: + continue + cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2) diff --git a/cv/detection/yolov7/pytorch/utils/torch_utils.py b/cv/detection/yolov7/pytorch/utils/torch_utils.py new file mode 100644 index 000000000..1e631b555 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/torch_utils.py @@ -0,0 +1,374 @@ +# YOLOR PyTorch utils + +import datetime +import logging +import math +import os +import platform +import subprocess +import time +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +try: + import thop # for FLOPS computation +except ImportError: + thop = None +logger = logging.getLogger(__name__) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """ + Decorator to make all processes in distributed training wait for each local_master to do something. + """ + if local_rank not in [-1, 0]: + torch.distributed.barrier() + yield + if local_rank == 0: + torch.distributed.barrier() + + +def init_torch_seeds(seed=0): + # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html + torch.manual_seed(seed) + if seed == 0: # slower, more reproducible + cudnn.benchmark, cudnn.deterministic = False, True + else: # faster, less reproducible + cudnn.benchmark, cudnn.deterministic = True, False + + +def date_modified(path=__file__): + # return human-readable file modification date, i.e. '2021-3-26' + t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) + return f'{t.year}-{t.month}-{t.day}' + + +def git_describe(path=Path(__file__).parent): # path must be a directory + # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe + s = f'git -C {path} describe --tags --long --always' + try: + return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] + except subprocess.CalledProcessError as e: + return '' # not a git repository + + +def select_device(device='', batch_size=None): + # device = 'cpu' or '0' or '0,1,2,3' + s = f'YOLOR 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string + cpu = device.lower() == 'cpu' + if cpu: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable + assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability + + cuda = not cpu and torch.cuda.is_available() + if cuda: + n = torch.cuda.device_count() + if n > 1 and batch_size: # check that batch_size is compatible with device_count + assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' + space = ' ' * len(s) + for i, d in enumerate(device.split(',') if device else range(n)): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB + else: + s += 'CPU\n' + + logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe + return torch.device('cuda:0' if cuda else 'cpu') + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(x, ops, n=100, device=None): + # profile a pytorch module or list of modules. Example usage: + # x = torch.randn(16, 3, 640, 640) # input + # m1 = lambda x: x * torch.sigmoid(x) + # m2 = nn.SiLU() + # profile(x, [m1, m2], n=100) # profile speed over 100 iterations + + device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + x = x.to(device) + x.requires_grad = True + print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') + print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, 'to') else m # device + m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type + dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS + except: + flops = 0 + + for _ in range(n): + t[0] = time_synchronized() + y = m(x) + t[1] = time_synchronized() + try: + _ = y.sum().backward() + t[2] = time_synchronized() + except: # no backward method + t[2] = float('nan') + dtf += (t[1] - t[0]) * 1000 / n # ms per op forward + dtb += (t[2] - t[1]) * 1000 / n # ms per op backward + + s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' + s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' + p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters + print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') + + +def is_parallel(model): + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0., 0. + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + print('Pruning model... ', end='') + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name='weight', amount=amount) # prune + prune.remove(m, 'weight') # make permanent + print(' %.3g global sparsity' % sparsity(model)) + + +def fuse_conv_and_bn(conv, bn): + # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = nn.Conv2d(conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + groups=conv.groups, + bias=True).requires_grad_(False).to(conv.weight.device) + + # prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, img_size=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPS + from thop import profile + stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 + img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input + flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS + img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float + fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS + except (ImportError, Exception): + fs = '' + + logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") + + +def load_classifier(name='resnet101', n=2): + # Loads a pretrained model reshaped to n-class output + model = torchvision.models.__dict__[name](pretrained=True) + + # ResNet model properties + # input_size = [3, 224, 224] + # input_space = 'RGB' + # input_range = [0, 1] + # mean = [0.485, 0.456, 0.406] + # std = [0.229, 0.224, 0.225] + + # Reshape output to n classes + filters = model.fc.weight.shape[1] + model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) + model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) + model.fc.out_features = n + return model + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + # scales img(bs,3,y,x) by ratio constrained to gs-multiple + if ratio == 1.0: + return img + else: + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + +class ModelEMA: + """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + This is intended to allow functionality like + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + A smoothed version of the weights is necessary for some training schemes to perform well. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + def __init__(self, model, decay=0.9999, updates=0): + # Create EMA + self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1. - d) * msd[k].detach() + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) + + +class BatchNormXd(torch.nn.modules.batchnorm._BatchNorm): + def _check_input_dim(self, input): + # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc + # is this method that is overwritten by the sub-class + # This original goal of this method was for tensor sanity checks + # If you're ok bypassing those sanity checks (eg. if you trust your inference + # to provide the right dimensional inputs), then you can just use this method + # for easy conversion from SyncBatchNorm + # (unfortunately, SyncBatchNorm does not store the original class - if it did + # we could return the one that was originally created) + return + +def revert_sync_batchnorm(module): + # this is very similar to the function that it is trying to revert: + # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679 + module_output = module + if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm): + new_cls = BatchNormXd + module_output = BatchNormXd(module.num_features, + module.eps, module.momentum, + module.affine, + module.track_running_stats) + if module.affine: + with torch.no_grad(): + module_output.weight = module.weight + module_output.bias = module.bias + module_output.running_mean = module.running_mean + module_output.running_var = module.running_var + module_output.num_batches_tracked = module.num_batches_tracked + if hasattr(module, "qconfig"): + module_output.qconfig = module.qconfig + for name, child in module.named_children(): + module_output.add_module(name, revert_sync_batchnorm(child)) + del module + return module_output + + +class TracedModel(nn.Module): + + def __init__(self, model=None, device=None, img_size=(640,640)): + super(TracedModel, self).__init__() + + print(" Convert model to Traced-model... ") + self.stride = model.stride + self.names = model.names + self.model = model + + self.model = revert_sync_batchnorm(self.model) + self.model.to('cpu') + self.model.eval() + + self.detect_layer = self.model.model[-1] + self.model.traced = True + + rand_example = torch.rand(1, 3, img_size, img_size) + + traced_script_module = torch.jit.trace(self.model, rand_example, strict=False) + #traced_script_module = torch.jit.script(self.model) + traced_script_module.save("traced_model.pt") + print(" traced_script_module saved! ") + self.model = traced_script_module + self.model.to(device) + self.detect_layer.to(device) + print(" model is traced! \n") + + def forward(self, x, augment=False, profile=False): + out = self.model(x) + out = self.detect_layer(out) + return out \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/utils/wandb_logging/__init__.py b/cv/detection/yolov7/pytorch/utils/wandb_logging/__init__.py new file mode 100644 index 000000000..84952a816 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/wandb_logging/__init__.py @@ -0,0 +1 @@ +# init \ No newline at end of file diff --git a/cv/detection/yolov7/pytorch/utils/wandb_logging/log_dataset.py b/cv/detection/yolov7/pytorch/utils/wandb_logging/log_dataset.py new file mode 100644 index 000000000..74cd6c6cd --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/wandb_logging/log_dataset.py @@ -0,0 +1,24 @@ +import argparse + +import yaml + +from wandb_utils import WandbLogger + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') + parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') + parser.add_argument('--project', type=str, default='YOLOR', help='name of W&B Project') + opt = parser.parse_args() + opt.resume = False # Explicitly disallow resume check for dataset upload job + + create_dataset_artifact(opt) diff --git a/cv/detection/yolov7/pytorch/utils/wandb_logging/wandb_utils.py b/cv/detection/yolov7/pytorch/utils/wandb_logging/wandb_utils.py new file mode 100644 index 000000000..aec7c5f48 --- /dev/null +++ b/cv/detection/yolov7/pytorch/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,306 @@ +import json +import sys +from pathlib import Path + +import torch +import yaml +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path +from utils.datasets import LoadImagesAndLabels +from utils.datasets import img2label_paths +from utils.general import colorstr, xywh2xyxy, check_dataset + +try: + import wandb + from wandb import init, finish +except ImportError: + wandb = None + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): + return from_string[len(prefix):] + + +def check_wandb_config_file(data_config_file): + wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path + if Path(wandb_config).is_file(): + return wandb_config + return data_config_file + + +def get_run_info(run_path): + run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) + run_id = run_path.stem + project = run_path.parent.stem + model_artifact_name = 'run_' + run_id + '_model' + return run_id, project, model_artifact_name + + +def check_wandb_resume(opt): + process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None + if isinstance(opt.resume, str): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + if opt.global_rank not in [-1, 0]: # For resuming DDP runs + run_id, project, model_artifact_name = get_run_info(opt.resume) + api = wandb.Api() + artifact = api.artifact(project + '/' + model_artifact_name + ':latest') + modeldir = artifact.download() + opt.weights = str(Path(modeldir) / "last.pt") + return True + return None + + +def process_wandb_config_ddp_mode(opt): + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + train_dir, val_dir = None, None + if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) + train_dir = train_artifact.download() + train_path = Path(train_dir) / 'data/images/' + data_dict['train'] = str(train_path) + + if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) + val_dir = val_artifact.download() + val_path = Path(val_dir) / 'data/images/' + data_dict['val'] = str(val_path) + if train_dir or val_dir: + ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') + with open(ddp_data_path, 'w') as f: + yaml.dump(data_dict, f) + opt.data = ddp_data_path + + +class WandbLogger(): + def __init__(self, opt, name, run_id, data_dict, job_type='Training'): + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict + # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + if isinstance(opt.resume, str): # checks resume from artifact + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + run_id, project, model_artifact_name = get_run_info(opt.resume) + model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name + assert wandb, 'install wandb to resume wandb runs' + # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config + self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') + opt.resume = model_artifact_name + elif self.wandb: + self.wandb_run = wandb.init(config=opt, + resume="allow", + project='YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem, + name=name, + job_type=job_type, + id=run_id) if not wandb.run else wandb.run + if self.wandb_run: + if self.job_type == 'Training': + if not opt.resume: + wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict + # Info useful for resuming from artifacts + self.wandb_run.config.opt = vars(opt) + self.wandb_run.config.data_dict = wandb_data_dict + self.data_dict = self.setup_training(opt, data_dict) + if self.job_type == 'Dataset Creation': + self.data_dict = self.check_and_upload_dataset(opt) + else: + prefix = colorstr('wandb: ') + print(f"{prefix}Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)") + + def check_and_upload_dataset(self, opt): + assert wandb, 'Install wandb to upload dataset' + check_dataset(self.data_dict) + config_path = self.log_dataset_artifact(opt.data, + opt.single_cls, + 'YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem) + print("Created dataset config file ", config_path) + with open(config_path) as f: + wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) + return wandb_data_dict + + def setup_training(self, opt, data_dict): + self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + modeldir, _ = self.download_model_artifact(opt) + if modeldir: + self.weights = Path(modeldir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( + self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ + config.opt['hyp'] + data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume + if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download + self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), + opt.artifact_alias) + self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), + opt.artifact_alias) + self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None + if self.train_artifact_path is not None: + train_path = Path(self.train_artifact_path) / 'data/images/' + data_dict['train'] = str(train_path) + if self.val_artifact_path is not None: + val_path = Path(self.val_artifact_path) / 'data/images/' + data_dict['val'] = str(val_path) + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + if self.val_artifact is not None: + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + return data_dict + + def download_dataset_artifact(self, path, alias): + if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): + dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) + assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" + datadir = dataset_artifact.download() + return datadir, dataset_artifact + return None, None + + def download_model_artifact(self, opt): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") + assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' + modeldir = model_artifact.download() + epochs_trained = model_artifact.metadata.get('epochs_trained') + total_epochs = model_artifact.metadata.get('total_epochs') + assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( + total_epochs) + return modeldir, model_artifact + return None, None + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score + }) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + print("Saving model artifact on epoch ", epoch + 1) + + def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): + with open(data_file) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) + names = {k: v for k, v in enumerate(names)} # to index dictionary + self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['train']), names, name='train') if data.get('train') else None + self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['val']), names, name='val') if data.get('val') else None + if data.get('train'): + data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') + if data.get('val'): + data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') + path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path + data.pop('download', None) + with open(path, 'w') as f: + yaml.dump(data, f) + + if self.job_type == 'Training': # builds correct artifact pipeline graph + self.wandb_run.use_artifact(self.val_artifact) + self.wandb_run.use_artifact(self.train_artifact) + self.val_artifact.wait() + self.val_table = self.val_artifact.get('val') + self.map_val_table_path() + else: + self.wandb_run.log_artifact(self.train_artifact) + self.wandb_run.log_artifact(self.val_artifact) + return path + + def map_val_table_path(self): + self.val_table_map = {} + print("Mapping dataset") + for i, data in enumerate(tqdm(self.val_table.data)): + self.val_table_map[data[3]] = data[0] + + def create_dataset_table(self, dataset, class_to_id, name='dataset'): + # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging + artifact = wandb.Artifact(name=name, type="dataset") + img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None + img_files = tqdm(dataset.img_files) if not img_files else img_files + for img_file in img_files: + if Path(img_file).is_dir(): + artifact.add_dir(img_file, name='data/images') + labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) + artifact.add_dir(labels_path, name='data/labels') + else: + artifact.add_file(img_file, name='data/images/' + Path(img_file).name) + label_file = Path(img2label_paths([img_file])[0]) + artifact.add_file(str(label_file), + name='data/labels/' + label_file.name) if label_file.exists() else None + table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) + for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): + height, width = shapes[0] + labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) + box_data, img_classes = [], {} + for cls, *xyxy in labels[:, 1:].tolist(): + cls = int(cls) + box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": cls, + "box_caption": "%s" % (class_to_id[cls]), + "scores": {"acc": 1}, + "domain": "pixel"}) + img_classes[cls] = class_to_id[cls] + boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space + table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), + Path(paths).name) + artifact.add(table, name) + return artifact + + def log_training_progress(self, predn, path, names): + if self.val_table and self.result_table: + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) + box_data = [] + total_conf = 0 + for *xyxy, conf, cls in predn.tolist(): + if conf >= 0.25: + box_data.append( + {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"}) + total_conf = total_conf + conf + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + id = self.val_table_map[Path(path).name] + self.result_table.add_data(self.current_epoch, + id, + wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), + total_conf / max(1, len(box_data)) + ) + + def log(self, log_dict): + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self, best_result=False): + if self.wandb_run: + wandb.log(self.log_dict) + self.log_dict = {} + if self.result_artifact: + train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") + self.result_artifact.add(train_results, 'result') + wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), + ('best' if best_result else '')]) + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + + def finish_run(self): + if self.wandb_run: + if self.log_dict: + wandb.log(self.log_dict) + wandb.run.finish() diff --git a/cv/instance_segmentation/yolact/pytorch/.gitignore b/cv/instance_segmentation/yolact/pytorch/.gitignore new file mode 100644 index 000000000..204abfe7d --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/.gitignore @@ -0,0 +1,161 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# IPython Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# dotenv +.env + +# virtualenv +venv/ +ENV/ + +# Spyder project settings +.spyderproject + +# Rope project settings +.ropeproject + +# atom remote-sync package +.remote-sync.json + +# weights +weights/ + +#DS_Store +.DS_Store + +# dev stuff +eval/ +eval.ipynb +dev.ipynb +.vscode/ + +# not ready +videos/ +templates/ +data/ssd_dataloader.py +data/datasets/ +doc/visualize.py +read_results.py +ssd300_120000/ +demos/live +webdemo.py +test_data_aug.py + +# attributes + +# pycharm +.idea/ + +# temp checkout soln +data/coco +data/sbd +data/cityscapes + +# pylint +.pylintrc + +# ssd.pytorch master branch (for merging) +ssd.pytorch/ + +# some datasets +data/coco/ +data/VOCdevkit/ +data/coco/images/ +data/coco/annotations/ +ap_data.pkl +results/ +logs/ +scripts/aws/ +scripts/gt.npy +scripts/proto.npy +scripts/info.txt +test.pkl +testeval.py +scripts/aws2/ +status.sh +train.sh +img/ +scripts/aws-ohio/ +scripts/aws3/ +data/config_dev.py +data/coco/ +data/sbd/ + +vid/ +vidres/ + +_.py diff --git a/cv/instance_segmentation/yolact/pytorch/CHANGELOG.md b/cv/instance_segmentation/yolact/pytorch/CHANGELOG.md new file mode 100644 index 000000000..7621d711f --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/CHANGELOG.md @@ -0,0 +1,57 @@ +# YOLACT Change Log + +This document will detail all changes I make. +I don't know how I'm going to be versioning things yet, so you get dates for now. + +``` +2020.01.25: + - Fixed the mask IoU branch crashing when all masks in a batch are discarded (fixes #302, #259). +2020.01.24: + - Fixed the conv layer detection during initialization to work with pytorch 1.4 (fixes #292). +2020.01.23: + - Fixed the video playback crashing if there's nothing in the scene (fixes #266). + - Fixed the logger logging the last loss as total loss instead of the actual total (fixes #254). + +2019.12.16 (v1.2): + - Added YOLACT++ implementation, paper, and code. + - Added DCN support (need to compile CUDA kernels if you want to use them, see README). + - Added a mask rescoring network trained with mask iou. + - Added configs with more anchors. + +2019.12.06: + - Made training much more stable (no more infs and hopefully fewer loss explosions) by ignoring + augmented boxes with < 4px of height and width (this includes 0 area boxes which caused the inf). + See #222 for details. + +2019.11.20: + - Fixed bug where saving videos wouldn't work when using cv2 not compiled with display support (#197). + +2019.11.06: + - Changed Cython import to only active when using traditional nms. + - Added cross-class fast NMS. + +2019.11.04: + - Fixed a bug where the learning rate auto-scaling wasn't being applied properly. + - Fixed a logging bug were lr was sometimes not properly logged after a resume. + +2019.10.25 (v1.1): + - Added proper Multi-GPU support. Simply increase your batch size to 8*num_gpus and everything will scale. + - I get an ~1.8x training speed increase when using 2 gpus and an ~3x increase when using 4. + - Added a logger that logs everything about your training. + - Check the Logging section of the README to see how to visualize your logs. (Not written yet) + - Savevideo now uses the evalvideo framework and suports --video_multiframe. It's much faster now! + - Added the ability to display fps right on the videos themselves by using --display_fps + - Evalvideo now doesn't crash when it runs out of frames. + - Pascal SBD is now officially supported! Check the training section for more details. + - Preserve_aspect_ratio kinda sorta works now, but it's iffy and the way I have it set up doesn't perform better. + - Added a ton of new config settings, most of which don't improve performance :/ + +2019.09.20 + - Fixed a bug where custom label maps weren't being applied properly because of global default argument initialization. +2019.08.29 + - Fixed a bug where the fpn conv layers weren't getting initialized with xavier since they were being overwritten by jit modules (see #127). +2019.08.04 + - Improved the matching algorithm used to match anchors to gt by making it less greedy (see #104). +2019.06.27 + - Sped up save video by ~8 ms per frame because I forgot to apply a speed fix I applied to the other modes. +``` diff --git a/cv/instance_segmentation/yolact/pytorch/LICENSE b/cv/instance_segmentation/yolact/pytorch/LICENSE new file mode 100644 index 000000000..47a22a0cd --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 Daniel Bolya + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/cv/instance_segmentation/yolact/pytorch/README.md b/cv/instance_segmentation/yolact/pytorch/README.md new file mode 100644 index 000000000..2035ab075 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/README.md @@ -0,0 +1,99 @@ +# **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s +``` + ██╗ ██╗ ██████╗ ██╗ █████╗ ██████╗████████╗ + ╚██╗ ██╔╝██╔═══██╗██║ ██╔══██╗██╔════╝╚══██╔══╝ + ╚████╔╝ ██║ ██║██║ ███████║██║ ██║ + ╚██╔╝ ██║ ██║██║ ██╔══██║██║ ██║ + ██║ ╚██████╔╝███████╗██║ ██║╚██████╗ ██║ + ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ +``` + +## Step 1: Installing packages +``` +# Cython needs to be installed before pycocotools +pip3 install cython +pip3 install opencv-python pillow pycocotools matplotlib +``` + +If you want to use YOLACT++, compile deformable convolutional layers (from [DCNv2](https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0)). + ```Shell + cd external/DCNv2 + python3 setup.py build develop + ``` + +## Step 2: Preparing datasets +Download the [COCO Dataset](https://cocodataset.org/#home) + +Modify the configuration file(data/coco.yaml) +``` +$ vim data/config.py +$ # 'train_images': the path of train images +$ # 'train_info': the path of train_info +$ # 'valid_images': the path of valid images +$ # 'valid_info': the path of valid_info +``` + +# Training +By default, we train on COCO. Make sure to download the entire dataset using the commands above. + - To train, grab an imagenet-pretrained model and put it in `./weights`. + - For Resnet101, download `resnet101_reducedfc.pth` from [here](https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing). + - For Resnet50, download `resnet50-19c8e357.pth` from [here](https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing). + - For Darknet53, download `darknet53.pth` from [here](https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing). + - Run one of the training commands below. + - Note that you can press ctrl+c while training and it will save an `*_interrupt.pth` file at the current iteration. + - All weights are saved in the `./weights` directory by default with the file name `__.pth`. +```Shell +# Trains using the base config with a batch size of 8 (the default). +python3 train.py --config=yolact_base_config + +# Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly. +python3 train.py --config=yolact_base_config --batch_size=5 + +# Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file's name. +python3 train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1 + +# Use the help option to see a description of all available command line arguments +python3 train.py --help +``` + +## Multi-GPU Support +YOLACT now supports multiple GPUs seamlessly during training: + + - Before running any of the scripts, run: `export CUDA_VISIBLE_DEVICES=[gpus]` + - Where you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3). + - You should still do this if only using 1 GPU. + - You can check the indices of your GPUs with `nvidia-smi`. + - Then, simply set the batch size to `8*num_gpus` with the training commands above. The training script will automatically scale the hyperparameters to the right values. + - If you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you're using. + - If you want to allocate the images per GPU specific for different GPUs, you can use `--batch_alloc=[alloc]` where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to `batch_size`. + +## Images +```Shell +# Display qualitative results on the specified image. +python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png + +# Process an image and save it to another file. +python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png + +# Process a whole folder of images. +python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder +``` +## Video +```Shell +# Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance. +# If you want, use "--display_fps" to draw the FPS directly on the frame. +python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4 + +# Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0. +python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0 + +# Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast! +python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4 +``` +As you can tell, `eval.py` can do a ton of stuff. Run the `--help` command to see everything it can do. +```Shell +python3 eval.py --help +``` + +## Reference +https://github.com/dbolya/yolact \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/backbone.py b/cv/instance_segmentation/yolact/pytorch/backbone.py new file mode 100644 index 000000000..4df59d023 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/backbone.py @@ -0,0 +1,459 @@ +import torch +import torch.nn as nn +import pickle + +from collections import OrderedDict + +try: + from dcn_v2 import DCN +except ImportError: + def DCN(*args, **kwdargs): + raise Exception('DCN could not be imported. If you want to use YOLACT++ models, compile DCN. Check the README for instructions.') + +class Bottleneck(nn.Module): + """ Adapted from torchvision.models.resnet """ + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=nn.BatchNorm2d, dilation=1, use_dcn=False): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False, dilation=dilation) + self.bn1 = norm_layer(planes) + if use_dcn: + self.conv2 = DCN(planes, planes, kernel_size=3, stride=stride, + padding=dilation, dilation=dilation, deformable_groups=1) + self.conv2.bias.data.zero_() + self.conv2.conv_offset_mask.weight.data.zero_() + self.conv2.conv_offset_mask.bias.data.zero_() + else: + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=dilation, bias=False, dilation=dilation) + self.bn2 = norm_layer(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False, dilation=dilation) + self.bn3 = norm_layer(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNetBackbone(nn.Module): + """ Adapted from torchvision.models.resnet """ + + def __init__(self, layers, dcn_layers=[0, 0, 0, 0], dcn_interval=1, atrous_layers=[], block=Bottleneck, norm_layer=nn.BatchNorm2d): + super().__init__() + + # These will be populated by _make_layer + self.num_base_layers = len(layers) + self.layers = nn.ModuleList() + self.channels = [] + self.norm_layer = norm_layer + self.dilation = 1 + self.atrous_layers = atrous_layers + + # From torchvision.models.resnet.Resnet + self.inplanes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = norm_layer(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self._make_layer(block, 64, layers[0], dcn_layers=dcn_layers[0], dcn_interval=dcn_interval) + self._make_layer(block, 128, layers[1], stride=2, dcn_layers=dcn_layers[1], dcn_interval=dcn_interval) + self._make_layer(block, 256, layers[2], stride=2, dcn_layers=dcn_layers[2], dcn_interval=dcn_interval) + self._make_layer(block, 512, layers[3], stride=2, dcn_layers=dcn_layers[3], dcn_interval=dcn_interval) + + # This contains every module that should be initialized by loading in pretrained weights. + # Any extra layers added onto this that won't be initialized by init_backbone will not be + # in this list. That way, Yolact::init_weights knows which backbone weights to initialize + # with xavier, and which ones to leave alone. + self.backbone_modules = [m for m in self.modules() if isinstance(m, nn.Conv2d)] + + + def _make_layer(self, block, planes, blocks, stride=1, dcn_layers=0, dcn_interval=1): + """ Here one layer means a string of n Bottleneck blocks. """ + downsample = None + + # This is actually just to create the connection between layers, and not necessarily to + # downsample. Even if the second condition is met, it only downsamples when stride != 1 + if stride != 1 or self.inplanes != planes * block.expansion: + if len(self.layers) in self.atrous_layers: + self.dilation += 1 + stride = 1 + + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False, + dilation=self.dilation), + self.norm_layer(planes * block.expansion), + ) + + layers = [] + use_dcn = (dcn_layers >= blocks) + layers.append(block(self.inplanes, planes, stride, downsample, self.norm_layer, self.dilation, use_dcn=use_dcn)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + use_dcn = ((i+dcn_layers) >= blocks) and (i % dcn_interval == 0) + layers.append(block(self.inplanes, planes, norm_layer=self.norm_layer, use_dcn=use_dcn)) + layer = nn.Sequential(*layers) + + self.channels.append(planes * block.expansion) + self.layers.append(layer) + + return layer + + def forward(self, x): + """ Returns a list of convouts for each layer. """ + + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + outs = [] + for layer in self.layers: + x = layer(x) + outs.append(x) + + return tuple(outs) + + def init_backbone(self, path): + """ Initializes the backbone weights for training. """ + state_dict = torch.load(path) + + # Replace layer1 -> layers.0 etc. + keys = list(state_dict) + for key in keys: + if key.startswith('layer'): + idx = int(key[5]) + new_key = 'layers.' + str(idx-1) + key[6:] + state_dict[new_key] = state_dict.pop(key) + + # Note: Using strict=False is berry scary. Triple check this. + self.load_state_dict(state_dict, strict=False) + + def add_layer(self, conv_channels=1024, downsample=2, depth=1, block=Bottleneck): + """ Add a downsample layer to the backbone as per what SSD does. """ + self._make_layer(block, conv_channels // block.expansion, blocks=depth, stride=downsample) + + + + +class ResNetBackboneGN(ResNetBackbone): + + def __init__(self, layers, num_groups=32): + super().__init__(layers, norm_layer=lambda x: nn.GroupNorm(num_groups, x)) + + def init_backbone(self, path): + """ The path here comes from detectron. So we load it differently. """ + with open(path, 'rb') as f: + state_dict = pickle.load(f, encoding='latin1') # From the detectron source + state_dict = state_dict['blobs'] + + our_state_dict_keys = list(self.state_dict().keys()) + new_state_dict = {} + + gn_trans = lambda x: ('gn_s' if x == 'weight' else 'gn_b') + layeridx2res = lambda x: 'res' + str(int(x)+2) + block2branch = lambda x: 'branch2' + ('a', 'b', 'c')[int(x[-1:])-1] + + # Transcribe each Detectron weights name to a Yolact weights name + for key in our_state_dict_keys: + parts = key.split('.') + transcribed_key = '' + + if (parts[0] == 'conv1'): + transcribed_key = 'conv1_w' + elif (parts[0] == 'bn1'): + transcribed_key = 'conv1_' + gn_trans(parts[1]) + elif (parts[0] == 'layers'): + if int(parts[1]) >= self.num_base_layers: continue + + transcribed_key = layeridx2res(parts[1]) + transcribed_key += '_' + parts[2] + '_' + + if parts[3] == 'downsample': + transcribed_key += 'branch1_' + + if parts[4] == '0': + transcribed_key += 'w' + else: + transcribed_key += gn_trans(parts[5]) + else: + transcribed_key += block2branch(parts[3]) + '_' + + if 'conv' in parts[3]: + transcribed_key += 'w' + else: + transcribed_key += gn_trans(parts[4]) + + new_state_dict[key] = torch.Tensor(state_dict[transcribed_key]) + + # strict=False because we may have extra unitialized layers at this point + self.load_state_dict(new_state_dict, strict=False) + + + + + + + +def darknetconvlayer(in_channels, out_channels, *args, **kwdargs): + """ + Implements a conv, activation, then batch norm. + Arguments are passed into the conv layer. + """ + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, *args, **kwdargs, bias=False), + nn.BatchNorm2d(out_channels), + # Darknet uses 0.1 here. + # See https://github.com/pjreddie/darknet/blob/680d3bde1924c8ee2d1c1dea54d3e56a05ca9a26/src/activations.h#L39 + nn.LeakyReLU(0.1, inplace=True) + ) + +class DarkNetBlock(nn.Module): + """ Note: channels is the lesser of the two. The output will be expansion * channels. """ + + expansion = 2 + + def __init__(self, in_channels, channels): + super().__init__() + + self.conv1 = darknetconvlayer(in_channels, channels, kernel_size=1) + self.conv2 = darknetconvlayer(channels, channels * self.expansion, kernel_size=3, padding=1) + + def forward(self, x): + return self.conv2(self.conv1(x)) + x + + + + +class DarkNetBackbone(nn.Module): + """ + An implementation of YOLOv3's Darnet53 in + https://pjreddie.com/media/files/papers/YOLOv3.pdf + + This is based off of the implementation of Resnet above. + """ + + def __init__(self, layers=[1, 2, 8, 8, 4], block=DarkNetBlock): + super().__init__() + + # These will be populated by _make_layer + self.num_base_layers = len(layers) + self.layers = nn.ModuleList() + self.channels = [] + + self._preconv = darknetconvlayer(3, 32, kernel_size=3, padding=1) + self.in_channels = 32 + + self._make_layer(block, 32, layers[0]) + self._make_layer(block, 64, layers[1]) + self._make_layer(block, 128, layers[2]) + self._make_layer(block, 256, layers[3]) + self._make_layer(block, 512, layers[4]) + + # This contains every module that should be initialized by loading in pretrained weights. + # Any extra layers added onto this that won't be initialized by init_backbone will not be + # in this list. That way, Yolact::init_weights knows which backbone weights to initialize + # with xavier, and which ones to leave alone. + self.backbone_modules = [m for m in self.modules() if isinstance(m, nn.Conv2d)] + + def _make_layer(self, block, channels, num_blocks, stride=2): + """ Here one layer means a string of n blocks. """ + layer_list = [] + + # The downsample layer + layer_list.append( + darknetconvlayer(self.in_channels, channels * block.expansion, + kernel_size=3, padding=1, stride=stride)) + + # Each block inputs channels and outputs channels * expansion + self.in_channels = channels * block.expansion + layer_list += [block(self.in_channels, channels) for _ in range(num_blocks)] + + self.channels.append(self.in_channels) + self.layers.append(nn.Sequential(*layer_list)) + + def forward(self, x): + """ Returns a list of convouts for each layer. """ + + x = self._preconv(x) + + outs = [] + for layer in self.layers: + x = layer(x) + outs.append(x) + + return tuple(outs) + + def add_layer(self, conv_channels=1024, stride=2, depth=1, block=DarkNetBlock): + """ Add a downsample layer to the backbone as per what SSD does. """ + self._make_layer(block, conv_channels // block.expansion, num_blocks=depth, stride=stride) + + def init_backbone(self, path): + """ Initializes the backbone weights for training. """ + # Note: Using strict=False is berry scary. Triple check this. + self.load_state_dict(torch.load(path), strict=False) + + + + + +class VGGBackbone(nn.Module): + """ + Args: + - cfg: A list of layers given as lists. Layers can be either 'M' signifying + a max pooling layer, a number signifying that many feature maps in + a conv layer, or a tuple of 'M' or a number and a kwdargs dict to pass + into the function that creates the layer (e.g. nn.MaxPool2d for 'M'). + - extra_args: A list of lists of arguments to pass into add_layer. + - norm_layers: Layers indices that need to pass through an l2norm layer. + """ + + def __init__(self, cfg, extra_args=[], norm_layers=[]): + super().__init__() + + self.channels = [] + self.layers = nn.ModuleList() + self.in_channels = 3 + self.extra_args = list(reversed(extra_args)) # So I can use it as a stack + + # Keeps track of what the corresponding key will be in the state dict of the + # pretrained model. For instance, layers.0.2 for us is 2 for the pretrained + # model but layers.1.1 is 5. + self.total_layer_count = 0 + self.state_dict_lookup = {} + + for idx, layer_cfg in enumerate(cfg): + self._make_layer(layer_cfg) + + self.norms = nn.ModuleList([nn.BatchNorm2d(self.channels[l]) for l in norm_layers]) + self.norm_lookup = {l: idx for idx, l in enumerate(norm_layers)} + + # These modules will be initialized by init_backbone, + # so don't overwrite their initialization later. + self.backbone_modules = [m for m in self.modules() if isinstance(m, nn.Conv2d)] + + def _make_layer(self, cfg): + """ + Each layer is a sequence of conv layers usually preceded by a max pooling. + Adapted from torchvision.models.vgg.make_layers. + """ + + layers = [] + + for v in cfg: + # VGG in SSD requires some special layers, so allow layers to be tuples of + # (, kwdargs dict) + args = None + if isinstance(v, tuple): + args = v[1] + v = v[0] + + # v should be either M or a number + if v == 'M': + # Set default arguments + if args is None: + args = {'kernel_size': 2, 'stride': 2} + + layers.append(nn.MaxPool2d(**args)) + else: + # See the comment in __init__ for an explanation of this + cur_layer_idx = self.total_layer_count + len(layers) + self.state_dict_lookup[cur_layer_idx] = '%d.%d' % (len(self.layers), len(layers)) + + # Set default arguments + if args is None: + args = {'kernel_size': 3, 'padding': 1} + + # Add the layers + layers.append(nn.Conv2d(self.in_channels, v, **args)) + layers.append(nn.ReLU(inplace=True)) + self.in_channels = v + + self.total_layer_count += len(layers) + self.channels.append(self.in_channels) + self.layers.append(nn.Sequential(*layers)) + + def forward(self, x): + """ Returns a list of convouts for each layer. """ + outs = [] + + for idx, layer in enumerate(self.layers): + x = layer(x) + + # Apply an l2norm module to the selected layers + # Note that this differs from the original implemenetation + if idx in self.norm_lookup: + x = self.norms[self.norm_lookup[idx]](x) + outs.append(x) + + return tuple(outs) + + def transform_key(self, k): + """ Transform e.g. features.24.bias to layers.4.1.bias """ + vals = k.split('.') + layerIdx = self.state_dict_lookup[int(vals[0])] + return 'layers.%s.%s' % (layerIdx, vals[1]) + + def init_backbone(self, path): + """ Initializes the backbone weights for training. """ + state_dict = torch.load(path) + state_dict = OrderedDict([(self.transform_key(k), v) for k,v in state_dict.items()]) + + self.load_state_dict(state_dict, strict=False) + + def add_layer(self, conv_channels=128, downsample=2): + """ Add a downsample layer to the backbone as per what SSD does. """ + if len(self.extra_args) > 0: + conv_channels, downsample = self.extra_args.pop() + + padding = 1 if downsample > 1 else 0 + + layer = nn.Sequential( + nn.Conv2d(self.in_channels, conv_channels, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(conv_channels, conv_channels*2, kernel_size=3, stride=downsample, padding=padding), + nn.ReLU(inplace=True) + ) + + self.in_channels = conv_channels*2 + self.channels.append(self.in_channels) + self.layers.append(layer) + + + + +def construct_backbone(cfg): + """ Constructs a backbone given a backbone config object (see config.py). """ + backbone = cfg.type(*cfg.args) + + # Add downsampling layers until we reach the number we need + num_layers = max(cfg.selected_layers) + 1 + + while len(backbone.layers) < num_layers: + backbone.add_layer() + + return backbone diff --git a/cv/instance_segmentation/yolact/pytorch/data/__init__.py b/cv/instance_segmentation/yolact/pytorch/data/__init__.py new file mode 100644 index 000000000..67c0f019a --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/data/__init__.py @@ -0,0 +1,6 @@ +from .config import * +from .coco import * + +import torch +import cv2 +import numpy as np diff --git a/cv/instance_segmentation/yolact/pytorch/data/coco.py b/cv/instance_segmentation/yolact/pytorch/data/coco.py new file mode 100644 index 000000000..f182c55bd --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/data/coco.py @@ -0,0 +1,284 @@ +import os +import os.path as osp +import sys +import torch +import torch.utils.data as data +import torch.nn.functional as F +import cv2 +import numpy as np +from .config import cfg +from pycocotools import mask as maskUtils +import random + +def get_label_map(): + if cfg.dataset.label_map is None: + return {x+1: x+1 for x in range(len(cfg.dataset.class_names))} + else: + return cfg.dataset.label_map + +class COCOAnnotationTransform(object): + """Transforms a COCO annotation into a Tensor of bbox coords and label index + Initilized with a dictionary lookup of classnames to indexes + """ + def __init__(self): + self.label_map = get_label_map() + + def __call__(self, target, width, height): + """ + Args: + target (dict): COCO target json annotation as a python dict + height (int): height + width (int): width + Returns: + a list containing lists of bounding boxes [bbox coords, class idx] + """ + scale = np.array([width, height, width, height]) + res = [] + for obj in target: + if 'bbox' in obj: + bbox = obj['bbox'] + label_idx = obj['category_id'] + if label_idx >= 0: + label_idx = self.label_map[label_idx] - 1 + final_box = list(np.array([bbox[0], bbox[1], bbox[0]+bbox[2], bbox[1]+bbox[3]])/scale) + final_box.append(label_idx) + res += [final_box] # [xmin, ymin, xmax, ymax, label_idx] + else: + print("No bbox found for object ", obj) + + return res + + +class COCODetection(data.Dataset): + """`MS Coco Detection `_ Dataset. + Args: + root (string): Root directory where images are downloaded to. + set_name (string): Name of the specific set of COCO images. + transform (callable, optional): A function/transform that augments the + raw images` + target_transform (callable, optional): A function/transform that takes + in the target (bbox) and transforms it. + prep_crowds (bool): Whether or not to prepare crowds for the evaluation step. + """ + + def __init__(self, image_path, info_file, transform=None, + target_transform=None, + dataset_name='MS COCO', has_gt=True): + # Do this here because we have too many things named COCO + from pycocotools.coco import COCO + + if target_transform is None: + target_transform = COCOAnnotationTransform() + + self.root = image_path + self.coco = COCO(info_file) + + self.ids = list(self.coco.imgToAnns.keys()) + if len(self.ids) == 0 or not has_gt: + self.ids = list(self.coco.imgs.keys()) + + self.transform = transform + self.target_transform = COCOAnnotationTransform() + + self.name = dataset_name + self.has_gt = has_gt + + def __getitem__(self, index): + """ + Args: + index (int): Index + Returns: + tuple: Tuple (image, (target, masks, num_crowds)). + target is the object returned by ``coco.loadAnns``. + """ + im, gt, masks, h, w, num_crowds = self.pull_item(index) + return im, (gt, masks, num_crowds) + + def __len__(self): + return len(self.ids) + + def pull_item(self, index): + """ + Args: + index (int): Index + Returns: + tuple: Tuple (image, target, masks, height, width, crowd). + target is the object returned by ``coco.loadAnns``. + Note that if no crowd annotations exist, crowd will be None + """ + img_id = self.ids[index] + + if self.has_gt: + ann_ids = self.coco.getAnnIds(imgIds=img_id) + + # Target has {'segmentation', 'area', iscrowd', 'image_id', 'bbox', 'category_id'} + target = [x for x in self.coco.loadAnns(ann_ids) if x['image_id'] == img_id] + else: + target = [] + + # Separate out crowd annotations. These are annotations that signify a large crowd of + # objects of said class, where there is no annotation for each individual object. Both + # during testing and training, consider these crowds as neutral. + crowd = [x for x in target if ('iscrowd' in x and x['iscrowd'])] + target = [x for x in target if not ('iscrowd' in x and x['iscrowd'])] + num_crowds = len(crowd) + + for x in crowd: + x['category_id'] = -1 + + # This is so we ensure that all crowd annotations are at the end of the array + target += crowd + + # The split here is to have compatibility with both COCO2014 and 2017 annotations. + # In 2014, images have the pattern COCO_{train/val}2014_%012d.jpg, while in 2017 it's %012d.jpg. + # Our script downloads the images as %012d.jpg so convert accordingly. + file_name = self.coco.loadImgs(img_id)[0]['file_name'] + + if file_name.startswith('COCO'): + file_name = file_name.split('_')[-1] + + path = osp.join(self.root, file_name) + assert osp.exists(path), 'Image path does not exist: {}'.format(path) + + img = cv2.imread(path) + height, width, _ = img.shape + + if len(target) > 0: + # Pool all the masks for this image into one [num_objects,height,width] matrix + masks = [self.coco.annToMask(obj).reshape(-1) for obj in target] + masks = np.vstack(masks) + masks = masks.reshape(-1, height, width) + + if self.target_transform is not None and len(target) > 0: + target = self.target_transform(target, width, height) + + if self.transform is not None: + if len(target) > 0: + target = np.array(target) + img, masks, boxes, labels = self.transform(img, masks, target[:, :4], + {'num_crowds': num_crowds, 'labels': target[:, 4]}) + + # I stored num_crowds in labels so I didn't have to modify the entirety of augmentations + num_crowds = labels['num_crowds'] + labels = labels['labels'] + + target = np.hstack((boxes, np.expand_dims(labels, axis=1))) + else: + img, _, _, _ = self.transform(img, np.zeros((1, height, width), dtype=np.float), np.array([[0, 0, 1, 1]]), + {'num_crowds': 0, 'labels': np.array([0])}) + masks = None + target = None + + if target.shape[0] == 0: + print('Warning: Augmentation output an example with no ground truth. Resampling...') + return self.pull_item(random.randint(0, len(self.ids)-1)) + + return torch.from_numpy(img).permute(2, 0, 1), target, masks, height, width, num_crowds + + def pull_image(self, index): + '''Returns the original image object at index in PIL form + + Note: not using self.__getitem__(), as any transformations passed in + could mess up this functionality. + + Argument: + index (int): index of img to show + Return: + cv2 img + ''' + img_id = self.ids[index] + path = self.coco.loadImgs(img_id)[0]['file_name'] + return cv2.imread(osp.join(self.root, path), cv2.IMREAD_COLOR) + + def pull_anno(self, index): + '''Returns the original annotation of image at index + + Note: not using self.__getitem__(), as any transformations passed in + could mess up this functionality. + + Argument: + index (int): index of img to get annotation of + Return: + list: [img_id, [(label, bbox coords),...]] + eg: ('001718', [('dog', (96, 13, 438, 332))]) + ''' + img_id = self.ids[index] + ann_ids = self.coco.getAnnIds(imgIds=img_id) + return self.coco.loadAnns(ann_ids) + + def __repr__(self): + fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' + fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) + fmt_str += ' Root Location: {}\n'.format(self.root) + tmp = ' Transforms (if any): ' + fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) + tmp = ' Target Transforms (if any): ' + fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) + return fmt_str + +def enforce_size(img, targets, masks, num_crowds, new_w, new_h): + """ Ensures that the image is the given size without distorting aspect ratio. """ + with torch.no_grad(): + _, h, w = img.size() + + if h == new_h and w == new_w: + return img, targets, masks, num_crowds + + # Resize the image so that it fits within new_w, new_h + w_prime = new_w + h_prime = h * new_w / w + + if h_prime > new_h: + w_prime *= new_h / h_prime + h_prime = new_h + + w_prime = int(w_prime) + h_prime = int(h_prime) + + # Do all the resizing + img = F.interpolate(img.unsqueeze(0), (h_prime, w_prime), mode='bilinear', align_corners=False) + img.squeeze_(0) + + # Act like each object is a color channel + masks = F.interpolate(masks.unsqueeze(0), (h_prime, w_prime), mode='bilinear', align_corners=False) + masks.squeeze_(0) + + # Scale bounding boxes (this will put them in the top left corner in the case of padding) + targets[:, [0, 2]] *= (w_prime / new_w) + targets[:, [1, 3]] *= (h_prime / new_h) + + # Finally, pad everything to be the new_w, new_h + pad_dims = (0, new_w - w_prime, 0, new_h - h_prime) + img = F.pad( img, pad_dims, mode='constant', value=0) + masks = F.pad(masks, pad_dims, mode='constant', value=0) + + return img, targets, masks, num_crowds + + + + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and (lists of annotations, masks) + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list, list, list) annotations for a given image are stacked + on 0 dim. The output gt is a tuple of annotations and masks. + """ + targets = [] + imgs = [] + masks = [] + num_crowds = [] + + for sample in batch: + imgs.append(sample[0]) + targets.append(torch.FloatTensor(sample[1][0])) + masks.append(torch.FloatTensor(sample[1][1])) + num_crowds.append(sample[1][2]) + + return imgs, (targets, masks, num_crowds) diff --git a/cv/instance_segmentation/yolact/pytorch/data/config.py b/cv/instance_segmentation/yolact/pytorch/data/config.py new file mode 100644 index 000000000..24dba3501 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/data/config.py @@ -0,0 +1,826 @@ +from backbone import ResNetBackbone, VGGBackbone, ResNetBackboneGN, DarkNetBackbone +from math import sqrt +import torch + +# for making bounding boxes pretty +COLORS = ((244, 67, 54), + (233, 30, 99), + (156, 39, 176), + (103, 58, 183), + ( 63, 81, 181), + ( 33, 150, 243), + ( 3, 169, 244), + ( 0, 188, 212), + ( 0, 150, 136), + ( 76, 175, 80), + (139, 195, 74), + (205, 220, 57), + (255, 235, 59), + (255, 193, 7), + (255, 152, 0), + (255, 87, 34), + (121, 85, 72), + (158, 158, 158), + ( 96, 125, 139)) + + +# These are in BGR and are for ImageNet +MEANS = (103.94, 116.78, 123.68) +STD = (57.38, 57.12, 58.40) + +COCO_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', + 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', + 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', + 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', + 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', + 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', + 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', + 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', + 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', + 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', + 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', + 'scissors', 'teddy bear', 'hair drier', 'toothbrush') + +COCO_LABEL_MAP = { 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, + 9: 9, 10: 10, 11: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16, + 18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24, + 27: 25, 28: 26, 31: 27, 32: 28, 33: 29, 34: 30, 35: 31, 36: 32, + 37: 33, 38: 34, 39: 35, 40: 36, 41: 37, 42: 38, 43: 39, 44: 40, + 46: 41, 47: 42, 48: 43, 49: 44, 50: 45, 51: 46, 52: 47, 53: 48, + 54: 49, 55: 50, 56: 51, 57: 52, 58: 53, 59: 54, 60: 55, 61: 56, + 62: 57, 63: 58, 64: 59, 65: 60, 67: 61, 70: 62, 72: 63, 73: 64, + 74: 65, 75: 66, 76: 67, 77: 68, 78: 69, 79: 70, 80: 71, 81: 72, + 82: 73, 84: 74, 85: 75, 86: 76, 87: 77, 88: 78, 89: 79, 90: 80} + + + +# ----------------------- CONFIG CLASS ----------------------- # + +class Config(object): + """ + Holds the configuration for anything you want it to. + To get the currently active config, call get_cfg(). + + To use, just do cfg.x instead of cfg['x']. + I made this because doing cfg['x'] all the time is dumb. + """ + + def __init__(self, config_dict): + for key, val in config_dict.items(): + self.__setattr__(key, val) + + def copy(self, new_config_dict={}): + """ + Copies this config into a new config object, making + the changes given by new_config_dict. + """ + + ret = Config(vars(self)) + + for key, val in new_config_dict.items(): + ret.__setattr__(key, val) + + return ret + + def replace(self, new_config_dict): + """ + Copies new_config_dict into this config object. + Note: new_config_dict can also be a config object. + """ + if isinstance(new_config_dict, Config): + new_config_dict = vars(new_config_dict) + + for key, val in new_config_dict.items(): + self.__setattr__(key, val) + + def print(self): + for k, v in vars(self).items(): + print(k, ' = ', v) + + + + + +# ----------------------- DATASETS ----------------------- # + +dataset_base = Config({ + 'name': 'Base Dataset', + + # Training images and annotations + 'train_images': './data/coco/images/train2017/', + 'train_info': 'path_to_annotation_file', + + # Validation images and annotations. + 'valid_images': './data/coco/images/val2017', + 'valid_info': 'path_to_annotation_file', + + # Whether or not to load GT. If this is False, eval.py quantitative evaluation won't work. + 'has_gt': True, + + # A list of names for each of you classes. + 'class_names': COCO_CLASSES, + + # COCO class ids aren't sequential, so this is a bandage fix. If your ids aren't sequential, + # provide a map from category_id -> index in class_names + 1 (the +1 is there because it's 1-indexed). + # If not specified, this just assumes category ids start at 1 and increase sequentially. + 'label_map': None +}) + +coco2014_dataset = dataset_base.copy({ + 'name': 'COCO 2014', + + 'train_info': './data/coco/annotations/instances_train2014.json', + 'valid_info': './data/coco/annotations/instances_val2014.json', + + 'label_map': COCO_LABEL_MAP +}) + +coco2017_dataset = dataset_base.copy({ + 'name': 'COCO 2017', + + 'train_info': './data/coco/annotations/instances_train2017.json', + 'valid_info': './data/coco/annotations/instances_val2017.json', + + 'label_map': COCO_LABEL_MAP +}) + +coco2017_testdev_dataset = dataset_base.copy({ + 'name': 'COCO 2017 Test-Dev', + + 'valid_info': './data/coco/annotations/image_info_test-dev2017.json', + 'has_gt': False, + + 'label_map': COCO_LABEL_MAP +}) + +PASCAL_CLASSES = ("aeroplane", "bicycle", "bird", "boat", "bottle", + "bus", "car", "cat", "chair", "cow", "diningtable", + "dog", "horse", "motorbike", "person", "pottedplant", + "sheep", "sofa", "train", "tvmonitor") + +pascal_sbd_dataset = dataset_base.copy({ + 'name': 'Pascal SBD 2012', + + 'train_images': './data/sbd/img', + 'valid_images': './data/sbd/img', + + 'train_info': './data/sbd/pascal_sbd_train.json', + 'valid_info': './data/sbd/pascal_sbd_val.json', + + 'class_names': PASCAL_CLASSES, +}) + + + + + +# ----------------------- TRANSFORMS ----------------------- # + +resnet_transform = Config({ + 'channel_order': 'RGB', + 'normalize': True, + 'subtract_means': False, + 'to_float': False, +}) + +vgg_transform = Config({ + # Note that though vgg is traditionally BGR, + # the channel order of vgg_reducedfc.pth is RGB. + 'channel_order': 'RGB', + 'normalize': False, + 'subtract_means': True, + 'to_float': False, +}) + +darknet_transform = Config({ + 'channel_order': 'RGB', + 'normalize': False, + 'subtract_means': False, + 'to_float': True, +}) + + + + + +# ----------------------- BACKBONES ----------------------- # + +backbone_base = Config({ + 'name': 'Base Backbone', + 'path': 'path/to/pretrained/weights', + 'type': object, + 'args': tuple(), + 'transform': resnet_transform, + + 'selected_layers': list(), + 'pred_scales': list(), + 'pred_aspect_ratios': list(), + + 'use_pixel_scales': False, + 'preapply_sqrt': True, + 'use_square_anchors': False, +}) + +resnet101_backbone = backbone_base.copy({ + 'name': 'ResNet101', + 'path': 'resnet101_reducedfc.pth', + 'type': ResNetBackbone, + 'args': ([3, 4, 23, 3],), + 'transform': resnet_transform, + + 'selected_layers': list(range(2, 8)), + 'pred_scales': [[1]]*6, + 'pred_aspect_ratios': [ [[0.66685089, 1.7073535, 0.87508774, 1.16524493, 0.49059086]] ] * 6, +}) + +resnet101_gn_backbone = backbone_base.copy({ + 'name': 'ResNet101_GN', + 'path': 'R-101-GN.pkl', + 'type': ResNetBackboneGN, + 'args': ([3, 4, 23, 3],), + 'transform': resnet_transform, + + 'selected_layers': list(range(2, 8)), + 'pred_scales': [[1]]*6, + 'pred_aspect_ratios': [ [[0.66685089, 1.7073535, 0.87508774, 1.16524493, 0.49059086]] ] * 6, +}) + +resnet101_dcn_inter3_backbone = resnet101_backbone.copy({ + 'name': 'ResNet101_DCN_Interval3', + 'args': ([3, 4, 23, 3], [0, 4, 23, 3], 3), +}) + +resnet50_backbone = resnet101_backbone.copy({ + 'name': 'ResNet50', + 'path': 'resnet50-19c8e357.pth', + 'type': ResNetBackbone, + 'args': ([3, 4, 6, 3],), + 'transform': resnet_transform, +}) + +resnet50_dcnv2_backbone = resnet50_backbone.copy({ + 'name': 'ResNet50_DCNv2', + 'args': ([3, 4, 6, 3], [0, 4, 6, 3]), +}) + +darknet53_backbone = backbone_base.copy({ + 'name': 'DarkNet53', + 'path': 'darknet53.pth', + 'type': DarkNetBackbone, + 'args': ([1, 2, 8, 8, 4],), + 'transform': darknet_transform, + + 'selected_layers': list(range(3, 9)), + 'pred_scales': [[3.5, 4.95], [3.6, 4.90], [3.3, 4.02], [2.7, 3.10], [2.1, 2.37], [1.8, 1.92]], + 'pred_aspect_ratios': [ [[1, sqrt(2), 1/sqrt(2), sqrt(3), 1/sqrt(3)][:n], [1]] for n in [3, 5, 5, 5, 3, 3] ], +}) + +vgg16_arch = [[64, 64], + [ 'M', 128, 128], + [ 'M', 256, 256, 256], + [('M', {'kernel_size': 2, 'stride': 2, 'ceil_mode': True}), 512, 512, 512], + [ 'M', 512, 512, 512], + [('M', {'kernel_size': 3, 'stride': 1, 'padding': 1}), + (1024, {'kernel_size': 3, 'padding': 6, 'dilation': 6}), + (1024, {'kernel_size': 1})]] + +vgg16_backbone = backbone_base.copy({ + 'name': 'VGG16', + 'path': 'vgg16_reducedfc.pth', + 'type': VGGBackbone, + 'args': (vgg16_arch, [(256, 2), (128, 2), (128, 1), (128, 1)], [3]), + 'transform': vgg_transform, + + 'selected_layers': [3] + list(range(5, 10)), + 'pred_scales': [[5, 4]]*6, + 'pred_aspect_ratios': [ [[1], [1, sqrt(2), 1/sqrt(2), sqrt(3), 1/sqrt(3)][:n]] for n in [3, 5, 5, 5, 3, 3] ], +}) + + + + + +# ----------------------- MASK BRANCH TYPES ----------------------- # + +mask_type = Config({ + # Direct produces masks directly as the output of each pred module. + # This is denoted as fc-mask in the paper. + # Parameters: mask_size, use_gt_bboxes + 'direct': 0, + + # Lincomb produces coefficients as the output of each pred module then uses those coefficients + # to linearly combine features from a prototype network to create image-sized masks. + # Parameters: + # - masks_to_train (int): Since we're producing (near) full image masks, it'd take too much + # vram to backprop on every single mask. Thus we select only a subset. + # - mask_proto_src (int): The input layer to the mask prototype generation network. This is an + # index in backbone.layers. Use to use the image itself instead. + # - mask_proto_net (list): A list of layers in the mask proto network with the last one + # being where the masks are taken from. Each conv layer is in + # the form (num_features, kernel_size, **kwdargs). An empty + # list means to use the source for prototype masks. If the + # kernel_size is negative, this creates a deconv layer instead. + # If the kernel_size is negative and the num_features is None, + # this creates a simple bilinear interpolation layer instead. + # - mask_proto_bias (bool): Whether to include an extra coefficient that corresponds to a proto + # mask of all ones. + # - mask_proto_prototype_activation (func): The activation to apply to each prototype mask. + # - mask_proto_mask_activation (func): After summing the prototype masks with the predicted + # coeffs, what activation to apply to the final mask. + # - mask_proto_coeff_activation (func): The activation to apply to the mask coefficients. + # - mask_proto_crop (bool): If True, crop the mask with the predicted bbox during training. + # - mask_proto_crop_expand (float): If cropping, the percent to expand the cropping bbox by + # in each direction. This is to make the model less reliant + # on perfect bbox predictions. + # - mask_proto_loss (str [l1|disj]): If not None, apply an l1 or disjunctive regularization + # loss directly to the prototype masks. + # - mask_proto_binarize_downsampled_gt (bool): Binarize GT after dowsnampling during training? + # - mask_proto_normalize_mask_loss_by_sqrt_area (bool): Whether to normalize mask loss by sqrt(sum(gt)) + # - mask_proto_reweight_mask_loss (bool): Reweight mask loss such that background is divided by + # #background and foreground is divided by #foreground. + # - mask_proto_grid_file (str): The path to the grid file to use with the next option. + # This should be a numpy.dump file with shape [numgrids, h, w] + # where h and w are w.r.t. the mask_proto_src convout. + # - mask_proto_use_grid (bool): Whether to add extra grid features to the proto_net input. + # - mask_proto_coeff_gate (bool): Add an extra set of sigmoided coefficients that is multiplied + # into the predicted coefficients in order to "gate" them. + # - mask_proto_prototypes_as_features (bool): For each prediction module, downsample the prototypes + # to the convout size of that module and supply the prototypes as input + # in addition to the already supplied backbone features. + # - mask_proto_prototypes_as_features_no_grad (bool): If the above is set, don't backprop gradients to + # to the prototypes from the network head. + # - mask_proto_remove_empty_masks (bool): Remove masks that are downsampled to 0 during loss calculations. + # - mask_proto_reweight_coeff (float): The coefficient to multiple the forground pixels with if reweighting. + # - mask_proto_coeff_diversity_loss (bool): Apply coefficient diversity loss on the coefficients so that the same + # instance has similar coefficients. + # - mask_proto_coeff_diversity_alpha (float): The weight to use for the coefficient diversity loss. + # - mask_proto_normalize_emulate_roi_pooling (bool): Normalize the mask loss to emulate roi pooling's affect on loss. + # - mask_proto_double_loss (bool): Whether to use the old loss in addition to any special new losses. + # - mask_proto_double_loss_alpha (float): The alpha to weight the above loss. + # - mask_proto_split_prototypes_by_head (bool): If true, this will give each prediction head its own prototypes. + # - mask_proto_crop_with_pred_box (bool): Whether to crop with the predicted box or the gt box. + 'lincomb': 1, +}) + + + + + +# ----------------------- ACTIVATION FUNCTIONS ----------------------- # + +activation_func = Config({ + 'tanh': torch.tanh, + 'sigmoid': torch.sigmoid, + 'softmax': lambda x: torch.nn.functional.softmax(x, dim=-1), + 'relu': lambda x: torch.nn.functional.relu(x, inplace=True), + 'none': lambda x: x, +}) + + + + + +# ----------------------- FPN DEFAULTS ----------------------- # + +fpn_base = Config({ + # The number of features to have in each FPN layer + 'num_features': 256, + + # The upsampling mode used + 'interpolation_mode': 'bilinear', + + # The number of extra layers to be produced by downsampling starting at P5 + 'num_downsample': 1, + + # Whether to down sample with a 3x3 stride 2 conv layer instead of just a stride 2 selection + 'use_conv_downsample': False, + + # Whether to pad the pred layers with 1 on each side (I forgot to add this at the start) + # This is just here for backwards compatibility + 'pad': True, + + # Whether to add relu to the downsampled layers. + 'relu_downsample_layers': False, + + # Whether to add relu to the regular layers + 'relu_pred_layers': True, +}) + + + + + +# ----------------------- CONFIG DEFAULTS ----------------------- # + +coco_base_config = Config({ + 'dataset': coco2014_dataset, + 'num_classes': 81, # This should include the background class + + 'max_iter': 400000, + + # The maximum number of detections for evaluation + 'max_num_detections': 100, + + # dw' = momentum * dw - lr * (grad + decay * w) + 'lr': 1e-3, + 'momentum': 0.9, + 'decay': 5e-4, + + # For each lr step, what to multiply the lr with + 'gamma': 0.1, + 'lr_steps': (280000, 360000, 400000), + + # Initial learning rate to linearly warmup from (if until > 0) + 'lr_warmup_init': 1e-4, + + # If > 0 then increase the lr linearly from warmup_init to lr each iter for until iters + 'lr_warmup_until': 500, + + # The terms to scale the respective loss by + 'conf_alpha': 1, + 'bbox_alpha': 1.5, + 'mask_alpha': 0.4 / 256 * 140 * 140, # Some funky equation. Don't worry about it. + + # Eval.py sets this if you just want to run YOLACT as a detector + 'eval_mask_branch': True, + + # Top_k examples to consider for NMS + 'nms_top_k': 200, + # Examples with confidence less than this are not considered by NMS + 'nms_conf_thresh': 0.05, + # Boxes with IoU overlap greater than this threshold will be culled during NMS + 'nms_thresh': 0.5, + + # See mask_type for details. + 'mask_type': mask_type.direct, + 'mask_size': 16, + 'masks_to_train': 100, + 'mask_proto_src': None, + 'mask_proto_net': [(256, 3, {}), (256, 3, {})], + 'mask_proto_bias': False, + 'mask_proto_prototype_activation': activation_func.relu, + 'mask_proto_mask_activation': activation_func.sigmoid, + 'mask_proto_coeff_activation': activation_func.tanh, + 'mask_proto_crop': True, + 'mask_proto_crop_expand': 0, + 'mask_proto_loss': None, + 'mask_proto_binarize_downsampled_gt': True, + 'mask_proto_normalize_mask_loss_by_sqrt_area': False, + 'mask_proto_reweight_mask_loss': False, + 'mask_proto_grid_file': 'data/grid.npy', + 'mask_proto_use_grid': False, + 'mask_proto_coeff_gate': False, + 'mask_proto_prototypes_as_features': False, + 'mask_proto_prototypes_as_features_no_grad': False, + 'mask_proto_remove_empty_masks': False, + 'mask_proto_reweight_coeff': 1, + 'mask_proto_coeff_diversity_loss': False, + 'mask_proto_coeff_diversity_alpha': 1, + 'mask_proto_normalize_emulate_roi_pooling': False, + 'mask_proto_double_loss': False, + 'mask_proto_double_loss_alpha': 1, + 'mask_proto_split_prototypes_by_head': False, + 'mask_proto_crop_with_pred_box': False, + + # SSD data augmentation parameters + # Randomize hue, vibrance, etc. + 'augment_photometric_distort': True, + # Have a chance to scale down the image and pad (to emulate smaller detections) + 'augment_expand': True, + # Potentialy sample a random crop from the image and put it in a random place + 'augment_random_sample_crop': True, + # Mirror the image with a probability of 1/2 + 'augment_random_mirror': True, + # Flip the image vertically with a probability of 1/2 + 'augment_random_flip': False, + # With uniform probability, rotate the image [0,90,180,270] degrees + 'augment_random_rot90': False, + + # Discard detections with width and height smaller than this (in absolute width and height) + 'discard_box_width': 4 / 550, + 'discard_box_height': 4 / 550, + + # If using batchnorm anywhere in the backbone, freeze the batchnorm layer during training. + # Note: any additional batch norm layers after the backbone will not be frozen. + 'freeze_bn': False, + + # Set this to a config object if you want an FPN (inherit from fpn_base). See fpn_base for details. + 'fpn': None, + + # Use the same weights for each network head + 'share_prediction_module': False, + + # For hard negative mining, instead of using the negatives that are leastl confidently background, + # use negatives that are most confidently not background. + 'ohem_use_most_confident': False, + + # Use focal loss as described in https://arxiv.org/pdf/1708.02002.pdf instead of OHEM + 'use_focal_loss': False, + 'focal_loss_alpha': 0.25, + 'focal_loss_gamma': 2, + + # The initial bias toward forground objects, as specified in the focal loss paper + 'focal_loss_init_pi': 0.01, + + # Keeps track of the average number of examples for each class, and weights the loss for that class accordingly. + 'use_class_balanced_conf': False, + + # Whether to use sigmoid focal loss instead of softmax, all else being the same. + 'use_sigmoid_focal_loss': False, + + # Use class[0] to be the objectness score and class[1:] to be the softmax predicted class. + # Note: at the moment this is only implemented if use_focal_loss is on. + 'use_objectness_score': False, + + # Adds a global pool + fc layer to the smallest selected layer that predicts the existence of each of the 80 classes. + # This branch is only evaluated during training time and is just there for multitask learning. + 'use_class_existence_loss': False, + 'class_existence_alpha': 1, + + # Adds a 1x1 convolution directly to the biggest selected layer that predicts a semantic segmentations for each of the 80 classes. + # This branch is only evaluated during training time and is just there for multitask learning. + 'use_semantic_segmentation_loss': False, + 'semantic_segmentation_alpha': 1, + + # Adds another branch to the netwok to predict Mask IoU. + 'use_mask_scoring': False, + 'mask_scoring_alpha': 1, + + # Match gt boxes using the Box2Pix change metric instead of the standard IoU metric. + # Note that the threshold you set for iou_threshold should be negative with this setting on. + 'use_change_matching': False, + + # Uses the same network format as mask_proto_net, except this time it's for adding extra head layers before the final + # prediction in prediction modules. If this is none, no extra layers will be added. + 'extra_head_net': None, + + # What params should the final head layers have (the ones that predict box, confidence, and mask coeffs) + 'head_layer_params': {'kernel_size': 3, 'padding': 1}, + + # Add extra layers between the backbone and the network heads + # The order is (bbox, conf, mask) + 'extra_layers': (0, 0, 0), + + # During training, to match detections with gt, first compute the maximum gt IoU for each prior. + # Then, any of those priors whose maximum overlap is over the positive threshold, mark as positive. + # For any priors whose maximum is less than the negative iou threshold, mark them as negative. + # The rest are neutral and not used in calculating the loss. + 'positive_iou_threshold': 0.5, + 'negative_iou_threshold': 0.5, + + # When using ohem, the ratio between positives and negatives (3 means 3 negatives to 1 positive) + 'ohem_negpos_ratio': 3, + + # If less than 1, anchors treated as a negative that have a crowd iou over this threshold with + # the crowd boxes will be treated as a neutral. + 'crowd_iou_threshold': 1, + + # This is filled in at runtime by Yolact's __init__, so don't touch it + 'mask_dim': None, + + # Input image size. + 'max_size': 300, + + # Whether or not to do post processing on the cpu at test time + 'force_cpu_nms': True, + + # Whether to use mask coefficient cosine similarity nms instead of bbox iou nms + 'use_coeff_nms': False, + + # Whether or not to have a separate branch whose sole purpose is to act as the coefficients for coeff_diversity_loss + # Remember to turn on coeff_diversity_loss, or these extra coefficients won't do anything! + # To see their effect, also remember to turn on use_coeff_nms. + 'use_instance_coeff': False, + 'num_instance_coeffs': 64, + + # Whether or not to tie the mask loss / box loss to 0 + 'train_masks': True, + 'train_boxes': True, + # If enabled, the gt masks will be cropped using the gt bboxes instead of the predicted ones. + # This speeds up training time considerably but results in much worse mAP at test time. + 'use_gt_bboxes': False, + + # Whether or not to preserve aspect ratio when resizing the image. + # If True, this will resize all images to be max_size^2 pixels in area while keeping aspect ratio. + # If False, all images are resized to max_size x max_size + 'preserve_aspect_ratio': False, + + # Whether or not to use the prediction module (c) from DSSD + 'use_prediction_module': False, + + # Whether or not to use the predicted coordinate scheme from Yolo v2 + 'use_yolo_regressors': False, + + # For training, bboxes are considered "positive" if their anchors have a 0.5 IoU overlap + # or greater with a ground truth box. If this is true, instead of using the anchor boxes + # for this IoU computation, the matching function will use the predicted bbox coordinates. + # Don't turn this on if you're not using yolo regressors! + 'use_prediction_matching': False, + + # A list of settings to apply after the specified iteration. Each element of the list should look like + # (iteration, config_dict) where config_dict is a dictionary you'd pass into a config object's init. + 'delayed_settings': [], + + # Use command-line arguments to set this. + 'no_jit': False, + + 'backbone': None, + 'name': 'base_config', + + # Fast Mask Re-scoring Network + # Inspried by Mask Scoring R-CNN (https://arxiv.org/abs/1903.00241) + # Do not crop out the mask with bbox but slide a convnet on the image-size mask, + # then use global pooling to get the final mask score + 'use_maskiou': False, + + # Archecture for the mask iou network. A (num_classes-1, 1, {}) layer is appended to the end. + 'maskiou_net': [], + + # Discard predicted masks whose area is less than this + 'discard_mask_area': -1, + + 'maskiou_alpha': 1.0, + 'rescore_mask': False, + 'rescore_bbox': False, + 'maskious_to_train': -1, +}) + + + + + +# ----------------------- YOLACT v1.0 CONFIGS ----------------------- # + +yolact_base_config = coco_base_config.copy({ + 'name': 'yolact_base', + + # Dataset stuff + 'dataset': coco2017_dataset, + 'num_classes': len(coco2017_dataset.class_names) + 1, + + # Image Size + 'max_size': 550, + + # Training params + 'lr_steps': (280000, 600000, 700000, 750000), + 'max_iter': 800000, + + # Backbone Settings + 'backbone': resnet101_backbone.copy({ + 'selected_layers': list(range(1, 4)), + 'use_pixel_scales': True, + 'preapply_sqrt': False, + 'use_square_anchors': True, # This is for backward compatability with a bug + + 'pred_aspect_ratios': [ [[1, 1/2, 2]] ]*5, + 'pred_scales': [[24], [48], [96], [192], [384]], + }), + + # FPN Settings + 'fpn': fpn_base.copy({ + 'use_conv_downsample': True, + 'num_downsample': 2, + }), + + # Mask Settings + 'mask_type': mask_type.lincomb, + 'mask_alpha': 6.125, + 'mask_proto_src': 0, + 'mask_proto_net': [(256, 3, {'padding': 1})] * 3 + [(None, -2, {}), (256, 3, {'padding': 1})] + [(32, 1, {})], + 'mask_proto_normalize_emulate_roi_pooling': True, + + # Other stuff + 'share_prediction_module': True, + 'extra_head_net': [(256, 3, {'padding': 1})], + + 'positive_iou_threshold': 0.5, + 'negative_iou_threshold': 0.4, + + 'crowd_iou_threshold': 0.7, + + 'use_semantic_segmentation_loss': True, +}) + +yolact_im400_config = yolact_base_config.copy({ + 'name': 'yolact_im400', + + 'max_size': 400, + 'backbone': yolact_base_config.backbone.copy({ + 'pred_scales': [[int(x[0] / yolact_base_config.max_size * 400)] for x in yolact_base_config.backbone.pred_scales], + }), +}) + +yolact_im700_config = yolact_base_config.copy({ + 'name': 'yolact_im700', + + 'masks_to_train': 300, + 'max_size': 700, + 'backbone': yolact_base_config.backbone.copy({ + 'pred_scales': [[int(x[0] / yolact_base_config.max_size * 700)] for x in yolact_base_config.backbone.pred_scales], + }), +}) + +yolact_darknet53_config = yolact_base_config.copy({ + 'name': 'yolact_darknet53', + + 'backbone': darknet53_backbone.copy({ + 'selected_layers': list(range(2, 5)), + + 'pred_scales': yolact_base_config.backbone.pred_scales, + 'pred_aspect_ratios': yolact_base_config.backbone.pred_aspect_ratios, + 'use_pixel_scales': True, + 'preapply_sqrt': False, + 'use_square_anchors': True, # This is for backward compatability with a bug + }), +}) + +yolact_resnet50_config = yolact_base_config.copy({ + 'name': 'yolact_resnet50', + + 'backbone': resnet50_backbone.copy({ + 'selected_layers': list(range(1, 4)), + + 'pred_scales': yolact_base_config.backbone.pred_scales, + 'pred_aspect_ratios': yolact_base_config.backbone.pred_aspect_ratios, + 'use_pixel_scales': True, + 'preapply_sqrt': False, + 'use_square_anchors': True, # This is for backward compatability with a bug + }), +}) + + +yolact_resnet50_pascal_config = yolact_resnet50_config.copy({ + 'name': None, # Will default to yolact_resnet50_pascal + + # Dataset stuff + 'dataset': pascal_sbd_dataset, + 'num_classes': len(pascal_sbd_dataset.class_names) + 1, + + 'max_iter': 120000, + 'lr_steps': (60000, 100000), + + 'backbone': yolact_resnet50_config.backbone.copy({ + 'pred_scales': [[32], [64], [128], [256], [512]], + 'use_square_anchors': False, + }) +}) + +# ----------------------- YOLACT++ CONFIGS ----------------------- # + +yolact_plus_base_config = yolact_base_config.copy({ + 'name': 'yolact_plus_base', + + 'backbone': resnet101_dcn_inter3_backbone.copy({ + 'selected_layers': list(range(1, 4)), + + 'pred_aspect_ratios': [ [[1, 1/2, 2]] ]*5, + 'pred_scales': [[i * 2 ** (j / 3.0) for j in range(3)] for i in [24, 48, 96, 192, 384]], + 'use_pixel_scales': True, + 'preapply_sqrt': False, + 'use_square_anchors': False, + }), + + 'use_maskiou': True, + 'maskiou_net': [(8, 3, {'stride': 2}), (16, 3, {'stride': 2}), (32, 3, {'stride': 2}), (64, 3, {'stride': 2}), (128, 3, {'stride': 2})], + 'maskiou_alpha': 25, + 'rescore_bbox': False, + 'rescore_mask': True, + + 'discard_mask_area': 5*5, +}) + +yolact_plus_resnet50_config = yolact_plus_base_config.copy({ + 'name': 'yolact_plus_resnet50', + + 'backbone': resnet50_dcnv2_backbone.copy({ + 'selected_layers': list(range(1, 4)), + + 'pred_aspect_ratios': [ [[1, 1/2, 2]] ]*5, + 'pred_scales': [[i * 2 ** (j / 3.0) for j in range(3)] for i in [24, 48, 96, 192, 384]], + 'use_pixel_scales': True, + 'preapply_sqrt': False, + 'use_square_anchors': False, + }), +}) + + +# Default config +cfg = yolact_base_config.copy() + +def set_cfg(config_name:str): + """ Sets the active config. Works even if cfg is already imported! """ + global cfg + + # Note this is not just an eval because I'm lazy, but also because it can + # be used like ssd300_config.copy({'max_size': 400}) for extreme fine-tuning + cfg.replace(eval(config_name)) + + if cfg.name is None: + cfg.name = config_name.split('_config')[0] + +def set_dataset(dataset_name:str): + """ Sets the dataset of the current config. 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cd $1 +fi + +if [ ! -d images ] + then + mkdir -p ./images +fi + +# Download the image data. +cd ./images +echo "Downloading MSCOCO train images ..." +curl -LO http://images.cocodataset.org/zips/train2017.zip +echo "Downloading MSCOCO val images ..." +curl -LO http://images.cocodataset.org/zips/val2017.zip + +cd ../ +if [ ! -d annotations ] + then + mkdir -p ./annotations +fi + +# Download the annotation data. +cd ./annotations +echo "Downloading MSCOCO train/val annotations ..." +curl -LO http://images.cocodataset.org/annotations/annotations_trainval2014.zip +curl -LO http://images.cocodataset.org/annotations/annotations_trainval2017.zip +echo "Finished downloading. Now extracting ..." + +# Unzip data +echo "Extracting train images ..." +unzip -qqjd ../images ../images/train2017.zip +echo "Extracting val images ..." +unzip -qqjd ../images ../images/val2017.zip +echo "Extracting annotations ..." +unzip -qqd .. ./annotations_trainval2014.zip +unzip -qqd .. ./annotations_trainval2017.zip + +echo "Removing zip files ..." +rm ../images/train2017.zip +rm ../images/val2017.zip +rm ./annotations_trainval2014.zip +rm ./annotations_trainval2017.zip + + +end=`date +%s` +runtime=$((end-start)) + +echo "Completed in " $runtime " seconds" diff --git a/cv/instance_segmentation/yolact/pytorch/data/scripts/COCO_test.sh b/cv/instance_segmentation/yolact/pytorch/data/scripts/COCO_test.sh new file mode 100644 index 000000000..c876e57ed --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/data/scripts/COCO_test.sh @@ -0,0 +1,62 @@ +#!/bin/bash + +start=`date +%s` + +# handle optional download dir +if [ -z "$1" ] + then + # navigate to ./data + echo "navigating to ./data/ ..." + mkdir -p ./data + cd ./data/ + mkdir -p ./coco + cd ./coco + mkdir -p ./images + mkdir -p ./annotations + else + # check if specified dir is valid + if [ ! -d $1 ]; then + echo $1 " is not a valid directory" + exit 0 + fi + echo "navigating to " $1 " ..." + cd $1 +fi + +if [ ! -d images ] + then + mkdir -p ./images +fi + +# Download the image data. +cd ./images +echo "Downloading MSCOCO test images ..." +curl -LO http://images.cocodataset.org/zips/test2017.zip + +cd ../ +if [ ! -d annotations ] + then + mkdir -p ./annotations +fi + +# Download the annotation data. +cd ./annotations +echo "Downloading MSCOCO test info ..." +curl -LO http://images.cocodataset.org/annotations/image_info_test2017.zip +echo "Finished downloading. Now extracting ..." + +# Unzip data +echo "Extracting train images ..." +unzip -qqjd ../images ../images/test2017.zip +echo "Extracting info ..." +unzip -qqd .. ./image_info_test2017.zip + +echo "Removing zip files ..." +rm ../images/test2017.zip +rm ./image_info_test2017.zip + + +end=`date +%s` +runtime=$((end-start)) + +echo "Completed in " $runtime " seconds" diff --git a/cv/instance_segmentation/yolact/pytorch/data/scripts/mix_sets.py b/cv/instance_segmentation/yolact/pytorch/data/scripts/mix_sets.py new file mode 100644 index 000000000..5f70eacb5 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/data/scripts/mix_sets.py @@ -0,0 +1,74 @@ +import json +import os +import sys +from collections import defaultdict + +usage_text = """ +This script creates a coco annotation file by mixing one or more existing annotation files. + +Usage: python data/scripts/mix_sets.py output_name [set1 range1 [set2 range2 [...]]] + +To use, specify the output annotation name and any number of set + range pairs, where the sets +are in the form instances_.json and ranges are python-evalable ranges. The resulting +json will be spit out as instances_.json in the same folder as the input sets. + +For instance, + python data/scripts/mix_sets.py trainval35k train2014 : val2014 :-5000 + +This will create an instance_trainval35k.json file with all images and corresponding annotations +from train2014 and the first 35000 images from val2014. + +You can also specify only one set: + python data/scripts/mix_sets.py minival5k val2014 -5000: + +This will take the last 5k images from val2014 and put it in instances_minival5k.json. +""" + +annotations_path = 'data/coco/annotations/instances_%s.json' +fields_to_combine = ('images', 'annotations') +fields_to_steal = ('info', 'categories', 'licenses') + +if __name__ == '__main__': + if len(sys.argv) < 4 or len(sys.argv) % 2 != 0: + print(usage_text) + exit() + + out_name = sys.argv[1] + sets = sys.argv[2:] + sets = [(sets[2*i], sets[2*i+1]) for i in range(len(sets)//2)] + + out = {x: [] for x in fields_to_combine} + + for idx, (set_name, range_str) in enumerate(sets): + print('Loading set %s...' % set_name) + with open(annotations_path % set_name, 'r') as f: + set_json = json.load(f) + + # "Steal" some fields that don't need to be combined from the first set + if idx == 0: + for field in fields_to_steal: + out[field] = set_json[field] + + print('Building image index...') + image_idx = {x['id']: x for x in set_json['images']} + + print('Collecting annotations...') + anns_idx = defaultdict(lambda: []) + + for ann in set_json['annotations']: + anns_idx[ann['image_id']].append(ann) + + export_ids = list(image_idx.keys()) + export_ids.sort() + export_ids = eval('export_ids[%s]' % range_str, {}, {'export_ids': export_ids}) + + print('Adding %d images...' % len(export_ids)) + for _id in export_ids: + out['images'].append(image_idx[_id]) + out['annotations'] += anns_idx[_id] + + print('Done.\n') + + print('Saving result...') + with open(annotations_path % (out_name), 'w') as out_file: + json.dump(out, out_file) diff --git 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    +
    +

    Select a configuration

    +
      +
    +
    +
    + + By Daniel Bolya + + diff --git a/cv/instance_segmentation/yolact/pytorch/web/iou.html b/cv/instance_segmentation/yolact/pytorch/web/iou.html new file mode 100644 index 000000000..15e6e1c89 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/iou.html @@ -0,0 +1,49 @@ + + + + + + + + + + IoU thingy + + + + + + + + + + +
    + + This text is displayed if your browser does not support HTML5 Canvas. + +
    +     + + diff --git a/cv/instance_segmentation/yolact/pytorch/web/scripts/index.js b/cv/instance_segmentation/yolact/pytorch/web/scripts/index.js new file mode 100644 index 000000000..fa412507f --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/scripts/index.js @@ -0,0 +1,17 @@ +$(document).ready(function() { + // Load in det_index and fill the config list with the appropriate elements + $.ajax({ + url: 'detindex', + dataType: 'text', + success: function (data) { + data = data.trim().split('\n'); + for (let i = 0; i < data.length; i++) { + name = data[i]; + + $('#config_list').append( + '
  1. ' + name + '
    2. ' + ); + } + } + }); +}); diff --git a/cv/instance_segmentation/yolact/pytorch/web/scripts/iou.js b/cv/instance_segmentation/yolact/pytorch/web/scripts/iou.js new file mode 100644 index 000000000..857400679 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/scripts/iou.js @@ -0,0 +1,482 @@ +// IoU added by Daniel Bolya +// +// Last updated November 2010 by Simon Sarris +// www.simonsarris.com +// sarris@acm.org +// +// Free to use and distribute at will +// So long as you are nice to people, etc + +// This is a self-executing function that I added only to stop this +// new script from interfering with the old one. It's a good idea in general, but not +// something I wanted to go over during this tutorial +(function(window) { + + + // holds all our boxes + var boxes2 = []; + + // New, holds the 8 tiny boxes that will be our selection handles + // the selection handles will be in this order: + // 0 1 2 + // 3 4 + // 5 6 7 + var selectionHandles = []; + + // Hold canvas information + var canvas; + var ctx; + var WIDTH; + var HEIGHT; + var INTERVAL = 20; // how often, in milliseconds, we check to see if a redraw is needed + + var isDrag = false; + var isResizeDrag = false; + var expectResize = -1; // New, will save the # of the selection handle if the mouse is over one. + var mx, my; // mouse coordinates + + // when set to true, the canvas will redraw everything + // invalidate() just sets this to false right now + // we want to call invalidate() whenever we make a change + var canvasValid = false; + + // The node (if any) being selected. + // If in the future we want to select multiple objects, this will get turned into an array + var mySel = null; + + // The selection color and width. Right now we have a red selection with a small width + var mySelColor = '#CC0000'; + var mySelWidth = 2; + var mySelBoxColor = 'darkred'; // New for selection boxes + var mySelBoxSize = 6; + + // we use a fake canvas to draw individual shapes for selection testing + var ghostcanvas; + var gctx; // fake canvas context + + // since we can drag from anywhere in a node + // instead of just its x/y corner, we need to save + // the offset of the mouse when we start dragging. + var offsetx, offsety; + + // Padding and border style widths for mouse offsets + var stylePaddingLeft, stylePaddingTop, styleBorderLeft, styleBorderTop; + + + + + // Box object to hold data + function Box2() { + this.x = 0; + this.y = 0; + this.w = 1; // default width and height? + this.h = 1; + this.fill = '#444444'; + } + + // New methods on the Box class + Box2.prototype = { + // we used to have a solo draw function + // but now each box is responsible for its own drawing + // mainDraw() will call this with the normal canvas + // myDown will call this with the ghost canvas with 'black' + draw: function(context, optionalColor) { + if (context === gctx) { + context.fillStyle = 'black'; // always want black for the ghost canvas + } else { + context.fillStyle = this.fill; + } + + // We can skip the drawing of elements that have moved off the screen: + if (this.x > WIDTH || this.y > HEIGHT) return; + if (this.x + this.w < 0 || this.y + this.h < 0) return; + + context.fillRect(this.x,this.y,this.w,this.h); + + // draw selection + // this is a stroke along the box and also 8 new selection handles + if (mySel === this) { + context.strokeStyle = mySelColor; + context.lineWidth = mySelWidth; + context.strokeRect(this.x,this.y,this.w,this.h); + + // draw the boxes + + var half = mySelBoxSize / 2; + + // 0 1 2 + // 3 4 + // 5 6 7 + + // top left, middle, right + selectionHandles[0].x = this.x-half; + selectionHandles[0].y = this.y-half; + + selectionHandles[1].x = this.x+this.w/2-half; + selectionHandles[1].y = this.y-half; + + selectionHandles[2].x = this.x+this.w-half; + selectionHandles[2].y = this.y-half; + + //middle left + selectionHandles[3].x = this.x-half; + selectionHandles[3].y = this.y+this.h/2-half; + + //middle right + selectionHandles[4].x = this.x+this.w-half; + selectionHandles[4].y = this.y+this.h/2-half; + + //bottom left, middle, right + selectionHandles[6].x = this.x+this.w/2-half; + selectionHandles[6].y = this.y+this.h-half; + + selectionHandles[5].x = this.x-half; + selectionHandles[5].y = this.y+this.h-half; + + selectionHandles[7].x = this.x+this.w-half; + selectionHandles[7].y = this.y+this.h-half; + + + context.fillStyle = mySelBoxColor; + for (var i = 0; i < 8; i ++) { + var cur = selectionHandles[i]; + context.fillRect(cur.x, cur.y, mySelBoxSize, mySelBoxSize); + } + } + + } // end draw + + } + + //Initialize a new Box, add it, and invalidate the canvas + function addRect(x, y, w, h, fill) { + var rect = new Box2; + rect.x = x; + rect.y = y; + rect.w = w + rect.h = h; + rect.fill = fill; + boxes2.push(rect); + invalidate(); + } + + // initialize our canvas, add a ghost canvas, set draw loop + // then add everything we want to intially exist on the canvas + function init2() { + canvas = document.getElementById('canvas2'); + canvas.style.width='50%'; + canvas.style.height='65%'; + canvas.width = canvas.offsetWidth; + canvas.height = canvas.offsetHeight; + HEIGHT = canvas.height; + WIDTH = canvas.width; + ctx = canvas.getContext('2d'); + ghostcanvas = document.createElement('canvas'); + ghostcanvas.height = HEIGHT; + ghostcanvas.width = WIDTH; + gctx = ghostcanvas.getContext('2d'); + + //fixes a problem where double clicking causes text to get selected on the canvas + canvas.onselectstart = function () { return false; } + + // fixes mouse co-ordinate problems when there's a border or padding + // see getMouse for more detail + if (document.defaultView && document.defaultView.getComputedStyle) { + stylePaddingLeft = parseInt(document.defaultView.getComputedStyle(canvas, null)['paddingLeft'], 10) || 0; + stylePaddingTop = parseInt(document.defaultView.getComputedStyle(canvas, null)['paddingTop'], 10) || 0; + styleBorderLeft = parseInt(document.defaultView.getComputedStyle(canvas, null)['borderLeftWidth'], 10) || 0; + styleBorderTop = parseInt(document.defaultView.getComputedStyle(canvas, null)['borderTopWidth'], 10) || 0; + } + + // make mainDraw() fire every INTERVAL milliseconds + setInterval(mainDraw, INTERVAL); + + // set our events. Up and down are for dragging, + // double click is for making new boxes + canvas.onmousedown = myDown; + canvas.onmouseup = myUp; + canvas.ondblclick = myDblClick; + canvas.onmousemove = myMove; + + // set up the selection handle boxes + for (var i = 0; i < 8; i ++) { + var rect = new Box2; + selectionHandles.push(rect); + } + + // add custom initialization here: + + + // add a large green rectangle + addRect(260, 70, WIDTH/2, HEIGHT/2, 'rgba(255, 210, 75, 0.7)'); + + // add a green-blue rectangle + addRect(240, 120, WIDTH/2, HEIGHT/2, 'rgba(255, 210, 75, 0.7)'); + + // add a smaller purple rectangle + // addRect(45, 60, 25, 25, 'rgba(150,150,250,0.7)'); + } + + + //wipes the canvas context + function clear(c) { + c.clearRect(0, 0, WIDTH, HEIGHT); + } + + // Main draw loop. + // While draw is called as often as the INTERVAL variable demands, + // It only ever does something if the canvas gets invalidated by our code + function mainDraw() { + if (canvasValid == false) { + clear(ctx); + + // Add stuff you want drawn in the background all the time here + + // draw all boxes + var l = boxes2.length; + for (var i = 0; i < l; i++) { + boxes2[i].draw(ctx); // we used to call drawshape, but now each box draws itself + } + + // Add stuff you want drawn on top all the time here + document.querySelector('#iou').innerHTML = computeIoU(boxes2[0], boxes2[1]); + + canvasValid = true; + } + } + + function computeIoU(a, b) { + var leftX = Math.max(a.x, b.x); + var rightX = Math.min(a.x+a.w, b.x+b.w); + + var topY = Math.max(a.y, b.y); + var botY = Math.min(a.y+a.h, b.y+b.h); + + if (rightX < leftX || botY < topY) + return 0; + + var inter = (rightX-leftX) * (botY-topY); + var areaA = a.w * a.h; + var areaB = b.w * b.h; + var union = areaA + areaB - inter; + + var iou = inter / union; + + return Math.round(iou * 100) / 100; + } + + // Happens when the mouse is moving inside the canvas + function myMove(e){ + if (isDrag) { + getMouse(e); + + mySel.x = mx - offsetx; + mySel.y = my - offsety; + + // something is changing position so we better invalidate the canvas! + invalidate(); + } else if (isResizeDrag) { + // time ro resize! + var oldx = mySel.x; + var oldy = mySel.y; + + // 0 1 2 + // 3 4 + // 5 6 7 + switch (expectResize) { + case 0: + mySel.x = mx; + mySel.y = my; + mySel.w += oldx - mx; + mySel.h += oldy - my; + break; + case 1: + mySel.y = my; + mySel.h += oldy - my; + break; + case 2: + mySel.y = my; + mySel.w = mx - oldx; + mySel.h += oldy - my; + break; + case 3: + mySel.x = mx; + mySel.w += oldx - mx; + break; + case 4: + mySel.w = mx - oldx; + break; + case 5: + mySel.x = mx; + mySel.w += oldx - mx; + mySel.h = my - oldy; + break; + case 6: + mySel.h = my - oldy; + break; + case 7: + mySel.w = mx - oldx; + mySel.h = my - oldy; + break; + } + + invalidate(); + } + + getMouse(e); + // if there's a selection see if we grabbed one of the selection handles + if (mySel !== null && !isResizeDrag) { + for (var i = 0; i < 8; i++) { + // 0 1 2 + // 3 4 + // 5 6 7 + + var cur = selectionHandles[i]; + + // we dont need to use the ghost context because + // selection handles will always be rectangles + if (mx >= cur.x && mx <= cur.x + mySelBoxSize && + my >= cur.y && my <= cur.y + mySelBoxSize) { + // we found one! + expectResize = i; + invalidate(); + + switch (i) { + case 0: + this.style.cursor='nw-resize'; + break; + case 1: + this.style.cursor='n-resize'; + break; + case 2: + this.style.cursor='ne-resize'; + break; + case 3: + this.style.cursor='w-resize'; + break; + case 4: + this.style.cursor='e-resize'; + break; + case 5: + this.style.cursor='sw-resize'; + break; + case 6: + this.style.cursor='s-resize'; + break; + case 7: + this.style.cursor='se-resize'; + break; + } + return; + } + + } + // not over a selection box, return to normal + isResizeDrag = false; + expectResize = -1; + this.style.cursor='auto'; + } + + } + + // Happens when the mouse is clicked in the canvas + function myDown(e){ + getMouse(e); + + //we are over a selection box + if (expectResize !== -1) { + isResizeDrag = true; + return; + } + + clear(gctx); + var l = boxes2.length; + for (var i = l-1; i >= 0; i--) { + // draw shape onto ghost context + boxes2[i].draw(gctx, 'black'); + + // get image data at the mouse x,y pixel + var imageData = gctx.getImageData(mx, my, 1, 1); + var index = (mx + my * imageData.width) * 4; + + // if the mouse pixel exists, select and break + if (imageData.data[3] > 0) { + mySel = boxes2[i]; + offsetx = mx - mySel.x; + offsety = my - mySel.y; + mySel.x = mx - offsetx; + mySel.y = my - offsety; + isDrag = true; + + invalidate(); + clear(gctx); + return; + } + + } + // havent returned means we have selected nothing + mySel = null; + // clear the ghost canvas for next time + clear(gctx); + // invalidate because we might need the selection border to disappear + invalidate(); + } + + function myUp(){ + isDrag = false; + isResizeDrag = false; + expectResize = -1; + } + + // adds a new node + function myDblClick(e) { + getMouse(e); + // for this method width and height determine the starting X and Y, too. + // so I left them as vars in case someone wanted to make them args for something and copy this code + // var width = 20; + // var height = 20; + // addRect(mx - (width / 2), my - (height / 2), width, height, 'rgba(220,205,65,0.7)'); + } + + + function invalidate() { + canvasValid = false; + } + + // Sets mx,my to the mouse position relative to the canvas + // unfortunately this can be tricky, we have to worry about padding and borders + function getMouse(e) { + var element = canvas, offsetX = 0, offsetY = 0; + + if (element.offsetParent) { + do { + offsetX += element.offsetLeft; + offsetY += element.offsetTop; + } while ((element = element.offsetParent)); + } + + // Add padding and border style widths to offset + offsetX += stylePaddingLeft; + offsetY += stylePaddingTop; + + offsetX += styleBorderLeft; + offsetY += styleBorderTop; + + mx = e.pageX - offsetX; + my = e.pageY - offsetY + } + + // If 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    +
    +
    + + By Daniel Bolya + + diff --git a/cv/instance_segmentation/yolact/pytorch/yolact.py b/cv/instance_segmentation/yolact/pytorch/yolact.py new file mode 100644 index 000000000..d83703bb7 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/yolact.py @@ -0,0 +1,724 @@ +import torch, torchvision +import torch.nn as nn +import torch.nn.functional as F +from torchvision.models.resnet import Bottleneck +import numpy as np +from itertools import product +from math import sqrt +from typing import List +from collections import defaultdict + +from data.config import cfg, mask_type +from layers import Detect +from layers.interpolate import InterpolateModule +from backbone import construct_backbone + +import torch.backends.cudnn as cudnn +from utils import timer +from utils.functions import MovingAverage, make_net + +# This is required for Pytorch 1.0.1 on Windows to initialize Cuda on some driver versions. +# See the bug report here: https://github.com/pytorch/pytorch/issues/17108 +torch.cuda.current_device() + +# As of March 10, 2019, Pytorch DataParallel still doesn't support JIT Script Modules +use_jit = torch.cuda.device_count() <= 1 +if not use_jit: + print('Multiple GPUs detected! Turning off JIT.') + +ScriptModuleWrapper = torch.jit.ScriptModule if use_jit else nn.Module +script_method_wrapper = torch.jit.script_method if use_jit else lambda fn, _rcn=None: fn + + + +class Concat(nn.Module): + def __init__(self, nets, extra_params): + super().__init__() + + self.nets = nn.ModuleList(nets) + self.extra_params = extra_params + + def forward(self, x): + # Concat each along the channel dimension + return torch.cat([net(x) for net in self.nets], dim=1, **self.extra_params) + +prior_cache = defaultdict(lambda: None) + +class PredictionModule(nn.Module): + """ + The (c) prediction module adapted from DSSD: + https://arxiv.org/pdf/1701.06659.pdf + + Note that this is slightly different to the module in the paper + because the Bottleneck block actually has a 3x3 convolution in + the middle instead of a 1x1 convolution. Though, I really can't + be arsed to implement it myself, and, who knows, this might be + better. + + Args: + - in_channels: The input feature size. + - out_channels: The output feature size (must be a multiple of 4). + - aspect_ratios: A list of lists of priorbox aspect ratios (one list per scale). + - scales: A list of priorbox scales relative to this layer's convsize. + For instance: If this layer has convouts of size 30x30 for + an image of size 600x600, the 'default' (scale + of 1) for this layer would produce bounding + boxes with an area of 20x20px. If the scale is + .5 on the other hand, this layer would consider + bounding boxes with area 10x10px, etc. + - parent: If parent is a PredictionModule, this module will use all the layers + from parent instead of from this module. + """ + + def __init__(self, in_channels, out_channels=1024, aspect_ratios=[[1]], scales=[1], parent=None, index=0): + super().__init__() + + self.num_classes = cfg.num_classes + self.mask_dim = cfg.mask_dim # Defined by Yolact + self.num_priors = sum(len(x)*len(scales) for x in aspect_ratios) + self.parent = [parent] # Don't include this in the state dict + self.index = index + self.num_heads = cfg.num_heads # Defined by Yolact + + if cfg.mask_proto_split_prototypes_by_head and cfg.mask_type == mask_type.lincomb: + self.mask_dim = self.mask_dim // self.num_heads + + if cfg.mask_proto_prototypes_as_features: + in_channels += self.mask_dim + + if parent is None: + if cfg.extra_head_net is None: + out_channels = in_channels + else: + self.upfeature, out_channels = make_net(in_channels, cfg.extra_head_net) + + if cfg.use_prediction_module: + self.block = Bottleneck(out_channels, out_channels // 4) + self.conv = nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=True) + self.bn = nn.BatchNorm2d(out_channels) + + self.bbox_layer = nn.Conv2d(out_channels, self.num_priors * 4, **cfg.head_layer_params) + self.conf_layer = nn.Conv2d(out_channels, self.num_priors * self.num_classes, **cfg.head_layer_params) + self.mask_layer = nn.Conv2d(out_channels, self.num_priors * self.mask_dim, **cfg.head_layer_params) + + if cfg.use_mask_scoring: + self.score_layer = nn.Conv2d(out_channels, self.num_priors, **cfg.head_layer_params) + + if cfg.use_instance_coeff: + self.inst_layer = nn.Conv2d(out_channels, self.num_priors * cfg.num_instance_coeffs, **cfg.head_layer_params) + + # What is this ugly lambda doing in the middle of all this clean prediction module code? + def make_extra(num_layers): + if num_layers == 0: + return lambda x: x + else: + # Looks more complicated than it is. This just creates an array of num_layers alternating conv-relu + return nn.Sequential(*sum([[ + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + nn.ReLU(inplace=True) + ] for _ in range(num_layers)], [])) + + self.bbox_extra, self.conf_extra, self.mask_extra = [make_extra(x) for x in cfg.extra_layers] + + if cfg.mask_type == mask_type.lincomb and cfg.mask_proto_coeff_gate: + self.gate_layer = nn.Conv2d(out_channels, self.num_priors * self.mask_dim, kernel_size=3, padding=1) + + self.aspect_ratios = aspect_ratios + self.scales = scales + + self.priors = None + self.last_conv_size = None + self.last_img_size = None + + def forward(self, x): + """ + Args: + - x: The convOut from a layer in the backbone network + Size: [batch_size, in_channels, conv_h, conv_w]) + + Returns a tuple (bbox_coords, class_confs, mask_output, prior_boxes) with sizes + - bbox_coords: [batch_size, conv_h*conv_w*num_priors, 4] + - class_confs: [batch_size, conv_h*conv_w*num_priors, num_classes] + - mask_output: [batch_size, conv_h*conv_w*num_priors, mask_dim] + - prior_boxes: [conv_h*conv_w*num_priors, 4] + """ + # In case we want to use another module's layers + src = self if self.parent[0] is None else self.parent[0] + + conv_h = x.size(2) + conv_w = x.size(3) + + if cfg.extra_head_net is not None: + x = src.upfeature(x) + + if cfg.use_prediction_module: + # The two branches of PM design (c) + a = src.block(x) + + b = src.conv(x) + b = src.bn(b) + b = F.relu(b) + + # TODO: Possibly switch this out for a product + x = a + b + + bbox_x = src.bbox_extra(x) + conf_x = src.conf_extra(x) + mask_x = src.mask_extra(x) + + bbox = src.bbox_layer(bbox_x).permute(0, 2, 3, 1).contiguous().view(x.size(0), -1, 4) + conf = src.conf_layer(conf_x).permute(0, 2, 3, 1).contiguous().view(x.size(0), -1, self.num_classes) + + if cfg.eval_mask_branch: + mask = src.mask_layer(mask_x).permute(0, 2, 3, 1).contiguous().view(x.size(0), -1, self.mask_dim) + else: + mask = torch.zeros(x.size(0), bbox.size(1), self.mask_dim, device=bbox.device) + + if cfg.use_mask_scoring: + score = src.score_layer(x).permute(0, 2, 3, 1).contiguous().view(x.size(0), -1, 1) + + if cfg.use_instance_coeff: + inst = src.inst_layer(x).permute(0, 2, 3, 1).contiguous().view(x.size(0), -1, cfg.num_instance_coeffs) + + # See box_utils.decode for an explanation of this + if cfg.use_yolo_regressors: + bbox[:, :, :2] = torch.sigmoid(bbox[:, :, :2]) - 0.5 + bbox[:, :, 0] /= conv_w + bbox[:, :, 1] /= conv_h + + if cfg.eval_mask_branch: + if cfg.mask_type == mask_type.direct: + mask = torch.sigmoid(mask) + elif cfg.mask_type == mask_type.lincomb: + mask = cfg.mask_proto_coeff_activation(mask) + + if cfg.mask_proto_coeff_gate: + gate = src.gate_layer(x).permute(0, 2, 3, 1).contiguous().view(x.size(0), -1, self.mask_dim) + mask = mask * torch.sigmoid(gate) + + if cfg.mask_proto_split_prototypes_by_head and cfg.mask_type == mask_type.lincomb: + mask = F.pad(mask, (self.index * self.mask_dim, (self.num_heads - self.index - 1) * self.mask_dim), mode='constant', value=0) + + priors = self.make_priors(conv_h, conv_w, x.device) + + preds = { 'loc': bbox, 'conf': conf, 'mask': mask, 'priors': priors } + + if cfg.use_mask_scoring: + preds['score'] = score + + if cfg.use_instance_coeff: + preds['inst'] = inst + + return preds + + def make_priors(self, conv_h, conv_w, device): + """ Note that priors are [x,y,width,height] where (x,y) is the center of the box. """ + global prior_cache + size = (conv_h, conv_w) + + with timer.env('makepriors'): + if self.last_img_size != (cfg._tmp_img_w, cfg._tmp_img_h): + prior_data = [] + + # Iteration order is important (it has to sync up with the convout) + for j, i in product(range(conv_h), range(conv_w)): + # +0.5 because priors are in center-size notation + x = (i + 0.5) / conv_w + y = (j + 0.5) / conv_h + + for ars in self.aspect_ratios: + for scale in self.scales: + for ar in ars: + if not cfg.backbone.preapply_sqrt: + ar = sqrt(ar) + + if cfg.backbone.use_pixel_scales: + w = scale * ar / cfg.max_size + h = scale / ar / cfg.max_size + else: + w = scale * ar / conv_w + h = scale / ar / conv_h + + # This is for backward compatability with a bug where I made everything square by accident + if cfg.backbone.use_square_anchors: + h = w + + prior_data += [x, y, w, h] + + self.priors = torch.Tensor(prior_data, device=device).view(-1, 4).detach() + self.priors.requires_grad = False + self.last_img_size = (cfg._tmp_img_w, cfg._tmp_img_h) + self.last_conv_size = (conv_w, conv_h) + prior_cache[size] = None + elif self.priors.device != device: + # This whole weird situation is so that DataParalell doesn't copy the priors each iteration + if prior_cache[size] is None: + prior_cache[size] = {} + + if device not in prior_cache[size]: + prior_cache[size][device] = self.priors.to(device) + + self.priors = prior_cache[size][device] + + return self.priors + +class FPN(ScriptModuleWrapper): + """ + Implements a general version of the FPN introduced in + https://arxiv.org/pdf/1612.03144.pdf + + Parameters (in cfg.fpn): + - num_features (int): The number of output features in the fpn layers. + - interpolation_mode (str): The mode to pass to F.interpolate. + - num_downsample (int): The number of downsampled layers to add onto the selected layers. + These extra layers are downsampled from the last selected layer. + + Args: + - in_channels (list): For each conv layer you supply in the forward pass, + how many features will it have? + """ + __constants__ = ['interpolation_mode', 'num_downsample', 'use_conv_downsample', 'relu_pred_layers', + 'lat_layers', 'pred_layers', 'downsample_layers', 'relu_downsample_layers'] + + def __init__(self, in_channels): + super().__init__() + + self.lat_layers = nn.ModuleList([ + nn.Conv2d(x, cfg.fpn.num_features, kernel_size=1) + for x in reversed(in_channels) + ]) + + # This is here for backwards compatability + padding = 1 if cfg.fpn.pad else 0 + self.pred_layers = nn.ModuleList([ + nn.Conv2d(cfg.fpn.num_features, cfg.fpn.num_features, kernel_size=3, padding=padding) + for _ in in_channels + ]) + + if cfg.fpn.use_conv_downsample: + self.downsample_layers = nn.ModuleList([ + nn.Conv2d(cfg.fpn.num_features, cfg.fpn.num_features, kernel_size=3, padding=1, stride=2) + for _ in range(cfg.fpn.num_downsample) + ]) + + self.interpolation_mode = cfg.fpn.interpolation_mode + self.num_downsample = cfg.fpn.num_downsample + self.use_conv_downsample = cfg.fpn.use_conv_downsample + self.relu_downsample_layers = cfg.fpn.relu_downsample_layers + self.relu_pred_layers = cfg.fpn.relu_pred_layers + + @script_method_wrapper + def forward(self, convouts:List[torch.Tensor]): + """ + Args: + - convouts (list): A list of convouts for the corresponding layers in in_channels. + Returns: + - A list of FPN convouts in the same order as x with extra downsample layers if requested. + """ + + out = [] + x = torch.zeros(1, device=convouts[0].device) + for i in range(len(convouts)): + out.append(x) + + # For backward compatability, the conv layers are stored in reverse but the input and output is + # given in the correct order. Thus, use j=-i-1 for the input and output and i for the conv layers. + j = len(convouts) + for lat_layer in self.lat_layers: + j -= 1 + + if j < len(convouts) - 1: + _, _, h, w = convouts[j].size() + x = F.interpolate(x, size=(h, w), mode=self.interpolation_mode, align_corners=False) + + x = x + lat_layer(convouts[j]) + out[j] = x + + # This janky second loop is here because TorchScript. + j = len(convouts) + for pred_layer in self.pred_layers: + j -= 1 + out[j] = pred_layer(out[j]) + + if self.relu_pred_layers: + F.relu(out[j], inplace=True) + + cur_idx = len(out) + + # In the original paper, this takes care of P6 + if self.use_conv_downsample: + for downsample_layer in self.downsample_layers: + out.append(downsample_layer(out[-1])) + else: + for idx in range(self.num_downsample): + # Note: this is an untested alternative to out.append(out[-1][:, :, ::2, ::2]). Thanks TorchScript. + out.append(nn.functional.max_pool2d(out[-1], 1, stride=2)) + + if self.relu_downsample_layers: + for idx in range(len(out) - cur_idx): + out[idx] = F.relu(out[idx + cur_idx], inplace=False) + + return out + +class FastMaskIoUNet(ScriptModuleWrapper): + + def __init__(self): + super().__init__() + input_channels = 1 + last_layer = [(cfg.num_classes-1, 1, {})] + self.maskiou_net, _ = make_net(input_channels, cfg.maskiou_net + last_layer, include_last_relu=True) + + def forward(self, x): + x = self.maskiou_net(x) + maskiou_p = F.max_pool2d(x, kernel_size=x.size()[2:]).squeeze(-1).squeeze(-1) + + return maskiou_p + + + +class Yolact(nn.Module): + """ + + + ██╗ ██╗ ██████╗ ██╗ █████╗ ██████╗████████╗ + ╚██╗ ██╔╝██╔═══██╗██║ ██╔══██╗██╔════╝╚══██╔══╝ + ╚████╔╝ ██║ ██║██║ ███████║██║ ██║ + ╚██╔╝ ██║ ██║██║ ██╔══██║██║ ██║ + ██║ ╚██████╔╝███████╗██║ ██║╚██████╗ ██║ + ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ + + + You can set the arguments by changing them in the backbone config object in config.py. + + Parameters (in cfg.backbone): + - selected_layers: The indices of the conv layers to use for prediction. + - pred_scales: A list with len(selected_layers) containing tuples of scales (see PredictionModule) + - pred_aspect_ratios: A list of lists of aspect ratios with len(selected_layers) (see PredictionModule) + """ + + def __init__(self): + super().__init__() + + self.backbone = construct_backbone(cfg.backbone) + + if cfg.freeze_bn: + self.freeze_bn() + + # Compute mask_dim here and add it back to the config. Make sure Yolact's constructor is called early! + if cfg.mask_type == mask_type.direct: + cfg.mask_dim = cfg.mask_size**2 + elif cfg.mask_type == mask_type.lincomb: + if cfg.mask_proto_use_grid: + self.grid = torch.Tensor(np.load(cfg.mask_proto_grid_file)) + self.num_grids = self.grid.size(0) + else: + self.num_grids = 0 + + self.proto_src = cfg.mask_proto_src + + if self.proto_src is None: in_channels = 3 + elif cfg.fpn is not None: in_channels = cfg.fpn.num_features + else: in_channels = self.backbone.channels[self.proto_src] + in_channels += self.num_grids + + # The include_last_relu=false here is because we might want to change it to another function + self.proto_net, cfg.mask_dim = make_net(in_channels, cfg.mask_proto_net, include_last_relu=False) + + if cfg.mask_proto_bias: + cfg.mask_dim += 1 + + + self.selected_layers = cfg.backbone.selected_layers + src_channels = self.backbone.channels + + if cfg.use_maskiou: + self.maskiou_net = FastMaskIoUNet() + + if cfg.fpn is not None: + # Some hacky rewiring to accomodate the FPN + self.fpn = FPN([src_channels[i] for i in self.selected_layers]) + self.selected_layers = list(range(len(self.selected_layers) + cfg.fpn.num_downsample)) + src_channels = [cfg.fpn.num_features] * len(self.selected_layers) + + + self.prediction_layers = nn.ModuleList() + cfg.num_heads = len(self.selected_layers) + + for idx, layer_idx in enumerate(self.selected_layers): + # If we're sharing prediction module weights, have every module's parent be the first one + parent = None + if cfg.share_prediction_module and idx > 0: + parent = self.prediction_layers[0] + + pred = PredictionModule(src_channels[layer_idx], src_channels[layer_idx], + aspect_ratios = cfg.backbone.pred_aspect_ratios[idx], + scales = cfg.backbone.pred_scales[idx], + parent = parent, + index = idx) + self.prediction_layers.append(pred) + + # Extra parameters for the extra losses + if cfg.use_class_existence_loss: + # This comes from the smallest layer selected + # Also note that cfg.num_classes includes background + self.class_existence_fc = nn.Linear(src_channels[-1], cfg.num_classes - 1) + + if cfg.use_semantic_segmentation_loss: + self.semantic_seg_conv = nn.Conv2d(src_channels[0], cfg.num_classes-1, kernel_size=1) + + # For use in evaluation + self.detect = Detect(cfg.num_classes, bkg_label=0, top_k=cfg.nms_top_k, + conf_thresh=cfg.nms_conf_thresh, nms_thresh=cfg.nms_thresh) + + def save_weights(self, path): + """ Saves the model's weights using compression because the file sizes were getting too big. """ + torch.save(self.state_dict(), path) + + def load_weights(self, path): + """ Loads weights from a compressed save file. """ + state_dict = torch.load(path) + + # For backward compatability, remove these (the new variable is called layers) + for key in list(state_dict.keys()): + if key.startswith('backbone.layer') and not key.startswith('backbone.layers'): + del state_dict[key] + + # Also for backward compatibility with v1.0 weights, do this check + if key.startswith('fpn.downsample_layers.'): + if cfg.fpn is not None and int(key.split('.')[2]) >= cfg.fpn.num_downsample: + del state_dict[key] + self.load_state_dict(state_dict) + + def init_weights(self, backbone_path): + """ Initialize weights for training. """ + # Initialize the backbone with the pretrained weights. + self.backbone.init_backbone(backbone_path) + + conv_constants = getattr(nn.Conv2d(1, 1, 1), '__constants__') + + # Quick lambda to test if one list contains the other + def all_in(x, y): + for _x in x: + if _x not in y: + return False + return True + + # Initialize the rest of the conv layers with xavier + for name, module in self.named_modules(): + # See issue #127 for why we need such a complicated condition if the module is a WeakScriptModuleProxy + # Broke in 1.3 (see issue #175), WeakScriptModuleProxy was turned into just ScriptModule. + # Broke in 1.4 (see issue #292), where RecursiveScriptModule is the new star of the show. + # Note that this might break with future pytorch updates, so let me know if it does + is_script_conv = False + if 'Script' in type(module).__name__: + # 1.4 workaround: now there's an original_name member so just use that + if hasattr(module, 'original_name'): + is_script_conv = 'Conv' in module.original_name + # 1.3 workaround: check if this has the same constants as a conv module + else: + is_script_conv = ( + all_in(module.__dict__['_constants_set'], conv_constants) + and all_in(conv_constants, module.__dict__['_constants_set'])) + + is_conv_layer = isinstance(module, nn.Conv2d) or is_script_conv + + if is_conv_layer and module not in self.backbone.backbone_modules: + nn.init.xavier_uniform_(module.weight.data) + + if module.bias is not None: + if cfg.use_focal_loss and 'conf_layer' in name: + if not cfg.use_sigmoid_focal_loss: + # Initialize the last layer as in the focal loss paper. + # Because we use softmax and not sigmoid, I had to derive an alternate expression + # on a notecard. Define pi to be the probability of outputting a foreground detection. + # Then let z = sum(exp(x)) - exp(x_0). Finally let c be the number of foreground classes. + # Chugging through the math, this gives us + # x_0 = log(z * (1 - pi) / pi) where 0 is the background class + # x_i = log(z / c) for all i > 0 + # For simplicity (and because we have a degree of freedom here), set z = 1. Then we have + # x_0 = log((1 - pi) / pi) note: don't split up the log for numerical stability + # x_i = -log(c) for all i > 0 + module.bias.data[0] = np.log((1 - cfg.focal_loss_init_pi) / cfg.focal_loss_init_pi) + module.bias.data[1:] = -np.log(module.bias.size(0) - 1) + else: + module.bias.data[0] = -np.log(cfg.focal_loss_init_pi / (1 - cfg.focal_loss_init_pi)) + module.bias.data[1:] = -np.log((1 - cfg.focal_loss_init_pi) / cfg.focal_loss_init_pi) + else: + module.bias.data.zero_() + + def train(self, mode=True): + super().train(mode) + + if cfg.freeze_bn: + self.freeze_bn() + + def freeze_bn(self, enable=False): + """ Adapted from https://discuss.pytorch.org/t/how-to-train-with-frozen-batchnorm/12106/8 """ + for module in self.modules(): + if isinstance(module, nn.BatchNorm2d): + module.train() if enable else module.eval() + + module.weight.requires_grad = enable + module.bias.requires_grad = enable + + def forward(self, x): + """ The input should be of size [batch_size, 3, img_h, img_w] """ + _, _, img_h, img_w = x.size() + cfg._tmp_img_h = img_h + cfg._tmp_img_w = img_w + + with timer.env('backbone'): + outs = self.backbone(x) + + if cfg.fpn is not None: + with timer.env('fpn'): + # Use backbone.selected_layers because we overwrote self.selected_layers + outs = [outs[i] for i in cfg.backbone.selected_layers] + outs = self.fpn(outs) + + proto_out = None + if cfg.mask_type == mask_type.lincomb and cfg.eval_mask_branch: + with timer.env('proto'): + proto_x = x if self.proto_src is None else outs[self.proto_src] + + if self.num_grids > 0: + grids = self.grid.repeat(proto_x.size(0), 1, 1, 1) + proto_x = torch.cat([proto_x, grids], dim=1) + + proto_out = self.proto_net(proto_x) + proto_out = cfg.mask_proto_prototype_activation(proto_out) + + if cfg.mask_proto_prototypes_as_features: + # Clone here because we don't want to permute this, though idk if contiguous makes this unnecessary + proto_downsampled = proto_out.clone() + + if cfg.mask_proto_prototypes_as_features_no_grad: + proto_downsampled = proto_out.detach() + + # Move the features last so the multiplication is easy + proto_out = proto_out.permute(0, 2, 3, 1).contiguous() + + if cfg.mask_proto_bias: + bias_shape = [x for x in proto_out.size()] + bias_shape[-1] = 1 + proto_out = torch.cat([proto_out, torch.ones(*bias_shape)], -1) + + + with timer.env('pred_heads'): + pred_outs = { 'loc': [], 'conf': [], 'mask': [], 'priors': [] } + + if cfg.use_mask_scoring: + pred_outs['score'] = [] + + if cfg.use_instance_coeff: + pred_outs['inst'] = [] + + for idx, pred_layer in zip(self.selected_layers, self.prediction_layers): + pred_x = outs[idx] + + if cfg.mask_type == mask_type.lincomb and cfg.mask_proto_prototypes_as_features: + # Scale the prototypes down to the current prediction layer's size and add it as inputs + proto_downsampled = F.interpolate(proto_downsampled, size=outs[idx].size()[2:], mode='bilinear', align_corners=False) + pred_x = torch.cat([pred_x, proto_downsampled], dim=1) + + # A hack for the way dataparallel works + if cfg.share_prediction_module and pred_layer is not self.prediction_layers[0]: + pred_layer.parent = [self.prediction_layers[0]] + + p = pred_layer(pred_x) + + for k, v in p.items(): + pred_outs[k].append(v) + + for k, v in pred_outs.items(): + pred_outs[k] = torch.cat(v, -2) + + if proto_out is not None: + pred_outs['proto'] = proto_out + + if self.training: + # For the extra loss functions + if cfg.use_class_existence_loss: + pred_outs['classes'] = self.class_existence_fc(outs[-1].mean(dim=(2, 3))) + + if cfg.use_semantic_segmentation_loss: + pred_outs['segm'] = self.semantic_seg_conv(outs[0]) + + return pred_outs + else: + if cfg.use_mask_scoring: + pred_outs['score'] = torch.sigmoid(pred_outs['score']) + + if cfg.use_focal_loss: + if cfg.use_sigmoid_focal_loss: + # Note: even though conf[0] exists, this mode doesn't train it so don't use it + pred_outs['conf'] = torch.sigmoid(pred_outs['conf']) + if cfg.use_mask_scoring: + pred_outs['conf'] *= pred_outs['score'] + elif cfg.use_objectness_score: + # See focal_loss_sigmoid in multibox_loss.py for details + objectness = torch.sigmoid(pred_outs['conf'][:, :, 0]) + pred_outs['conf'][:, :, 1:] = objectness[:, :, None] * F.softmax(pred_outs['conf'][:, :, 1:], -1) + pred_outs['conf'][:, :, 0 ] = 1 - objectness + else: + pred_outs['conf'] = F.softmax(pred_outs['conf'], -1) + else: + + if cfg.use_objectness_score: + objectness = torch.sigmoid(pred_outs['conf'][:, :, 0]) + + pred_outs['conf'][:, :, 1:] = (objectness > 0.10)[..., None] \ + * F.softmax(pred_outs['conf'][:, :, 1:], dim=-1) + + else: + pred_outs['conf'] = F.softmax(pred_outs['conf'], -1) + + return self.detect(pred_outs, self) + + + + +# Some testing code +if __name__ == '__main__': + from utils.functions import init_console + init_console() + + # Use the first argument to set the config if you want + import sys + if len(sys.argv) > 1: + from data.config import set_cfg + set_cfg(sys.argv[1]) + + net = Yolact() + net.train() + net.init_weights(backbone_path='weights/' + cfg.backbone.path) + + # GPU + net = net.cuda() + torch.set_default_tensor_type('torch.cuda.FloatTensor') + + x = torch.zeros((1, 3, cfg.max_size, cfg.max_size)) + y = net(x) + + for p in net.prediction_layers: + print(p.last_conv_size) + + print() + for k, a in y.items(): + print(k + ': ', a.size(), torch.sum(a)) + exit() + + net(x) + # timer.disable('pass2') + avg = MovingAverage() + try: + while True: + timer.reset() + with timer.env('everything else'): + net(x) + avg.add(timer.total_time()) + print('\033[2J') # Moves console cursor to 0,0 + timer.print_stats() + print('Avg fps: %.2f\tAvg ms: %.2f ' % (1/avg.get_avg(), avg.get_avg()*1000)) + except KeyboardInterrupt: + pass diff --git a/cv/tracking/deep_sort/pytorch/.gitignore b/cv/tracking/deep_sort/pytorch/.gitignore new file mode 100644 index 000000000..37ed2f4dc --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/.gitignore @@ -0,0 +1,13 @@ +# Folders +__pycache__/ +build/ +*.egg-info + + +# Files +*.weights +*.t7 +*.mp4 +*.avi +*.so +*.txt diff --git a/cv/tracking/deep_sort/pytorch/.gitmodules b/cv/tracking/deep_sort/pytorch/.gitmodules new file mode 100644 index 000000000..bbbb4fe6e --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/.gitmodules @@ -0,0 +1,6 @@ +[submodule "thirdparty/fast-reid"] + path = thirdparty/fast-reid + url = https://github.com/JDAI-CV/fast-reid.git +[submodule "thirdparty/mmdetection"] + path = thirdparty/mmdetection + url = https://github.com/open-mmlab/mmdetection.git diff --git a/cv/tracking/deep_sort/pytorch/LICENSE b/cv/tracking/deep_sort/pytorch/LICENSE new file mode 100644 index 000000000..92a1ed5dc --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Ziqiang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/README.md b/cv/tracking/deep_sort/pytorch/README.md new file mode 100644 index 000000000..359b673c9 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/README.md @@ -0,0 +1,145 @@ +## Introduction +This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in [PAPER](https://arxiv.org/abs/1703.07402) is FasterRCNN , and the original source code is [HERE](https://github.com/nwojke/deep_sort). +However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use **YOLOv3** to generate bboxes instead of FasterRCNN. + +We just need to train the RE-ID model! + +## Preparing datasets +Download the [Market-1501](https://zheng-lab.cecs.anu.edu.au/Project/project_reid.html) + +Change the data path as your own in ./deep_sort/deep/create_market1501_datasets.py file. +``` +python3 create_market1501_datasets.py +``` + +In train path, 000-751 is train data and remain is test data, delete test path and move 752-1501 to this. + + +## Training +The original model used in paper is in original_model.py, and its parameter here [original_ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6). + +To train the model, first you need download Market1501dataset or Mars dataset. + +Then you can try [train.py](deep_sort/deep/train.py) to train your own parameter and evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py). +! +``` +cd deep_sort/deep/ +python3 train.py --data-dir your path +``` + +## Quick Start +0. Check all dependencies installed +```bash +pip install -r requirements.txt +``` +for user in china, you can specify pypi source to accelerate install like: +```bash +pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + +1. Clone this repository +``` +git clone git@github.com:ZQPei/deep_sort_pytorch.git +``` + +2. Download YOLOv3 parameters +``` +cd detector/YOLOv3/weight/ +wget https://pjreddie.com/media/files/yolov3.weights +wget https://pjreddie.com/media/files/yolov3-tiny.weights +cd ../../../ +``` + +3. Download deepsort parameters ckpt.t7 +``` +cd deep_sort/deep/checkpoint +# download ckpt.t7 from +https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder +cd ../../../ +``` + +4. Compile nms module +```bash +cd detector/YOLOv3/nms +sh build.sh +cd ../../.. +``` + +Notice: +If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either `gcc version too low` or `libraries missing`. + +5. (Optional) Prepare third party submodules + +[fast-reid](https://github.com/JDAI-CV/fast-reid) + +This library supports bagtricks, AGW and other mainstream ReID methods through providing an fast-reid adapter. + +to prepare our bundled fast-reid, then follow instructions in its README to install it. + +Please refer to `configs/fastreid.yaml` for a sample of using fast-reid. See [Model Zoo](https://github.com/JDAI-CV/fast-reid/blob/master/docs/MODEL_ZOO.md) for available methods and trained models. + +[MMDetection](https://github.com/open-mmlab/mmdetection) + +This library supports Faster R-CNN and other mainstream detection methods through providing an MMDetection adapter. + +to prepare our bundled MMDetection, then follow instructions in its README to install it. + +Please refer to `configs/mmdet.yaml` for a sample of using MMDetection. See [Model Zoo](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) for available methods and trained models. + +Run + +``` +git submodule update --init --recursive +``` + + +6. Run demo +``` +usage: deepsort.py [-h] + [--fastreid] + [--config_fastreid CONFIG_FASTREID] + [--mmdet] + [--config_mmdetection CONFIG_MMDETECTION] + [--config_detection CONFIG_DETECTION] + [--config_deepsort CONFIG_DEEPSORT] [--display] + [--frame_interval FRAME_INTERVAL] + [--display_width DISPLAY_WIDTH] + [--display_height DISPLAY_HEIGHT] [--save_path SAVE_PATH] + [--cpu] [--camera CAM] + VIDEO_PATH + +# yolov3 + deepsort +python deepsort.py [VIDEO_PATH] + +# yolov3_tiny + deepsort +python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml + +# yolov3 + deepsort on webcam +python3 deepsort.py /dev/video0 --camera 0 + +# yolov3_tiny + deepsort on webcam +python3 deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0 + +# fast-reid + deepsort +python deepsort.py [VIDEO_PATH] --fastreid [--config_fastreid ./configs/fastreid.yaml] + +# MMDetection + deepsort +python deepsort.py [VIDEO_PATH] --mmdet [--config_mmdetection ./configs/mmdet.yaml] +``` +Use `--display` to enable display. +Results will be saved to `./output/results.avi` and `./output/results.txt`. + +All files above can also be accessed from BaiduDisk! +linker：[BaiduDisk](https://pan.baidu.com/s/1YJ1iPpdFTlUyLFoonYvozg) +passwd：fbuw + +## References +- paper: [Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402) + +- code: [nwojke/deep_sort](https://github.com/nwojke/deep_sort) + +- paper: [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf) + +- code: [Joseph Redmon/yolov3](https://pjreddie.com/darknet/yolo/) + +https://github.com/ZQPei/deep_sort_pytorch diff --git a/cv/tracking/deep_sort/pytorch/configs/deep_sort.yaml b/cv/tracking/deep_sort/pytorch/configs/deep_sort.yaml new file mode 100644 index 000000000..f06834d32 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/configs/deep_sort.yaml @@ -0,0 +1,10 @@ +DEEPSORT: + REID_CKPT: "./deep_sort/deep/checkpoint/ckpt.t7" + MAX_DIST: 0.2 + MIN_CONFIDENCE: 0.3 + NMS_MAX_OVERLAP: 0.5 + MAX_IOU_DISTANCE: 0.7 + MAX_AGE: 70 + N_INIT: 3 + NN_BUDGET: 100 + \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/configs/fastreid.yaml b/cv/tracking/deep_sort/pytorch/configs/fastreid.yaml new file mode 100644 index 000000000..60b37f710 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/configs/fastreid.yaml @@ -0,0 +1,3 @@ +FASTREID: + CFG: "thirdparty/fast-reid/configs/Market1501/bagtricks_R50.yml" + CHECKPOINT: "deep_sort/deep/checkpoint/market_bot_R50.pth" \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/configs/mmdet.yaml b/cv/tracking/deep_sort/pytorch/configs/mmdet.yaml new file mode 100644 index 000000000..e1b7e5882 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/configs/mmdet.yaml @@ -0,0 +1,5 @@ +MMDET: + CFG: "thirdparty/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py" + CHECKPOINT: "detector/MMDet/weight/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth" + + SCORE_THRESH: 0.5 \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/configs/yolov3.yaml b/cv/tracking/deep_sort/pytorch/configs/yolov3.yaml new file mode 100644 index 000000000..a08476ae5 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/configs/yolov3.yaml @@ -0,0 +1,7 @@ +YOLOV3: + CFG: "./detector/YOLOv3/cfg/yolo_v3.cfg" + WEIGHT: "./detector/YOLOv3/weight/yolov3.weights" + CLASS_NAMES: "./detector/YOLOv3/cfg/coco.names" + + SCORE_THRESH: 0.5 + NMS_THRESH: 0.4 diff --git a/cv/tracking/deep_sort/pytorch/configs/yolov3_tiny.yaml b/cv/tracking/deep_sort/pytorch/configs/yolov3_tiny.yaml new file mode 100644 index 000000000..1261e6859 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/configs/yolov3_tiny.yaml @@ -0,0 +1,7 @@ +YOLOV3: + CFG: "./detector/YOLOv3/cfg/yolov3-tiny.cfg" + WEIGHT: "./detector/YOLOv3/weight/yolov3-tiny.weights" + CLASS_NAMES: "./detector/YOLOv3/cfg/coco.names" + + SCORE_THRESH: 0.5 + NMS_THRESH: 0.4 \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/README.md b/cv/tracking/deep_sort/pytorch/deep_sort/README.md new file mode 100644 index 000000000..e89c9b3ea --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/README.md @@ -0,0 +1,3 @@ +# Deep Sort + +This is the implemention of deep sort with pytorch. \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/__init__.py b/cv/tracking/deep_sort/pytorch/deep_sort/__init__.py new file mode 100644 index 000000000..9148db420 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/__init__.py @@ -0,0 +1,28 @@ +from .deep_sort import DeepSort + + +__all__ = ['DeepSort', 'build_tracker'] + + +def build_tracker(cfg, use_cuda): + if cfg.USE_FASTREID: + return DeepSort(model_path=cfg.FASTREID.CHECKPOINT, model_config=cfg.FASTREID.CFG, + max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, + nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, + max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=use_cuda) + + else: + return DeepSort(model_path=cfg.DEEPSORT.REID_CKPT, + max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, + nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, + max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=use_cuda) + + + + + + + + + + diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/__init__.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/checkpoint/.gitkeep b/cv/tracking/deep_sort/pytorch/deep_sort/deep/checkpoint/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py new file mode 100644 index 000000000..2e4bc7f9a --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py @@ -0,0 +1,128 @@ +import os +from PIL import Image +from shutil import copyfile, copytree, rmtree, move + +PATH_DATASET = '/home/datasets/cv/Market-1501-v15.09.15' # 需要处理的文件夹 +PATH_NEW_DATASET = '/home/wenfeng.zhang/datasets/market1501' # 处理后的文件夹 +PATH_ALL_IMAGES = PATH_NEW_DATASET + '/all_images' +PATH_TRAIN = PATH_NEW_DATASET + '/train' +PATH_TEST = PATH_NEW_DATASET + '/test' + + +# 定义创建目录函数 +def mymkdir(path): + path = path.strip() # 去除首位空格 + path = path.rstrip("\\") # 去除尾部 \ 符号 + isExists = os.path.exists(path) # 判断路径是否存在 + if not isExists: + os.makedirs(path) # 如果不存在则创建目录 + print(path + ' 创建成功') + return True + else: + # 如果目录存在则不创建，并提示目录已存在 + print(path + ' 目录已存在') + return False + + +class BatchRename(): + ''' + 批量重命名文件夹中的图片文件 + ''' + + def __init__(self): + self.path = PATH_DATASET # 表示需要命名处理的文件夹 + + # 修改图像尺寸 + def resize(self): + for aroot, dirs, files in os.walk(self.path): + # aroot是self.path目录下的所有子目录（含self.path）,dir是self.path下所有的文件夹的列表. + filelist = files # 注意此处仅是该路径下的其中一个列表 + # print('list', list) + + # filelist = os.listdir(self.path) #获取文件路径 + total_num = len(filelist) # 获取文件长度（个数） + + for item in filelist: + if item.endswith('.jpg'): # 初始的图片的格式为jpg格式的（或者源文件是png格式及其他格式，后面的转换格式就可以调整为自己需要的格式即可） + src = os.path.join(os.path.abspath(aroot), item) + + # 修改图片尺寸到128宽*256高 + im = Image.open(src) + out = im.resize((128, 256), Image.ANTIALIAS) # resize image with high-quality + out.save(src) # 原路径保存 + + def rename(self): + + for aroot, dirs, files in os.walk(self.path): + # aroot是self.path目录下的所有子目录（含self.path）,dir是self.path下所有的文件夹的列表. + filelist = files # 注意此处仅是该路径下的其中一个列表 + # print('list', list) + + # filelist = os.listdir(self.path) #获取文件路径 + total_num = len(filelist) # 获取文件长度（个数） + + i = 1 # 表示文件的命名是从1开始的 + for item in filelist: + if item.endswith('.jpg'): # 初始的图片的格式为jpg格式的（或者源文件是png格式及其他格式，后面的转换格式就可以调整为自己需要的格式即可） + src = os.path.join(os.path.abspath(aroot), item) + + # 根据图片名创建图片目录 + dirname = str(item.split('_')[0]) + # 为相同车辆创建目录 + # new_dir = os.path.join(self.path, '..', 'bbox_all', dirname) + new_dir = os.path.join(PATH_ALL_IMAGES, dirname) + if not os.path.isdir(new_dir): + mymkdir(new_dir) + + # 获得new_dir中的图片数 + num_pic = len(os.listdir(new_dir)) + + dst = os.path.join(os.path.abspath(new_dir), + dirname + 'C1T0001F' + str(num_pic + 1) + '.jpg') + # 处理后的格式也为jpg格式的，当然这里可以改成png格式 C1T0001F见mars.py filenames 相机ID，跟踪指数 + # dst = os.path.join(os.path.abspath(self.path), '0000' + format(str(i), '0>3s') + '.jpg') 这种情况下的命名格式为0000000.jpg形式，可以自主定义想要的格式 + try: + copyfile(src, dst) # os.rename(src, dst) + print('converting %s to %s ...' % (src, dst)) + i = i + 1 + except: + continue + print('total %d to rename & converted %d jpgs' % (total_num, i)) + + def split(self): + # --------------------------------------- + # train_test + images_path = PATH_ALL_IMAGES + train_save_path = PATH_TRAIN + test_save_path = PATH_TEST + if not os.path.isdir(train_save_path): + os.mkdir(train_save_path) + os.mkdir(test_save_path) + + for _, dirs, _ in os.walk(images_path, topdown=True): + for i, dir in enumerate(dirs): + for root, _, files in os.walk(images_path + '/' + dir, topdown=True): + for j, file in enumerate(files): + if (j == 0): # test dataset；每个车辆的第一幅图片 + print("序号：%s 文件夹： %s 图片：%s 归为测试集" % (i + 1, root, file)) + src_path = root + '/' + file + dst_dir = test_save_path + '/' + dir + if not os.path.isdir(dst_dir): + os.mkdir(dst_dir) + dst_path = dst_dir + '/' + file + move(src_path, dst_path) + else: + src_path = root + '/' + file + dst_dir = train_save_path + '/' + dir + if not os.path.isdir(dst_dir): + os.mkdir(dst_dir) + dst_path = dst_dir + '/' + file + move(src_path, dst_path) + rmtree(PATH_ALL_IMAGES) + + +if __name__ == '__main__': + demo = BatchRename() + demo.resize() + demo.rename() + demo.split() \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/evaluate.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/evaluate.py new file mode 100644 index 000000000..85eaa6fc3 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/evaluate.py @@ -0,0 +1,15 @@ +import torch + +features = torch.load("features.pth") +qf = features["qf"] +ql = features["ql"] +gf = features["gf"] +gl = features["gl"] + +scores = qf.mm(gf.t()) +res = scores.topk(5, dim=1)[1][:,0] +top1correct = gl[res].eq(ql).sum().item() + +print("Acc top1:{:.3f}".format(top1correct/ql.size(0))) + + diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/feature_extractor.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/feature_extractor.py new file mode 100644 index 000000000..a35283eec --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/feature_extractor.py @@ -0,0 +1,95 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import cv2 +import logging + +from .model import Net +from fastreid.config import get_cfg +from fastreid.engine import DefaultTrainer +from fastreid.utils.checkpoint import Checkpointer + +class Extractor(object): + def __init__(self, model_path, use_cuda=True): + self.net = Net(reid=True) + self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict'] + self.net.load_state_dict(state_dict) + logger = logging.getLogger("root.tracker") + logger.info("Loading weights from {}... Done!".format(model_path)) + self.net.to(self.device) + self.size = (64, 128) + self.norm = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) + + + + def _preprocess(self, im_crops): + """ + TODO: + 1. to float with scale from 0 to 1 + 2. resize to (64, 128) as Market1501 dataset did + 3. concatenate to a numpy array + 3. to torch Tensor + 4. normalize + """ + def _resize(im, size): + return cv2.resize(im.astype(np.float32)/255., size) + + im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() + return im_batch + + + def __call__(self, im_crops): + im_batch = self._preprocess(im_crops) + with torch.no_grad(): + im_batch = im_batch.to(self.device) + features = self.net(im_batch) + return features.cpu().numpy() + +class FastReIDExtractor(object): + def __init__(self, model_config, model_path, use_cuda=True): + cfg = get_cfg() + cfg.merge_from_file(model_config) + cfg.MODEL.BACKBONE.PRETRAIN = False + self.net = DefaultTrainer.build_model(cfg) + self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" + + Checkpointer(self.net).load(model_path) + logger = logging.getLogger("root.tracker") + logger.info("Loading weights from {}... Done!".format(model_path)) + self.net.to(self.device) + self.net.eval() + height, width = cfg.INPUT.SIZE_TEST + self.size = (width, height) + self.norm = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) + + + def _preprocess(self, im_crops): + def _resize(im, size): + return cv2.resize(im.astype(np.float32)/255., size) + + im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() + return im_batch + + + def __call__(self, im_crops): + im_batch = self._preprocess(im_crops) + with torch.no_grad(): + im_batch = im_batch.to(self.device) + features = self.net(im_batch) + return features.cpu().numpy() + + + +if __name__ == '__main__': + img = cv2.imread("demo.jpg")[:,:,(2,1,0)] + extr = Extractor("checkpoint/ckpt.t7") + feature = extr(img) + print(feature.shape) + diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/model.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/model.py new file mode 100644 index 000000000..bc92106d9 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/model.py @@ -0,0 +1,104 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class BasicBlock(nn.Module): + def __init__(self, c_in, c_out,is_downsample=False): + super(BasicBlock,self).__init__() + self.is_downsample = is_downsample + if is_downsample: + self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False) + else: + self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(c_out) + self.relu = nn.ReLU(True) + self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(c_out) + if is_downsample: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), + nn.BatchNorm2d(c_out) + ) + elif c_in != c_out: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), + nn.BatchNorm2d(c_out) + ) + self.is_downsample = True + + def forward(self,x): + y = self.conv1(x) + y = self.bn1(y) + y = self.relu(y) + y = self.conv2(y) + y = self.bn2(y) + if self.is_downsample: + x = self.downsample(x) + return F.relu(x.add(y),True) + +def make_layers(c_in,c_out,repeat_times, is_downsample=False): + blocks = [] + for i in range(repeat_times): + if i ==0: + blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),] + else: + blocks += [BasicBlock(c_out,c_out),] + return nn.Sequential(*blocks) + +class Net(nn.Module): + def __init__(self, num_classes=751 ,reid=False): + super(Net,self).__init__() + # 3 128 64 + self.conv = nn.Sequential( + nn.Conv2d(3,64,3,stride=1,padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + # nn.Conv2d(32,32,3,stride=1,padding=1), + # nn.BatchNorm2d(32), + # nn.ReLU(inplace=True), + nn.MaxPool2d(3,2,padding=1), + ) + # 32 64 32 + self.layer1 = make_layers(64,64,2,False) + # 32 64 32 + self.layer2 = make_layers(64,128,2,True) + # 64 32 16 + self.layer3 = make_layers(128,256,2,True) + # 128 16 8 + self.layer4 = make_layers(256,512,2,True) + # 256 8 4 + self.avgpool = nn.AvgPool2d((8,4),1) + # 256 1 1 + self.reid = reid + self.classifier = nn.Sequential( + nn.Linear(512, 256), + nn.BatchNorm1d(256), + nn.ReLU(inplace=True), + nn.Dropout(), + nn.Linear(256, num_classes), + ) + + def forward(self, x): + x = self.conv(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.avgpool(x) + x = x.view(x.size(0),-1) + # B x 128 + if self.reid: + x = x.div(x.norm(p=2,dim=1,keepdim=True)) + return x + # classifier + x = self.classifier(x) + return x + + +if __name__ == '__main__': + net = Net() + x = torch.randn(4,3,128,64) + y = net(x) + import ipdb; ipdb.set_trace() + + diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/original_model.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/original_model.py new file mode 100644 index 000000000..72453a639 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/original_model.py @@ -0,0 +1,106 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class BasicBlock(nn.Module): + def __init__(self, c_in, c_out,is_downsample=False): + super(BasicBlock,self).__init__() + self.is_downsample = is_downsample + if is_downsample: + self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False) + else: + self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(c_out) + self.relu = nn.ReLU(True) + self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(c_out) + if is_downsample: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), + nn.BatchNorm2d(c_out) + ) + elif c_in != c_out: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), + nn.BatchNorm2d(c_out) + ) + self.is_downsample = True + + def forward(self,x): + y = self.conv1(x) + y = self.bn1(y) + y = self.relu(y) + y = self.conv2(y) + y = self.bn2(y) + if self.is_downsample: + x = self.downsample(x) + return F.relu(x.add(y),True) + +def make_layers(c_in,c_out,repeat_times, is_downsample=False): + blocks = [] + for i in range(repeat_times): + if i ==0: + blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),] + else: + blocks += [BasicBlock(c_out,c_out),] + return nn.Sequential(*blocks) + +class Net(nn.Module): + def __init__(self, num_classes=625 ,reid=False): + super(Net,self).__init__() + # 3 128 64 + self.conv = nn.Sequential( + nn.Conv2d(3,32,3,stride=1,padding=1), + nn.BatchNorm2d(32), + nn.ELU(inplace=True), + nn.Conv2d(32,32,3,stride=1,padding=1), + nn.BatchNorm2d(32), + nn.ELU(inplace=True), + nn.MaxPool2d(3,2,padding=1), + ) + # 32 64 32 + self.layer1 = make_layers(32,32,2,False) + # 32 64 32 + self.layer2 = make_layers(32,64,2,True) + # 64 32 16 + self.layer3 = make_layers(64,128,2,True) + # 128 16 8 + self.dense = nn.Sequential( + nn.Dropout(p=0.6), + nn.Linear(128*16*8, 128), + nn.BatchNorm1d(128), + nn.ELU(inplace=True) + ) + # 256 1 1 + self.reid = reid + self.batch_norm = nn.BatchNorm1d(128) + self.classifier = nn.Sequential( + nn.Linear(128, num_classes), + ) + + def forward(self, x): + x = self.conv(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + + x = x.view(x.size(0),-1) + if self.reid: + x = self.dense[0](x) + x = self.dense[1](x) + x = x.div(x.norm(p=2,dim=1,keepdim=True)) + return x + x = self.dense(x) + # B x 128 + # classifier + x = self.classifier(x) + return x + + +if __name__ == '__main__': + net = Net(reid=True) + x = torch.randn(4,3,128,64) + y = net(x) + import ipdb; ipdb.set_trace() + + diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/test.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/test.py new file mode 100644 index 000000000..ecac0ad5c --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/test.py @@ -0,0 +1,77 @@ +import torch +import torch.backends.cudnn as cudnn +import torchvision + +import argparse +import os + +from model import Net + +parser = argparse.ArgumentParser(description="Train on market1501") +parser.add_argument("--data-dir",default='data',type=str) +parser.add_argument("--no-cuda",action="store_true") +parser.add_argument("--gpu-id",default=0,type=int) +args = parser.parse_args() + +# device +device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu" +if torch.cuda.is_available() and not args.no_cuda: + cudnn.benchmark = True + +# data loader +root = args.data_dir +query_dir = os.path.join(root,"query") +gallery_dir = os.path.join(root,"gallery") +transform = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128,64)), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +]) +queryloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(query_dir, transform=transform), + batch_size=64, shuffle=False +) +galleryloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(gallery_dir, transform=transform), + batch_size=64, shuffle=False +) + +# net definition +net = Net(reid=True) +assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!" +print('Loading from checkpoint/ckpt.t7') +checkpoint = torch.load("./checkpoint/ckpt.t7") +net_dict = checkpoint['net_dict'] +net.load_state_dict(net_dict, strict=False) +net.eval() +net.to(device) + +# compute features +query_features = torch.tensor([]).float() +query_labels = torch.tensor([]).long() +gallery_features = torch.tensor([]).float() +gallery_labels = torch.tensor([]).long() + +with torch.no_grad(): + for idx,(inputs,labels) in enumerate(queryloader): + inputs = inputs.to(device) + features = net(inputs).cpu() + query_features = torch.cat((query_features, features), dim=0) + query_labels = torch.cat((query_labels, labels)) + + for idx,(inputs,labels) in enumerate(galleryloader): + inputs = inputs.to(device) + features = net(inputs).cpu() + gallery_features = torch.cat((gallery_features, features), dim=0) + gallery_labels = torch.cat((gallery_labels, labels)) + +gallery_labels -= 2 + +# save features +features = { + "qf": query_features, + "ql": query_labels, + "gf": gallery_features, + "gl": gallery_labels +} +torch.save(features,"features.pth") \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/train.jpg b/cv/tracking/deep_sort/pytorch/deep_sort/deep/train.jpg new file mode 100644 index 0000000000000000000000000000000000000000..de464c999a57744c0e79556828615b9c7b0689c8 GIT binary patch literal 31654 zcmeFZ1yo#1);8R@y99zJxCeI#BzVx^?gV#t4IwzeJy>uE?(V_eoyMVY=kMg+x&PeU zJ9B4d&HJwJTkC69b+b;Ns;*sCyY{o6z3V*AJgxxHB}FAf0T2)n07LK};Bg-C3IGoa z3kM4W4+jT_fB=t(jE#bfgoKQbiG_wu^pu2{=qVu~DLEq*>2rEALPBaz8u}N^Y;0^K 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b/cv/tracking/deep_sort/pytorch/deep_sort/deep/train.py new file mode 100644 index 000000000..a2d463a94 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/train.py @@ -0,0 +1,189 @@ +import argparse +import os +import time + +import numpy as np +import matplotlib.pyplot as plt +import torch +import torch.backends.cudnn as cudnn +import torchvision + +from model import Net + +parser = argparse.ArgumentParser(description="Train on market1501") +parser.add_argument("--data-dir",default='data',type=str) +parser.add_argument("--no-cuda",action="store_true") +parser.add_argument("--gpu-id",default=0,type=int) +parser.add_argument("--lr",default=0.1, type=float) +parser.add_argument("--interval",'-i',default=20,type=int) +parser.add_argument('--resume', '-r',action='store_true') +args = parser.parse_args() + +# device +device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu" +if torch.cuda.is_available() and not args.no_cuda: + cudnn.benchmark = True + +# data loading +root = args.data_dir +train_dir = os.path.join(root,"train") +test_dir = os.path.join(root,"test") +transform_train = torchvision.transforms.Compose([ + torchvision.transforms.RandomCrop((128,64),padding=4), + torchvision.transforms.RandomHorizontalFlip(), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +]) +transform_test = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128,64)), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +]) +trainloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(train_dir, transform=transform_train), + batch_size=64,shuffle=True +) +testloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(test_dir, transform=transform_test), + batch_size=64,shuffle=True +) +num_classes = max(len(trainloader.dataset.classes), len(testloader.dataset.classes)) + +# net definition +start_epoch = 0 +net = Net(num_classes=num_classes) +if args.resume: + assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!" + print('Loading from checkpoint/ckpt.t7') + checkpoint = torch.load("./checkpoint/ckpt.t7") + # import ipdb; ipdb.set_trace() + net_dict = checkpoint['net_dict'] + net.load_state_dict(net_dict) + best_acc = checkpoint['acc'] + start_epoch = checkpoint['epoch'] +net.to(device) + +# loss and optimizer +criterion = torch.nn.CrossEntropyLoss() +optimizer = torch.optim.SGD(net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4) +best_acc = 0. + +# train function for each epoch +def train(epoch): + print("\nEpoch : %d"%(epoch+1)) + net.train() + training_loss = 0. + train_loss = 0. + correct = 0 + total = 0 + interval = args.interval + start = time.time() + for idx, (inputs, labels) in enumerate(trainloader): + # forward + inputs,labels = inputs.to(device),labels.to(device) + outputs = net(inputs) + loss = criterion(outputs, labels) + + # backward + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # accumurating + training_loss += loss.item() + train_loss += loss.item() + correct += outputs.max(dim=1)[1].eq(labels).sum().item() + total += labels.size(0) + + # print + if (idx+1)%interval == 0: + end = time.time() + print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format( + 100.*(idx+1)/len(trainloader), end-start, training_loss/interval, correct, total, 100.*correct/total + )) + training_loss = 0. + start = time.time() + + return train_loss/len(trainloader), 1.- correct/total + +def test(epoch): + global best_acc + net.eval() + test_loss = 0. + correct = 0 + total = 0 + start = time.time() + with torch.no_grad(): + for idx, (inputs, labels) in enumerate(testloader): + inputs, labels = inputs.to(device), labels.to(device) + outputs = net(inputs) + loss = criterion(outputs, labels) + + test_loss += loss.item() + correct += outputs.max(dim=1)[1].eq(labels).sum().item() + total += labels.size(0) + + print("Testing ...") + end = time.time() + print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format( + 100.*(idx+1)/len(testloader), end-start, test_loss/len(testloader), correct, total, 100.*correct/total + )) + + # saving checkpoint + acc = 100.*correct/total + if acc > best_acc: + best_acc = acc + print("Saving parameters to checkpoint/ckpt.t7") + checkpoint = { + 'net_dict':net.state_dict(), + 'acc':acc, + 'epoch':epoch, + } + if not os.path.isdir('checkpoint'): + os.mkdir('checkpoint') + torch.save(checkpoint, './checkpoint/ckpt1.t7') + + return test_loss/len(testloader), 1.- correct/total + +# plot figure +x_epoch = [] +record = {'train_loss':[], 'train_err':[], 'test_loss':[], 'test_err':[]} +fig = plt.figure() +ax0 = fig.add_subplot(121, title="loss") +ax1 = fig.add_subplot(122, title="top1err") +def draw_curve(epoch, train_loss, train_err, test_loss, test_err): + global record + record['train_loss'].append(train_loss) + record['train_err'].append(train_err) + record['test_loss'].append(test_loss) + record['test_err'].append(test_err) + + x_epoch.append(epoch) + ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train') + ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val') + ax1.plot(x_epoch, record['train_err'], 'bo-', label='train') + ax1.plot(x_epoch, record['test_err'], 'ro-', label='val') + if epoch == 0: + ax0.legend() + ax1.legend() + fig.savefig("train.jpg") + +# lr decay +def lr_decay(): + global optimizer + for params in optimizer.param_groups: + params['lr'] *= 0.1 + lr = params['lr'] + print("Learning rate adjusted to {}".format(lr)) + +def main(): + for epoch in range(start_epoch, start_epoch+40): + train_loss, train_err = train(epoch) + test_loss, test_err = test(epoch) + draw_curve(epoch, train_loss, train_err, test_loss, test_err) + if (epoch+1)%20==0: + lr_decay() + + +if __name__ == '__main__': + main() diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep_sort.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep_sort.py new file mode 100644 index 000000000..0f54b4767 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep_sort.py @@ -0,0 +1,117 @@ +import numpy as np +import torch + +from .deep.feature_extractor import Extractor, FastReIDExtractor +from .sort.nn_matching import NearestNeighborDistanceMetric +from .sort.preprocessing import non_max_suppression +from .sort.detection import Detection +from .sort.tracker import Tracker + + +__all__ = ['DeepSort'] + + +class DeepSort(object): + def __init__(self, model_path, model_config=None, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True): + self.min_confidence = min_confidence + self.nms_max_overlap = nms_max_overlap + + if model_config is None: + self.extractor = Extractor(model_path, use_cuda=use_cuda) + else: + self.extractor = FastReIDExtractor(model_config, model_path, use_cuda=use_cuda) + + max_cosine_distance = max_dist + metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget) + self.tracker = Tracker(metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init) + + def update(self, bbox_xywh, confidences, ori_img): + self.height, self.width = ori_img.shape[:2] + # generate detections + features = self._get_features(bbox_xywh, ori_img) + bbox_tlwh = self._xywh_to_tlwh(bbox_xywh) + detections = [Detection(bbox_tlwh[i], conf, features[i]) for i,conf in enumerate(confidences) if conf>self.min_confidence] + + # run on non-maximum supression + boxes = np.array([d.tlwh for d in detections]) + scores = np.array([d.confidence for d in detections]) + indices = non_max_suppression(boxes, self.nms_max_overlap, scores) + detections = [detections[i] for i in indices] + + # update tracker + self.tracker.predict() + self.tracker.update(detections) + + # output bbox identities + outputs = [] + for track in self.tracker.tracks: + if not track.is_confirmed() or track.time_since_update > 1: + continue + box = track.to_tlwh() + x1,y1,x2,y2 = self._tlwh_to_xyxy(box) + track_id = track.track_id + outputs.append(np.array([x1,y1,x2,y2,track_id], dtype=np.int)) + if len(outputs) > 0: + outputs = np.stack(outputs,axis=0) + return outputs + + + """ + TODO: + Convert bbox from xc_yc_w_h to xtl_ytl_w_h + Thanks JieChen91@github.com for reporting this bug! + """ + @staticmethod + def _xywh_to_tlwh(bbox_xywh): + if isinstance(bbox_xywh, np.ndarray): + bbox_tlwh = bbox_xywh.copy() + elif isinstance(bbox_xywh, torch.Tensor): + bbox_tlwh = bbox_xywh.clone() + bbox_tlwh[:,0] = bbox_xywh[:,0] - bbox_xywh[:,2]/2. + bbox_tlwh[:,1] = bbox_xywh[:,1] - bbox_xywh[:,3]/2. + return bbox_tlwh + + + def _xywh_to_xyxy(self, bbox_xywh): + x,y,w,h = bbox_xywh + x1 = max(int(x-w/2),0) + x2 = min(int(x+w/2),self.width-1) + y1 = max(int(y-h/2),0) + y2 = min(int(y+h/2),self.height-1) + return x1,y1,x2,y2 + + def _tlwh_to_xyxy(self, bbox_tlwh): + """ + TODO: + Convert bbox from xtl_ytl_w_h to xc_yc_w_h + Thanks JieChen91@github.com for reporting this bug! + """ + x,y,w,h = bbox_tlwh + x1 = max(int(x),0) + x2 = min(int(x+w),self.width-1) + y1 = max(int(y),0) + y2 = min(int(y+h),self.height-1) + return x1,y1,x2,y2 + + def _xyxy_to_tlwh(self, bbox_xyxy): + x1,y1,x2,y2 = bbox_xyxy + + t = x1 + l = y1 + w = int(x2-x1) + h = int(y2-y1) + return t,l,w,h + + def _get_features(self, bbox_xywh, ori_img): + im_crops = [] + for box in bbox_xywh: + x1,y1,x2,y2 = self._xywh_to_xyxy(box) + im = ori_img[y1:y2,x1:x2] + im_crops.append(im) + if im_crops: + features = self.extractor(im_crops) + else: + features = np.array([]) + return features + + diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/__init__.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/detection.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/detection.py new file mode 100644 index 000000000..87fc5fd79 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/detection.py @@ -0,0 +1,49 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np + + +class Detection(object): + """ + This class represents a bounding box detection in a single image. + + Parameters + ---------- + tlwh : array_like + Bounding box in format `(x, y, w, h)`. + confidence : float + Detector confidence score. + feature : array_like + A feature vector that describes the object contained in this image. + + Attributes + ---------- + tlwh : ndarray + Bounding box in format `(top left x, top left y, width, height)`. + confidence : ndarray + Detector confidence score. + feature : ndarray | NoneType + A feature vector that describes the object contained in this image. + + """ + + def __init__(self, tlwh, confidence, feature): + self.tlwh = np.asarray(tlwh, dtype=np.float) + self.confidence = float(confidence) + self.feature = np.asarray(feature, dtype=np.float32) + + def to_tlbr(self): + """Convert bounding box to format `(min x, min y, max x, max y)`, i.e., + `(top left, bottom right)`. + """ + ret = self.tlwh.copy() + ret[2:] += ret[:2] + return ret + + def to_xyah(self): + """Convert bounding box to format `(center x, center y, aspect ratio, + height)`, where the aspect ratio is `width / height`. + """ + ret = self.tlwh.copy() + ret[:2] += ret[2:] / 2 + ret[2] /= ret[3] + return ret diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/iou_matching.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/iou_matching.py new file mode 100644 index 000000000..481e930da --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/iou_matching.py @@ -0,0 +1,81 @@ +# vim: expandtab:ts=4:sw=4 +from __future__ import absolute_import +import numpy as np +from . import linear_assignment + + +def iou(bbox, candidates): + """Computer intersection over union. + + Parameters + ---------- + bbox : ndarray + A bounding box in format `(top left x, top left y, width, height)`. + candidates : ndarray + A matrix of candidate bounding boxes (one per row) in the same format + as `bbox`. + + Returns + ------- + ndarray + The intersection over union in [0, 1] between the `bbox` and each + candidate. A higher score means a larger fraction of the `bbox` is + occluded by the candidate. + + """ + bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:] + candidates_tl = candidates[:, :2] + candidates_br = candidates[:, :2] + candidates[:, 2:] + + tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis], + np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]] + br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis], + np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]] + wh = np.maximum(0., br - tl) + + area_intersection = wh.prod(axis=1) + area_bbox = bbox[2:].prod() + area_candidates = candidates[:, 2:].prod(axis=1) + return area_intersection / (area_bbox + area_candidates - area_intersection) + + +def iou_cost(tracks, detections, track_indices=None, + detection_indices=None): + """An intersection over union distance metric. + + Parameters + ---------- + tracks : List[deep_sort.track.Track] + A list of tracks. + detections : List[deep_sort.detection.Detection] + A list of detections. + track_indices : Optional[List[int]] + A list of indices to tracks that should be matched. Defaults to + all `tracks`. + detection_indices : Optional[List[int]] + A list of indices to detections that should be matched. Defaults + to all `detections`. + + Returns + ------- + ndarray + Returns a cost matrix of shape + len(track_indices), len(detection_indices) where entry (i, j) is + `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`. + + """ + if track_indices is None: + track_indices = np.arange(len(tracks)) + if detection_indices is None: + detection_indices = np.arange(len(detections)) + + cost_matrix = np.zeros((len(track_indices), len(detection_indices))) + for row, track_idx in enumerate(track_indices): + if tracks[track_idx].time_since_update > 1: + cost_matrix[row, :] = linear_assignment.INFTY_COST + continue + + bbox = tracks[track_idx].to_tlwh() + candidates = np.asarray([detections[i].tlwh for i in detection_indices]) + cost_matrix[row, :] = 1. - iou(bbox, candidates) + return cost_matrix diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/kalman_filter.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/kalman_filter.py new file mode 100644 index 000000000..91d14f6ab --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/kalman_filter.py @@ -0,0 +1,229 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np +import scipy.linalg + + +""" +Table for the 0.95 quantile of the chi-square distribution with N degrees of +freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv +function and used as Mahalanobis gating threshold. +""" +chi2inv95 = { + 1: 3.8415, + 2: 5.9915, + 3: 7.8147, + 4: 9.4877, + 5: 11.070, + 6: 12.592, + 7: 14.067, + 8: 15.507, + 9: 16.919} + + +class KalmanFilter(object): + """ + A simple Kalman filter for tracking bounding boxes in image space. + + The 8-dimensional state space + + x, y, a, h, vx, vy, va, vh + + contains the bounding box center position (x, y), aspect ratio a, height h, + and their respective velocities. + + Object motion follows a constant velocity model. The bounding box location + (x, y, a, h) is taken as direct observation of the state space (linear + observation model). + + """ + + def __init__(self): + ndim, dt = 4, 1. + + # Create Kalman filter model matrices. + self._motion_mat = np.eye(2 * ndim, 2 * ndim) + for i in range(ndim): + self._motion_mat[i, ndim + i] = dt + self._update_mat = np.eye(ndim, 2 * ndim) + + # Motion and observation uncertainty are chosen relative to the current + # state estimate. These weights control the amount of uncertainty in + # the model. This is a bit hacky. + self._std_weight_position = 1. / 20 + self._std_weight_velocity = 1. / 160 + + def initiate(self, measurement): + """Create track from unassociated measurement. + + Parameters + ---------- + measurement : ndarray + Bounding box coordinates (x, y, a, h) with center position (x, y), + aspect ratio a, and height h. + + Returns + ------- + (ndarray, ndarray) + Returns the mean vector (8 dimensional) and covariance matrix (8x8 + dimensional) of the new track. Unobserved velocities are initialized + to 0 mean. + + """ + mean_pos = measurement + mean_vel = np.zeros_like(mean_pos) + mean = np.r_[mean_pos, mean_vel] + + std = [ + 2 * self._std_weight_position * measurement[3], + 2 * self._std_weight_position * measurement[3], + 1e-2, + 2 * self._std_weight_position * measurement[3], + 10 * self._std_weight_velocity * measurement[3], + 10 * self._std_weight_velocity * measurement[3], + 1e-5, + 10 * self._std_weight_velocity * measurement[3]] + covariance = np.diag(np.square(std)) + return mean, covariance + + def predict(self, mean, covariance): + """Run Kalman filter prediction step. + + Parameters + ---------- + mean : ndarray + The 8 dimensional mean vector of the object state at the previous + time step. + covariance : ndarray + The 8x8 dimensional covariance matrix of the object state at the + previous time step. + + Returns + ------- + (ndarray, ndarray) + Returns the mean vector and covariance matrix of the predicted + state. Unobserved velocities are initialized to 0 mean. + + """ + std_pos = [ + self._std_weight_position * mean[3], + self._std_weight_position * mean[3], + 1e-2, + self._std_weight_position * mean[3]] + std_vel = [ + self._std_weight_velocity * mean[3], + self._std_weight_velocity * mean[3], + 1e-5, + self._std_weight_velocity * mean[3]] + motion_cov = np.diag(np.square(np.r_[std_pos, std_vel])) + + mean = np.dot(self._motion_mat, mean) + covariance = np.linalg.multi_dot(( + self._motion_mat, covariance, self._motion_mat.T)) + motion_cov + + return mean, covariance + + def project(self, mean, covariance): + """Project state distribution to measurement space. + + Parameters + ---------- + mean : ndarray + The state's mean vector (8 dimensional array). + covariance : ndarray + The state's covariance matrix (8x8 dimensional). + + Returns + ------- + (ndarray, ndarray) + Returns the projected mean and covariance matrix of the given state + estimate. + + """ + std = [ + self._std_weight_position * mean[3], + self._std_weight_position * mean[3], + 1e-1, + self._std_weight_position * mean[3]] + innovation_cov = np.diag(np.square(std)) + + mean = np.dot(self._update_mat, mean) + covariance = np.linalg.multi_dot(( + self._update_mat, covariance, self._update_mat.T)) + return mean, covariance + innovation_cov + + def update(self, mean, covariance, measurement): + """Run Kalman filter correction step. + + Parameters + ---------- + mean : ndarray + The predicted state's mean vector (8 dimensional). + covariance : ndarray + The state's covariance matrix (8x8 dimensional). + measurement : ndarray + The 4 dimensional measurement vector (x, y, a, h), where (x, y) + is the center position, a the aspect ratio, and h the height of the + bounding box. + + Returns + ------- + (ndarray, ndarray) + Returns the measurement-corrected state distribution. + + """ + projected_mean, projected_cov = self.project(mean, covariance) + + chol_factor, lower = scipy.linalg.cho_factor( + projected_cov, lower=True, check_finite=False) + kalman_gain = scipy.linalg.cho_solve( + (chol_factor, lower), np.dot(covariance, self._update_mat.T).T, + check_finite=False).T + innovation = measurement - projected_mean + + new_mean = mean + np.dot(innovation, kalman_gain.T) + new_covariance = covariance - np.linalg.multi_dot(( + kalman_gain, projected_cov, kalman_gain.T)) + return new_mean, new_covariance + + def gating_distance(self, mean, covariance, measurements, + only_position=False): + """Compute gating distance between state distribution and measurements. + + A suitable distance threshold can be obtained from `chi2inv95`. If + `only_position` is False, the chi-square distribution has 4 degrees of + freedom, otherwise 2. + + Parameters + ---------- + mean : ndarray + Mean vector over the state distribution (8 dimensional). + covariance : ndarray + Covariance of the state distribution (8x8 dimensional). + measurements : ndarray + An Nx4 dimensional matrix of N measurements, each in + format (x, y, a, h) where (x, y) is the bounding box center + position, a the aspect ratio, and h the height. + only_position : Optional[bool] + If True, distance computation is done with respect to the bounding + box center position only. + + Returns + ------- + ndarray + Returns an array of length N, where the i-th element contains the + squared Mahalanobis distance between (mean, covariance) and + `measurements[i]`. + + """ + mean, covariance = self.project(mean, covariance) + if only_position: + mean, covariance = mean[:2], covariance[:2, :2] + measurements = measurements[:, :2] + + cholesky_factor = np.linalg.cholesky(covariance) + d = measurements - mean + z = scipy.linalg.solve_triangular( + cholesky_factor, d.T, lower=True, check_finite=False, + overwrite_b=True) + squared_maha = np.sum(z * z, axis=0) + return squared_maha diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/linear_assignment.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/linear_assignment.py new file mode 100644 index 000000000..f8c78bbd2 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/linear_assignment.py @@ -0,0 +1,192 @@ +# vim: expandtab:ts=4:sw=4 +from __future__ import absolute_import +import numpy as np +# from sklearn.utils.linear_assignment_ import linear_assignment +from scipy.optimize import linear_sum_assignment as linear_assignment +from . import kalman_filter + + +INFTY_COST = 1e+5 + + +def min_cost_matching( + distance_metric, max_distance, tracks, detections, track_indices=None, + detection_indices=None): + """Solve linear assignment problem. + + Parameters + ---------- + distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray + The distance metric is given a list of tracks and detections as well as + a list of N track indices and M detection indices. The metric should + return the NxM dimensional cost matrix, where element (i, j) is the + association cost between the i-th track in the given track indices and + the j-th detection in the given detection_indices. + max_distance : float + Gating threshold. Associations with cost larger than this value are + disregarded. + tracks : List[track.Track] + A list of predicted tracks at the current time step. + detections : List[detection.Detection] + A list of detections at the current time step. + track_indices : List[int] + List of track indices that maps rows in `cost_matrix` to tracks in + `tracks` (see description above). + detection_indices : List[int] + List of detection indices that maps columns in `cost_matrix` to + detections in `detections` (see description above). + + Returns + ------- + (List[(int, int)], List[int], List[int]) + Returns a tuple with the following three entries: + * A list of matched track and detection indices. + * A list of unmatched track indices. + * A list of unmatched detection indices. + + """ + if track_indices is None: + track_indices = np.arange(len(tracks)) + if detection_indices is None: + detection_indices = np.arange(len(detections)) + + if len(detection_indices) == 0 or len(track_indices) == 0: + return [], track_indices, detection_indices # Nothing to match. + + cost_matrix = distance_metric( + tracks, detections, track_indices, detection_indices) + cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5 + + row_indices, col_indices = linear_assignment(cost_matrix) + + matches, unmatched_tracks, unmatched_detections = [], [], [] + for col, detection_idx in enumerate(detection_indices): + if col not in col_indices: + unmatched_detections.append(detection_idx) + for row, track_idx in enumerate(track_indices): + if row not in row_indices: + unmatched_tracks.append(track_idx) + for row, col in zip(row_indices, col_indices): + track_idx = track_indices[row] + detection_idx = detection_indices[col] + if cost_matrix[row, col] > max_distance: + unmatched_tracks.append(track_idx) + unmatched_detections.append(detection_idx) + else: + matches.append((track_idx, detection_idx)) + return matches, unmatched_tracks, unmatched_detections + + +def matching_cascade( + distance_metric, max_distance, cascade_depth, tracks, detections, + track_indices=None, detection_indices=None): + """Run matching cascade. + + Parameters + ---------- + distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray + The distance metric is given a list of tracks and detections as well as + a list of N track indices and M detection indices. The metric should + return the NxM dimensional cost matrix, where element (i, j) is the + association cost between the i-th track in the given track indices and + the j-th detection in the given detection indices. + max_distance : float + Gating threshold. Associations with cost larger than this value are + disregarded. + cascade_depth: int + The cascade depth, should be se to the maximum track age. + tracks : List[track.Track] + A list of predicted tracks at the current time step. + detections : List[detection.Detection] + A list of detections at the current time step. + track_indices : Optional[List[int]] + List of track indices that maps rows in `cost_matrix` to tracks in + `tracks` (see description above). Defaults to all tracks. + detection_indices : Optional[List[int]] + List of detection indices that maps columns in `cost_matrix` to + detections in `detections` (see description above). Defaults to all + detections. + + Returns + ------- + (List[(int, int)], List[int], List[int]) + Returns a tuple with the following three entries: + * A list of matched track and detection indices. + * A list of unmatched track indices. + * A list of unmatched detection indices. + + """ + if track_indices is None: + track_indices = list(range(len(tracks))) + if detection_indices is None: + detection_indices = list(range(len(detections))) + + unmatched_detections = detection_indices + matches = [] + for level in range(cascade_depth): + if len(unmatched_detections) == 0: # No detections left + break + + track_indices_l = [ + k for k in track_indices + if tracks[k].time_since_update == 1 + level + ] + if len(track_indices_l) == 0: # Nothing to match at this level + continue + + matches_l, _, unmatched_detections = \ + min_cost_matching( + distance_metric, max_distance, tracks, detections, + track_indices_l, unmatched_detections) + matches += matches_l + unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches)) + return matches, unmatched_tracks, unmatched_detections + + +def gate_cost_matrix( + kf, cost_matrix, tracks, detections, track_indices, detection_indices, + gated_cost=INFTY_COST, only_position=False): + """Invalidate infeasible entries in cost matrix based on the state + distributions obtained by Kalman filtering. + + Parameters + ---------- + kf : The Kalman filter. + cost_matrix : ndarray + The NxM dimensional cost matrix, where N is the number of track indices + and M is the number of detection indices, such that entry (i, j) is the + association cost between `tracks[track_indices[i]]` and + `detections[detection_indices[j]]`. + tracks : List[track.Track] + A list of predicted tracks at the current time step. + detections : List[detection.Detection] + A list of detections at the current time step. + track_indices : List[int] + List of track indices that maps rows in `cost_matrix` to tracks in + `tracks` (see description above). + detection_indices : List[int] + List of detection indices that maps columns in `cost_matrix` to + detections in `detections` (see description above). + gated_cost : Optional[float] + Entries in the cost matrix corresponding to infeasible associations are + set this value. Defaults to a very large value. + only_position : Optional[bool] + If True, only the x, y position of the state distribution is considered + during gating. Defaults to False. + + Returns + ------- + ndarray + Returns the modified cost matrix. + + """ + gating_dim = 2 if only_position else 4 + gating_threshold = kalman_filter.chi2inv95[gating_dim] + measurements = np.asarray( + [detections[i].to_xyah() for i in detection_indices]) + for row, track_idx in enumerate(track_indices): + track = tracks[track_idx] + gating_distance = kf.gating_distance( + track.mean, track.covariance, measurements, only_position) + cost_matrix[row, gating_distance > gating_threshold] = gated_cost + return cost_matrix diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/nn_matching.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/nn_matching.py new file mode 100644 index 000000000..1d03d6f51 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/nn_matching.py @@ -0,0 +1,177 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np + + +def _pdist(a, b): + """Compute pair-wise squared distance between points in `a` and `b`. + + Parameters + ---------- + a : array_like + An NxM matrix of N samples of dimensionality M. + b : array_like + An LxM matrix of L samples of dimensionality M. + + Returns + ------- + ndarray + Returns a matrix of size len(a), len(b) such that eleement (i, j) + contains the squared distance between `a[i]` and `b[j]`. + + """ + a, b = np.asarray(a), np.asarray(b) + if len(a) == 0 or len(b) == 0: + return np.zeros((len(a), len(b))) + a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1) + r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :] + r2 = np.clip(r2, 0., float(np.inf)) + return r2 + + +def _cosine_distance(a, b, data_is_normalized=False): + """Compute pair-wise cosine distance between points in `a` and `b`. + + Parameters + ---------- + a : array_like + An NxM matrix of N samples of dimensionality M. + b : array_like + An LxM matrix of L samples of dimensionality M. + data_is_normalized : Optional[bool] + If True, assumes rows in a and b are unit length vectors. + Otherwise, a and b are explicitly normalized to lenght 1. + + Returns + ------- + ndarray + Returns a matrix of size len(a), len(b) such that eleement (i, j) + contains the squared distance between `a[i]` and `b[j]`. + + """ + if not data_is_normalized: + a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True) + b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True) + return 1. - np.dot(a, b.T) + + +def _nn_euclidean_distance(x, y): + """ Helper function for nearest neighbor distance metric (Euclidean). + + Parameters + ---------- + x : ndarray + A matrix of N row-vectors (sample points). + y : ndarray + A matrix of M row-vectors (query points). + + Returns + ------- + ndarray + A vector of length M that contains for each entry in `y` the + smallest Euclidean distance to a sample in `x`. + + """ + distances = _pdist(x, y) + return np.maximum(0.0, distances.min(axis=0)) + + +def _nn_cosine_distance(x, y): + """ Helper function for nearest neighbor distance metric (cosine). + + Parameters + ---------- + x : ndarray + A matrix of N row-vectors (sample points). + y : ndarray + A matrix of M row-vectors (query points). + + Returns + ------- + ndarray + A vector of length M that contains for each entry in `y` the + smallest cosine distance to a sample in `x`. + + """ + distances = _cosine_distance(x, y) + return distances.min(axis=0) + + +class NearestNeighborDistanceMetric(object): + """ + A nearest neighbor distance metric that, for each target, returns + the closest distance to any sample that has been observed so far. + + Parameters + ---------- + metric : str + Either "euclidean" or "cosine". + matching_threshold: float + The matching threshold. Samples with larger distance are considered an + invalid match. + budget : Optional[int] + If not None, fix samples per class to at most this number. Removes + the oldest samples when the budget is reached. + + Attributes + ---------- + samples : Dict[int -> List[ndarray]] + A dictionary that maps from target identities to the list of samples + that have been observed so far. + + """ + + def __init__(self, metric, matching_threshold, budget=None): + + + if metric == "euclidean": + self._metric = _nn_euclidean_distance + elif metric == "cosine": + self._metric = _nn_cosine_distance + else: + raise ValueError( + "Invalid metric; must be either 'euclidean' or 'cosine'") + self.matching_threshold = matching_threshold + self.budget = budget + self.samples = {} + + def partial_fit(self, features, targets, active_targets): + """Update the distance metric with new data. + + Parameters + ---------- + features : ndarray + An NxM matrix of N features of dimensionality M. + targets : ndarray + An integer array of associated target identities. + active_targets : List[int] + A list of targets that are currently present in the scene. + + """ + for feature, target in zip(features, targets): + self.samples.setdefault(target, []).append(feature) + if self.budget is not None: + self.samples[target] = self.samples[target][-self.budget:] + self.samples = {k: self.samples[k] for k in active_targets} + + def distance(self, features, targets): + """Compute distance between features and targets. + + Parameters + ---------- + features : ndarray + An NxM matrix of N features of dimensionality M. + targets : List[int] + A list of targets to match the given `features` against. + + Returns + ------- + ndarray + Returns a cost matrix of shape len(targets), len(features), where + element (i, j) contains the closest squared distance between + `targets[i]` and `features[j]`. + + """ + cost_matrix = np.zeros((len(targets), len(features))) + for i, target in enumerate(targets): + cost_matrix[i, :] = self._metric(self.samples[target], features) + return cost_matrix diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/preprocessing.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/preprocessing.py new file mode 100644 index 000000000..de31eeae6 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/preprocessing.py @@ -0,0 +1,73 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np +import cv2 + + +def non_max_suppression(boxes, max_bbox_overlap, scores=None): + """Suppress overlapping detections. + + Original code from [1]_ has been adapted to include confidence score. + + .. [1] http://www.pyimagesearch.com/2015/02/16/ + faster-non-maximum-suppression-python/ + + Examples + -------- + + >>> boxes = [d.roi for d in detections] + >>> scores = [d.confidence for d in detections] + >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores) + >>> detections = [detections[i] for i in indices] + + Parameters + ---------- + boxes : ndarray + Array of ROIs (x, y, width, height). + max_bbox_overlap : float + ROIs that overlap more than this values are suppressed. + scores : Optional[array_like] + Detector confidence score. + + Returns + ------- + List[int] + Returns indices of detections that have survived non-maxima suppression. + + """ + if len(boxes) == 0: + return [] + + boxes = boxes.astype(np.float) + pick = [] + + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + boxes[:, 0] + y2 = boxes[:, 3] + boxes[:, 1] + + area = (x2 - x1 + 1) * (y2 - y1 + 1) + if scores is not None: + idxs = np.argsort(scores) + else: + idxs = np.argsort(y2) + + while len(idxs) > 0: + last = len(idxs) - 1 + i = idxs[last] + pick.append(i) + + xx1 = np.maximum(x1[i], x1[idxs[:last]]) + yy1 = np.maximum(y1[i], y1[idxs[:last]]) + xx2 = np.minimum(x2[i], x2[idxs[:last]]) + yy2 = np.minimum(y2[i], y2[idxs[:last]]) + + w = np.maximum(0, xx2 - xx1 + 1) + h = np.maximum(0, yy2 - yy1 + 1) + + overlap = (w * h) / area[idxs[:last]] + + idxs = np.delete( + idxs, np.concatenate( + ([last], np.where(overlap > max_bbox_overlap)[0]))) + + return pick diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/track.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/track.py new file mode 100644 index 000000000..9322e371b --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/track.py @@ -0,0 +1,166 @@ +# vim: expandtab:ts=4:sw=4 + + +class TrackState: + """ + Enumeration type for the single target track state. Newly created tracks are + classified as `tentative` until enough evidence has been collected. Then, + the track state is changed to `confirmed`. Tracks that are no longer alive + are classified as `deleted` to mark them for removal from the set of active + tracks. + + """ + + Tentative = 1 + Confirmed = 2 + Deleted = 3 + + +class Track: + """ + A single target track with state space `(x, y, a, h)` and associated + velocities, where `(x, y)` is the center of the bounding box, `a` is the + aspect ratio and `h` is the height. + + Parameters + ---------- + mean : ndarray + Mean vector of the initial state distribution. + covariance : ndarray + Covariance matrix of the initial state distribution. + track_id : int + A unique track identifier. + n_init : int + Number of consecutive detections before the track is confirmed. The + track state is set to `Deleted` if a miss occurs within the first + `n_init` frames. + max_age : int + The maximum number of consecutive misses before the track state is + set to `Deleted`. + feature : Optional[ndarray] + Feature vector of the detection this track originates from. If not None, + this feature is added to the `features` cache. + + Attributes + ---------- + mean : ndarray + Mean vector of the initial state distribution. + covariance : ndarray + Covariance matrix of the initial state distribution. + track_id : int + A unique track identifier. + hits : int + Total number of measurement updates. + age : int + Total number of frames since first occurance. + time_since_update : int + Total number of frames since last measurement update. + state : TrackState + The current track state. + features : List[ndarray] + A cache of features. On each measurement update, the associated feature + vector is added to this list. + + """ + + def __init__(self, mean, covariance, track_id, n_init, max_age, + feature=None): + self.mean = mean + self.covariance = covariance + self.track_id = track_id + self.hits = 1 + self.age = 1 + self.time_since_update = 0 + + self.state = TrackState.Tentative + self.features = [] + if feature is not None: + self.features.append(feature) + + self._n_init = n_init + self._max_age = max_age + + def to_tlwh(self): + """Get current position in bounding box format `(top left x, top left y, + width, height)`. + + Returns + ------- + ndarray + The bounding box. + + """ + ret = self.mean[:4].copy() + ret[2] *= ret[3] + ret[:2] -= ret[2:] / 2 + return ret + + def to_tlbr(self): + """Get current position in bounding box format `(min x, miny, max x, + max y)`. + + Returns + ------- + ndarray + The bounding box. + + """ + ret = self.to_tlwh() + ret[2:] = ret[:2] + ret[2:] + return ret + + def predict(self, kf): + """Propagate the state distribution to the current time step using a + Kalman filter prediction step. + + Parameters + ---------- + kf : kalman_filter.KalmanFilter + The Kalman filter. + + """ + self.mean, self.covariance = kf.predict(self.mean, self.covariance) + self.age += 1 + self.time_since_update += 1 + + def update(self, kf, detection): + """Perform Kalman filter measurement update step and update the feature + cache. + + Parameters + ---------- + kf : kalman_filter.KalmanFilter + The Kalman filter. + detection : Detection + The associated detection. + + """ + self.mean, self.covariance = kf.update( + self.mean, self.covariance, detection.to_xyah()) + self.features.append(detection.feature) + + self.hits += 1 + self.time_since_update = 0 + if self.state == TrackState.Tentative and self.hits >= self._n_init: + self.state = TrackState.Confirmed + + def mark_missed(self): + """Mark this track as missed (no association at the current time step). + """ + if self.state == TrackState.Tentative: + self.state = TrackState.Deleted + elif self.time_since_update > self._max_age: + self.state = TrackState.Deleted + + def is_tentative(self): + """Returns True if this track is tentative (unconfirmed). + """ + return self.state == TrackState.Tentative + + def is_confirmed(self): + """Returns True if this track is confirmed.""" + return self.state == TrackState.Confirmed + + def is_deleted(self): + """Returns True if this track is dead and should be deleted.""" + return self.state == TrackState.Deleted diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/sort/tracker.py b/cv/tracking/deep_sort/pytorch/deep_sort/sort/tracker.py new file mode 100644 index 000000000..56dbda13a --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deep_sort/sort/tracker.py @@ -0,0 +1,138 @@ +# vim: expandtab:ts=4:sw=4 +from __future__ import absolute_import +import numpy as np +from . import kalman_filter +from . import linear_assignment +from . import iou_matching +from .track import Track + + +class Tracker: + """ + This is the multi-target tracker. + + Parameters + ---------- + metric : nn_matching.NearestNeighborDistanceMetric + A distance metric for measurement-to-track association. + max_age : int + Maximum number of missed misses before a track is deleted. + n_init : int + Number of consecutive detections before the track is confirmed. The + track state is set to `Deleted` if a miss occurs within the first + `n_init` frames. + + Attributes + ---------- + metric : nn_matching.NearestNeighborDistanceMetric + The distance metric used for measurement to track association. + max_age : int + Maximum number of missed misses before a track is deleted. + n_init : int + Number of frames that a track remains in initialization phase. + kf : kalman_filter.KalmanFilter + A Kalman filter to filter target trajectories in image space. + tracks : List[Track] + The list of active tracks at the current time step. + + """ + + def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3): + self.metric = metric + self.max_iou_distance = max_iou_distance + self.max_age = max_age + self.n_init = n_init + + self.kf = kalman_filter.KalmanFilter() + self.tracks = [] + self._next_id = 1 + + def predict(self): + """Propagate track state distributions one time step forward. + + This function should be called once every time step, before `update`. + """ + for track in self.tracks: + track.predict(self.kf) + + def update(self, detections): + """Perform measurement update and track management. + + Parameters + ---------- + detections : List[deep_sort.detection.Detection] + A list of detections at the current time step. + + """ + # Run matching cascade. + matches, unmatched_tracks, unmatched_detections = \ + self._match(detections) + + # Update track set. + for track_idx, detection_idx in matches: + self.tracks[track_idx].update( + self.kf, detections[detection_idx]) + for track_idx in unmatched_tracks: + self.tracks[track_idx].mark_missed() + for detection_idx in unmatched_detections: + self._initiate_track(detections[detection_idx]) + self.tracks = [t for t in self.tracks if not t.is_deleted()] + + # Update distance metric. + active_targets = [t.track_id for t in self.tracks if t.is_confirmed()] + features, targets = [], [] + for track in self.tracks: + if not track.is_confirmed(): + continue + features += track.features + targets += [track.track_id for _ in track.features] + track.features = [] + self.metric.partial_fit( + np.asarray(features), np.asarray(targets), active_targets) + + def _match(self, detections): + + def gated_metric(tracks, dets, track_indices, detection_indices): + features = np.array([dets[i].feature for i in detection_indices]) + targets = np.array([tracks[i].track_id for i in track_indices]) + cost_matrix = self.metric.distance(features, targets) + cost_matrix = linear_assignment.gate_cost_matrix( + self.kf, cost_matrix, tracks, dets, track_indices, + detection_indices) + + return cost_matrix + + # Split track set into confirmed and unconfirmed tracks. + confirmed_tracks = [ + i for i, t in enumerate(self.tracks) if t.is_confirmed()] + unconfirmed_tracks = [ + i for i, t in enumerate(self.tracks) if not t.is_confirmed()] + + # Associate confirmed tracks using appearance features. + matches_a, unmatched_tracks_a, unmatched_detections = \ + linear_assignment.matching_cascade( + gated_metric, self.metric.matching_threshold, self.max_age, + self.tracks, detections, confirmed_tracks) + + # Associate remaining tracks together with unconfirmed tracks using IOU. + iou_track_candidates = unconfirmed_tracks + [ + k for k in unmatched_tracks_a if + self.tracks[k].time_since_update == 1] + unmatched_tracks_a = [ + k for k in unmatched_tracks_a if + self.tracks[k].time_since_update != 1] + matches_b, unmatched_tracks_b, unmatched_detections = \ + linear_assignment.min_cost_matching( + iou_matching.iou_cost, self.max_iou_distance, self.tracks, + detections, iou_track_candidates, unmatched_detections) + + matches = matches_a + matches_b + unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b)) + return matches, unmatched_tracks, unmatched_detections + + def _initiate_track(self, detection): + mean, covariance = self.kf.initiate(detection.to_xyah()) + self.tracks.append(Track( + mean, covariance, self._next_id, self.n_init, self.max_age, + detection.feature)) + self._next_id += 1 diff --git a/cv/tracking/deep_sort/pytorch/deepsort.py b/cv/tracking/deep_sort/pytorch/deepsort.py new file mode 100644 index 000000000..355b86606 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/deepsort.py @@ -0,0 +1,172 @@ +import os +import cv2 +import time +import argparse +import torch +import warnings +import numpy as np +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), 'thirdparty/fast-reid')) + + +from detector import build_detector +from deep_sort import build_tracker +from utils.draw import draw_boxes +from utils.parser import get_config +from utils.log import get_logger +from utils.io import write_results + + + +class VideoTracker(object): + def __init__(self, cfg, args, video_path): + self.cfg = cfg + self.args = args + self.video_path = video_path + self.logger = get_logger("root") + + use_cuda = args.use_cuda and torch.cuda.is_available() + if not use_cuda: + warnings.warn("Running in cpu mode which maybe very slow!", UserWarning) + + if args.display: + cv2.namedWindow("test", cv2.WINDOW_NORMAL) + cv2.resizeWindow("test", args.display_width, args.display_height) + + if args.cam != -1: + print("Using webcam " + str(args.cam)) + self.vdo = cv2.VideoCapture(args.cam) + else: + self.vdo = cv2.VideoCapture() + self.detector = build_detector(cfg, use_cuda=use_cuda) + self.deepsort = build_tracker(cfg, use_cuda=use_cuda) + self.class_names = self.detector.class_names + + def __enter__(self): + if self.args.cam != -1: + ret, frame = self.vdo.read() + assert ret, "Error: Camera error" + self.im_width = frame.shape[0] + self.im_height = frame.shape[1] + + else: + assert os.path.isfile(self.video_path), "Path error" + self.vdo.open(self.video_path) + self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH)) + self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT)) + assert self.vdo.isOpened() + + if self.args.save_path: + os.makedirs(self.args.save_path, exist_ok=True) + + # path of saved video and results + self.save_video_path = os.path.join(self.args.save_path, "results.avi") + self.save_results_path = os.path.join(self.args.save_path, "results.txt") + + # create video writer + fourcc = cv2.VideoWriter_fourcc(*'MJPG') + self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 20, (self.im_width, self.im_height)) + + # logging + self.logger.info("Save results to {}".format(self.args.save_path)) + + return self + + def __exit__(self, exc_type, exc_value, exc_traceback): + if exc_type: + print(exc_type, exc_value, exc_traceback) + + def run(self): + results = [] + idx_frame = 0 + while self.vdo.grab(): + idx_frame += 1 + if idx_frame % self.args.frame_interval: + continue + + start = time.time() + _, ori_im = self.vdo.retrieve() + im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB) + + # do detection + bbox_xywh, cls_conf, cls_ids = self.detector(im) + + # select person class + mask = cls_ids == 0 + + bbox_xywh = bbox_xywh[mask] + # bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector + bbox_xywh[:, 3:] *= 1.2 + cls_conf = cls_conf[mask] + + # do tracking + outputs = self.deepsort.update(bbox_xywh, cls_conf, im) + + # draw boxes for visualization + if len(outputs) > 0: + bbox_tlwh = [] + bbox_xyxy = outputs[:, :4] + identities = outputs[:, -1] + ori_im = draw_boxes(ori_im, bbox_xyxy, identities) + + for bb_xyxy in bbox_xyxy: + bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy)) + + results.append((idx_frame - 1, bbox_tlwh, identities)) + + end = time.time() + + if self.args.display: + cv2.imshow("test", ori_im) + cv2.waitKey(1) + + if self.args.save_path: + self.writer.write(ori_im) + + # save results + write_results(self.save_results_path, results, 'mot') + + # logging + self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \ + .format(end - start, 1 / (end - start), bbox_xywh.shape[0], len(outputs))) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("VIDEO_PATH", type=str) + parser.add_argument("--config_mmdetection", type=str, default="./configs/mmdet.yaml") + parser.add_argument("--config_detection", type=str, default="./configs/yolov3.yaml") + parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml") + parser.add_argument("--config_fastreid", type=str, default="./configs/fastreid.yaml") + parser.add_argument("--fastreid", action="store_true") + parser.add_argument("--mmdet", action="store_true") + # parser.add_argument("--ignore_display", dest="display", action="store_false", default=True) + parser.add_argument("--display", action="store_true") + parser.add_argument("--frame_interval", type=int, default=1) + parser.add_argument("--display_width", type=int, default=800) + parser.add_argument("--display_height", type=int, default=600) + parser.add_argument("--save_path", type=str, default="./output/") + parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True) + parser.add_argument("--camera", action="store", dest="cam", type=int, default="-1") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + cfg = get_config() + if args.mmdet: + cfg.merge_from_file(args.config_mmdetection) + cfg.USE_MMDET = True + else: + cfg.merge_from_file(args.config_detection) + cfg.USE_MMDET = False + cfg.merge_from_file(args.config_deepsort) + if args.fastreid: + cfg.merge_from_file(args.config_fastreid) + cfg.USE_FASTREID = True + else: + cfg.USE_FASTREID = False + + with VideoTracker(cfg, args, video_path=args.VIDEO_PATH) as vdo_trk: + vdo_trk.run() diff --git a/cv/tracking/deep_sort/pytorch/demo/1.jpg b/cv/tracking/deep_sort/pytorch/demo/1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..29626c7e02b17d272c47c01d2947958cd8898d34 GIT binary patch literal 253964 zcmZ^~2T&7V`1iYXkkA!FmEKE8=uME`rAY6cAYBj;kR~;N(xg}E9i(>xp*JCb(mMhw 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z5fNK<@lAcksto7di5Ke>#M;D*H<ZjoEbvhV@#gu9RridDkc_T1u^5%?Qve(k>jz@K zjOP2XMZEMiv9mNkHq-CVzc0V%`u%q%^AruvWg7U!&@#^Q{mx<$f5PEA5rpQithL$O mhdEv;n?#4}i&NxGtpc?9uf0u4UhT8cnS2kA=eIckVETW%>R4g` literal 0 HcmV?d00001 diff --git a/cv/tracking/deep_sort/pytorch/detector/MMDet/__init__.py b/cv/tracking/deep_sort/pytorch/detector/MMDet/__init__.py new file mode 100644 index 000000000..53a482bb0 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/MMDet/__init__.py @@ -0,0 +1,2 @@ +from .detector import MMDet +__all__ = ['MMDet'] \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/MMDet/detector.py b/cv/tracking/deep_sort/pytorch/detector/MMDet/detector.py new file mode 100644 index 000000000..d23afb491 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/MMDet/detector.py @@ -0,0 +1,55 @@ +import logging +import numpy as np +import torch + +from mmdet.apis import init_detector, inference_detector +from .mmdet_utils import xyxy_to_xywh + +class MMDet(object): + def __init__(self, cfg_file, checkpoint_file, score_thresh=0.7, + is_xywh=False, use_cuda=True): + # net definition + self.device = "cuda" if use_cuda else "cpu" + self.net = init_detector(cfg_file, checkpoint_file, device=self.device) + logger = logging.getLogger("root.detector") + logger.info('Loading weights from %s... Done!' % (checkpoint_file)) + + #constants + self.score_thresh = score_thresh + self.use_cuda = use_cuda + self.is_xywh = is_xywh + self.class_names = self.net.CLASSES + self.num_classes = len(self.class_names) + + def __call__(self, ori_img): + # forward + bbox_result = inference_detector(self.net, ori_img) + bboxes = np.vstack(bbox_result) + + if len(bboxes) == 0: + bbox = np.array([]).reshape([0, 4]) + cls_conf = np.array([]) + cls_ids = np.array([]) + return bbox, cls_conf, cls_ids + + bbox = bboxes[:, :4] + cls_conf = bboxes[:, 4] + cls_ids = [ + np.full(bbox.shape[0], i, dtype=np.int32) + for i, bbox in enumerate(bbox_result) + ] + cls_ids = np.concatenate(cls_ids) + + selected_idx = cls_conf > self.score_thresh + bbox = bbox[selected_idx, :] + cls_conf = cls_conf[selected_idx] + cls_ids = cls_ids[selected_idx] + + if self.is_xywh: + bbox = xyxy_to_xywh(bbox) + + return bbox, cls_conf, cls_ids + + + + diff --git a/cv/tracking/deep_sort/pytorch/detector/MMDet/mmdet_utils.py b/cv/tracking/deep_sort/pytorch/detector/MMDet/mmdet_utils.py new file mode 100644 index 000000000..6a44c5800 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/MMDet/mmdet_utils.py @@ -0,0 +1,15 @@ +import torch +import numpy as np + +def xyxy_to_xywh(boxes_xyxy): + if isinstance(boxes_xyxy, torch.Tensor): + boxes_xywh = boxes_xyxy.clone() + elif isinstance(boxes_xyxy, np.ndarray): + boxes_xywh = boxes_xyxy.copy() + + boxes_xywh[:, 0] = (boxes_xyxy[:, 0] + boxes_xyxy[:, 2]) / 2. + boxes_xywh[:, 1] = (boxes_xyxy[:, 1] + boxes_xyxy[:, 3]) / 2. + boxes_xywh[:, 2] = boxes_xyxy[:, 2] - boxes_xyxy[:, 0] + boxes_xywh[:, 3] = boxes_xyxy[:, 3] - boxes_xyxy[:, 1] + + return boxes_xywh \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/README.md b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/README.md new file mode 100644 index 000000000..ef8e168b7 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/README.md @@ -0,0 +1,11 @@ +# YOLOv3 for detection + +This is an implemention of YOLOv3 with only the forward part. + +If you want to train YOLOv3 on your custom dataset, please search `YOLOv3` on github. + +## Quick forward +```bash +cd YOLOv3 +python +``` \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/__init__.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/__init__.py new file mode 100644 index 000000000..fff6a6100 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/__init__.py @@ -0,0 +1,9 @@ +import sys +sys.path.append("detector/YOLOv3") + + +from .detector import YOLOv3 +__all__ = ['YOLOv3'] + + + diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg.py new file mode 100644 index 000000000..57127eef5 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg.py @@ -0,0 +1,248 @@ +import torch +from .yolo_utils import convert2cpu + + +def parse_cfg(cfgfile): + blocks = [] + fp = open(cfgfile) + block = None + line = fp.readline() + while line != '': + line = line.rstrip() + if line == '' or line[0] == '#': + line = fp.readline() + continue + elif line[0] == '[': + if block: + blocks.append(block) + block = dict() + block['type'] = line.lstrip('[').rstrip(']') + # set default value + if block['type'] == 'convolutional': + block['batch_normalize'] = 0 + else: + key, value = line.split('=') + key = key.strip() + if key == 'type': + key = '_type' + value = value.strip() + block[key] = value + line = fp.readline() + + if block: + blocks.append(block) + fp.close() + return blocks + + +def print_cfg(blocks): + print('layer filters size input output'); + prev_width = 416 + prev_height = 416 + prev_filters = 3 + out_filters = [] + out_widths = [] + out_heights = [] + ind = -2 + for block in blocks: + ind += 1 + if block['type'] == 'net': + prev_width = int(block['width']) + prev_height = int(block['height']) + continue + elif block['type'] == 'convolutional': + filters = int(block['filters']) + kernel_size = int(block['size']) + stride = int(block['stride']) + is_pad = int(block['pad']) + pad = (kernel_size - 1) // 2 if is_pad else 0 + width = (prev_width + 2 * pad - kernel_size) // stride + 1 + height = (prev_height + 2 * pad - kernel_size) // stride + 1 + print('%5d %-6s %4d %d x %d / %d %3d x %3d x%4d -> %3d x %3d x%4d' % ( + ind, 'conv', filters, kernel_size, kernel_size, stride, prev_width, prev_height, prev_filters, width, + height, filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'maxpool': + pool_size = int(block['size']) + stride = int(block['stride']) + width = prev_width // stride + height = prev_height // stride + print('%5d %-6s %d x %d / %d %3d x %3d x%4d -> %3d x %3d x%4d' % ( + ind, 'max', pool_size, pool_size, stride, prev_width, prev_height, prev_filters, width, height, filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'avgpool': + width = 1 + height = 1 + print('%5d %-6s %3d x %3d x%4d -> %3d' % ( + ind, 'avg', prev_width, prev_height, prev_filters, prev_filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'softmax': + print('%5d %-6s -> %3d' % (ind, 'softmax', prev_filters)) + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'cost': + print('%5d %-6s -> %3d' % (ind, 'cost', prev_filters)) + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'reorg': + stride = int(block['stride']) + filters = stride * stride * prev_filters + width = prev_width // stride + height = prev_height // stride + print('%5d %-6s / %d %3d x %3d x%4d -> %3d x %3d x%4d' % ( + ind, 'reorg', stride, prev_width, prev_height, prev_filters, width, height, filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'upsample': + stride = int(block['stride']) + filters = prev_filters + width = prev_width * stride + height = prev_height * stride + print('%5d %-6s * %d %3d x %3d x%4d -> %3d x %3d x%4d' % ( + ind, 'upsample', stride, prev_width, prev_height, prev_filters, width, height, filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'route': + layers = block['layers'].split(',') + layers = [int(i) if int(i) > 0 else int(i) + ind for i in layers] + if len(layers) == 1: + print('%5d %-6s %d' % (ind, 'route', layers[0])) + prev_width = out_widths[layers[0]] + prev_height = out_heights[layers[0]] + prev_filters = out_filters[layers[0]] + elif len(layers) == 2: + print('%5d %-6s %d %d' % (ind, 'route', layers[0], layers[1])) + prev_width = out_widths[layers[0]] + prev_height = out_heights[layers[0]] + assert (prev_width == out_widths[layers[1]]) + assert (prev_height == out_heights[layers[1]]) + prev_filters = out_filters[layers[0]] + out_filters[layers[1]] + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] in ['region', 'yolo']: + print('%5d %-6s' % (ind, 'detection')) + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'shortcut': + from_id = int(block['from']) + from_id = from_id if from_id > 0 else from_id + ind + print('%5d %-6s %d' % (ind, 'shortcut', from_id)) + prev_width = out_widths[from_id] + prev_height = out_heights[from_id] + prev_filters = out_filters[from_id] + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'connected': + filters = int(block['output']) + print('%5d %-6s %d -> %3d' % (ind, 'connected', prev_filters, filters)) + prev_filters = filters + out_widths.append(1) + out_heights.append(1) + out_filters.append(prev_filters) + else: + print('unknown type %s' % (block['type'])) + + +def load_conv(buf, start, conv_model): + num_w = conv_model.weight.numel() + num_b = conv_model.bias.numel() + # print("start: {}, num_w: {}, num_b: {}".format(start, num_w, num_b)) + # by ysyun, use .view_as() + conv_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]).view_as(conv_model.bias.data)); + start = start + num_b + conv_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_w]).view_as(conv_model.weight.data)); + start = start + num_w + return start + + +def save_conv(fp, conv_model): + if conv_model.bias.is_cuda: + convert2cpu(conv_model.bias.data).numpy().tofile(fp) + convert2cpu(conv_model.weight.data).numpy().tofile(fp) + else: + conv_model.bias.data.numpy().tofile(fp) + conv_model.weight.data.numpy().tofile(fp) + + +def load_conv_bn(buf, start, conv_model, bn_model): + num_w = conv_model.weight.numel() + num_b = bn_model.bias.numel() + bn_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b])); + start = start + num_b + bn_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_b])); + start = start + num_b + bn_model.running_mean.copy_(torch.from_numpy(buf[start:start + num_b])); + start = start + num_b + bn_model.running_var.copy_(torch.from_numpy(buf[start:start + num_b])); + start = start + num_b + # conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w])); start = start + num_w + conv_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_w]).view_as(conv_model.weight.data)); + start = start + num_w + return start + + +def save_conv_bn(fp, conv_model, bn_model): + if bn_model.bias.is_cuda: + convert2cpu(bn_model.bias.data).numpy().tofile(fp) + convert2cpu(bn_model.weight.data).numpy().tofile(fp) + convert2cpu(bn_model.running_mean).numpy().tofile(fp) + convert2cpu(bn_model.running_var).numpy().tofile(fp) + convert2cpu(conv_model.weight.data).numpy().tofile(fp) + else: + bn_model.bias.data.numpy().tofile(fp) + bn_model.weight.data.numpy().tofile(fp) + bn_model.running_mean.numpy().tofile(fp) + bn_model.running_var.numpy().tofile(fp) + conv_model.weight.data.numpy().tofile(fp) + + +def load_fc(buf, start, fc_model): + num_w = fc_model.weight.numel() + num_b = fc_model.bias.numel() + fc_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b])); + start = start + num_b + fc_model.weight.data.copy_(torch.from_numpy(buf[start:start + num_w])); + start = start + num_w + return start + + +def save_fc(fp, fc_model): + fc_model.bias.data.numpy().tofile(fp) + fc_model.weight.data.numpy().tofile(fp) + + +if __name__ == '__main__': + import sys + + blocks = parse_cfg('cfg/yolo.cfg') + if len(sys.argv) == 2: + blocks = parse_cfg(sys.argv[1]) + print_cfg(blocks) diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/coco.data b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/coco.data new file mode 100644 index 000000000..b7e31be30 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/coco.data @@ -0,0 +1,5 @@ +train = coco_train.txt +valid = coco_test.txt +names = data/coco.names +backup = backup +gpus = 0,1,2,3 diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/coco.names b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/coco.names new file mode 100644 index 000000000..ca76c80b5 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/coco.names @@ -0,0 +1,80 @@ +person +bicycle +car +motorbike +aeroplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +sofa +pottedplant +bed +diningtable +toilet +tvmonitor +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/darknet19_448.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/darknet19_448.cfg new file mode 100644 index 000000000..133c688eb --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/darknet19_448.cfg @@ -0,0 +1,200 @@ +[net] +batch=128 +subdivisions=4 +height=448 +width=448 +max_crop=512 +channels=3 +momentum=0.9 +decay=0.0005 + +learning_rate=0.001 +policy=poly +power=4 +max_batches=100000 + +angle=7 +hue = .1 +saturation=.75 +exposure=.75 +aspect=.75 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[avgpool] + +[softmax] +groups=1 + +[cost] +type=sse + diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/tiny-yolo-voc.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/tiny-yolo-voc.cfg new file mode 100644 index 000000000..ab2c066a2 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/tiny-yolo-voc.cfg @@ -0,0 +1,134 @@ +[net] +batch=64 +subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +max_batches = 40200 +policy=steps +steps=-1,100,20000,30000 +scales=.1,10,.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=125 +activation=linear + +[region] +anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 +bias_match=1 +classes=20 +coords=4 +num=5 +softmax=1 +jitter=.2 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/tiny-yolo.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/tiny-yolo.cfg new file mode 100644 index 000000000..ac5770e78 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/tiny-yolo.cfg @@ -0,0 +1,140 @@ +[net] +# Training +# batch=64 +# subdivisions=2 +# Testing +batch=1 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=425 +activation=linear + +[region] +anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 +bias_match=1 +classes=80 +coords=4 +num=5 +softmax=1 +jitter=.2 +rescore=0 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 + diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc.data b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc.data new file mode 100644 index 000000000..3329357e7 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc.data @@ -0,0 +1,5 @@ +train = data/voc_train.txt +valid = data/2007_test.txt +names = data/voc.names +backup = backup +gpus = 3 diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc.names b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc.names new file mode 100644 index 000000000..8420ab35e --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc.names @@ -0,0 +1,20 @@ +aeroplane +bicycle +bird +boat +bottle +bus +car +cat +chair +cow +diningtable +dog +horse +motorbike +person +pottedplant +sheep +sofa +train +tvmonitor diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc_gaotie.data b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc_gaotie.data new file mode 100644 index 000000000..66495ec54 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/voc_gaotie.data @@ -0,0 +1,5 @@ +train = data/gaotie_trainval.txt +valid = data/gaotie_test.txt +names = data/voc.names +backup = backup +gpus = 3 \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo-voc.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo-voc.cfg new file mode 100644 index 000000000..d5bdfc1c5 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo-voc.cfg @@ -0,0 +1,258 @@ +[net] +# Testing +batch=64 +subdivisions=8 +# Training +# batch=64 +# subdivisions=8 +height=416 +width=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 80200 +policy=steps +steps=-1,500,40000,60000 +scales=0.1,10,.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + + +####### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[route] +layers=-9 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=64 +activation=leaky + +[reorg] +stride=2 + +[route] +layers=-1,-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=125 +activation=linear + + +[region] +anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071 +bias_match=1 +classes=20 +coords=4 +num=5 +softmax=1 +jitter=.3 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo.cfg new file mode 100644 index 000000000..2a0cd98fb --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo.cfg @@ -0,0 +1,258 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + + +####### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[route] +layers=-9 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=64 +activation=leaky + +[reorg] +stride=2 + +[route] +layers=-1,-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=425 +activation=linear + + +[region] +anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 +bias_match=1 +classes=80 +coords=4 +num=5 +softmax=1 +jitter=.3 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo_v3.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo_v3.cfg new file mode 100644 index 000000000..f388ebd08 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolo_v3.cfg @@ -0,0 +1,789 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=16 +subdivisions=4 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=20,25 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolov3-tiny.cfg b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolov3-tiny.cfg new file mode 100644 index 000000000..cfca3cfa6 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/cfg/yolov3-tiny.cfg @@ -0,0 +1,182 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/darknet.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/darknet.py new file mode 100644 index 000000000..da6d3c0e7 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/darknet.py @@ -0,0 +1,435 @@ +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from .cfg import * +from .region_layer import RegionLayer +from .yolo_layer import YoloLayer + +class MaxPoolStride1(nn.Module): + def __init__(self): + super(MaxPoolStride1, self).__init__() + + def forward(self, x): + x = F.max_pool2d(F.pad(x, (0,1,0,1), mode='replicate'), 2, stride=1) + return x + +class Upsample(nn.Module): + def __init__(self, stride=2): + super(Upsample, self).__init__() + self.stride = stride + def forward(self, x): + stride = self.stride + assert(x.data.dim() == 4) + B = x.data.size(0) + C = x.data.size(1) + H = x.data.size(2) + W = x.data.size(3) + ws = stride + hs = stride + x = x.view(B, C, H, 1, W, 1).expand(B, C, H, hs, W, ws).contiguous().view(B, C, H*hs, W*ws) + return x + +class Reorg(nn.Module): + def __init__(self, stride=2): + super(Reorg, self).__init__() + self.stride = stride + def forward(self, x): + stride = self.stride + assert(x.data.dim() == 4) + B = x.data.size(0) + C = x.data.size(1) + H = x.data.size(2) + W = x.data.size(3) + assert(H % stride == 0) + assert(W % stride == 0) + ws = stride + hs = stride + x = x.view(B, C, H//hs, hs, W//ws, ws).transpose(3,4).contiguous() + x = x.view(B, C, (H//hs)*(W//ws), hs*ws).transpose(2,3).contiguous() + x = x.view(B, C, hs*ws, H//hs, W//ws).transpose(1,2).contiguous() + x = x.view(B, hs*ws*C, H//hs, W//ws) + return x + +class GlobalAvgPool2d(nn.Module): + def __init__(self): + super(GlobalAvgPool2d, self).__init__() + + def forward(self, x): + N = x.data.size(0) + C = x.data.size(1) + H = x.data.size(2) + W = x.data.size(3) + x = F.avg_pool2d(x, (H, W)) + x = x.view(N, C) + return x + +# for route and shortcut +class EmptyModule(nn.Module): + def __init__(self): + super(EmptyModule, self).__init__() + + def forward(self, x): + return x + +# support route shortcut and reorg + +class Darknet(nn.Module): + def getLossLayers(self): + loss_layers = [] + for m in self.models: + if isinstance(m, RegionLayer) or isinstance(m, YoloLayer): + loss_layers.append(m) + return loss_layers + + def __init__(self, cfgfile, use_cuda=True): + super(Darknet, self).__init__() + self.use_cuda = use_cuda + self.blocks = parse_cfg(cfgfile) + self.models = self.create_network(self.blocks) # merge conv, bn,leaky + self.loss_layers = self.getLossLayers() + + #self.width = int(self.blocks[0]['width']) + #self.height = int(self.blocks[0]['height']) + + if len(self.loss_layers) > 0: + last = len(self.loss_layers)-1 + self.anchors = self.loss_layers[last].anchors + self.num_anchors = self.loss_layers[last].num_anchors + self.anchor_step = self.loss_layers[last].anchor_step + self.num_classes = self.loss_layers[last].num_classes + + # default format : major=0, minor=1 + self.header = torch.IntTensor([0,1,0,0]) + self.seen = 0 + + def forward(self, x): + ind = -2 + self.loss_layers = None + outputs = dict() + out_boxes = dict() + outno = 0 + for block in self.blocks: + ind = ind + 1 + + if block['type'] == 'net': + continue + elif block['type'] in ['convolutional', 'maxpool', 'reorg', 'upsample', 'avgpool', 'softmax', 'connected']: + x = self.models[ind](x) + outputs[ind] = x + elif block['type'] == 'route': + layers = block['layers'].split(',') + layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] + if len(layers) == 1: + x = outputs[layers[0]] + elif len(layers) == 2: + x1 = outputs[layers[0]] + x2 = outputs[layers[1]] + x = torch.cat((x1,x2),1) + outputs[ind] = x + elif block['type'] == 'shortcut': + from_layer = int(block['from']) + activation = block['activation'] + from_layer = from_layer if from_layer > 0 else from_layer + ind + x1 = outputs[from_layer] + x2 = outputs[ind-1] + x = x1 + x2 + if activation == 'leaky': + x = F.leaky_relu(x, 0.1, inplace=True) + elif activation == 'relu': + x = F.relu(x, inplace=True) + outputs[ind] = x + elif block['type'] in [ 'region', 'yolo']: + boxes = self.models[ind].get_mask_boxes(x) + out_boxes[outno]= boxes + outno += 1 + outputs[ind] = None + elif block['type'] == 'cost': + continue + else: + print('unknown type %s' % (block['type'])) + return x if outno == 0 else out_boxes + + def print_network(self): + print_cfg(self.blocks) + + def create_network(self, blocks): + models = nn.ModuleList() + + prev_filters = 3 + out_filters =[] + prev_stride = 1 + out_strides = [] + conv_id = 0 + ind = -2 + for block in blocks: + ind += 1 + if block['type'] == 'net': + prev_filters = int(block['channels']) + self.width = int(block['width']) + self.height = int(block['height']) + continue + elif block['type'] == 'convolutional': + conv_id = conv_id + 1 + batch_normalize = int(block['batch_normalize']) + filters = int(block['filters']) + kernel_size = int(block['size']) + stride = int(block['stride']) + is_pad = int(block['pad']) + pad = (kernel_size-1)//2 if is_pad else 0 + activation = block['activation'] + model = nn.Sequential() + if batch_normalize: + model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False)) + model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters)) + #model.add_module('bn{0}'.format(conv_id), BN2d(filters)) + else: + model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad)) + if activation == 'leaky': + model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True)) + elif activation == 'relu': + model.add_module('relu{0}'.format(conv_id), nn.ReLU(inplace=True)) + prev_filters = filters + out_filters.append(prev_filters) + prev_stride = stride * prev_stride + out_strides.append(prev_stride) + models.append(model) + elif block['type'] == 'maxpool': + pool_size = int(block['size']) + stride = int(block['stride']) + if stride > 1: + model = nn.MaxPool2d(pool_size, stride) + else: + model = MaxPoolStride1() + out_filters.append(prev_filters) + prev_stride = stride * prev_stride + out_strides.append(prev_stride) + models.append(model) + elif block['type'] == 'avgpool': + model = GlobalAvgPool2d() + out_filters.append(prev_filters) + models.append(model) + elif block['type'] == 'softmax': + model = nn.Softmax() + out_strides.append(prev_stride) + out_filters.append(prev_filters) + models.append(model) + elif block['type'] == 'cost': + if block['_type'] == 'sse': + model = nn.MSELoss(size_average=True) + elif block['_type'] == 'L1': + model = nn.L1Loss(size_average=True) + elif block['_type'] == 'smooth': + model = nn.SmoothL1Loss(size_average=True) + out_filters.append(1) + out_strides.append(prev_stride) + models.append(model) + elif block['type'] == 'reorg': + stride = int(block['stride']) + prev_filters = stride * stride * prev_filters + out_filters.append(prev_filters) + prev_stride = prev_stride * stride + out_strides.append(prev_stride) + models.append(Reorg(stride)) + elif block['type'] == 'upsample': + stride = int(block['stride']) + out_filters.append(prev_filters) + prev_stride = prev_stride / stride + out_strides.append(prev_stride) + #models.append(nn.Upsample(scale_factor=stride, mode='nearest')) + models.append(Upsample(stride)) + elif block['type'] == 'route': + layers = block['layers'].split(',') + ind = len(models) + layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] + if len(layers) == 1: + prev_filters = out_filters[layers[0]] + prev_stride = out_strides[layers[0]] + elif len(layers) == 2: + assert(layers[0] == ind - 1) + prev_filters = out_filters[layers[0]] + out_filters[layers[1]] + prev_stride = out_strides[layers[0]] + out_filters.append(prev_filters) + out_strides.append(prev_stride) + models.append(EmptyModule()) + elif block['type'] == 'shortcut': + ind = len(models) + prev_filters = out_filters[ind-1] + out_filters.append(prev_filters) + prev_stride = out_strides[ind-1] + out_strides.append(prev_stride) + models.append(EmptyModule()) + elif block['type'] == 'connected': + filters = int(block['output']) + if block['activation'] == 'linear': + model = nn.Linear(prev_filters, filters) + elif block['activation'] == 'leaky': + model = nn.Sequential( + nn.Linear(prev_filters, filters), + nn.LeakyReLU(0.1, inplace=True)) + elif block['activation'] == 'relu': + model = nn.Sequential( + nn.Linear(prev_filters, filters), + nn.ReLU(inplace=True)) + prev_filters = filters + out_filters.append(prev_filters) + out_strides.append(prev_stride) + models.append(model) + elif block['type'] == 'region': + region_layer = RegionLayer(use_cuda=self.use_cuda) + anchors = block['anchors'].split(',') + region_layer.anchors = [float(i) for i in anchors] + region_layer.num_classes = int(block['classes']) + region_layer.num_anchors = int(block['num']) + region_layer.anchor_step = len(region_layer.anchors)//region_layer.num_anchors + region_layer.rescore = int(block['rescore']) + region_layer.object_scale = float(block['object_scale']) + region_layer.noobject_scale = float(block['noobject_scale']) + region_layer.class_scale = float(block['class_scale']) + region_layer.coord_scale = float(block['coord_scale']) + region_layer.thresh = float(block['thresh']) + out_filters.append(prev_filters) + out_strides.append(prev_stride) + models.append(region_layer) + elif block['type'] == 'yolo': + yolo_layer = YoloLayer(use_cuda=self.use_cuda) + anchors = block['anchors'].split(',') + anchor_mask = block['mask'].split(',') + yolo_layer.anchor_mask = [int(i) for i in anchor_mask] + yolo_layer.anchors = [float(i) for i in anchors] + yolo_layer.num_classes = int(block['classes']) + yolo_layer.num_anchors = int(block['num']) + yolo_layer.anchor_step = len(yolo_layer.anchors)//yolo_layer.num_anchors + try: + yolo_layer.rescore = int(block['rescore']) + except: + pass + yolo_layer.ignore_thresh = float(block['ignore_thresh']) + yolo_layer.truth_thresh = float(block['truth_thresh']) + yolo_layer.stride = prev_stride + yolo_layer.nth_layer = ind + yolo_layer.net_width = self.width + yolo_layer.net_height = self.height + out_filters.append(prev_filters) + out_strides.append(prev_stride) + models.append(yolo_layer) + else: + print('unknown type %s' % (block['type'])) + + return models + + def load_binfile(self, weightfile): + fp = open(weightfile, 'rb') + + version = np.fromfile(fp, count=3, dtype=np.int32) + version = [int(i) for i in version] + if version[0]*10+version[1] >=2 and version[0] < 1000 and version[1] < 1000: + seen = np.fromfile(fp, count=1, dtype=np.int64) + else: + seen = np.fromfile(fp, count=1, dtype=np.int32) + self.header = torch.from_numpy(np.concatenate((version, seen), axis=0)) + self.seen = int(seen) + body = np.fromfile(fp, dtype=np.float32) + fp.close() + return body + + def load_weights(self, weightfile): + buf = self.load_binfile(weightfile) + + start = 0 + ind = -2 + for block in self.blocks: + if start >= buf.size: + break + ind = ind + 1 + if block['type'] == 'net': + continue + elif block['type'] == 'convolutional': + model = self.models[ind] + batch_normalize = int(block['batch_normalize']) + if batch_normalize: + start = load_conv_bn(buf, start, model[0], model[1]) + else: + start = load_conv(buf, start, model[0]) + elif block['type'] == 'connected': + model = self.models[ind] + if block['activation'] != 'linear': + start = load_fc(buf, start, model[0]) + else: + start = load_fc(buf, start, model) + elif block['type'] == 'maxpool': + pass + elif block['type'] == 'reorg': + pass + elif block['type'] == 'upsample': + pass + elif block['type'] == 'route': + pass + elif block['type'] == 'shortcut': + pass + elif block['type'] == 'region': + pass + elif block['type'] == 'yolo': + pass + elif block['type'] == 'avgpool': + pass + elif block['type'] == 'softmax': + pass + elif block['type'] == 'cost': + pass + else: + print('unknown type %s' % (block['type'])) + + def save_weights(self, outfile, cutoff=0): + if cutoff <= 0: + cutoff = len(self.blocks)-1 + + fp = open(outfile, 'wb') + self.header[3] = self.seen + header = np.array(self.header[0:3].numpy(), np.int32) + header.tofile(fp) + if (self.header[0]*10+self.header[1]) >= 2: + seen = np.array(self.seen, np.int64) + else: + seen = np.array(self.seen, np.int32) + seen.tofile(fp) + + ind = -1 + for blockId in range(1, cutoff+1): + ind = ind + 1 + block = self.blocks[blockId] + if block['type'] == 'convolutional': + model = self.models[ind] + batch_normalize = int(block['batch_normalize']) + if batch_normalize: + save_conv_bn(fp, model[0], model[1]) + else: + save_conv(fp, model[0]) + elif block['type'] == 'connected': + model = self.models[ind] + if block['activation'] != 'linear': + save_fc(fc, model) + else: + save_fc(fc, model[0]) + elif block['type'] == 'maxpool': + pass + elif block['type'] == 'reorg': + pass + elif block['type'] == 'upsample': + pass + elif block['type'] == 'route': + pass + elif block['type'] == 'shortcut': + pass + elif block['type'] == 'region': + pass + elif block['type'] == 'yolo': + pass + elif block['type'] == 'avgpool': + pass + elif block['type'] == 'softmax': + pass + elif block['type'] == 'cost': + pass + else: + print('unknown type %s' % (block['type'])) + fp.close() diff --git 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b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/detect.py @@ -0,0 +1,131 @@ +import sys +import time +from PIL import Image, ImageDraw +#from models.tiny_yolo import TinyYoloNet +from yolo_utils import * +from darknet import Darknet + +import cv2 + +namesfile=None +def detect(cfgfile, weightfile, imgfolder): + m = Darknet(cfgfile) + + #m.print_network() + m.load_weights(weightfile) + print('Loading weights from %s... Done!' % (weightfile)) + + # if m.num_classes == 20: + # namesfile = 'data/voc.names' + # elif m.num_classes == 80: + # namesfile = 'data/coco.names' + # else: + # namesfile = 'data/names' + + use_cuda = True + if use_cuda: + m.cuda() + + imgfiles = [x for x in os.listdir(imgfolder) if x[-4:] == '.jpg'] + imgfiles.sort() + for imgname in imgfiles: + imgfile = os.path.join(imgfolder,imgname) + + img = Image.open(imgfile).convert('RGB') + sized = img.resize((m.width, m.height)) + + #for i in range(2): + start = time.time() + boxes = do_detect(m, sized, 0.5, 0.4, use_cuda) + finish = time.time() + #if i == 1: + print('%s: Predicted in %f seconds.' % (imgfile, (finish-start))) + + class_names = load_class_names(namesfile) + img = plot_boxes(img, boxes, 'result/{}'.format(os.path.basename(imgfile)), class_names) + img = np.array(img) + cv2.imshow('{}'.format(os.path.basename(imgfolder)), img) + cv2.resizeWindow('{}'.format(os.path.basename(imgfolder)), 1000,800) + cv2.waitKey(1000) + +def detect_cv2(cfgfile, weightfile, imgfile): + import cv2 + m = Darknet(cfgfile) + + m.print_network() + m.load_weights(weightfile) + print('Loading weights from %s... Done!' % (weightfile)) + + if m.num_classes == 20: + namesfile = 'data/voc.names' + elif m.num_classes == 80: + namesfile = 'data/coco.names' + else: + namesfile = 'data/names' + + use_cuda = True + if use_cuda: + m.cuda() + + img = cv2.imread(imgfile) + sized = cv2.resize(img, (m.width, m.height)) + sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB) + + for i in range(2): + start = time.time() + boxes = do_detect(m, sized, 0.5, 0.4, use_cuda) + finish = time.time() + if i == 1: + print('%s: Predicted in %f seconds.' % (imgfile, (finish-start))) + + class_names = load_class_names(namesfile) + plot_boxes_cv2(img, boxes, savename='predictions.jpg', class_names=class_names) + +def detect_skimage(cfgfile, weightfile, imgfile): + from skimage import io + from skimage.transform import resize + m = Darknet(cfgfile) + + m.print_network() + m.load_weights(weightfile) + print('Loading weights from %s... Done!' % (weightfile)) + + if m.num_classes == 20: + namesfile = 'data/voc.names' + elif m.num_classes == 80: + namesfile = 'data/coco.names' + else: + namesfile = 'data/names' + + use_cuda = True + if use_cuda: + m.cuda() + + img = io.imread(imgfile) + sized = resize(img, (m.width, m.height)) * 255 + + for i in range(2): + start = time.time() + boxes = do_detect(m, sized, 0.5, 0.4, use_cuda) + finish = time.time() + if i == 1: + print('%s: Predicted in %f seconds.' % (imgfile, (finish-start))) + + class_names = load_class_names(namesfile) + plot_boxes_cv2(img, boxes, savename='predictions.jpg', class_names=class_names) + +if __name__ == '__main__': + if len(sys.argv) == 5: + cfgfile = sys.argv[1] + weightfile = sys.argv[2] + imgfolder = sys.argv[3] + cv2.namedWindow('{}'.format(os.path.basename(imgfolder)), cv2.WINDOW_NORMAL ) + cv2.resizeWindow('{}'.format(os.path.basename(imgfolder)), 1000,800) + globals()["namesfile"] = sys.argv[4] + detect(cfgfile, weightfile, imgfolder) + #detect_cv2(cfgfile, weightfile, imgfile) + #detect_skimage(cfgfile, weightfile, imgfile) + else: + print('Usage: ') + print(' python detect.py cfgfile weightfile imgfolder names') + #detect('cfg/tiny-yolo-voc.cfg', 'tiny-yolo-voc.weights', 'data/person.jpg', version=1) diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/detector.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/detector.py new file mode 100644 index 000000000..8fb302b92 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/detector.py @@ -0,0 +1,102 @@ +import torch +import logging +import numpy as np +import cv2 + +from .darknet import Darknet +from .yolo_utils import get_all_boxes, nms, post_process, xywh_to_xyxy, xyxy_to_xywh +from .nms import boxes_nms + + +class YOLOv3(object): + def __init__(self, cfgfile, weightfile, namesfile, score_thresh=0.7, conf_thresh=0.01, nms_thresh=0.45, + is_xywh=False, use_cuda=True): + # net definition + self.net = Darknet(cfgfile) + self.net.load_weights(weightfile) + logger = logging.getLogger("root.detector") + logger.info('Loading weights from %s... Done!' % (weightfile)) + self.device = "cuda" if use_cuda else "cpu" + self.net.eval() + self.net.to(self.device) + + # constants + self.size = self.net.width, self.net.height + self.score_thresh = score_thresh + self.conf_thresh = conf_thresh + self.nms_thresh = nms_thresh + self.use_cuda = use_cuda + self.is_xywh = is_xywh + self.num_classes = self.net.num_classes + self.class_names = self.load_class_names(namesfile) + + def __call__(self, ori_img): + # img to tensor + assert isinstance(ori_img, np.ndarray), "input must be a numpy array!" + img = ori_img.astype(np.float) / 255. + + img = cv2.resize(img, self.size) + img = torch.from_numpy(img).float().permute(2, 0, 1).unsqueeze(0) + + # forward + with torch.no_grad(): + img = img.to(self.device) + out_boxes = self.net(img) + boxes = get_all_boxes(out_boxes, self.conf_thresh, self.num_classes, + use_cuda=self.use_cuda) # batch size is 1 + # boxes = nms(boxes, self.nms_thresh) + + boxes = post_process(boxes, self.net.num_classes, self.conf_thresh, self.nms_thresh)[0].cpu() + boxes = boxes[boxes[:, -2] > self.score_thresh, :] # bbox xmin ymin xmax ymax + + if len(boxes) == 0: + bbox = torch.FloatTensor([]).reshape([0, 4]) + cls_conf = torch.FloatTensor([]) + cls_ids = torch.LongTensor([]) + else: + height, width = ori_img.shape[:2] + bbox = boxes[:, :4] + if self.is_xywh: + # bbox x y w h + bbox = xyxy_to_xywh(bbox) + + bbox *= torch.FloatTensor([[width, height, width, height]]) + cls_conf = boxes[:, 5] + cls_ids = boxes[:, 6].long() + return bbox.numpy(), cls_conf.numpy(), cls_ids.numpy() + + def load_class_names(self, namesfile): + with open(namesfile, 'r', encoding='utf8') as fp: + class_names = [line.strip() for line in fp.readlines()] + return class_names + + +def demo(): + import os + from vizer.draw import draw_boxes + + yolo = YOLOv3("cfg/yolo_v3.cfg", "weight/yolov3.weights", "cfg/coco.names") + print("yolo.size =", yolo.size) + root = "./demo" + resdir = os.path.join(root, "results") + os.makedirs(resdir, exist_ok=True) + files = [os.path.join(root, file) for file in os.listdir(root) if file.endswith('.jpg')] + files.sort() + for filename in files: + img = cv2.imread(filename) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + bbox, cls_conf, cls_ids = yolo(img) + + if bbox is not None: + img = draw_boxes(img, bbox, cls_ids, cls_conf, class_name_map=yolo.class_names) + # save results + cv2.imwrite(os.path.join(resdir, os.path.basename(filename)), img[:, :, (2, 1, 0)]) + # imshow + # cv2.namedWindow("yolo", cv2.WINDOW_NORMAL) + # cv2.resizeWindow("yolo", 600,600) + # cv2.imshow("yolo",res[:,:,(2,1,0)]) + # cv2.waitKey(0) + + +if __name__ == "__main__": + demo() diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/__init__.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/__init__.py new file mode 100644 index 000000000..4da700723 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/__init__.py @@ -0,0 +1 @@ +from .nms import boxes_nms \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/build.sh b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/build.sh new file mode 100644 index 000000000..44766a2e2 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/build.sh @@ -0,0 +1,5 @@ +cd ext + +python build.py build_ext develop + +cd .. diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/__init__.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/build.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/build.py new file mode 100644 index 000000000..66973bcf1 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/build.py @@ -0,0 +1,58 @@ +import glob +import os + +import torch +from setuptools import setup +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +requirements = ["torch"] + + +def get_extensions(): + extensions_dir = os.path.dirname(os.path.abspath(__file__)) + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + sources = main_file + source_cpu + extension = CppExtension + + extra_compile_args = {"cxx": []} + define_macros = [] + + if torch.cuda.is_available() and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + + sources = [os.path.join(extensions_dir, s) for s in sources] + + include_dirs = [extensions_dir] + + ext_modules = [ + extension( + "torch_extension", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + + return ext_modules + + +setup( + name="torch_extension", + version="0.1", + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}) diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cpu/nms_cpu.cpp b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cpu/nms_cpu.cpp new file mode 100644 index 000000000..5b3f93ca0 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cpu/nms_cpu.cpp @@ -0,0 +1,75 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include "cpu/vision.h" + + +template <typename scalar_t> +at::Tensor nms_cpu_kernel(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold) { + AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor"); + AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor"); + AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + } + + auto x1_t = dets.select(1, 0).contiguous(); + auto y1_t = dets.select(1, 1).contiguous(); + auto x2_t = dets.select(1, 2).contiguous(); + auto y2_t = dets.select(1, 3).contiguous(); + + at::Tensor areas_t = (x2_t - x1_t) * (y2_t - y1_t); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte).device(at::kCPU)); + + auto suppressed = suppressed_t.data<uint8_t>(); + auto order = order_t.data<int64_t>(); + auto x1 = x1_t.data<scalar_t>(); + auto y1 = y1_t.data<scalar_t>(); + auto x2 = x2_t.data<scalar_t>(); + auto y2 = y2_t.data<scalar_t>(); + auto areas = areas_t.data<scalar_t>(); + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) + continue; + auto ix1 = x1[i]; + auto iy1 = y1[i]; + auto ix2 = x2[i]; + auto iy2 = y2[i]; + auto iarea = areas[i]; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) + continue; + auto xx1 = std::max(ix1, x1[j]); + auto yy1 = std::max(iy1, y1[j]); + auto xx2 = std::min(ix2, x2[j]); + auto yy2 = std::min(iy2, y2[j]); + + auto w = std::max(static_cast<scalar_t>(0), xx2 - xx1); + auto h = std::max(static_cast<scalar_t>(0), yy2 - yy1); + auto inter = w * h; + auto ovr = inter / (iarea + areas[j] - inter); + if (ovr >= threshold) + suppressed[j] = 1; + } + } + return at::nonzero(suppressed_t == 0).squeeze(1); +} + +at::Tensor nms_cpu(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold) { + at::Tensor result; + AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] { + result = nms_cpu_kernel<scalar_t>(dets, scores, threshold); + }); + return result; +} \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cpu/vision.h b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cpu/vision.h new file mode 100644 index 000000000..b3529ad78 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cpu/vision.h @@ -0,0 +1,7 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include <torch/extension.h> + +at::Tensor nms_cpu(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold); diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cuda/nms.cu b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cuda/nms.cu new file mode 100644 index 000000000..2eb452510 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cuda/nms.cu @@ -0,0 +1,131 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include <ATen/ATen.h> +#include <ATen/cuda/CUDAContext.h> + +#include <THC/THC.h> +#include <THC/THCDeviceUtils.cuh> + +#include <vector> +#include <iostream> + +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +__device__ inline float devIoU(float const * const a, float const * const b) { + float left = max(a[0], b[0]), right = min(a[2], b[2]); + float top = max(a[1], b[1]), bottom = min(a[3], b[3]); + float width = max(right - left, 0.f), height = max(bottom - top, 0.f); + float interS = width * height; + float Sa = (a[2] - a[0]) * (a[3] - a[1]); + float Sb = (b[2] - b[0]) * (b[3] - b[1]); + return interS / (Sa + Sb - interS); +} + +__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, + const float *dev_boxes, unsigned long long *dev_mask) { + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + __shared__ float block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const float *cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +// boxes is a N x 5 tensor +at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) { + using scalar_t = float; + AT_ASSERTM(boxes.type().is_cuda(), "boxes must be a CUDA tensor"); + auto scores = boxes.select(1, 4); + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto boxes_sorted = boxes.index_select(0, order_t); + + int boxes_num = boxes.size(0); + + const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock); + + scalar_t* boxes_dev = boxes_sorted.data<scalar_t>(); + + THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState + + unsigned long long* mask_dev = NULL; + //THCudaCheck(THCudaMalloc(state, (void**) &mask_dev, + // boxes_num * col_blocks * sizeof(unsigned long long))); + + mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long)); + + dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock), + THCCeilDiv(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + nms_kernel<<<blocks, threads>>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector<unsigned long long> mask_host(boxes_num * col_blocks); + THCudaCheck(cudaMemcpy(&mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost)); + + std::vector<unsigned long long> remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data<int64_t>(); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + THCudaFree(state, mask_dev); + // TODO improve this part + return std::get<0>(order_t.index({ + keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to( + order_t.device(), keep.scalar_type()) + }).sort(0, false)); +} \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cuda/vision.h b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cuda/vision.h new file mode 100644 index 000000000..b5bd90778 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/cuda/vision.h @@ -0,0 +1,7 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include <torch/extension.h> + +at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh); + + diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/nms.h b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/nms.h new file mode 100644 index 000000000..312fed4a7 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/nms.h @@ -0,0 +1,28 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + + +at::Tensor nms(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold) { + + if (dets.type().is_cuda()) { +#ifdef WITH_CUDA + // TODO raise error if not compiled with CUDA + if (dets.numel() == 0) + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + auto b = at::cat({dets, scores.unsqueeze(1)}, 1); + return nms_cuda(b, threshold); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + + at::Tensor result = nms_cpu(dets, scores, threshold); + return result; +} diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/vision.cpp b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/vision.cpp new file mode 100644 index 000000000..726b77bd4 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/ext/vision.cpp @@ -0,0 +1,7 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include "nms.h" + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("nms", &nms, "non-maximum suppression"); +} diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/nms.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/nms.py new file mode 100644 index 000000000..1b4a2db86 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/nms.py @@ -0,0 +1,34 @@ +import warnings +import torchvision + +try: + import torch + import torch_extension + + _nms = torch_extension.nms +except ImportError: + if torchvision.__version__ >= '0.3.0': + _nms = torchvision.ops.nms + else: + from .python_nms import python_nms + + _nms = python_nms + warnings.warn('You are using python version NMS, which is very very slow. Try compile c++ NMS ' + 'using `cd ext & python build.py build_ext develop`') + + +def boxes_nms(boxes, scores, nms_thresh, max_count=-1): + """ Performs non-maximum suppression, run on GPU or CPU according to + boxes's device. + Args: + boxes(Tensor): `xyxy` mode boxes, use absolute coordinates(or relative coordinates), shape is (n, 4) + scores(Tensor): scores, shape is (n, ) + nms_thresh(float): thresh + max_count (int): if > 0, then only the top max_proposals are kept after non-maximum suppression + Returns: + indices kept. + """ + keep = _nms(boxes, scores, nms_thresh) + if max_count > 0: + keep = keep[:max_count] + return keep diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/python_nms.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/python_nms.py new file mode 100644 index 000000000..bd8a4ba23 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/nms/python_nms.py @@ -0,0 +1,59 @@ +import torch +import numpy as np + + +def python_nms(boxes, scores, nms_thresh): + """ Performs non-maximum suppression using numpy + Args: + boxes(Tensor): `xyxy` mode boxes, use absolute coordinates(not support relative coordinates), + shape is (n, 4) + scores(Tensor): scores, shape is (n, ) + nms_thresh(float): thresh + Returns: + indices kept. + """ + if boxes.numel() == 0: + return torch.empty((0,), dtype=torch.long) + # Use numpy to run nms. Running nms in PyTorch code on CPU is really slow. + origin_device = boxes.device + cpu_device = torch.device('cpu') + boxes = boxes.to(cpu_device).numpy() + scores = scores.to(cpu_device).numpy() + + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + areas = (x2 - x1) * (y2 - y1) + order = np.argsort(scores)[::-1] + num_detections = boxes.shape[0] + suppressed = np.zeros((num_detections,), dtype=np.bool) + for _i in range(num_detections): + i = order[_i] + if suppressed[i]: + continue + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + + for _j in range(_i + 1, num_detections): + j = order[_j] + if suppressed[j]: + continue + + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0, xx2 - xx1) + h = max(0, yy2 - yy1) + + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + if ovr >= nms_thresh: + suppressed[j] = True + keep = np.nonzero(suppressed == 0)[0] + keep = torch.from_numpy(keep).to(origin_device) + return keep diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/region_layer.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/region_layer.py new file mode 100644 index 000000000..c55ef376e --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/region_layer.py @@ -0,0 +1,185 @@ +import math +import sys +import time +import torch +import torch.nn as nn +from .yolo_utils import bbox_iou, multi_bbox_ious, convert2cpu + + +class RegionLayer(nn.Module): + def __init__(self, num_classes=0, anchors=[], num_anchors=1, use_cuda=None): + super(RegionLayer, self).__init__() + use_cuda = torch.cuda.is_available() and (True if use_cuda is None else use_cuda) + self.device = torch.device("cuda" if use_cuda else "cpu") + self.num_classes = num_classes + self.num_anchors = num_anchors + self.anchor_step = len(anchors) // num_anchors + # self.anchors = torch.stack(torch.FloatTensor(anchors).split(self.anchor_step)).to(self.device) + self.anchors = torch.FloatTensor(anchors).view(self.num_anchors, self.anchor_step).to(self.device) + self.rescore = 1 + self.coord_scale = 1 + self.noobject_scale = 1 + self.object_scale = 5 + self.class_scale = 1 + self.thresh = 0.6 + self.seen = 0 + + def build_targets(self, pred_boxes, target, nH, nW): + nB = target.size(0) + nA = self.num_anchors + conf_mask = torch.ones(nB, nA, nH, nW) * self.noobject_scale + coord_mask = torch.zeros(nB, nA, nH, nW) + cls_mask = torch.zeros(nB, nA, nH, nW) + tcoord = torch.zeros(4, nB, nA, nH, nW) + tconf = torch.zeros(nB, nA, nH, nW) + tcls = torch.zeros(nB, nA, nH, nW) + + nAnchors = nA * nH * nW + nPixels = nH * nW + nGT = 0 # number of ground truth + nRecall = 0 + # it works faster on CPU than on GPU. + anchors = self.anchors.to("cpu") + + if self.seen < 12800: + tcoord[0].fill_(0.5) + tcoord[1].fill_(0.5) + coord_mask.fill_(1) + + for b in range(nB): + cur_pred_boxes = pred_boxes[b * nAnchors:(b + 1) * nAnchors].t() + cur_ious = torch.zeros(nAnchors) + tbox = target[b].view(-1, 5).to("cpu") + for t in range(50): + if tbox[t][1] == 0: + break + gx, gw = [i * nW for i in (tbox[t][1], tbox[t][3])] + gy, gh = [i * nH for i in (tbox[t][2], tbox[t][4])] + cur_gt_boxes = torch.FloatTensor([gx, gy, gw, gh]).repeat(nAnchors, 1).t() + cur_ious = torch.max(cur_ious, multi_bbox_ious(cur_pred_boxes, cur_gt_boxes, x1y1x2y2=False)) + ignore_ix = cur_ious > self.thresh + conf_mask[b][ignore_ix.view(nA, nH, nW)] = 0 + + for t in range(50): + if tbox[t][1] == 0: + break + nGT += 1 + gx, gw = [i * nW for i in (tbox[t][1], tbox[t][3])] + gy, gh = [i * nH for i in (tbox[t][2], tbox[t][4])] + gw, gh = gw.float(), gh.float() + gi, gj = int(gx), int(gy) + + tmp_gt_boxes = torch.FloatTensor([0, 0, gw, gh]).repeat(nA, 1).t() + anchor_boxes = torch.cat((torch.zeros(nA, 2), anchors), 1).t() + tmp_ious = multi_bbox_ious(tmp_gt_boxes, anchor_boxes, x1y1x2y2=False) + best_iou, best_n = torch.max(tmp_ious, 0) + + if self.anchor_step == 4: # this part is not tested. + tmp_ious_mask = (tmp_ious == best_iou) + if tmp_ious_mask.sum() > 0: + gt_pos = torch.FloatTensor([gi, gj, gx, gy]).repeat(nA, 1).t() + an_pos = anchor_boxes[4:6] # anchor_boxes are consisted of [0 0 aw ah ax ay] + dist = pow(((gt_pos[0] + an_pos[0]) - gt_pos[2]), 2) + pow( + ((gt_pos[1] + an_pos[1]) - gt_pos[3]), 2) + dist[1 - tmp_ious_mask] = 10000 # set the large number for the small ious + _, best_n = torch.min(dist, 0) + + gt_box = torch.FloatTensor([gx, gy, gw, gh]) + pred_box = pred_boxes[b * nAnchors + best_n * nPixels + gj * nW + gi] + iou = bbox_iou(gt_box, pred_box, x1y1x2y2=False) + + coord_mask[b][best_n][gj][gi] = 1 + cls_mask[b][best_n][gj][gi] = 1 + conf_mask[b][best_n][gj][gi] = self.object_scale + tcoord[0][b][best_n][gj][gi] = gx - gi + tcoord[1][b][best_n][gj][gi] = gy - gj + tcoord[2][b][best_n][gj][gi] = math.log(gw / anchors[best_n][0]) + tcoord[3][b][best_n][gj][gi] = math.log(gh / anchors[best_n][1]) + tcls[b][best_n][gj][gi] = tbox[t][0] + tconf[b][best_n][gj][gi] = iou if self.rescore else 1. + if iou > 0.5: + nRecall += 1 + + return nGT, nRecall, coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls + + def get_mask_boxes(self, output): + if not isinstance(self.anchors, torch.Tensor): + self.anchors = torch.FloatTensor(self.anchors).view(self.num_anchors, self.anchor_step).to(self.device) + masked_anchors = self.anchors.view(-1) + num_anchors = torch.IntTensor([self.num_anchors]).to(self.device) + return {'x': output, 'a': masked_anchors, 'n': num_anchors} + + def forward(self, output, target): + # output : BxAs*(4+1+num_classes)*H*W + t0 = time.time() + nB = output.data.size(0) # batch size + nA = self.num_anchors + nC = self.num_classes + nH = output.data.size(2) + nW = output.data.size(3) + cls_anchor_dim = nB * nA * nH * nW + + if not isinstance(self.anchors, torch.Tensor): + self.anchors = torch.FloatTensor(self.anchors).view(self.num_anchors, self.anchor_step).to(self.device) + + output = output.view(nB, nA, (5 + nC), nH, nW) + cls_grid = torch.linspace(5, 5 + nC - 1, nC).long().to(self.device) + ix = torch.LongTensor(range(0, 5)).to(self.device) + pred_boxes = torch.FloatTensor(4, cls_anchor_dim).to(self.device) + + coord = output.index_select(2, ix[0:4]).view(nB * nA, -1, nH * nW).transpose(0, 1).contiguous().view(-1, + cls_anchor_dim) # x, y, w, h + coord[0:2] = coord[0:2].sigmoid() # x, y + conf = output.index_select(2, ix[4]).view(nB, nA, nH, nW).sigmoid() + cls = output.index_select(2, cls_grid) + cls = cls.view(nB * nA, nC, nH * nW).transpose(1, 2).contiguous().view(cls_anchor_dim, nC) + + t1 = time.time() + grid_x = torch.linspace(0, nW - 1, nW).repeat(nB * nA, nH, 1).view(cls_anchor_dim).to(self.device) + grid_y = torch.linspace(0, nH - 1, nH).repeat(nW, 1).t().repeat(nB * nA, 1, 1).view(cls_anchor_dim).to( + self.device) + anchor_w = self.anchors.index_select(1, ix[0]).repeat(1, nB * nH * nW).view(cls_anchor_dim) + anchor_h = self.anchors.index_select(1, ix[1]).repeat(1, nB * nH * nW).view(cls_anchor_dim) + + pred_boxes[0] = coord[0] + grid_x + pred_boxes[1] = coord[1] + grid_y + pred_boxes[2] = coord[2].exp() * anchor_w + pred_boxes[3] = coord[3].exp() * anchor_h + # for build_targets. it works faster on CPU than on GPU + pred_boxes = convert2cpu(pred_boxes.transpose(0, 1).contiguous().view(-1, 4)).detach() + + t2 = time.time() + nGT, nRecall, coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls = \ + self.build_targets(pred_boxes, target.detach(), nH, nW) + + cls_mask = (cls_mask == 1) + tcls = tcls[cls_mask].long().view(-1) + cls_mask = cls_mask.view(-1, 1).repeat(1, nC).to(self.device) + cls = cls[cls_mask].view(-1, nC) + + nProposals = int((conf > 0.25).sum()) + + tcoord = tcoord.view(4, cls_anchor_dim).to(self.device) + tconf, tcls = tconf.to(self.device), tcls.to(self.device) + coord_mask, conf_mask = coord_mask.view(cls_anchor_dim).to(self.device), conf_mask.sqrt().to(self.device) + + t3 = time.time() + loss_coord = self.coord_scale * nn.MSELoss(size_average=False)(coord * coord_mask, tcoord * coord_mask) / 2 + # sqrt(object_scale)/2 is almost equal to 1. + loss_conf = nn.MSELoss(size_average=False)(conf * conf_mask, tconf * conf_mask) / 2 + loss_cls = self.class_scale * nn.CrossEntropyLoss(size_average=False)(cls, tcls) if cls.size(0) > 0 else 0 + loss = loss_coord + loss_conf + loss_cls + t4 = time.time() + if False: + print('-' * 30) + print(' activation : %f' % (t1 - t0)) + print(' create pred_boxes : %f' % (t2 - t1)) + print(' build targets : %f' % (t3 - t2)) + print(' create loss : %f' % (t4 - t3)) + print(' total : %f' % (t4 - t0)) + print('%d: nGT %3d, nRC %3d, nPP %3d, loss: box %6.3f, conf %6.3f, class %6.3f, total %7.3f' + % (self.seen, nGT, nRecall, nProposals, loss_coord, loss_conf, loss_cls, loss)) + if math.isnan(loss.item()): + print(conf, tconf) + sys.exit(0) + return loss diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/weight/.gitkeep b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/weight/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/yolo_layer.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/yolo_layer.py new file mode 100644 index 000000000..578969faf --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/yolo_layer.py @@ -0,0 +1,181 @@ +import math +import sys +import time +import torch +import torch.nn as nn +from .yolo_utils import bbox_iou, multi_bbox_ious, convert2cpu + + +class YoloLayer(nn.Module): + def __init__(self, anchor_mask=[], num_classes=0, anchors=[], num_anchors=1, use_cuda=None): + super(YoloLayer, self).__init__() + use_cuda = torch.cuda.is_available() and (True if use_cuda is None else use_cuda) + self.device = torch.device("cuda" if use_cuda else "cpu") + + self.anchor_mask = anchor_mask + self.num_classes = num_classes + self.anchors = anchors + self.num_anchors = num_anchors + self.anchor_step = len(anchors) // num_anchors + self.rescore = 0 + self.ignore_thresh = 0.5 + self.truth_thresh = 1. + self.stride = 32 + self.nth_layer = 0 + self.seen = 0 + self.net_width = 0 + self.net_height = 0 + + def get_mask_boxes(self, output): + masked_anchors = [] + for m in self.anchor_mask: + masked_anchors += self.anchors[m * self.anchor_step:(m + 1) * self.anchor_step] + masked_anchors = [anchor / self.stride for anchor in masked_anchors] + + masked_anchors = torch.FloatTensor(masked_anchors).to(self.device) + num_anchors = torch.IntTensor([len(self.anchor_mask)]).to(self.device) + return {'x': output, 'a': masked_anchors, 'n': num_anchors} + + def build_targets(self, pred_boxes, target, anchors, nA, nH, nW): + nB = target.size(0) + anchor_step = anchors.size(1) # anchors[nA][anchor_step] + conf_mask = torch.ones(nB, nA, nH, nW) + coord_mask = torch.zeros(nB, nA, nH, nW) + cls_mask = torch.zeros(nB, nA, nH, nW) + tcoord = torch.zeros(4, nB, nA, nH, nW) + tconf = torch.zeros(nB, nA, nH, nW) + tcls = torch.zeros(nB, nA, nH, nW) + twidth, theight = self.net_width / self.stride, self.net_height / self.stride + + nAnchors = nA * nH * nW + nPixels = nH * nW + nGT = 0 + nRecall = 0 + nRecall75 = 0 + + # it works faster on CPU than on GPU. + anchors = anchors.to("cpu") + + for b in range(nB): + cur_pred_boxes = pred_boxes[b * nAnchors:(b + 1) * nAnchors].t() + cur_ious = torch.zeros(nAnchors) + tbox = target[b].view(-1, 5).to("cpu") + for t in range(50): + if tbox[t][1] == 0: + break + gx, gy = tbox[t][1] * nW, tbox[t][2] * nH + gw, gh = tbox[t][3] * twidth, tbox[t][4] * theight + cur_gt_boxes = torch.FloatTensor([gx, gy, gw, gh]).repeat(nAnchors, 1).t() + cur_ious = torch.max(cur_ious, multi_bbox_ious(cur_pred_boxes, cur_gt_boxes, x1y1x2y2=False)) + ignore_ix = cur_ious > self.ignore_thresh + conf_mask[b][ignore_ix.view(nA, nH, nW)] = 0 + + for t in range(50): + if tbox[t][1] == 0: + break + nGT += 1 + gx, gy = tbox[t][1] * nW, tbox[t][2] * nH + gw, gh = tbox[t][3] * twidth, tbox[t][4] * theight + gw, gh = gw.float(), gh.float() + gi, gj = int(gx), int(gy) + + tmp_gt_boxes = torch.FloatTensor([0, 0, gw, gh]).repeat(nA, 1).t() + anchor_boxes = torch.cat((torch.zeros(nA, anchor_step), anchors), 1).t() + _, best_n = torch.max(multi_bbox_ious(tmp_gt_boxes, anchor_boxes, x1y1x2y2=False), 0) + + gt_box = torch.FloatTensor([gx, gy, gw, gh]) + pred_box = pred_boxes[b * nAnchors + best_n * nPixels + gj * nW + gi] + iou = bbox_iou(gt_box, pred_box, x1y1x2y2=False) + + coord_mask[b][best_n][gj][gi] = 1 + cls_mask[b][best_n][gj][gi] = 1 + conf_mask[b][best_n][gj][gi] = 1 + tcoord[0][b][best_n][gj][gi] = gx - gi + tcoord[1][b][best_n][gj][gi] = gy - gj + tcoord[2][b][best_n][gj][gi] = math.log(gw / anchors[best_n][0]) + tcoord[3][b][best_n][gj][gi] = math.log(gh / anchors[best_n][1]) + tcls[b][best_n][gj][gi] = tbox[t][0] + tconf[b][best_n][gj][gi] = iou if self.rescore else 1. + + if iou > 0.5: + nRecall += 1 + if iou > 0.75: + nRecall75 += 1 + + return nGT, nRecall, nRecall75, coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls + + def forward(self, output, target): + # output : BxAs*(4+1+num_classes)*H*W + mask_tuple = self.get_mask_boxes(output) + t0 = time.time() + nB = output.data.size(0) # batch size + nA = mask_tuple['n'].item() # num_anchors + nC = self.num_classes + nH = output.data.size(2) + nW = output.data.size(3) + anchor_step = mask_tuple['a'].size(0) // nA + anchors = mask_tuple['a'].view(nA, anchor_step).to(self.device) + cls_anchor_dim = nB * nA * nH * nW + + output = output.view(nB, nA, (5 + nC), nH, nW) + cls_grid = torch.linspace(5, 5 + nC - 1, nC).long().to(self.device) + ix = torch.LongTensor(range(0, 5)).to(self.device) + pred_boxes = torch.FloatTensor(4, cls_anchor_dim).to(self.device) + + coord = output.index_select(2, ix[0:4]).view(nB * nA, -1, nH * nW).transpose(0, 1).contiguous().view(-1, + cls_anchor_dim) # x, y, w, h + coord[0:2] = coord[0:2].sigmoid() # x, y + conf = output.index_select(2, ix[4]).view(nB, nA, nH, nW).sigmoid() + cls = output.index_select(2, cls_grid) + cls = cls.view(nB * nA, nC, nH * nW).transpose(1, 2).contiguous().view(cls_anchor_dim, nC) + + t1 = time.time() + grid_x = torch.linspace(0, nW - 1, nW).repeat(nB * nA, nH, 1).view(cls_anchor_dim).to(self.device) + grid_y = torch.linspace(0, nH - 1, nH).repeat(nW, 1).t().repeat(nB * nA, 1, 1).view(cls_anchor_dim).to( + self.device) + anchor_w = anchors.index_select(1, ix[0]).repeat(1, nB * nH * nW).view(cls_anchor_dim) + anchor_h = anchors.index_select(1, ix[1]).repeat(1, nB * nH * nW).view(cls_anchor_dim) + + pred_boxes[0] = coord[0] + grid_x + pred_boxes[1] = coord[1] + grid_y + pred_boxes[2] = coord[2].exp() * anchor_w + pred_boxes[3] = coord[3].exp() * anchor_h + # for build_targets. it works faster on CPU than on GPU + pred_boxes = convert2cpu(pred_boxes.transpose(0, 1).contiguous().view(-1, 4)).detach() + + t2 = time.time() + nGT, nRecall, nRecall75, coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls = \ + self.build_targets(pred_boxes, target.detach(), anchors.detach(), nA, nH, nW) + + cls_mask = (cls_mask == 1) + tcls = tcls[cls_mask].long().view(-1) + cls_mask = cls_mask.view(-1, 1).repeat(1, nC).to(self.device) + cls = cls[cls_mask].view(-1, nC) + + nProposals = int((conf > 0.25).sum()) + + tcoord = tcoord.view(4, cls_anchor_dim).to(self.device) + tconf, tcls = tconf.to(self.device), tcls.to(self.device) + coord_mask, conf_mask = coord_mask.view(cls_anchor_dim).to(self.device), conf_mask.to(self.device) + + t3 = time.time() + loss_coord = nn.MSELoss(size_average=False)(coord * coord_mask, tcoord * coord_mask) / 2 + loss_conf = nn.MSELoss(size_average=False)(conf * conf_mask, tconf * conf_mask) + loss_cls = nn.CrossEntropyLoss(size_average=False)(cls, tcls) if cls.size(0) > 0 else 0 + loss = loss_coord + loss_conf + loss_cls + + t4 = time.time() + if False: + print('-' * 30) + print(' activation : %f' % (t1 - t0)) + print(' create pred_boxes : %f' % (t2 - t1)) + print(' build targets : %f' % (t3 - t2)) + print(' create loss : %f' % (t4 - t3)) + print(' total : %f' % (t4 - t0)) + print( + '%d: Layer(%03d) nGT %3d, nRC %3d, nRC75 %3d, nPP %3d, loss: box %6.3f, conf %6.3f, class %6.3f, total %7.3f' + % (self.seen, self.nth_layer, nGT, nRecall, nRecall75, nProposals, loss_coord, loss_conf, loss_cls, loss)) + if math.isnan(loss.item()): + print(conf, tconf) + sys.exit(0) + return loss diff --git a/cv/tracking/deep_sort/pytorch/detector/YOLOv3/yolo_utils.py b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/yolo_utils.py new file mode 100644 index 000000000..b546eeff0 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/YOLOv3/yolo_utils.py @@ -0,0 +1,589 @@ +import os +import time +import math +import torch +import numpy as np +from PIL import Image, ImageDraw +import struct # get_image_size +import imghdr # get_image_size + + +def sigmoid(x): + return 1.0 / (math.exp(-x) + 1.) + + +def softmax(x): + x = torch.exp(x - torch.max(x)) + x /= x.sum() + return x + + +def bbox_iou(box1, box2, x1y1x2y2=True): + if x1y1x2y2: + x1_min = min(box1[0], box2[0]) + x2_max = max(box1[2], box2[2]) + y1_min = min(box1[1], box2[1]) + y2_max = max(box1[3], box2[3]) + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + else: + w1, h1 = box1[2], box1[3] + w2, h2 = box2[2], box2[3] + x1_min = min(box1[0] - w1 / 2.0, box2[0] - w2 / 2.0) + x2_max = max(box1[0] + w1 / 2.0, box2[0] + w2 / 2.0) + y1_min = min(box1[1] - h1 / 2.0, box2[1] - h2 / 2.0) + y2_max = max(box1[1] + h1 / 2.0, box2[1] + h2 / 2.0) + + w_union = x2_max - x1_min + h_union = y2_max - y1_min + w_cross = w1 + w2 - w_union + h_cross = h1 + h2 - h_union + carea = 0 + if w_cross <= 0 or h_cross <= 0: + return 0.0 + + area1 = w1 * h1 + area2 = w2 * h2 + carea = w_cross * h_cross + uarea = area1 + area2 - carea + return float(carea / uarea) + + +def multi_bbox_ious(boxes1, boxes2, x1y1x2y2=True): + if x1y1x2y2: + x1_min = torch.min(boxes1[0], boxes2[0]) + x2_max = torch.max(boxes1[2], boxes2[2]) + y1_min = torch.min(boxes1[1], boxes2[1]) + y2_max = torch.max(boxes1[3], boxes2[3]) + w1, h1 = boxes1[2] - boxes1[0], boxes1[3] - boxes1[1] + w2, h2 = boxes2[2] - boxes2[0], boxes2[3] - boxes2[1] + else: + w1, h1 = boxes1[2], boxes1[3] + w2, h2 = boxes2[2], boxes2[3] + x1_min = torch.min(boxes1[0] - w1 / 2.0, boxes2[0] - w2 / 2.0) + x2_max = torch.max(boxes1[0] + w1 / 2.0, boxes2[0] + w2 / 2.0) + y1_min = torch.min(boxes1[1] - h1 / 2.0, boxes2[1] - h2 / 2.0) + y2_max = torch.max(boxes1[1] + h1 / 2.0, boxes2[1] + h2 / 2.0) + + w_union = x2_max - x1_min + h_union = y2_max - y1_min + w_cross = w1 + w2 - w_union + h_cross = h1 + h2 - h_union + mask = (((w_cross <= 0) + (h_cross <= 0)) > 0) + area1 = w1 * h1 + area2 = w2 * h2 + carea = w_cross * h_cross + carea[mask] = 0 + uarea = area1 + area2 - carea + return carea / uarea + + +from .nms import boxes_nms + + +def post_process(boxes, num_classes, conf_thresh=0.01, nms_thresh=0.45, obj_thresh=0.3): + batch_size = boxes.size(0) + + # nms + results_boxes = [] + for batch_id in range(batch_size): + processed_boxes = [] + for cls_id in range(num_classes): + mask = (boxes[batch_id, :, -1] == cls_id) * (boxes[batch_id, :, 4] > obj_thresh) + masked_boxes = boxes[batch_id, mask] + + keep = boxes_nms(masked_boxes[:, :4], masked_boxes[:, 5], nms_thresh) + + nmsed_boxes = masked_boxes[keep, :] + + processed_boxes.append(nmsed_boxes) + processed_boxes = torch.cat(processed_boxes, dim=0) + + results_boxes.append(processed_boxes) + + return results_boxes + + +def xywh_to_xyxy(boxes_xywh): + boxes_xyxy = boxes_xywh.copy() + boxes_xyxy[:, 0] = boxes_xywh[:, 0] - boxes_xywh[:, 2] / 2. + boxes_xyxy[:, 0] = boxes_xywh[:, 0] - boxes_xywh[:, 2] / 2. + boxes_xyxy[:, 0] = boxes_xywh[:, 0] - boxes_xywh[:, 2] / 2. + boxes_xyxy[:, 0] = boxes_xywh[:, 0] - boxes_xywh[:, 2] / 2. + + return boxes_xyxy + + +def xyxy_to_xywh(boxes_xyxy): + if isinstance(boxes_xyxy, torch.Tensor): + boxes_xywh = boxes_xyxy.clone() + elif isinstance(boxes_xyxy, np.ndarray): + boxes_xywh = boxes_xyxy.copy() + + boxes_xywh[:, 0] = (boxes_xyxy[:, 0] + boxes_xyxy[:, 2]) / 2. + boxes_xywh[:, 1] = (boxes_xyxy[:, 1] + boxes_xyxy[:, 3]) / 2. + boxes_xywh[:, 2] = boxes_xyxy[:, 2] - boxes_xyxy[:, 0] + boxes_xywh[:, 3] = boxes_xyxy[:, 3] - boxes_xyxy[:, 1] + + return boxes_xywh + + +def nms(boxes, nms_thresh): + if len(boxes) == 0: + return boxes + + det_confs = torch.zeros(len(boxes)) + for i in range(len(boxes)): + det_confs[i] = boxes[i][4] + + _, sortIds = torch.sort(det_confs, descending=True) + out_boxes = [] + for i in range(len(boxes)): + box_i = boxes[sortIds[i]] + if box_i[4] > 0: + out_boxes.append(box_i) + for j in range(i + 1, len(boxes)): + box_j = boxes[sortIds[j]] + if bbox_iou(box_i, box_j, x1y1x2y2=False) > nms_thresh: + # print(box_i, box_j, bbox_iou(box_i, box_j, x1y1x2y2=False)) + box_j[4] = 0 + return out_boxes + + +def convert2cpu(gpu_matrix): + return torch.FloatTensor(gpu_matrix.size()).copy_(gpu_matrix) + + +def convert2cpu_long(gpu_matrix): + return torch.LongTensor(gpu_matrix.size()).copy_(gpu_matrix) + + +def get_all_boxes(output, conf_thresh, num_classes, only_objectness=1, validation=False, use_cuda=True): + # total number of inputs (batch size) + # first element (x) for first tuple (x, anchor_mask, num_anchor) + batchsize = output[0]['x'].data.size(0) + + all_boxes = [] + for i in range(len(output)): + pred, anchors, num_anchors = output[i]['x'].data, output[i]['a'], output[i]['n'].item() + boxes = get_region_boxes(pred, conf_thresh, num_classes, anchors, num_anchors, \ + only_objectness=only_objectness, validation=validation, use_cuda=use_cuda) + + all_boxes.append(boxes) + return torch.cat(all_boxes, dim=1) + + +def get_region_boxes(output, obj_thresh, num_classes, anchors, num_anchors, only_objectness=1, validation=False, + use_cuda=True): + device = torch.device("cuda" if use_cuda else "cpu") + anchors = anchors.to(device) + anchor_step = anchors.size(0) // num_anchors + if output.dim() == 3: + output = output.unsqueeze(0) + batch = output.size(0) + assert (output.size(1) == (5 + num_classes) * num_anchors) + h = output.size(2) + w = output.size(3) + cls_anchor_dim = batch * num_anchors * h * w + + # all_boxes = [] + output = output.view(batch * num_anchors, 5 + num_classes, h * w).transpose(0, 1).contiguous().view(5 + num_classes, + cls_anchor_dim) + + grid_x = torch.linspace(0, w - 1, w).repeat(batch * num_anchors, h, 1).view(cls_anchor_dim).to(device) + grid_y = torch.linspace(0, h - 1, h).repeat(w, 1).t().repeat(batch * num_anchors, 1, 1).view(cls_anchor_dim).to( + device) + ix = torch.LongTensor(range(0, 2)).to(device) + anchor_w = anchors.view(num_anchors, anchor_step).index_select(1, ix[0]).repeat(1, batch, h * w).view( + cls_anchor_dim) + anchor_h = anchors.view(num_anchors, anchor_step).index_select(1, ix[1]).repeat(1, batch, h * w).view( + cls_anchor_dim) + + xs, ys = torch.sigmoid(output[0]) + grid_x, torch.sigmoid(output[1]) + grid_y + ws, hs = torch.exp(output[2]) * anchor_w.detach(), torch.exp(output[3]) * anchor_h.detach() + det_confs = torch.sigmoid(output[4]) + + # by ysyun, dim=1 means input is 2D or even dimension else dim=0 + cls_confs = torch.nn.Softmax(dim=1)(output[5:5 + num_classes].transpose(0, 1)).detach() + cls_max_confs, cls_max_ids = torch.max(cls_confs, 1) + cls_max_confs = cls_max_confs.view(-1) + cls_max_ids = cls_max_ids.view(-1).float() + + # sz_hw = h*w + # sz_hwa = sz_hw*num_anchors + # det_confs = convert2cpu(det_confs) + # cls_max_confs = convert2cpu(cls_max_confs) + # cls_max_ids = convert2cpu_long(cls_max_ids) + # xs, ys = convert2cpu(xs), convert2cpu(ys) + # ws, hs = convert2cpu(ws), convert2cpu(hs) + + cls_confs = det_confs * cls_max_confs + + # boxes = [xs/w, ys/h, ws/w, hs/h, det_confs, cls_confs, cls_max_ids] + xs, ys, ws, hs = xs / w, ys / h, ws / w, hs / h + x1, y1, x2, y2 = torch.clamp_min(xs - ws / 2., 0.), torch.clamp_min(ys - hs / 2., 0.), torch.clamp_max(xs + ws / 2., + 1.), torch.clamp_max( + ys + hs / 2., 1.) + boxes = [x1, y1, x2, y2, det_confs, cls_confs, cls_max_ids] + boxes = list(map(lambda x: x.view(batch, -1), boxes)) + boxes = torch.stack(boxes, dim=2) + + # for b in range(batch): + # boxes = [] + # for cy in range(h): + # for cx in range(w): + # for i in range(num_anchors): + # ind = b*sz_hwa + i*sz_hw + cy*w + cx + # det_conf = det_confs[ind] + # if only_objectness: + # conf = det_confs[ind] + # else: + # conf = det_confs[ind] * cls_max_confs[ind] + + # if conf > conf_thresh: + # bcx = xs[ind] + # bcy = ys[ind] + # bw = ws[ind] + # bh = hs[ind] + # cls_max_conf = cls_max_confs[ind] + # cls_max_id = cls_max_ids[ind] + # box = [bcx/w, bcy/h, bw/w, bh/h, det_conf, cls_max_conf, cls_max_id] + + # boxes.append(box) + # all_boxes.append(boxes) + return boxes + + +# def get_all_boxes(output, conf_thresh, num_classes, only_objectness=1, validation=False, use_cuda=True): +# # total number of inputs (batch size) +# # first element (x) for first tuple (x, anchor_mask, num_anchor) +# tot = output[0]['x'].data.size(0) +# all_boxes = [[] for i in range(tot)] +# for i in range(len(output)): +# pred, anchors, num_anchors = output[i]['x'].data, output[i]['a'], output[i]['n'].item() +# b = get_region_boxes(pred, conf_thresh, num_classes, anchors, num_anchors, \ +# only_objectness=only_objectness, validation=validation, use_cuda=use_cuda) +# for t in range(tot): +# all_boxes[t] += b[t] +# return all_boxes + +# def get_region_boxes(output, conf_thresh, num_classes, anchors, num_anchors, only_objectness=1, validation=False, use_cuda=True): +# device = torch.device("cuda" if use_cuda else "cpu") +# anchors = anchors.to(device) +# anchor_step = anchors.size(0)//num_anchors +# if output.dim() == 3: +# output = output.unsqueeze(0) +# batch = output.size(0) +# assert(output.size(1) == (5+num_classes)*num_anchors) +# h = output.size(2) +# w = output.size(3) +# cls_anchor_dim = batch*num_anchors*h*w + +# t0 = time.time() +# all_boxes = [] +# output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, cls_anchor_dim) + +# grid_x = torch.linspace(0, w-1, w).repeat(batch*num_anchors, h, 1).view(cls_anchor_dim).to(device) +# grid_y = torch.linspace(0, h-1, h).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(cls_anchor_dim).to(device) +# ix = torch.LongTensor(range(0,2)).to(device) +# anchor_w = anchors.view(num_anchors, anchor_step).index_select(1, ix[0]).repeat(1, batch, h*w).view(cls_anchor_dim) +# anchor_h = anchors.view(num_anchors, anchor_step).index_select(1, ix[1]).repeat(1, batch, h*w).view(cls_anchor_dim) + +# xs, ys = torch.sigmoid(output[0]) + grid_x, torch.sigmoid(output[1]) + grid_y +# ws, hs = torch.exp(output[2]) * anchor_w.detach(), torch.exp(output[3]) * anchor_h.detach() +# det_confs = torch.sigmoid(output[4]) + +# # by ysyun, dim=1 means input is 2D or even dimension else dim=0 +# cls_confs = torch.nn.Softmax(dim=1)(output[5:5+num_classes].transpose(0,1)).detach() +# cls_max_confs, cls_max_ids = torch.max(cls_confs, 1) +# cls_max_confs = cls_max_confs.view(-1) +# cls_max_ids = cls_max_ids.view(-1) +# t1 = time.time() + +# sz_hw = h*w +# sz_hwa = sz_hw*num_anchors +# det_confs = convert2cpu(det_confs) +# cls_max_confs = convert2cpu(cls_max_confs) +# cls_max_ids = convert2cpu_long(cls_max_ids) +# xs, ys = convert2cpu(xs), convert2cpu(ys) +# ws, hs = convert2cpu(ws), convert2cpu(hs) +# if validation: +# cls_confs = convert2cpu(cls_confs.view(-1, num_classes)) + +# t2 = time.time() +# for b in range(batch): +# boxes = [] +# for cy in range(h): +# for cx in range(w): +# for i in range(num_anchors): +# ind = b*sz_hwa + i*sz_hw + cy*w + cx +# det_conf = det_confs[ind] +# if only_objectness: +# conf = det_confs[ind] +# else: +# conf = det_confs[ind] * cls_max_confs[ind] + +# if conf > conf_thresh: +# bcx = xs[ind] +# bcy = ys[ind] +# bw = ws[ind] +# bh = hs[ind] +# cls_max_conf = cls_max_confs[ind] +# cls_max_id = cls_max_ids[ind] +# box = [bcx/w, bcy/h, bw/w, bh/h, det_conf, cls_max_conf, cls_max_id] +# if (not only_objectness) and validation: +# for c in range(num_classes): +# tmp_conf = cls_confs[ind][c] +# if c != cls_max_id and det_confs[ind]*tmp_conf > conf_thresh: +# box.append(tmp_conf) +# box.append(c) +# boxes.append(box) +# all_boxes.append(boxes) +# t3 = time.time() +# if False: +# print('---------------------------------') +# print('matrix computation : %f' % (t1-t0)) +# print(' gpu to cpu : %f' % (t2-t1)) +# print(' boxes filter : %f' % (t3-t2)) +# print('---------------------------------') +# return all_boxes + +def plot_boxes_cv2(img, boxes, savename=None, class_names=None, color=None): + import cv2 + colors = torch.FloatTensor([[1, 0, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0], [1, 1, 0], [1, 0, 0]]) + + def get_color(c, x, max_val): + ratio = float(x) / max_val * 5 + i = int(math.floor(ratio)) + j = int(math.ceil(ratio)) + ratio -= i + r = (1 - ratio) * colors[i][c] + ratio * colors[j][c] + return int(r * 255) + + width = img.shape[1] + height = img.shape[0] + for i in range(len(boxes)): + box = boxes[i] + x1 = int(round((box[0] - box[2] / 2.0) * width)) + y1 = int(round((box[1] - box[3] / 2.0) * height)) + x2 = int(round((box[0] + box[2] / 2.0) * width)) + y2 = int(round((box[1] + box[3] / 2.0) * height)) + + if color: + rgb = color + else: + rgb = (255, 0, 0) + if len(box) >= 7 and class_names: + cls_conf = box[5] + cls_id = box[6] + # print('%s: %f' % (class_names[cls_id], cls_conf)) + classes = len(class_names) + offset = cls_id * 123457 % classes + red = get_color(2, offset, classes) + green = get_color(1, offset, classes) + blue = get_color(0, offset, classes) + if color is None: + rgb = (red, green, blue) + img = cv2.putText(img, class_names[cls_id], (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1.2, rgb, 1) + img = cv2.rectangle(img, (x1, y1), (x2, y2), rgb, 1) + if savename: + print("save plot results to %s" % savename) + cv2.imwrite(savename, img) + return img + + +def plot_boxes(img, boxes, savename=None, class_names=None): + colors = torch.FloatTensor([[1, 0, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0], [1, 1, 0], [1, 0, 0]]) + + def get_color(c, x, max_val): + ratio = float(x) / max_val * 5 + i = int(math.floor(ratio)) + j = int(math.ceil(ratio)) + ratio -= i + r = (1 - ratio) * colors[i][c] + ratio * colors[j][c] + return int(r * 255) + + width = img.width + height = img.height + draw = ImageDraw.Draw(img) + print("%d box(es) is(are) found" % len(boxes)) + for i in range(len(boxes)): + box = boxes[i] + x1 = (box[0] - box[2] / 2.0) * width + y1 = (box[1] - box[3] / 2.0) * height + x2 = (box[0] + box[2] / 2.0) * width + y2 = (box[1] + box[3] / 2.0) * height + + rgb = (255, 0, 0) + if len(box) >= 7 and class_names: + cls_conf = box[5] + cls_id = box[6] + print('%s: %f' % (class_names[cls_id], cls_conf)) + classes = len(class_names) + offset = cls_id * 123457 % classes + red = get_color(2, offset, classes) + green = get_color(1, offset, classes) + blue = get_color(0, offset, classes) + rgb = (red, green, blue) + draw.text((x1, y1), class_names[cls_id], fill=rgb) + draw.rectangle([x1, y1, x2, y2], outline=rgb) + if savename: + print("save plot results to %s" % savename) + img.save(savename) + return img + + +def read_truths(lab_path): + if not os.path.exists(lab_path): + return np.array([]) + if os.path.getsize(lab_path): + truths = np.loadtxt(lab_path) + truths = truths.reshape(truths.size // 5, 5) # to avoid single truth problem + return truths + else: + return np.array([]) + + +def read_truths_args(lab_path, min_box_scale): + truths = read_truths(lab_path) + new_truths = [] + for i in range(truths.shape[0]): + if truths[i][3] < min_box_scale: + continue + new_truths.append([truths[i][0], truths[i][1], truths[i][2], truths[i][3], truths[i][4]]) + return np.array(new_truths) + + +def load_class_names(namesfile): + class_names = [] + with open(namesfile, 'r', encoding='utf8') as fp: + lines = fp.readlines() + for line in lines: + class_names.append(line.strip()) + return class_names + + +def image2torch(img): + if isinstance(img, Image.Image): + width = img.width + height = img.height + img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes())) + img = img.view(height, width, 3).transpose(0, 1).transpose(0, 2).contiguous() + img = img.view(1, 3, height, width) + img = img.float().div(255.0) + elif type(img) == np.ndarray: # cv2 image + img = torch.from_numpy(img.transpose(2, 0, 1)).float().div(255.0).unsqueeze(0) + else: + print("unknown image type") + exit(-1) + return img + + +def do_detect(model, img, conf_thresh, nms_thresh, use_cuda=True): + model.eval() + t0 = time.time() + img = image2torch(img) + t1 = time.time() + + img = img.to(torch.device("cuda" if use_cuda else "cpu")) + t2 = time.time() + + out_boxes = model(img) + boxes = get_all_boxes(out_boxes, conf_thresh, model.num_classes, use_cuda=use_cuda)[0] + + t3 = time.time() + boxes = nms(boxes, nms_thresh) + t4 = time.time() + + if False: + print('-----------------------------------') + print(' image to tensor : %f' % (t1 - t0)) + print(' tensor to cuda : %f' % (t2 - t1)) + print(' predict : %f' % (t3 - t2)) + print(' nms : %f' % (t4 - t3)) + print(' total : %f' % (t4 - t0)) + print('-----------------------------------') + return boxes + + +def read_data_cfg(datacfg): + options = dict() + options['gpus'] = '0,1,2,3' + options['num_workers'] = '10' + with open(datacfg) as fp: + lines = fp.readlines() + + for line in lines: + line = line.strip() + if line == '': + continue + key, value = line.split('=') + key = key.strip() + value = value.strip() + options[key] = value + return options + + +def scale_bboxes(bboxes, width, height): + import copy + dets = copy.deepcopy(bboxes) + for i in range(len(dets)): + dets[i][0] = dets[i][0] * width + dets[i][1] = dets[i][1] * height + dets[i][2] = dets[i][2] * width + dets[i][3] = dets[i][3] * height + return dets + + +def file_lines(thefilepath): + count = 0 + thefile = open(thefilepath, 'rb') + while True: + buffer = thefile.read(8192 * 1024) + if not buffer: + break + count += buffer.count(b'\n') + thefile.close() + return count + + +def get_image_size(fname): + """ + Determine the image type of fhandle and return its size. + from draco + """ + with open(fname, 'rb') as fhandle: + head = fhandle.read(24) + if len(head) != 24: + return + if imghdr.what(fname) == 'png': + check = struct.unpack('>i', head[4:8])[0] + if check != 0x0d0a1a0a: + return + width, height = struct.unpack('>ii', head[16:24]) + elif imghdr.what(fname) == 'gif': + width, height = struct.unpack('<HH', head[6:10]) + elif imghdr.what(fname) == 'jpeg' or imghdr.what(fname) == 'jpg': + try: + fhandle.seek(0) # Read 0xff next + size = 2 + ftype = 0 + while not 0xc0 <= ftype <= 0xcf: + fhandle.seek(size, 1) + byte = fhandle.read(1) + while ord(byte) == 0xff: + byte = fhandle.read(1) + ftype = ord(byte) + size = struct.unpack('>H', fhandle.read(2))[0] - 2 + # We are at a SOFn block + fhandle.seek(1, 1) # Skip `precision' byte. + height, width = struct.unpack('>HH', fhandle.read(4)) + except Exception: # IGNORE:W0703 + return + else: + return + return width, height + + +def logging(message): + print('%s %s' % (time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), message)) diff --git a/cv/tracking/deep_sort/pytorch/detector/__init__.py b/cv/tracking/deep_sort/pytorch/detector/__init__.py new file mode 100644 index 000000000..27f86e139 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/detector/__init__.py @@ -0,0 +1,15 @@ +from .YOLOv3 import YOLOv3 +from .MMDet import MMDet + + +__all__ = ['build_detector'] + +def build_detector(cfg, use_cuda): + if cfg.USE_MMDET: + return MMDet(cfg.MMDET.CFG, cfg.MMDET.CHECKPOINT, + score_thresh=cfg.MMDET.SCORE_THRESH, + is_xywh=True, use_cuda=use_cuda) + else: + return YOLOv3(cfg.YOLOV3.CFG, cfg.YOLOV3.WEIGHT, cfg.YOLOV3.CLASS_NAMES, + score_thresh=cfg.YOLOV3.SCORE_THRESH, nms_thresh=cfg.YOLOV3.NMS_THRESH, + is_xywh=True, use_cuda=use_cuda) diff --git a/cv/tracking/deep_sort/pytorch/ped_det_server.py b/cv/tracking/deep_sort/pytorch/ped_det_server.py new file mode 100644 index 000000000..4d67916e9 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/ped_det_server.py @@ -0,0 +1,155 @@ +""" +This module gets video in input and outputs the +json file with coordination of bboxes in the video. + +""" +from os.path import basename, splitext, join, isfile, isdir, dirname +from os import makedirs + +from tqdm import tqdm +import cv2 +import argparse +import torch + +from detector import build_detector +from deep_sort import build_tracker +from utils.tools import tik_tok, is_video +from utils.draw import compute_color_for_labels +from utils.parser import get_config +from utils.json_logger import BboxToJsonLogger +import warnings + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--VIDEO_PATH", type=str, default="./demo/ped.avi") + parser.add_argument("--config_detection", type=str, default="./configs/yolov3.yaml") + parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml") + parser.add_argument("--write-fps", type=int, default=20) + parser.add_argument("--frame_interval", type=int, default=1) + parser.add_argument("--save_path", type=str, default="./output") + parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True) + args = parser.parse_args() + + assert isfile(args.VIDEO_PATH), "Error: Video not found" + assert is_video(args.VIDEO_PATH), "Error: Not Supported format" + if args.frame_interval < 1: args.frame_interval = 1 + + return args + + +class VideoTracker(object): + def __init__(self, cfg, args): + self.cfg = cfg + self.args = args + use_cuda = args.use_cuda and torch.cuda.is_available() + if not use_cuda: + warnings.warn("Running in cpu mode!") + + self.vdo = cv2.VideoCapture() + self.detector = build_detector(cfg, use_cuda=use_cuda) + self.deepsort = build_tracker(cfg, use_cuda=use_cuda) + self.class_names = self.detector.class_names + + # Configure output video and json + self.logger = BboxToJsonLogger() + filename, extension = splitext(basename(self.args.VIDEO_PATH)) + self.output_file = join(self.args.save_path, f'{filename}.avi') + self.json_output = join(self.args.save_path, f'{filename}.json') + if not isdir(dirname(self.json_output)): + makedirs(dirname(self.json_output)) + + def __enter__(self): + self.vdo.open(self.args.VIDEO_PATH) + self.total_frames = int(cv2.VideoCapture.get(self.vdo, cv2.CAP_PROP_FRAME_COUNT)) + self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH)) + self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + video_details = {'frame_width': self.im_width, + 'frame_height': self.im_height, + 'frame_rate': self.args.write_fps, + 'video_name': self.args.VIDEO_PATH} + codec = cv2.VideoWriter_fourcc(*'XVID') + self.writer = cv2.VideoWriter(self.output_file, codec, self.args.write_fps, + (self.im_width, self.im_height)) + self.logger.add_video_details(**video_details) + + assert self.vdo.isOpened() + return self + + def __exit__(self, exc_type, exc_value, exc_traceback): + if exc_type: + print(exc_type, exc_value, exc_traceback) + + def run(self): + idx_frame = 0 + pbar = tqdm(total=self.total_frames + 1) + while self.vdo.grab(): + if idx_frame % args.frame_interval == 0: + _, ori_im = self.vdo.retrieve() + timestamp = self.vdo.get(cv2.CAP_PROP_POS_MSEC) + frame_id = int(self.vdo.get(cv2.CAP_PROP_POS_FRAMES)) + self.logger.add_frame(frame_id=frame_id, timestamp=timestamp) + self.detection(frame=ori_im, frame_id=frame_id) + self.save_frame(ori_im) + idx_frame += 1 + pbar.update() + self.logger.json_output(self.json_output) + + @tik_tok + def detection(self, frame, frame_id): + im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # do detection + bbox_xywh, cls_conf, cls_ids = self.detector(im) + if bbox_xywh is not None: + # select person class + mask = cls_ids == 0 + + bbox_xywh = bbox_xywh[mask] + bbox_xywh[:, 3:] *= 1.2 # bbox dilation just in case bbox too small + cls_conf = cls_conf[mask] + + # do tracking + outputs = self.deepsort.update(bbox_xywh, cls_conf, im) + + # draw boxes for visualization + if len(outputs) > 0: + frame = self.draw_boxes(img=frame, frame_id=frame_id, output=outputs) + + def draw_boxes(self, img, frame_id, output, offset=(0, 0)): + for i, box in enumerate(output): + x1, y1, x2, y2, identity = [int(ii) for ii in box] + self.logger.add_bbox_to_frame(frame_id=frame_id, + bbox_id=identity, + top=y1, + left=x1, + width=x2 - x1, + height=y2 - y1) + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + + # box text and bar + self.logger.add_label_to_bbox(frame_id=frame_id, bbox_id=identity, category='pedestrian', confidence=0.9) + color = compute_color_for_labels(identity) + label = '{}{:d}'.format("", identity) + t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 3) + cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1) + cv2.putText(img, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2) + return img + + def save_frame(self, frame) -> None: + if frame is not None: self.writer.write(frame) + + +if __name__ == "__main__": + args = parse_args() + cfg = get_config() + cfg.merge_from_file(args.config_detection) + cfg.merge_from_file(args.config_deepsort) + + with VideoTracker(cfg, args) as vdo_trk: + vdo_trk.run() + diff --git a/cv/tracking/deep_sort/pytorch/scripts/yolov3_deepsort.sh b/cv/tracking/deep_sort/pytorch/scripts/yolov3_deepsort.sh new file mode 100644 index 000000000..ed1311e8b --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/scripts/yolov3_deepsort.sh @@ -0,0 +1 @@ +python yolov3_deepsort.py [VIDEO_PATH] --config_detection \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/scripts/yolov3_tiny_deepsort.sh b/cv/tracking/deep_sort/pytorch/scripts/yolov3_tiny_deepsort.sh new file mode 100644 index 000000000..24316f178 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/scripts/yolov3_tiny_deepsort.sh @@ -0,0 +1 @@ +python yolov3_deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/utils/__init__.py b/cv/tracking/deep_sort/pytorch/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/tracking/deep_sort/pytorch/utils/asserts.py b/cv/tracking/deep_sort/pytorch/utils/asserts.py new file mode 100644 index 000000000..59a73cc04 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/asserts.py @@ -0,0 +1,13 @@ +from os import environ + + +def assert_in(file, files_to_check): + if file not in files_to_check: + raise AssertionError("{} does not exist in the list".format(str(file))) + return True + + +def assert_in_env(check_list: list): + for item in check_list: + assert_in(item, environ.keys()) + return True diff --git a/cv/tracking/deep_sort/pytorch/utils/draw.py b/cv/tracking/deep_sort/pytorch/utils/draw.py new file mode 100644 index 000000000..148d92c9d --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/draw.py @@ -0,0 +1,36 @@ +import numpy as np +import cv2 + +palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) + + +def compute_color_for_labels(label): + """ + Simple function that adds fixed color depending on the class + """ + color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] + return tuple(color) + + +def draw_boxes(img, bbox, identities=None, offset=(0,0)): + for i,box in enumerate(bbox): + x1,y1,x2,y2 = [int(i) for i in box] + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + # box text and bar + id = int(identities[i]) if identities is not None else 0 + color = compute_color_for_labels(id) + label = '{}{:d}'.format("", id) + t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0] + cv2.rectangle(img,(x1, y1),(x2,y2),color,3) + cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1) + cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2) + return img + + + +if __name__ == '__main__': + for i in range(82): + print(compute_color_for_labels(i)) diff --git a/cv/tracking/deep_sort/pytorch/utils/evaluation.py b/cv/tracking/deep_sort/pytorch/utils/evaluation.py new file mode 100644 index 000000000..100179407 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/evaluation.py @@ -0,0 +1,103 @@ +import os +import numpy as np +import copy +import motmetrics as mm +mm.lap.default_solver = 'lap' +from utils.io import read_results, unzip_objs + + +class Evaluator(object): + + def __init__(self, data_root, seq_name, data_type): + self.data_root = data_root + self.seq_name = seq_name + self.data_type = data_type + + self.load_annotations() + self.reset_accumulator() + + def load_annotations(self): + assert self.data_type == 'mot' + + gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt') + self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True) + self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True) + + def reset_accumulator(self): + self.acc = mm.MOTAccumulator(auto_id=True) + + def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False): + # results + trk_tlwhs = np.copy(trk_tlwhs) + trk_ids = np.copy(trk_ids) + + # gts + gt_objs = self.gt_frame_dict.get(frame_id, []) + gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2] + + # ignore boxes + ignore_objs = self.gt_ignore_frame_dict.get(frame_id, []) + ignore_tlwhs = unzip_objs(ignore_objs)[0] + + + # remove ignored results + keep = np.ones(len(trk_tlwhs), dtype=bool) + iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5) + if len(iou_distance) > 0: + match_is, match_js = mm.lap.linear_sum_assignment(iou_distance) + match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js]) + match_ious = iou_distance[match_is, match_js] + + match_js = np.asarray(match_js, dtype=int) + match_js = match_js[np.logical_not(np.isnan(match_ious))] + keep[match_js] = False + trk_tlwhs = trk_tlwhs[keep] + trk_ids = trk_ids[keep] + + # get distance matrix + iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5) + + # acc + self.acc.update(gt_ids, trk_ids, iou_distance) + + if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'): + events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics + else: + events = None + return events + + def eval_file(self, filename): + self.reset_accumulator() + + result_frame_dict = read_results(filename, self.data_type, is_gt=False) + frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys()))) + for frame_id in frames: + trk_objs = result_frame_dict.get(frame_id, []) + trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2] + self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False) + + return self.acc + + @staticmethod + def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')): + names = copy.deepcopy(names) + if metrics is None: + metrics = mm.metrics.motchallenge_metrics + metrics = copy.deepcopy(metrics) + + mh = mm.metrics.create() + summary = mh.compute_many( + accs, + metrics=metrics, + names=names, + generate_overall=True + ) + + return summary + + @staticmethod + def save_summary(summary, filename): + import pandas as pd + writer = pd.ExcelWriter(filename) + summary.to_excel(writer) + writer.save() diff --git a/cv/tracking/deep_sort/pytorch/utils/io.py b/cv/tracking/deep_sort/pytorch/utils/io.py new file mode 100644 index 000000000..2dc9afd24 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/io.py @@ -0,0 +1,133 @@ +import os +from typing import Dict +import numpy as np + +# from utils.log import get_logger + + +def write_results(filename, results, data_type): + if data_type == 'mot': + save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n' + elif data_type == 'kitti': + save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' + else: + raise ValueError(data_type) + + with open(filename, 'w') as f: + for frame_id, tlwhs, track_ids in results: + if data_type == 'kitti': + frame_id -= 1 + for tlwh, track_id in zip(tlwhs, track_ids): + if track_id < 0: + continue + x1, y1, w, h = tlwh + x2, y2 = x1 + w, y1 + h + line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h) + f.write(line) + + +# def write_results(filename, results_dict: Dict, data_type: str): +# if not filename: +# return +# path = os.path.dirname(filename) +# if not os.path.exists(path): +# os.makedirs(path) + +# if data_type in ('mot', 'mcmot', 'lab'): +# save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n' +# elif data_type == 'kitti': +# save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n' +# else: +# raise ValueError(data_type) + +# with open(filename, 'w') as f: +# for frame_id, frame_data in results_dict.items(): +# if data_type == 'kitti': +# frame_id -= 1 +# for tlwh, track_id in frame_data: +# if track_id < 0: +# continue +# x1, y1, w, h = tlwh +# x2, y2 = x1 + w, y1 + h +# line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0) +# f.write(line) +# logger.info('Save results to {}'.format(filename)) + + +def read_results(filename, data_type: str, is_gt=False, is_ignore=False): + if data_type in ('mot', 'lab'): + read_fun = read_mot_results + else: + raise ValueError('Unknown data type: {}'.format(data_type)) + + return read_fun(filename, is_gt, is_ignore) + + +""" +labels={'ped', ... % 1 +'person_on_vhcl', ... % 2 +'car', ... % 3 +'bicycle', ... % 4 +'mbike', ... % 5 +'non_mot_vhcl', ... % 6 +'static_person', ... % 7 +'distractor', ... % 8 +'occluder', ... % 9 +'occluder_on_grnd', ... %10 +'occluder_full', ... % 11 +'reflection', ... % 12 +'crowd' ... % 13 +}; +""" + + +def read_mot_results(filename, is_gt, is_ignore): + valid_labels = {1} + ignore_labels = {2, 7, 8, 12} + results_dict = dict() + if os.path.isfile(filename): + with open(filename, 'r') as f: + for line in f.readlines(): + linelist = line.split(',') + if len(linelist) < 7: + continue + fid = int(linelist[0]) + if fid < 1: + continue + results_dict.setdefault(fid, list()) + + if is_gt: + if 'MOT16-' in filename or 'MOT17-' in filename: + label = int(float(linelist[7])) + mark = int(float(linelist[6])) + if mark == 0 or label not in valid_labels: + continue + score = 1 + elif is_ignore: + if 'MOT16-' in filename or 'MOT17-' in filename: + label = int(float(linelist[7])) + vis_ratio = float(linelist[8]) + if label not in ignore_labels and vis_ratio >= 0: + continue + else: + continue + score = 1 + else: + score = float(linelist[6]) + + tlwh = tuple(map(float, linelist[2:6])) + target_id = int(linelist[1]) + + results_dict[fid].append((tlwh, target_id, score)) + + return results_dict + + +def unzip_objs(objs): + if len(objs) > 0: + tlwhs, ids, scores = zip(*objs) + else: + tlwhs, ids, scores = [], [], [] + tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4) + + return tlwhs, ids, scores \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/utils/json_logger.py b/cv/tracking/deep_sort/pytorch/utils/json_logger.py new file mode 100644 index 000000000..0afd0b45d --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/json_logger.py @@ -0,0 +1,383 @@ +""" +References: + https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f +""" +import json +from os import makedirs +from os.path import exists, join +from datetime import datetime + + +class JsonMeta(object): + HOURS = 3 + MINUTES = 59 + SECONDS = 59 + PATH_TO_SAVE = 'LOGS' + DEFAULT_FILE_NAME = 'remaining' + + +class BaseJsonLogger(object): + """ + This is the base class that returns __dict__ of its own + it also returns the dicts of objects in the attributes that are list instances + + """ + + def dic(self): + # returns dicts of objects + out = {} + for k, v in self.__dict__.items(): + if hasattr(v, 'dic'): + out[k] = v.dic() + elif isinstance(v, list): + out[k] = self.list(v) + else: + out[k] = v + return out + + @staticmethod + def list(values): + # applies the dic method on items in the list + return [v.dic() if hasattr(v, 'dic') else v for v in values] + + +class Label(BaseJsonLogger): + """ + For each bounding box there are various categories with confidences. Label class keeps track of that information. + """ + + def __init__(self, category: str, confidence: float): + self.category = category + self.confidence = confidence + + +class Bbox(BaseJsonLogger): + """ + This module stores the information for each frame and use them in JsonParser + Attributes: + labels (list): List of label module. + top (int): + left (int): + width (int): + height (int): + + Args: + bbox_id (float): + top (int): + left (int): + width (int): + height (int): + + References: + Check Label module for better understanding. + + + """ + + def __init__(self, bbox_id, top, left, width, height): + self.labels = [] + self.bbox_id = bbox_id + self.top = top + self.left = left + self.width = width + self.height = height + + def add_label(self, category, confidence): + # adds category and confidence only if top_k is not exceeded. + self.labels.append(Label(category, confidence)) + + def labels_full(self, value): + return len(self.labels) == value + + +class Frame(BaseJsonLogger): + """ + This module stores the information for each frame and use them in JsonParser + Attributes: + timestamp (float): The elapsed time of captured frame + frame_id (int): The frame number of the captured video + bboxes (list of Bbox objects): Stores the list of bbox objects. + + References: + Check Bbox class for better information + + Args: + timestamp (float): + frame_id (int): + + """ + + def __init__(self, frame_id: int, timestamp: float = None): + self.frame_id = frame_id + self.timestamp = timestamp + self.bboxes = [] + + def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int): + bboxes_ids = [bbox.bbox_id for bbox in self.bboxes] + if bbox_id not in bboxes_ids: + self.bboxes.append(Bbox(bbox_id, top, left, width, height)) + else: + raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id)) + + def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float): + bboxes = {bbox.id: bbox for bbox in self.bboxes} + if bbox_id in bboxes.keys(): + res = bboxes.get(bbox_id) + res.add_label(category, confidence) + else: + raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id)) + + +class BboxToJsonLogger(BaseJsonLogger): + """ + ُ This module is designed to automate the task of logging jsons. An example json is used + to show the contents of json file shortly + Example: + { + "video_details": { + "frame_width": 1920, + "frame_height": 1080, + "frame_rate": 20, + "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi" + }, + "frames": [ + { + "frame_id": 329, + "timestamp": 3365.1254 + "bboxes": [ + { + "labels": [ + { + "category": "pedestrian", + "confidence": 0.9 + } + ], + "bbox_id": 0, + "top": 1257, + "left": 138, + "width": 68, + "height": 109 + } + ] + }], + + Attributes: + frames (dict): It's a dictionary that maps each frame_id to json attributes. + video_details (dict): information about video file. + top_k_labels (int): shows the allowed number of labels + start_time (datetime object): we use it to automate the json output by time. + + Args: + top_k_labels (int): shows the allowed number of labels + + """ + + def __init__(self, top_k_labels: int = 1): + self.frames = {} + self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None, + video_name=None) + self.top_k_labels = top_k_labels + self.start_time = datetime.now() + + def set_top_k(self, value): + self.top_k_labels = value + + def frame_exists(self, frame_id: int) -> bool: + """ + Args: + frame_id (int): + + Returns: + bool: true if frame_id is recognized + """ + return frame_id in self.frames.keys() + + def add_frame(self, frame_id: int, timestamp: float = None) -> None: + """ + Args: + frame_id (int): + timestamp (float): opencv captured frame time property + + Raises: + ValueError: if frame_id would not exist in class frames attribute + + Returns: + None + + """ + if not self.frame_exists(frame_id): + self.frames[frame_id] = Frame(frame_id, timestamp) + else: + raise ValueError("Frame id: {} already exists".format(frame_id)) + + def bbox_exists(self, frame_id: int, bbox_id: int) -> bool: + """ + Args: + frame_id: + bbox_id: + + Returns: + bool: if bbox exists in frame bboxes list + """ + bboxes = [] + if self.frame_exists(frame_id=frame_id): + bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes] + return bbox_id in bboxes + + def find_bbox(self, frame_id: int, bbox_id: int): + """ + + Args: + frame_id: + bbox_id: + + Returns: + bbox_id (int): + + Raises: + ValueError: if bbox_id does not exist in the bbox list of specific frame. + """ + if not self.bbox_exists(frame_id, bbox_id): + raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id)) + bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes} + return bboxes.get(bbox_id) + + def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None: + """ + + Args: + frame_id (int): + bbox_id (int): + top (int): + left (int): + width (int): + height (int): + + Returns: + None + + Raises: + ValueError: if bbox_id already exist in frame information with frame_id + ValueError: if frame_id does not exist in frames attribute + """ + if self.frame_exists(frame_id): + frame = self.frames[frame_id] + if not self.bbox_exists(frame_id, bbox_id): + frame.add_bbox(bbox_id, top, left, width, height) + else: + raise ValueError( + "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id)) + else: + raise ValueError("frame with frame_id: {} does not exist".format(frame_id)) + + def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float): + """ + Args: + frame_id: + bbox_id: + category: + confidence: the confidence value returned from yolo detection + + Returns: + None + + Raises: + ValueError: if labels quota (top_k_labels) exceeds. + """ + bbox = self.find_bbox(frame_id, bbox_id) + if not bbox.labels_full(self.top_k_labels): + bbox.add_label(category, confidence) + else: + raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id)) + + def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None, + video_name: str = None): + self.video_details['frame_width'] = frame_width + self.video_details['frame_height'] = frame_height + self.video_details['frame_rate'] = frame_rate + self.video_details['video_name'] = video_name + + def output(self): + output = {'video_details': self.video_details} + result = list(self.frames.values()) + output['frames'] = [item.dic() for item in result] + return output + + def json_output(self, output_name): + """ + Args: + output_name: + + Returns: + None + + Notes: + It creates the json output with `output_name` name. + """ + if not output_name.endswith('.json'): + output_name += '.json' + with open(output_name, 'w') as file: + json.dump(self.output(), file) + file.close() + + def set_start(self): + self.start_time = datetime.now() + + def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0, + seconds: int = 60) -> None: + """ + Notes: + Creates folder and then periodically stores the jsons on that address. + + Args: + output_dir (str): the directory where output files will be stored + hours (int): + minutes (int): + seconds (int): + + Returns: + None + + """ + end = datetime.now() + interval = 0 + interval += abs(min([hours, JsonMeta.HOURS]) * 3600) + interval += abs(min([minutes, JsonMeta.MINUTES]) * 60) + interval += abs(min([seconds, JsonMeta.SECONDS])) + diff = (end - self.start_time).seconds + + if diff > interval: + output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json' + if not exists(output_dir): + makedirs(output_dir) + output = join(output_dir, output_name) + self.json_output(output_name=output) + self.frames = {} + self.start_time = datetime.now() + + def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE): + """ + saves as the number of frames quota increases higher. + :param frames_quota: + :param frame_counter: + :param output_dir: + :return: + """ + pass + + def flush(self, output_dir): + """ + Notes: + We use this function to output jsons whenever possible. + like the time that we exit the while loop of opencv. + + Args: + output_dir: + + Returns: + None + + """ + filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json' + output = join(output_dir, filename) + self.json_output(output_name=output) diff --git a/cv/tracking/deep_sort/pytorch/utils/log.py b/cv/tracking/deep_sort/pytorch/utils/log.py new file mode 100644 index 000000000..5b8c94090 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/log.py @@ -0,0 +1,17 @@ +import logging + + +def get_logger(name='root'): + formatter = logging.Formatter( + # fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s') + fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + + handler = logging.StreamHandler() + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + logger.addHandler(handler) + return logger + + diff --git a/cv/tracking/deep_sort/pytorch/utils/parser.py b/cv/tracking/deep_sort/pytorch/utils/parser.py new file mode 100644 index 000000000..c2b007955 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/parser.py @@ -0,0 +1,38 @@ +import os +import yaml +from easydict import EasyDict as edict + +class YamlParser(edict): + """ + This is yaml parser based on EasyDict. + """ + def __init__(self, cfg_dict=None, config_file=None): + if cfg_dict is None: + cfg_dict = {} + + if config_file is not None: + assert(os.path.isfile(config_file)) + with open(config_file, 'r') as fo: + cfg_dict.update(yaml.safe_load(fo.read())) + + super(YamlParser, self).__init__(cfg_dict) + + + def merge_from_file(self, config_file): + with open(config_file, 'r') as fo: + self.update(yaml.safe_load(fo.read())) + + + def merge_from_dict(self, config_dict): + self.update(config_dict) + + +def get_config(config_file=None): + return YamlParser(config_file=config_file) + + +if __name__ == "__main__": + cfg = YamlParser(config_file="../configs/yolov3.yaml") + cfg.merge_from_file("../configs/deep_sort.yaml") + + import ipdb; ipdb.set_trace() diff --git a/cv/tracking/deep_sort/pytorch/utils/tools.py b/cv/tracking/deep_sort/pytorch/utils/tools.py new file mode 100644 index 000000000..965fb69c2 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/utils/tools.py @@ -0,0 +1,39 @@ +from functools import wraps +from time import time + + +def is_video(ext: str): + """ + Returns true if ext exists in + allowed_exts for video files. + + Args: + ext: + + Returns: + + """ + + allowed_exts = ('.mp4', '.webm', '.ogg', '.avi', '.wmv', '.mkv', '.3gp') + return any((ext.endswith(x) for x in allowed_exts)) + + +def tik_tok(func): + """ + keep track of time for each process. + Args: + func: + + Returns: + + """ + @wraps(func) + def _time_it(*args, **kwargs): + start = time() + try: + return func(*args, **kwargs) + finally: + end_ = time() + print("time: {:.03f}s, fps: {:.03f}".format(end_ - start, 1 / (end_ - start))) + + return _time_it diff --git a/cv/tracking/deep_sort/pytorch/webserver/.env b/cv/tracking/deep_sort/pytorch/webserver/.env new file mode 100644 index 000000000..de09f70af --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/webserver/.env @@ -0,0 +1,13 @@ +project_root="C:\Users\ZQ_deep_sort_pytorch" +model_type="yolov3" +output_dir="public/" +json_output="json_output/" # ignored for the moment in ped_det_online_server.py +reid_ckpt="deep_sort/deep/checkpoint/ckpt.t7" +yolov3_cfg="detector/YOLOv3/cfg/yolo_v3.cfg" +yolov3_weight="detector/YOLOv3/weight/yolov3.weights" +yolov3_tiny_cfg="detector/YOLOv3/cfg/yolov3-tiny.cfg" +yolov3_tiny_weight="detector/YOLOv3/weight/yolov3-tiny.weights" +yolov3_class_names="detector/YOLOv3/cfg/coco.names" +analysis_output="video_analysis/" +app="flask_stream_server.py" +camera_stream= "rtsp://user@111.222.333.444:somesecretcode" diff --git a/cv/tracking/deep_sort/pytorch/webserver/__init__.py b/cv/tracking/deep_sort/pytorch/webserver/__init__.py 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diff --git a/cv/tracking/deep_sort/pytorch/webserver/readme.md b/cv/tracking/deep_sort/pytorch/webserver/readme.md new file mode 100644 index 000000000..37d1996ee --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/webserver/readme.md @@ -0,0 +1,45 @@ +# Stream pedestrian detection web server + +### Requirements + +- python = 3.7 +- redis +- flask +- opencv +- pytorch +- dotenv + +Please note that you need to install redis on your system. + +### The architecture. + +![web server architecture](images/arc.png) + +1 - `RealTimeTracking` reads frames from rtsp link using threads +(Using threads make the web server robust against network packet loss) + +2 - In `RealTimeTracking.run` function in each iteration frame is stored in redis cache on server. + +3 - Now we can serve the frames on redis to clients. + +4 - To start the pedestrian detection, after running +`rtsp_webserver.py`, send a GET request on `127.0.0.1:8888/run` +with setting these GET method parameters + +| Param | Value | Description | +| :-------------: | :-------------: | :-------------: | +| run | 1/0 | to start the tracking set 1/ to stop tracking service set it as 0| +| camera_stream | 'rtsp://ip:port/admin...' | provide it with valid rtsp link | + +for example: + + (to start the service) 127.0.0.1:8888/run?run=1 + (to stop the service) 127.0.0.1:8888/run?run=0 + (to change the camera) + 1- 127.0.0.1:8888/run?run=0 (first stop the current service) + 2- 127.0.0.1:8888/run?run=0&camera_stream=rtsp://ip:port/admin... (then start it with another rtsp link) + +![web server architecture](images/request.png) + + +5 - get pedestrian detection stream in `127.0.0.1:8888` diff --git a/cv/tracking/deep_sort/pytorch/webserver/rtsp_threaded_tracker.py b/cv/tracking/deep_sort/pytorch/webserver/rtsp_threaded_tracker.py new file mode 100644 index 000000000..aa10b6983 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/webserver/rtsp_threaded_tracker.py @@ -0,0 +1,108 @@ +import warnings +from os import getenv +import sys +from os.path import dirname, abspath + +sys.path.append(dirname(dirname(abspath(__file__)))) + +import torch +from deep_sort import build_tracker +from detector import build_detector +import cv2 +from utils.draw import compute_color_for_labels +from concurrent.futures import ThreadPoolExecutor +from redis import Redis + +redis_cache = Redis('127.0.0.1') + + +class RealTimeTracking(object): + """ + This class is built to get frame from rtsp link and continuously + assign each frame to an attribute namely as frame in order to + compensate the network packet loss. then we use flask to give it + as service to client. + Args: + args: parse_args inputs + cfg: deepsort dict and yolo-model cfg from server_cfg file + + """ + + def __init__(self, cfg, args): + # Create a VideoCapture object + self.cfg = cfg + self.args = args + use_cuda = self.args.use_cuda and torch.cuda.is_available() + + if not use_cuda: + warnings.warn(UserWarning("Running in cpu mode!")) + + self.detector = build_detector(cfg, use_cuda=use_cuda) + self.deepsort = build_tracker(cfg, use_cuda=use_cuda) + self.class_names = self.detector.class_names + + self.vdo = cv2.VideoCapture(self.args.input) + self.status, self.frame = None, None + self.total_frames = int(cv2.VideoCapture.get(self.vdo, cv2.CAP_PROP_FRAME_COUNT)) + self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH)) + self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + self.output_frame = None + + self.thread = ThreadPoolExecutor(max_workers=1) + self.thread.submit(self.update) + + def update(self): + while True: + if self.vdo.isOpened(): + (self.status, self.frame) = self.vdo.read() + + def run(self): + print('streaming started ...') + while getenv('in_progress') != 'off': + try: + frame = self.frame.copy() + self.detection(frame=frame) + frame_to_bytes = cv2.imencode('.jpg', frame)[1].tobytes() + redis_cache.set('frame', frame_to_bytes) + except AttributeError: + pass + print('streaming stopped ...') + + + def detection(self, frame): + im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # do detection + bbox_xywh, cls_conf, cls_ids = self.detector(im) + if bbox_xywh is not None: + # select person class + mask = cls_ids == 0 + + bbox_xywh = bbox_xywh[mask] + bbox_xywh[:, 3:] *= 1.2 # bbox dilation just in case bbox too small + cls_conf = cls_conf[mask] + + # do tracking + outputs = self.deepsort.update(bbox_xywh, cls_conf, im) + + # draw boxes for visualization + if len(outputs) > 0: + self.draw_boxes(img=frame, output=outputs) + + @staticmethod + def draw_boxes(img, output, offset=(0, 0)): + for i, box in enumerate(output): + x1, y1, x2, y2, identity = [int(ii) for ii in box] + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + + # box text and bar + color = compute_color_for_labels(identity) + label = '{}{:d}'.format("", identity) + t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 3) + cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1) + cv2.putText(img, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2) + return img diff --git a/cv/tracking/deep_sort/pytorch/webserver/rtsp_webserver.py b/cv/tracking/deep_sort/pytorch/webserver/rtsp_webserver.py new file mode 100644 index 000000000..6a164ca35 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/webserver/rtsp_webserver.py @@ -0,0 +1,163 @@ +""" + +# TODO: Load ML model with redis and keep it for sometime. + 1- detector/yolov3/detector.py |=> yolov3 weightfile -> redis cache + 2- deepsort/deep/feature_extractor |=> model_path -> redis cache + 3- Use tmpfs (Insert RAM as a virtual disk and store model state): https://pypi.org/project/memory-tempfile/ + +""" +from os.path import join +from os import getenv, environ +from dotenv import load_dotenv +import argparse +from threading import Thread + +from redis import Redis +from flask import Response, Flask, jsonify, request, abort + +from rtsp_threaded_tracker import RealTimeTracking +from server_cfg import model, deep_sort_dict +from config.config import DevelopmentConfig +from utils.parser import get_config + +redis_cache = Redis('127.0.0.1') +app = Flask(__name__) +environ['in_progress'] = 'off' + + +def parse_args(): + """ + Parses the arguments + Returns: + argparse Namespace + """ + assert 'project_root' in environ.keys() + project_root = getenv('project_root') + parser = argparse.ArgumentParser() + + parser.add_argument("--input", + type=str, + default=getenv('camera_stream')) + + parser.add_argument("--model", + type=str, + default=join(project_root, + getenv('model_type'))) + + parser.add_argument("--cpu", + dest="use_cuda", + action="store_false", default=True) + args = parser.parse_args() + + return args + + +def gen(): + """ + + Returns: video frames from redis cache + + """ + while True: + frame = redis_cache.get('frame') + if frame is not None: + yield b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n' + + +def pedestrian_tracking(cfg, args): + """ + starts the pedestrian detection on rtsp link + Args: + cfg: + args: + + Returns: + + """ + tracker = RealTimeTracking(cfg, args) + tracker.run() + + +def trigger_process(cfg, args): + """ + triggers pedestrian_tracking process on rtsp link using a thread + Args: + cfg: + args: + + Returns: + """ + try: + t = Thread(target=pedestrian_tracking, args=(cfg, args)) + t.start() + return jsonify({"message": "Pedestrian detection started successfully"}) + except Exception: + return jsonify({'message': "Unexpected exception occured in process"}) + + +@app.errorhandler(400) +def bad_argument(error): + return jsonify({'message': error.description['message']}) + + +# Routes +@app.route('/stream', methods=['GET']) +def stream(): + """ + Provides video frames on http link + Returns: + + """ + return Response(gen(), + mimetype='multipart/x-mixed-replace; boundary=frame') + + +@app.route("/run", methods=['GET']) +def process_manager(): + """ + request parameters: + run (bool): 1 -> start the pedestrian tracking + 0 -> stop it + camera_stream: str -> rtsp link to security camera + + :return: + """ + # data = request.args + data = request.args + status = data['run'] + status = int(status) if status.isnumeric() else abort(400, {'message': f"bad argument for run {data['run']}"}) + if status == 1: + # if pedestrian tracking is not running, start it off! + try: + if environ.get('in_progress', 'off') == 'off': + global cfg, args + vdo = data.get('camera_stream') + if vdo is not None: + args.input = int(vdo) + environ['in_progress'] = 'on' + return trigger_process(cfg, args) + elif environ.get('in_progress') == 'on': + # if pedestrian tracking is running, don't start another one (we are short of gpu resources) + return jsonify({"message": " Pedestrian detection is already in progress."}) + except Exception: + environ['in_progress'] = 'off' + return abort(503) + elif status == 0: + if environ.get('in_progress', 'off') == 'off': + return jsonify({"message": "pedestrian detection is already terminated!"}) + else: + environ['in_progress'] = 'off' + return jsonify({"message": "Pedestrian detection terminated!"}) + + +if __name__ == '__main__': + load_dotenv() + app.config.from_object(DevelopmentConfig) + + # BackProcess Initialization + args = parse_args() + cfg = get_config() + cfg.merge_from_dict(model) + cfg.merge_from_dict(deep_sort_dict) + # Start the flask app + app.run() diff --git a/cv/tracking/deep_sort/pytorch/webserver/server_cfg.py b/cv/tracking/deep_sort/pytorch/webserver/server_cfg.py new file mode 100644 index 000000000..64950fe81 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/webserver/server_cfg.py @@ -0,0 +1,62 @@ +"""""" +import sys +from os.path import dirname, abspath, isfile + +sys.path.append(dirname(dirname(abspath(__file__)))) + +from dotenv import load_dotenv +from utils.asserts import assert_in_env +from os import getenv +from os.path import join + +load_dotenv('.env') +# Configure deep sort info +deep_sort_info = dict(REID_CKPT=join(getenv('project_root'), getenv('reid_ckpt')), + MAX_DIST=0.2, + MIN_CONFIDENCE=.3, + NMS_MAX_OVERLAP=0.5, + MAX_IOU_DISTANCE=0.7, + N_INIT=3, + MAX_AGE=70, + NN_BUDGET=100) +deep_sort_dict = {'DEEPSORT': deep_sort_info} + +# Configure yolov3 info + +yolov3_info = dict(CFG=join(getenv('project_root'), getenv('yolov3_cfg')), + WEIGHT=join(getenv('project_root'), getenv('yolov3_weight')), + CLASS_NAMES=join(getenv('project_root'), getenv('yolov3_class_names')), + SCORE_THRESH=0.5, + NMS_THRESH=0.4 + ) +yolov3_dict = {'YOLOV3': yolov3_info} + +# Configure yolov3-tiny info + +yolov3_tiny_info = dict(CFG=join(getenv('project_root'), getenv('yolov3_tiny_cfg')), + WEIGHT=join(getenv('project_root'), getenv('yolov3_tiny_weight')), + CLASS_NAMES=join(getenv('project_root'), getenv('yolov3_class_names')), + SCORE_THRESH=0.5, + NMS_THRESH=0.4 + ) +yolov3_tiny_dict = {'YOLOV3': yolov3_tiny_info} + + +check_list = ['project_root', 'reid_ckpt', 'yolov3_class_names', 'model_type', 'yolov3_cfg', 'yolov3_weight', + 'yolov3_tiny_cfg', 'yolov3_tiny_weight', 'yolov3_class_names'] + +if assert_in_env(check_list): + assert isfile(deep_sort_info['REID_CKPT']) + if getenv('model_type') == 'yolov3': + assert isfile(yolov3_info['WEIGHT']) + assert isfile(yolov3_info['CFG']) + assert isfile(yolov3_info['CLASS_NAMES']) + model = yolov3_dict.copy() + + elif getenv('model_type') == 'yolov3_tiny': + assert isfile(yolov3_tiny_info['WEIGHT']) + assert isfile(yolov3_tiny_info['CFG']) + assert isfile(yolov3_tiny_info['CLASS_NAMES']) + model = yolov3_tiny_dict.copy() + else: + raise ValueError("Value '{}' for model_type is not valid".format(getenv('model_type'))) diff --git a/cv/tracking/deep_sort/pytorch/webserver/templates/index.html b/cv/tracking/deep_sort/pytorch/webserver/templates/index.html new file mode 100644 index 000000000..f329e71d6 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/webserver/templates/index.html @@ -0,0 +1,9 @@ +<html lang="en"> + <head> + <title>دوربین طبقه 2: راهرو 2 </title> + </head> + <body> + <h1>طبقه 2 </h1> + <img src="{{ url_for('video_feed') }}"> + </body> +</html> \ No newline at end of file diff --git a/cv/tracking/deep_sort/pytorch/yolov3_deepsort_eval.py b/cv/tracking/deep_sort/pytorch/yolov3_deepsort_eval.py new file mode 100644 index 000000000..f0036d288 --- /dev/null +++ b/cv/tracking/deep_sort/pytorch/yolov3_deepsort_eval.py @@ -0,0 +1,87 @@ +import os +import os.path as osp +import logging +import argparse +from pathlib import Path + +from utils.log import get_logger +from yolov3_deepsort import VideoTracker +from utils.parser import get_config + +import motmetrics as mm +mm.lap.default_solver = 'lap' +from utils.evaluation import Evaluator + +def mkdir_if_missing(dir): + os.makedirs(dir, exist_ok=True) + +def main(data_root='', seqs=('',), args=""): + logger = get_logger() + logger.setLevel(logging.INFO) + data_type = 'mot' + result_root = os.path.join(Path(data_root), "mot_results") + mkdir_if_missing(result_root) + + cfg = get_config() + cfg.merge_from_file(args.config_detection) + cfg.merge_from_file(args.config_deepsort) + + # run tracking + accs = [] + for seq in seqs: + logger.info('start seq: {}'.format(seq)) + result_filename = os.path.join(result_root, '{}.txt'.format(seq)) + video_path = data_root+"/"+seq+"/video/video.mp4" + + with VideoTracker(cfg, args, video_path, result_filename) as vdo_trk: + vdo_trk.run() + + # eval + logger.info('Evaluate seq: {}'.format(seq)) + evaluator = Evaluator(data_root, seq, data_type) + accs.append(evaluator.eval_file(result_filename)) + + # get summary + metrics = mm.metrics.motchallenge_metrics + mh = mm.metrics.create() + summary = Evaluator.get_summary(accs, seqs, metrics) + strsummary = mm.io.render_summary( + summary, + formatters=mh.formatters, + namemap=mm.io.motchallenge_metric_names + ) + print(strsummary) + Evaluator.save_summary(summary, os.path.join(result_root, 'summary_global.xlsx')) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_detection", type=str, default="./configs/yolov3.yaml") + parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml") + parser.add_argument("--ignore_display", dest="display", action="store_false", default=False) + parser.add_argument("--frame_interval", type=int, default=1) + parser.add_argument("--display_width", type=int, default=800) + parser.add_argument("--display_height", type=int, default=600) + parser.add_argument("--save_path", type=str, default="./demo/demo.avi") + parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True) + parser.add_argument("--camera", action="store", dest="cam", type=int, default="-1") + return parser.parse_args() + +if __name__ == '__main__': + args = parse_args() + + seqs_str = '''MOT16-02 + MOT16-04 + MOT16-05 + MOT16-09 + MOT16-10 + MOT16-11 + MOT16-13 + ''' + data_root = 'data/dataset/MOT16/train/' + + seqs = [seq.strip() for seq in seqs_str.split()] + + main(data_root=data_root, + seqs=seqs, + args=args) \ No newline at end of file -- Gitee From bbd38e2596f3b9e189008837191f517f7c90bd20 Mon Sep 17 00:00:00 2001 From: "wenfeng.zhang" <wenfeng.zhang@iluvatar.ai> Date: Thu, 9 Mar 2023 16:21:09 +0800 Subject: [PATCH 2/2] modify readme.md file --- .../pointpillars/pytorch/README.md | 37 ++++++++-------- cv/detection/yolov7/pytorch/README.md | 17 ++++++++ .../yolact/pytorch/README.md | 34 ++++----------- cv/tracking/deep_sort/pytorch/README.md | 42 +++++++++++-------- .../deep/create_market1501_datasets.py | 4 +- 5 files changed, 71 insertions(+), 63 deletions(-) diff --git a/cv/3d_detection/pointpillars/pytorch/README.md b/cv/3d_detection/pointpillars/pytorch/README.md index fa7eca008..038037e3e 100755 --- a/cv/3d_detection/pointpillars/pytorch/README.md +++ b/cv/3d_detection/pointpillars/pytorch/README.md @@ -23,14 +23,14 @@ python3 setup.py develop 1. Download Download [point cloud](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_velodyne.zip)(29GB), [images](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_image_2.zip)(12 GB), [calibration files](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_calib.zip)(16 MB)和[labels](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_label_2.zip)(5 MB)。 - Or you can download from fileserve, - ``` - wget http://10.150.9.95/swapp/datasets/cv/3d_detection/kitti.tar.gz - tar -xzvf kitti.tar.gz - ``` Format the datasets as follows: ``` kitti + |- ImageSets + |- train.txt + |- val.txt + |- test.txt + |- trainval.txt |- training |- calib (#7481 .txt) |- image_2 (#7481 .png) @@ -41,12 +41,17 @@ python3 setup.py develop |- image_2 (#7518 .png) |- velodyne (#7418 .bin) ``` - + The train.txt、val.txt、test.txt and trainval.txt you can get from: + ``` + wget https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/test.txt + wget https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/train.txt + wget https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/val.txt + wget https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/trainval.txt + ``` 2. Pre-process KITTI datasets First ``` - cd PointPillars/ - python pre_process_kitti.py --data_root your_path_to_kitti + python3 pre_process_kitti.py --data_root your_path_to_kitti ``` Now, we have datasets as follows: @@ -76,7 +81,6 @@ python3 setup.py develop ### Single GPU training ``` -cd PointPillars/ python3 train.py --data_root your_path_to_kitti ``` ### Multiple GPU training @@ -86,32 +90,29 @@ python3 -m torch.distributed.launch --nproc_per_node 8 train_dist.py --data_root ## [Evaluation] ``` -cd PointPillars/ -python3 evaluate.py --ckpt pretrained/epoch_160.pth --data_root your_path_to_kitti +python3 evaluate.py --ckpt pretrained/your_weights.pth --data_root your_path_to_kitti ``` ## [Test] ``` -cd PointPillars/ - # 1. infer and visualize point cloud detection -python3 test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path +python3 test.py --ckpt pretrained/your_weights.pth --pc_path your_pc_path # 2. infer and visualize point cloud detection and gound truth. -python3 test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path --gt_path your_gt_path +python3 test.py --ckpt pretrained/your_weights.pth --pc_path your_pc_path --calib_path your_calib_path --gt_path your_gt_path # 3. infer and visualize point cloud & image detection -python3 test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path --img_path your_img_path +python3 test.py --ckpt pretrained/your_weights.pth --pc_path your_pc_path --calib_path your_calib_path --img_path your_img_path e.g. [infer on val set 000134] -python3 test.py --ckpt pretrained/epoch_160.pth --pc_path /home/lifa/data/KITTI/training/velodyne_reduced/000134.bin +python3 test.py --ckpt pretrained/your_weights.pth --pc_path /home/lifa/data/KITTI/training/velodyne_reduced/000134.bin or -python3 test.py --ckpt pretrained/epoch_160.pth --pc_path data/kitti/training/velodyne_reduced/000134.bin --calib_path data/kitti/training/calib/000134.txt --img_path data/kitti/training/image_2/000134.png --gt_path data/kitti/training/label_2/000134.txt +python3 test.py --ckpt pretrained/your_weights.pth --pc_path data/kitti/training/velodyne_reduced/000134.bin --calib_path data/kitti/training/calib/000134.txt --img_path data/kitti/training/image_2/000134.png --gt_path data/kitti/training/label_2/000134.txt ``` diff --git a/cv/detection/yolov7/pytorch/README.md b/cv/detection/yolov7/pytorch/README.md index 0a86f09d3..80e2e1da7 100644 --- a/cv/detection/yolov7/pytorch/README.md +++ b/cv/detection/yolov7/pytorch/README.md @@ -17,6 +17,23 @@ $ # path: the root of coco data $ # train: the relative path of train images $ # val: the relative path of valid images ``` +The train2017.txt and val2017.txt file you can get from: +``` +wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip +``` +The datasets format as follows: +``` + coco + |- iamges + |- train2017 + |- val2017 + |- labels + |- train2017 + |- val2017 + |- train2017.txt + |- val2017.txt + + ``` ## Training diff --git a/cv/instance_segmentation/yolact/pytorch/README.md b/cv/instance_segmentation/yolact/pytorch/README.md index 2035ab075..87a4f73d2 100644 --- a/cv/instance_segmentation/yolact/pytorch/README.md +++ b/cv/instance_segmentation/yolact/pytorch/README.md @@ -7,6 +7,9 @@ ██║ ╚██████╔╝███████╗██║ ██║╚██████╗ ██║ ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ ``` +A simple, fully convolutional model for real-time instance segmentation. This is the code for papers: + - [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) + - [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218) ## Step 1: Installing packages ``` @@ -24,7 +27,7 @@ If you want to use YOLACT++, compile deformable convolutional layers (from [DCNv ## Step 2: Preparing datasets Download the [COCO Dataset](https://cocodataset.org/#home) -Modify the configuration file(data/coco.yaml) +Modify the configuration file(data/config.py) ``` $ vim data/config.py $ # 'train_images': the path of train images @@ -66,33 +69,12 @@ YOLACT now supports multiple GPUs seamlessly during training: - Then, simply set the batch size to `8*num_gpus` with the training commands above. The training script will automatically scale the hyperparameters to the right values. - If you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you're using. - If you want to allocate the images per GPU specific for different GPUs, you can use `--batch_alloc=[alloc]` where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to `batch_size`. + - The learning rate should divide the number of gpus. -## Images -```Shell -# Display qualitative results on the specified image. -python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png - -# Process an image and save it to another file. -python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png - -# Process a whole folder of images. -python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder -``` -## Video -```Shell -# Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance. -# If you want, use "--display_fps" to draw the FPS directly on the frame. -python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4 - -# Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0. -python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0 - -# Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast! -python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4 +For example: use 8 GPUs to train. ``` -As you can tell, `eval.py` can do a ton of stuff. Run the `--help` command to see everything it can do. -```Shell -python3 eval.py --help +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +python3 train.py --config=yolact_base_config --batch_size 64 --lr 0.000125 ``` ## Reference diff --git a/cv/tracking/deep_sort/pytorch/README.md b/cv/tracking/deep_sort/pytorch/README.md index 359b673c9..0b84dcbcb 100644 --- a/cv/tracking/deep_sort/pytorch/README.md +++ b/cv/tracking/deep_sort/pytorch/README.md @@ -12,8 +12,22 @@ Change the data path as your own in ./deep_sort/deep/create_market1501_datasets. python3 create_market1501_datasets.py ``` -In train path, 000-751 is train data and remain is test data, delete test path and move 752-1501 to this. +In train path, 000-751 is train data and remain is test data, move 752-1501 to test path. +Format the datasets as follows: +``` + market1501 + |- train + |- 0000 + |- 0001 + ... + |- 0751 + |- test + |- 0752 + |- 0753 + ... + |- 1501 +``` ## Training The original model used in paper is in original_model.py, and its parameter here [original_ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6). @@ -21,7 +35,6 @@ The original model used in paper is in original_model.py, and its parameter here To train the model, first you need download Market1501dataset or Mars dataset. Then you can try [train.py](deep_sort/deep/train.py) to train your own parameter and evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py). -! ``` cd deep_sort/deep/ python3 train.py --data-dir your path @@ -30,19 +43,14 @@ python3 train.py --data-dir your path ## Quick Start 0. Check all dependencies installed ```bash -pip install -r requirements.txt +pip3 install -r requirements.txt ``` for user in china, you can specify pypi source to accelerate install like: ```bash -pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple -``` - -1. Clone this repository -``` -git clone git@github.com:ZQPei/deep_sort_pytorch.git +pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` -2. Download YOLOv3 parameters +1. Download YOLOv3 parameters ``` cd detector/YOLOv3/weight/ wget https://pjreddie.com/media/files/yolov3.weights @@ -50,7 +58,7 @@ wget https://pjreddie.com/media/files/yolov3-tiny.weights cd ../../../ ``` -3. Download deepsort parameters ckpt.t7 +2. Download deepsort parameters ckpt.t7 ``` cd deep_sort/deep/checkpoint # download ckpt.t7 from @@ -58,7 +66,7 @@ https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this cd ../../../ ``` -4. Compile nms module +3. Compile nms module ```bash cd detector/YOLOv3/nms sh build.sh @@ -68,7 +76,7 @@ cd ../../.. Notice: If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either `gcc version too low` or `libraries missing`. -5. (Optional) Prepare third party submodules +4. (Optional) Prepare third party submodules [fast-reid](https://github.com/JDAI-CV/fast-reid) @@ -109,10 +117,10 @@ usage: deepsort.py [-h] VIDEO_PATH # yolov3 + deepsort -python deepsort.py [VIDEO_PATH] +python3 deepsort.py [VIDEO_PATH] # yolov3_tiny + deepsort -python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml +python3 deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml # yolov3 + deepsort on webcam python3 deepsort.py /dev/video0 --camera 0 @@ -121,10 +129,10 @@ python3 deepsort.py /dev/video0 --camera 0 python3 deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0 # fast-reid + deepsort -python deepsort.py [VIDEO_PATH] --fastreid [--config_fastreid ./configs/fastreid.yaml] +python3 deepsort.py [VIDEO_PATH] --fastreid [--config_fastreid ./configs/fastreid.yaml] # MMDetection + deepsort -python deepsort.py [VIDEO_PATH] --mmdet [--config_mmdetection ./configs/mmdet.yaml] +python3 deepsort.py [VIDEO_PATH] --mmdet [--config_mmdetection ./configs/mmdet.yaml] ``` Use `--display` to enable display. Results will be saved to `./output/results.avi` and `./output/results.txt`. diff --git a/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py b/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py index 2e4bc7f9a..a360e482e 100644 --- a/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py +++ b/cv/tracking/deep_sort/pytorch/deep_sort/deep/create_market1501_datasets.py @@ -2,8 +2,8 @@ import os from PIL import Image from shutil import copyfile, copytree, rmtree, move -PATH_DATASET = '/home/datasets/cv/Market-1501-v15.09.15' # 需要处理的文件夹 -PATH_NEW_DATASET = '/home/wenfeng.zhang/datasets/market1501' # 处理后的文件夹 +PATH_DATASET = './Market-1501-v15.09.15' # 需要处理的文件夹 +PATH_NEW_DATASET = './market1501' # 处理后的文件夹 PATH_ALL_IMAGES = PATH_NEW_DATASET + '/all_images' PATH_TRAIN = PATH_NEW_DATASET + '/train' PATH_TEST = PATH_NEW_DATASET + '/test' -- Gitee

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zl4KN@l7-L!$!)iiu<6@gh%LFIMA%%XO+_e{C=n|V7?ykUG&cJbZK-MX?vhRgaVDrH zjjSy$(ZUh{3n3V)3d=^c8MZZE3Q8d&K^3K(Qr@VCN`ea)rH%k#eaetY&SyU}0NymJ zaPLycXJbYnG{B5VF>+vF1koPb)m;cNn)ido-p^3$?6w&uiJvqxk|$gdVM5}y%}NZoiWq&Z)rHe# zIydd3(Y^CGR6xmH6*+~-k?0LZp|t7%)h%`jb+d>n)(~6^uh!aQbKlD$;^chLx}g|l zA8G+kXwJY%Dh$O44#`T0;ymTtt1h~;53}J~6T_IgB|EzXV;XR3IVaImihRjJN=y(` zAF^-wt`qkj>{8~{IRv1ny6YbTWM3S{wML@3@nCJ95UGmo0dmUL=%E%OIVGrGU*4Qw zDB;J$dn&mv>v=w7!RGDk-NZ3gq-*#f2}<@-;wHztIov9+hVCd$yWOKP-<3E~(t6?C za=DYEMh#(Nj@NT~t)v^e5kX90!ZkPg09A#O8KVgjrX+=k3X+1w9F!6{p_JXSEUg(B zRs;*FRjKi2SHU=^*t%OmsbExVC4^9n5^$zvv^NryAe?dr6bR-OD8sDQ%9WNvou{`g zTymHt9~tI3XDKxxmMT=7$10`SLC#ZpJ1<{*qZwwV(i~FTJQC6>p%t%&QA!l5{1^M{ zx6>b0JA^<=nOmiEq(Y#aN+pGH#gv}xsWC&Z4$)eHn@%W>RLp6G1qgI{NX}mK*QPdH zG(H!OtCXrz6$19jCfFz~0<{M{5JXUw1vwW3B8U=1.0.1 + - cudatoolkit + - cudnn + - pytorch::cuda100 + - matplotlib + - git # to download COCO dataset + - curl # to download COCO dataset + - unzip # to download COCO dataset + - conda-forge::bash # to download COCO dataset + - pip: + - opencv-python + - pillow <7.0 # bug PILLOW_VERSION in torchvision, must be < 7.0 until torchvision is upgraded + - pycocotools + - PyQt5 # needed on KDE/Qt envs for matplotlib + diff --git a/cv/instance_segmentation/yolact/pytorch/eval.py b/cv/instance_segmentation/yolact/pytorch/eval.py new file mode 100644 index 000000000..547bc0aae --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/eval.py @@ -0,0 +1,1107 @@ +from data import COCODetection, get_label_map, MEANS, COLORS +from yolact import Yolact +from utils.augmentations import BaseTransform, FastBaseTransform, Resize +from utils.functions import MovingAverage, ProgressBar +from layers.box_utils import jaccard, center_size, mask_iou +from utils import timer +from utils.functions import SavePath +from layers.output_utils import postprocess, undo_image_transformation +import pycocotools + +from data import cfg, set_cfg, set_dataset + +import numpy as np +import torch +import torch.backends.cudnn as cudnn +from torch.autograd import Variable +import argparse +import time +import random +import cProfile +import pickle +import json +import os +from collections import defaultdict +from pathlib import Path +from collections import OrderedDict +from PIL import Image + +import matplotlib.pyplot as plt +import cv2 + +def str2bool(v): + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + +def parse_args(argv=None): + parser = argparse.ArgumentParser( + description='YOLACT COCO Evaluation') + parser.add_argument('--trained_model', + default='weights/ssd300_mAP_77.43_v2.pth', type=str, + help='Trained state_dict file path to open. If "interrupt", this will open the interrupt file.') + parser.add_argument('--top_k', default=5, type=int, + help='Further restrict the number of predictions to parse') + parser.add_argument('--cuda', default=True, type=str2bool, + help='Use cuda to evaulate model') + parser.add_argument('--fast_nms', default=True, type=str2bool, + help='Whether to use a faster, but not entirely correct version of NMS.') + parser.add_argument('--cross_class_nms', default=False, type=str2bool, + help='Whether compute NMS cross-class or per-class.') + parser.add_argument('--display_masks', default=True, type=str2bool, + help='Whether or not to display masks over bounding boxes') + parser.add_argument('--display_bboxes', default=True, type=str2bool, + help='Whether or not to display bboxes around masks') + parser.add_argument('--display_text', default=True, type=str2bool, + help='Whether or not to display text (class [score])') + parser.add_argument('--display_scores', default=True, type=str2bool, + help='Whether or not to display scores in addition to classes') + parser.add_argument('--display', dest='display', action='store_true', + help='Display qualitative results instead of quantitative ones.') + parser.add_argument('--shuffle', dest='shuffle', action='store_true', + help='Shuffles the images when displaying them. Doesn\'t have much of an effect when display is off though.') + parser.add_argument('--ap_data_file', default='results/ap_data.pkl', type=str, + help='In quantitative mode, the file to save detections before calculating mAP.') + parser.add_argument('--resume', dest='resume', action='store_true', + help='If display not set, this resumes mAP calculations from the ap_data_file.') + parser.add_argument('--max_images', default=-1, type=int, + help='The maximum number of images from the dataset to consider. Use -1 for all.') + parser.add_argument('--output_coco_json', dest='output_coco_json', action='store_true', + help='If display is not set, instead of processing IoU values, this just dumps detections into the coco json file.') + parser.add_argument('--bbox_det_file', default='results/bbox_detections.json', type=str, + help='The output file for coco bbox results if --coco_results is set.') + parser.add_argument('--mask_det_file', default='results/mask_detections.json', type=str, + help='The output file for coco mask results if --coco_results is set.') + parser.add_argument('--config', default=None, + help='The config object to use.') + parser.add_argument('--output_web_json', dest='output_web_json', action='store_true', + help='If display is not set, instead of processing IoU values, this dumps detections for usage with the detections viewer web thingy.') + parser.add_argument('--web_det_path', default='web/dets/', type=str, + help='If output_web_json is set, this is the path to dump detections into.') + parser.add_argument('--no_bar', dest='no_bar', action='store_true', + help='Do not output the status bar. This is useful for when piping to a file.') + parser.add_argument('--display_lincomb', default=False, type=str2bool, + help='If the config uses lincomb masks, output a visualization of how those masks are created.') + parser.add_argument('--benchmark', default=False, dest='benchmark', action='store_true', + help='Equivalent to running display mode but without displaying an image.') + parser.add_argument('--no_sort', default=False, dest='no_sort', action='store_true', + help='Do not sort images by hashed image ID.') + parser.add_argument('--seed', default=None, type=int, + help='The seed to pass into random.seed. Note: this is only really for the shuffle and does not (I think) affect cuda stuff.') + parser.add_argument('--mask_proto_debug', default=False, dest='mask_proto_debug', action='store_true', + help='Outputs stuff for scripts/compute_mask.py.') + parser.add_argument('--no_crop', default=False, dest='crop', action='store_false', + help='Do not crop output masks with the predicted bounding box.') + parser.add_argument('--image', default=None, type=str, + help='A path to an image to use for display.') + parser.add_argument('--images', default=None, type=str, + help='An input folder of images and output folder to save detected images. Should be in the format input->output.') + parser.add_argument('--video', default=None, type=str, + help='A path to a video to evaluate on. Passing in a number will use that index webcam.') + parser.add_argument('--video_multiframe', default=1, type=int, + help='The number of frames to evaluate in parallel to make videos play at higher fps.') + parser.add_argument('--score_threshold', default=0, type=float, + help='Detections with a score under this threshold will not be considered. This currently only works in display mode.') + parser.add_argument('--dataset', default=None, type=str, + help='If specified, override the dataset specified in the config with this one (example: coco2017_dataset).') + parser.add_argument('--detect', default=False, dest='detect', action='store_true', + help='Don\'t evauluate the mask branch at all and only do object detection. This only works for --display and --benchmark.') + parser.add_argument('--display_fps', default=False, dest='display_fps', action='store_true', + help='When displaying / saving video, draw the FPS on the frame') + parser.add_argument('--emulate_playback', default=False, dest='emulate_playback', action='store_true', + help='When saving a video, emulate the framerate that you\'d get running in real-time mode.') + + parser.set_defaults(no_bar=False, display=False, resume=False, output_coco_json=False, output_web_json=False, shuffle=False, + benchmark=False, no_sort=False, no_hash=False, mask_proto_debug=False, crop=True, detect=False, display_fps=False, + emulate_playback=False) + + global args + args = parser.parse_args(argv) + + if args.output_web_json: + args.output_coco_json = True + + if args.seed is not None: + random.seed(args.seed) + +iou_thresholds = [x / 100 for x in range(50, 100, 5)] +coco_cats = {} # Call prep_coco_cats to fill this +coco_cats_inv = {} +color_cache = defaultdict(lambda: {}) + +def prep_display(dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''): + """ + Note: If undo_transform=False then im_h and im_w are allowed to be None. + """ + if undo_transform: + img_numpy = undo_image_transformation(img, w, h) + img_gpu = torch.Tensor(img_numpy).cuda() + else: + img_gpu = img / 255.0 + h, w, _ = img.shape + + with timer.env('Postprocess'): + save = cfg.rescore_bbox + cfg.rescore_bbox = True + t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb, + crop_masks = args.crop, + score_threshold = args.score_threshold) + cfg.rescore_bbox = save + + with timer.env('Copy'): + idx = t[1].argsort(0, descending=True)[:args.top_k] + + if cfg.eval_mask_branch: + # Masks are drawn on the GPU, so don't copy + masks = t[3][idx] + classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]] + + num_dets_to_consider = min(args.top_k, classes.shape[0]) + for j in range(num_dets_to_consider): + if scores[j] < args.score_threshold: + num_dets_to_consider = j + break + + # Quick and dirty lambda for selecting the color for a particular index + # Also keeps track of a per-gpu color cache for maximum speed + def get_color(j, on_gpu=None): + global color_cache + color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS) + + if on_gpu is not None and color_idx in color_cache[on_gpu]: + return color_cache[on_gpu][color_idx] + else: + color = COLORS[color_idx] + if not undo_transform: + # The image might come in as RGB or BRG, depending + color = (color[2], color[1], color[0]) + if on_gpu is not None: + color = torch.Tensor(color).to(on_gpu).float() / 255. + color_cache[on_gpu][color_idx] = color + return color + + # First, draw the masks on the GPU where we can do it really fast + # Beware: very fast but possibly unintelligible mask-drawing code ahead + # I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice + if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0: + # After this, mask is of size [num_dets, h, w, 1] + masks = masks[:num_dets_to_consider, :, :, None] + + # Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1]) + colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0) + masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha + + # This is 1 everywhere except for 1-mask_alpha where the mask is + inv_alph_masks = masks * (-mask_alpha) + 1 + + # I did the math for this on pen and paper. This whole block should be equivalent to: + # for j in range(num_dets_to_consider): + # img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j] + masks_color_summand = masks_color[0] + if num_dets_to_consider > 1: + inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0) + masks_color_cumul = masks_color[1:] * inv_alph_cumul + masks_color_summand += masks_color_cumul.sum(dim=0) + + img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand + + if args.display_fps: + # Draw the box for the fps on the GPU + font_face = cv2.FONT_HERSHEY_DUPLEX + font_scale = 0.6 + font_thickness = 1 + + text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0] + + img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha + + + # Then draw the stuff that needs to be done on the cpu + # Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason + img_numpy = (img_gpu * 255).byte().cpu().numpy() + + if args.display_fps: + # Draw the text on the CPU + text_pt = (4, text_h + 2) + text_color = [255, 255, 255] + + cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA) + + if num_dets_to_consider == 0: + return img_numpy + + if args.display_text or args.display_bboxes: + for j in reversed(range(num_dets_to_consider)): + x1, y1, x2, y2 = boxes[j, :] + color = get_color(j) + score = scores[j] + + if args.display_bboxes: + cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1) + + if args.display_text: + _class = cfg.dataset.class_names[classes[j]] + text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class + + font_face = cv2.FONT_HERSHEY_DUPLEX + font_scale = 0.6 + font_thickness = 1 + + text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0] + + text_pt = (x1, y1 - 3) + text_color = [255, 255, 255] + + cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1) + cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA) + + + return img_numpy + +def prep_benchmark(dets_out, h, w): + with timer.env('Postprocess'): + t = postprocess(dets_out, w, h, crop_masks=args.crop, score_threshold=args.score_threshold) + + with timer.env('Copy'): + classes, scores, boxes, masks = [x[:args.top_k] for x in t] + if isinstance(scores, list): + box_scores = scores[0].cpu().numpy() + mask_scores = scores[1].cpu().numpy() + else: + scores = scores.cpu().numpy() + classes = classes.cpu().numpy() + boxes = boxes.cpu().numpy() + masks = masks.cpu().numpy() + + with timer.env('Sync'): + # Just in case + torch.cuda.synchronize() + +def prep_coco_cats(): + """ Prepare inverted table for category id lookup given a coco cats object. """ + for coco_cat_id, transformed_cat_id_p1 in get_label_map().items(): + transformed_cat_id = transformed_cat_id_p1 - 1 + coco_cats[transformed_cat_id] = coco_cat_id + coco_cats_inv[coco_cat_id] = transformed_cat_id + + +def get_coco_cat(transformed_cat_id): + """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """ + return coco_cats[transformed_cat_id] + +def get_transformed_cat(coco_cat_id): + """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """ + return coco_cats_inv[coco_cat_id] + + +class Detections: + + def __init__(self): + self.bbox_data = [] + self.mask_data = [] + + def add_bbox(self, image_id:int, category_id:int, bbox:list, score:float): + """ Note that bbox should be a list or tuple of (x1, y1, x2, y2) """ + bbox = [bbox[0], bbox[1], bbox[2]-bbox[0], bbox[3]-bbox[1]] + + # Round to the nearest 10th to avoid huge file sizes, as COCO suggests + bbox = [round(float(x)*10)/10 for x in bbox] + + self.bbox_data.append({ + 'image_id': int(image_id), + 'category_id': get_coco_cat(int(category_id)), + 'bbox': bbox, + 'score': float(score) + }) + + def add_mask(self, image_id:int, category_id:int, segmentation:np.ndarray, score:float): + """ The segmentation should be the full mask, the size of the image and with size [h, w]. """ + rle = pycocotools.mask.encode(np.asfortranarray(segmentation.astype(np.uint8))) + rle['counts'] = rle['counts'].decode('ascii') # json.dump doesn't like bytes strings + + self.mask_data.append({ + 'image_id': int(image_id), + 'category_id': get_coco_cat(int(category_id)), + 'segmentation': rle, + 'score': float(score) + }) + + def dump(self): + dump_arguments = [ + (self.bbox_data, args.bbox_det_file), + (self.mask_data, args.mask_det_file) + ] + + for data, path in dump_arguments: + with open(path, 'w') as f: + json.dump(data, f) + + def dump_web(self): + """ Dumps it in the format for my web app. Warning: bad code ahead! """ + config_outs = ['preserve_aspect_ratio', 'use_prediction_module', + 'use_yolo_regressors', 'use_prediction_matching', + 'train_masks'] + + output = { + 'info' : { + 'Config': {key: getattr(cfg, key) for key in config_outs}, + } + } + + image_ids = list(set([x['image_id'] for x in self.bbox_data])) + image_ids.sort() + image_lookup = {_id: idx for idx, _id in enumerate(image_ids)} + + output['images'] = [{'image_id': image_id, 'dets': []} for image_id in image_ids] + + # These should already be sorted by score with the way prep_metrics works. + for bbox, mask in zip(self.bbox_data, self.mask_data): + image_obj = output['images'][image_lookup[bbox['image_id']]] + image_obj['dets'].append({ + 'score': bbox['score'], + 'bbox': bbox['bbox'], + 'category': cfg.dataset.class_names[get_transformed_cat(bbox['category_id'])], + 'mask': mask['segmentation'], + }) + + with open(os.path.join(args.web_det_path, '%s.json' % cfg.name), 'w') as f: + json.dump(output, f) + + + + +def _mask_iou(mask1, mask2, iscrowd=False): + with timer.env('Mask IoU'): + ret = mask_iou(mask1, mask2, iscrowd) + return ret.cpu() + +def _bbox_iou(bbox1, bbox2, iscrowd=False): + with timer.env('BBox IoU'): + ret = jaccard(bbox1, bbox2, iscrowd) + return ret.cpu() + +def prep_metrics(ap_data, dets, img, gt, gt_masks, h, w, num_crowd, image_id, detections:Detections=None): + """ Returns a list of APs for this image, with each element being for a class """ + if not args.output_coco_json: + with timer.env('Prepare gt'): + gt_boxes = torch.Tensor(gt[:, :4]) + gt_boxes[:, [0, 2]] *= w + gt_boxes[:, [1, 3]] *= h + gt_classes = list(gt[:, 4].astype(int)) + gt_masks = torch.Tensor(gt_masks).view(-1, h*w) + + if num_crowd > 0: + split = lambda x: (x[-num_crowd:], x[:-num_crowd]) + crowd_boxes , gt_boxes = split(gt_boxes) + crowd_masks , gt_masks = split(gt_masks) + crowd_classes, gt_classes = split(gt_classes) + + with timer.env('Postprocess'): + classes, scores, boxes, masks = postprocess(dets, w, h, crop_masks=args.crop, score_threshold=args.score_threshold) + + if classes.size(0) == 0: + return + + classes = list(classes.cpu().numpy().astype(int)) + if isinstance(scores, list): + box_scores = list(scores[0].cpu().numpy().astype(float)) + mask_scores = list(scores[1].cpu().numpy().astype(float)) + else: + scores = list(scores.cpu().numpy().astype(float)) + box_scores = scores + mask_scores = scores + masks = masks.view(-1, h*w).cuda() + boxes = boxes.cuda() + + + if args.output_coco_json: + with timer.env('JSON Output'): + boxes = boxes.cpu().numpy() + masks = masks.view(-1, h, w).cpu().numpy() + for i in range(masks.shape[0]): + # Make sure that the bounding box actually makes sense and a mask was produced + if (boxes[i, 3] - boxes[i, 1]) * (boxes[i, 2] - boxes[i, 0]) > 0: + detections.add_bbox(image_id, classes[i], boxes[i,:], box_scores[i]) + detections.add_mask(image_id, classes[i], masks[i,:,:], mask_scores[i]) + return + + with timer.env('Eval Setup'): + num_pred = len(classes) + num_gt = len(gt_classes) + + mask_iou_cache = _mask_iou(masks, gt_masks) + bbox_iou_cache = _bbox_iou(boxes.float(), gt_boxes.float()) + + if num_crowd > 0: + crowd_mask_iou_cache = _mask_iou(masks, crowd_masks, iscrowd=True) + crowd_bbox_iou_cache = _bbox_iou(boxes.float(), crowd_boxes.float(), iscrowd=True) + else: + crowd_mask_iou_cache = None + crowd_bbox_iou_cache = None + + box_indices = sorted(range(num_pred), key=lambda i: -box_scores[i]) + mask_indices = sorted(box_indices, key=lambda i: -mask_scores[i]) + + iou_types = [ + ('box', lambda i,j: bbox_iou_cache[i, j].item(), + lambda i,j: crowd_bbox_iou_cache[i,j].item(), + lambda i: box_scores[i], box_indices), + ('mask', lambda i,j: mask_iou_cache[i, j].item(), + lambda i,j: crowd_mask_iou_cache[i,j].item(), + lambda i: mask_scores[i], mask_indices) + ] + + timer.start('Main loop') + for _class in set(classes + gt_classes): + ap_per_iou = [] + num_gt_for_class = sum([1 for x in gt_classes if x == _class]) + + for iouIdx in range(len(iou_thresholds)): + iou_threshold = iou_thresholds[iouIdx] + + for iou_type, iou_func, crowd_func, score_func, indices in iou_types: + gt_used = [False] * len(gt_classes) + + ap_obj = ap_data[iou_type][iouIdx][_class] + ap_obj.add_gt_positives(num_gt_for_class) + + for i in indices: + if classes[i] != _class: + continue + + max_iou_found = iou_threshold + max_match_idx = -1 + for j in range(num_gt): + if gt_used[j] or gt_classes[j] != _class: + continue + + iou = iou_func(i, j) + + if iou > max_iou_found: + max_iou_found = iou + max_match_idx = j + + if max_match_idx >= 0: + gt_used[max_match_idx] = True + ap_obj.push(score_func(i), True) + else: + # If the detection matches a crowd, we can just ignore it + matched_crowd = False + + if num_crowd > 0: + for j in range(len(crowd_classes)): + if crowd_classes[j] != _class: + continue + + iou = crowd_func(i, j) + + if iou > iou_threshold: + matched_crowd = True + break + + # All this crowd code so that we can make sure that our eval code gives the + # same result as COCOEval. There aren't even that many crowd annotations to + # begin with, but accuracy is of the utmost importance. + if not matched_crowd: + ap_obj.push(score_func(i), False) + timer.stop('Main loop') + + +class APDataObject: + """ + Stores all the information necessary to calculate the AP for one IoU and one class. + Note: I type annotated this because why not. + """ + + def __init__(self): + self.data_points = [] + self.num_gt_positives = 0 + + def push(self, score:float, is_true:bool): + self.data_points.append((score, is_true)) + + def add_gt_positives(self, num_positives:int): + """ Call this once per image. """ + self.num_gt_positives += num_positives + + def is_empty(self) -> bool: + return len(self.data_points) == 0 and self.num_gt_positives == 0 + + def get_ap(self) -> float: + """ Warning: result not cached. """ + + if self.num_gt_positives == 0: + return 0 + + # Sort descending by score + self.data_points.sort(key=lambda x: -x[0]) + + precisions = [] + recalls = [] + num_true = 0 + num_false = 0 + + # Compute the precision-recall curve. The x axis is recalls and the y axis precisions. + for datum in self.data_points: + # datum[1] is whether the detection a true or false positive + if datum[1]: num_true += 1 + else: num_false += 1 + + precision = num_true / (num_true + num_false) + recall = num_true / self.num_gt_positives + + precisions.append(precision) + recalls.append(recall) + + # Smooth the curve by computing [max(precisions[i:]) for i in range(len(precisions))] + # Basically, remove any temporary dips from the curve. + # At least that's what I think, idk. COCOEval did it so I do too. + for i in range(len(precisions)-1, 0, -1): + if precisions[i] > precisions[i-1]: + precisions[i-1] = precisions[i] + + # Compute the integral of precision(recall) d_recall from recall=0->1 using fixed-length riemann summation with 101 bars. + y_range = [0] * 101 # idx 0 is recall == 0.0 and idx 100 is recall == 1.00 + x_range = np.array([x / 100 for x in range(101)]) + recalls = np.array(recalls) + + # I realize this is weird, but all it does is find the nearest precision(x) for a given x in x_range. + # Basically, if the closest recall we have to 0.01 is 0.009 this sets precision(0.01) = precision(0.009). + # I approximate the integral this way, because that's how COCOEval does it. + indices = np.searchsorted(recalls, x_range, side='left') + for bar_idx, precision_idx in enumerate(indices): + if precision_idx < len(precisions): + y_range[bar_idx] = precisions[precision_idx] + + # Finally compute the riemann sum to get our integral. + # avg([precision(x) for x in 0:0.01:1]) + return sum(y_range) / len(y_range) + +def badhash(x): + """ + Just a quick and dirty hash function for doing a deterministic shuffle based on image_id. + + Source: + https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key + """ + x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF + x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF + x = ((x >> 16) ^ x) & 0xFFFFFFFF + return x + +def evalimage(net:Yolact, path:str, save_path:str=None): + frame = torch.from_numpy(cv2.imread(path)).cuda().float() + batch = FastBaseTransform()(frame.unsqueeze(0)) + preds = net(batch) + + img_numpy = prep_display(preds, frame, None, None, undo_transform=False) + + if save_path is None: + img_numpy = img_numpy[:, :, (2, 1, 0)] + + if save_path is None: + plt.imshow(img_numpy) + plt.title(path) + plt.show() + else: + cv2.imwrite(save_path, img_numpy) + +def evalimages(net:Yolact, input_folder:str, output_folder:str): + if not os.path.exists(output_folder): + os.mkdir(output_folder) + + print() + for p in Path(input_folder).glob('*'): + path = str(p) + name = os.path.basename(path) + name = '.'.join(name.split('.')[:-1]) + '.png' + out_path = os.path.join(output_folder, name) + + evalimage(net, path, out_path) + print(path + ' -> ' + out_path) + print('Done.') + +from multiprocessing.pool import ThreadPool +from queue import Queue + +class CustomDataParallel(torch.nn.DataParallel): + """ A Custom Data Parallel class that properly gathers lists of dictionaries. """ + def gather(self, outputs, output_device): + # Note that I don't actually want to convert everything to the output_device + return sum(outputs, []) + +def evalvideo(net:Yolact, path:str, out_path:str=None): + # If the path is a digit, parse it as a webcam index + is_webcam = path.isdigit() + + # If the input image size is constant, this make things faster (hence why we can use it in a video setting). + cudnn.benchmark = True + + if is_webcam: + vid = cv2.VideoCapture(int(path)) + else: + vid = cv2.VideoCapture(path) + + if not vid.isOpened(): + print('Could not open video "%s"' % path) + exit(-1) + + target_fps = round(vid.get(cv2.CAP_PROP_FPS)) + frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) + frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + if is_webcam: + num_frames = float('inf') + else: + num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT)) + + net = CustomDataParallel(net).cuda() + transform = torch.nn.DataParallel(FastBaseTransform()).cuda() + frame_times = MovingAverage(100) + fps = 0 + frame_time_target = 1 / target_fps + running = True + fps_str = '' + vid_done = False + frames_displayed = 0 + + if out_path is not None: + out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), target_fps, (frame_width, frame_height)) + + def cleanup_and_exit(): + print() + pool.terminate() + vid.release() + if out_path is not None: + out.release() + cv2.destroyAllWindows() + exit() + + def get_next_frame(vid): + frames = [] + for idx in range(args.video_multiframe): + frame = vid.read()[1] + if frame is None: + return frames + frames.append(frame) + return frames + + def transform_frame(frames): + with torch.no_grad(): + frames = [torch.from_numpy(frame).cuda().float() for frame in frames] + return frames, transform(torch.stack(frames, 0)) + + def eval_network(inp): + with torch.no_grad(): + frames, imgs = inp + num_extra = 0 + while imgs.size(0) < args.video_multiframe: + imgs = torch.cat([imgs, imgs[0].unsqueeze(0)], dim=0) + num_extra += 1 + out = net(imgs) + if num_extra > 0: + out = out[:-num_extra] + return frames, out + + def prep_frame(inp, fps_str): + with torch.no_grad(): + frame, preds = inp + return prep_display(preds, frame, None, None, undo_transform=False, class_color=True, fps_str=fps_str) + + frame_buffer = Queue() + video_fps = 0 + + # All this timing code to make sure that + def play_video(): + try: + nonlocal frame_buffer, running, video_fps, is_webcam, num_frames, frames_displayed, vid_done + + video_frame_times = MovingAverage(100) + frame_time_stabilizer = frame_time_target + last_time = None + stabilizer_step = 0.0005 + progress_bar = ProgressBar(30, num_frames) + + while running: + frame_time_start = time.time() + + if not frame_buffer.empty(): + next_time = time.time() + if last_time is not None: + video_frame_times.add(next_time - last_time) + video_fps = 1 / video_frame_times.get_avg() + if out_path is None: + cv2.imshow(path, frame_buffer.get()) + else: + out.write(frame_buffer.get()) + frames_displayed += 1 + last_time = next_time + + if out_path is not None: + if video_frame_times.get_avg() == 0: + fps = 0 + else: + fps = 1 / video_frame_times.get_avg() + progress = frames_displayed / num_frames * 100 + progress_bar.set_val(frames_displayed) + + print('\rProcessing Frames %s %6d / %6d (%5.2f%%) %5.2f fps ' + % (repr(progress_bar), frames_displayed, num_frames, progress, fps), end='') + + + # This is split because you don't want savevideo to require cv2 display functionality (see #197) + if out_path is None and cv2.waitKey(1) == 27: + # Press Escape to close + running = False + if not (frames_displayed < num_frames): + running = False + + if not vid_done: + buffer_size = frame_buffer.qsize() + if buffer_size < args.video_multiframe: + frame_time_stabilizer += stabilizer_step + elif buffer_size > args.video_multiframe: + frame_time_stabilizer -= stabilizer_step + if frame_time_stabilizer < 0: + frame_time_stabilizer = 0 + + new_target = frame_time_stabilizer if is_webcam else max(frame_time_stabilizer, frame_time_target) + else: + new_target = frame_time_target + + next_frame_target = max(2 * new_target - video_frame_times.get_avg(), 0) + target_time = frame_time_start + next_frame_target - 0.001 # Let's just subtract a millisecond to be safe + + if out_path is None or args.emulate_playback: + # This gives more accurate timing than if sleeping the whole amount at once + while time.time() < target_time: + time.sleep(0.001) + else: + # Let's not starve the main thread, now + time.sleep(0.001) + except: + # See issue #197 for why this is necessary + import traceback + traceback.print_exc() + + + extract_frame = lambda x, i: (x[0][i] if x[1][i]['detection'] is None else x[0][i].to(x[1][i]['detection']['box'].device), [x[1][i]]) + + # Prime the network on the first frame because I do some thread unsafe things otherwise + print('Initializing model... ', end='') + first_batch = eval_network(transform_frame(get_next_frame(vid))) + print('Done.') + + # For each frame the sequence of functions it needs to go through to be processed (in reversed order) + sequence = [prep_frame, eval_network, transform_frame] + pool = ThreadPool(processes=len(sequence) + args.video_multiframe + 2) + pool.apply_async(play_video) + active_frames = [{'value': extract_frame(first_batch, i), 'idx': 0} for i in range(len(first_batch[0]))] + + print() + if out_path is None: print('Press Escape to close.') + try: + while vid.isOpened() and running: + # Hard limit on frames in buffer so we don't run out of memory >.> + while frame_buffer.qsize() > 100: + time.sleep(0.001) + + start_time = time.time() + + # Start loading the next frames from the disk + if not vid_done: + next_frames = pool.apply_async(get_next_frame, args=(vid,)) + else: + next_frames = None + + if not (vid_done and len(active_frames) == 0): + # For each frame in our active processing queue, dispatch a job + # for that frame using the current function in the sequence + for frame in active_frames: + _args = [frame['value']] + if frame['idx'] == 0: + _args.append(fps_str) + frame['value'] = pool.apply_async(sequence[frame['idx']], args=_args) + + # For each frame whose job was the last in the sequence (i.e. for all final outputs) + for frame in active_frames: + if frame['idx'] == 0: + frame_buffer.put(frame['value'].get()) + + # Remove the finished frames from the processing queue + active_frames = [x for x in active_frames if x['idx'] > 0] + + # Finish evaluating every frame in the processing queue and advanced their position in the sequence + for frame in list(reversed(active_frames)): + frame['value'] = frame['value'].get() + frame['idx'] -= 1 + + if frame['idx'] == 0: + # Split this up into individual threads for prep_frame since it doesn't support batch size + active_frames += [{'value': extract_frame(frame['value'], i), 'idx': 0} for i in range(1, len(frame['value'][0]))] + frame['value'] = extract_frame(frame['value'], 0) + + # Finish loading in the next frames and add them to the processing queue + if next_frames is not None: + frames = next_frames.get() + if len(frames) == 0: + vid_done = True + else: + active_frames.append({'value': frames, 'idx': len(sequence)-1}) + + # Compute FPS + frame_times.add(time.time() - start_time) + fps = args.video_multiframe / frame_times.get_avg() + else: + fps = 0 + + fps_str = 'Processing FPS: %.2f | Video Playback FPS: %.2f | Frames in Buffer: %d' % (fps, video_fps, frame_buffer.qsize()) + if not args.display_fps: + print('\r' + fps_str + ' ', end='') + + except KeyboardInterrupt: + print('\nStopping...') + + cleanup_and_exit() + +def evaluate(net:Yolact, dataset, train_mode=False): + net.detect.use_fast_nms = args.fast_nms + net.detect.use_cross_class_nms = args.cross_class_nms + cfg.mask_proto_debug = args.mask_proto_debug + + # TODO Currently we do not support Fast Mask Re-scroing in evalimage, evalimages, and evalvideo + if args.image is not None: + if ':' in args.image: + inp, out = args.image.split(':') + evalimage(net, inp, out) + else: + evalimage(net, args.image) + return + elif args.images is not None: + inp, out = args.images.split(':') + evalimages(net, inp, out) + return + elif args.video is not None: + if ':' in args.video: + inp, out = args.video.split(':') + evalvideo(net, inp, out) + else: + evalvideo(net, args.video) + return + + frame_times = MovingAverage() + dataset_size = len(dataset) if args.max_images < 0 else min(args.max_images, len(dataset)) + progress_bar = ProgressBar(30, dataset_size) + + print() + + if not args.display and not args.benchmark: + # For each class and iou, stores tuples (score, isPositive) + # Index ap_data[type][iouIdx][classIdx] + ap_data = { + 'box' : [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds], + 'mask': [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds] + } + detections = Detections() + else: + timer.disable('Load Data') + + dataset_indices = list(range(len(dataset))) + + if args.shuffle: + random.shuffle(dataset_indices) + elif not args.no_sort: + # Do a deterministic shuffle based on the image ids + # + # I do this because on python 3.5 dictionary key order is *random*, while in 3.6 it's + # the order of insertion. That means on python 3.6, the images come in the order they are in + # in the annotations file. For some reason, the first images in the annotations file are + # the hardest. To combat this, I use a hard-coded hash function based on the image ids + # to shuffle the indices we use. That way, no matter what python version or how pycocotools + # handles the data, we get the same result every time. + hashed = [badhash(x) for x in dataset.ids] + dataset_indices.sort(key=lambda x: hashed[x]) + + dataset_indices = dataset_indices[:dataset_size] + + try: + # Main eval loop + for it, image_idx in enumerate(dataset_indices): + timer.reset() + + with timer.env('Load Data'): + img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx) + + # Test flag, do not upvote + if cfg.mask_proto_debug: + with open('scripts/info.txt', 'w') as f: + f.write(str(dataset.ids[image_idx])) + np.save('scripts/gt.npy', gt_masks) + + batch = Variable(img.unsqueeze(0)) + if args.cuda: + batch = batch.cuda() + + with timer.env('Network Extra'): + preds = net(batch) + # Perform the meat of the operation here depending on our mode. + if args.display: + img_numpy = prep_display(preds, img, h, w) + elif args.benchmark: + prep_benchmark(preds, h, w) + else: + prep_metrics(ap_data, preds, img, gt, gt_masks, h, w, num_crowd, dataset.ids[image_idx], detections) + + # First couple of images take longer because we're constructing the graph. + # Since that's technically initialization, don't include those in the FPS calculations. + if it > 1: + frame_times.add(timer.total_time()) + + if args.display: + if it > 1: + print('Avg FPS: %.4f' % (1 / frame_times.get_avg())) + plt.imshow(img_numpy) + plt.title(str(dataset.ids[image_idx])) + plt.show() + elif not args.no_bar: + if it > 1: fps = 1 / frame_times.get_avg() + else: fps = 0 + progress = (it+1) / dataset_size * 100 + progress_bar.set_val(it+1) + print('\rProcessing Images %s %6d / %6d (%5.2f%%) %5.2f fps ' + % (repr(progress_bar), it+1, dataset_size, progress, fps), end='') + + + + if not args.display and not args.benchmark: + print() + if args.output_coco_json: + print('Dumping detections...') + if args.output_web_json: + detections.dump_web() + else: + detections.dump() + else: + if not train_mode: + print('Saving data...') + with open(args.ap_data_file, 'wb') as f: + pickle.dump(ap_data, f) + + return calc_map(ap_data) + elif args.benchmark: + print() + print() + print('Stats for the last frame:') + timer.print_stats() + avg_seconds = frame_times.get_avg() + print('Average: %5.2f fps, %5.2f ms' % (1 / frame_times.get_avg(), 1000*avg_seconds)) + + except KeyboardInterrupt: + print('Stopping...') + + +def calc_map(ap_data): + print('Calculating mAP...') + aps = [{'box': [], 'mask': []} for _ in iou_thresholds] + + for _class in range(len(cfg.dataset.class_names)): + for iou_idx in range(len(iou_thresholds)): + for iou_type in ('box', 'mask'): + ap_obj = ap_data[iou_type][iou_idx][_class] + + if not ap_obj.is_empty(): + aps[iou_idx][iou_type].append(ap_obj.get_ap()) + + all_maps = {'box': OrderedDict(), 'mask': OrderedDict()} + + # Looking back at it, this code is really hard to read :/ + for iou_type in ('box', 'mask'): + all_maps[iou_type]['all'] = 0 # Make this first in the ordereddict + for i, threshold in enumerate(iou_thresholds): + mAP = sum(aps[i][iou_type]) / len(aps[i][iou_type]) * 100 if len(aps[i][iou_type]) > 0 else 0 + all_maps[iou_type][int(threshold*100)] = mAP + all_maps[iou_type]['all'] = (sum(all_maps[iou_type].values()) / (len(all_maps[iou_type].values())-1)) + + print_maps(all_maps) + + # Put in a prettier format so we can serialize it to json during training + all_maps = {k: {j: round(u, 2) for j, u in v.items()} for k, v in all_maps.items()} + return all_maps + +def print_maps(all_maps): + # Warning: hacky + make_row = lambda vals: (' %5s |' * len(vals)) % tuple(vals) + make_sep = lambda n: ('-------+' * n) + + print() + print(make_row([''] + [('.%d ' % x if isinstance(x, int) else x + ' ') for x in all_maps['box'].keys()])) + print(make_sep(len(all_maps['box']) + 1)) + for iou_type in ('box', 'mask'): + print(make_row([iou_type] + ['%.2f' % x if x < 100 else '%.1f' % x for x in all_maps[iou_type].values()])) + print(make_sep(len(all_maps['box']) + 1)) + print() + + + +if __name__ == '__main__': + parse_args() + + if args.config is not None: + set_cfg(args.config) + + if args.trained_model == 'interrupt': + args.trained_model = SavePath.get_interrupt('weights/') + elif args.trained_model == 'latest': + args.trained_model = SavePath.get_latest('weights/', cfg.name) + + if args.config is None: + model_path = SavePath.from_str(args.trained_model) + # TODO: Bad practice? Probably want to do a name lookup instead. + args.config = model_path.model_name + '_config' + print('Config not specified. Parsed %s from the file name.\n' % args.config) + set_cfg(args.config) + + if args.detect: + cfg.eval_mask_branch = False + + if args.dataset is not None: + set_dataset(args.dataset) + + with torch.no_grad(): + if not os.path.exists('results'): + os.makedirs('results') + + if args.cuda: + cudnn.fastest = True + torch.set_default_tensor_type('torch.cuda.FloatTensor') + else: + torch.set_default_tensor_type('torch.FloatTensor') + + if args.resume and not args.display: + with open(args.ap_data_file, 'rb') as f: + ap_data = pickle.load(f) + calc_map(ap_data) + exit() + + if args.image is None and args.video is None and args.images is None: + dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info, + transform=BaseTransform(), has_gt=cfg.dataset.has_gt) + prep_coco_cats() + else: + dataset = None + + print('Loading model...', end='') + net = Yolact() + net.load_weights(args.trained_model) + net.eval() + print(' Done.') + + if args.cuda: + net = net.cuda() + + evaluate(net, dataset) + + diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/.gitignore b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/.gitignore new file mode 100644 index 000000000..5891c61e6 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/.gitignore @@ -0,0 +1,11 @@ +.vscode +.idea +*.so +*.o +*pyc +_ext +build +DCNv2.egg-info +dist +vendor/ + diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/LICENSE b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/LICENSE new file mode 100644 index 000000000..b2e3b5207 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2019, Charles Shang +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/README.md b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/README.md new file mode 100644 index 000000000..0c0ea7979 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/README.md @@ -0,0 +1,24 @@ +# DCNv2 latest + + + +Since DCN is used in many models and performance well but in industry this op support is not very well. Including pytorch, onnx, tensorrt etc. This repo is make DCNv2 available at all versions in pytorch. + +![image-20210217183103121](https://gitee.com/jinfagang/picbed/raw/master/img/image-20210217183103121.png) + +Pytorch 1.7 inferenced in CenterNet-DLA model. It works on Pytorch 1.7 so that you can use it in your RTX 30 series cards. + + + +## Updates + +- **2021.03.24**: It was confirmed PyTorch 1.8 is OK with master branch, feel free to use it. +- **2021.02.18**: Happy new year! PyTorch 1.7 finally supported on master branch! **for lower version theoretically also works, if not, pls fire an issue to me!**. +- **2020.09.23**: Now master branch works for pytorch 1.6 by default, for older version you gonna need separated one. +- **2020.08.25**: Check out pytorch1.6 branch for pytorch 1.6 support, you will meet an error like `THCudaBlas_Sgemv undefined` if you using pytorch 1.6 build master branch. master branch now work for pytorch 1.5; + + + +## Contact + +If you have any question, please using this platform post questions: http://t.manaai.cn diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/__init__.py b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/dcn_v2.py b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/dcn_v2.py new file mode 100644 index 000000000..ce654b5cb --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/dcn_v2.py @@ -0,0 +1,400 @@ +#!/usr/bin/env python +from __future__ import absolute_import, division, print_function + +import math + +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +import _ext as _backend + + +class _DCNv2(Function): + @staticmethod + def forward( + ctx, input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups + ): + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.kernel_size = _pair(weight.shape[2:4]) + ctx.deformable_groups = deformable_groups + output = _backend.dcn_v2_forward( + input, + weight, + bias, + offset, + mask, + ctx.kernel_size[0], + ctx.kernel_size[1], + ctx.stride[0], + ctx.stride[1], + ctx.padding[0], + ctx.padding[1], + ctx.dilation[0], + ctx.dilation[1], + ctx.deformable_groups, + ) + ctx.save_for_backward(input, offset, mask, weight, bias) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input, grad_offset, grad_mask, grad_weight, grad_bias = _backend.dcn_v2_backward( + input, + weight, + bias, + offset, + mask, + grad_output, + ctx.kernel_size[0], + ctx.kernel_size[1], + ctx.stride[0], + ctx.stride[1], + ctx.padding[0], + ctx.padding[1], + ctx.dilation[0], + ctx.dilation[1], + ctx.deformable_groups, + ) + + return grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None + + @staticmethod + def symbolic( + g, input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups + ): + from torch.nn.modules.utils import _pair + + stride = _pair(stride) + padding = _pair(padding) + dilation = _pair(dilation) + # as of trt 7, the dcn operation will be translated again by modifying the onnx file + # so the exporting code is kept to resemble the forward() + return g.op( + "DCNv2_2", + input, + offset, + mask, + weight, + bias, + stride_i=stride, + padding_i=padding, + dilation_i=dilation, + deformable_groups_i=deformable_groups, + ) + + +dcn_v2_conv = _DCNv2.apply + + +class DCNv2(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation=1, + deformable_groups=1, + ): + super(DCNv2, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.deformable_groups = deformable_groups + + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size)) + self.bias = nn.Parameter(torch.Tensor(out_channels)) + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1.0 / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + self.bias.data.zero_() + + def forward(self, input, offset, mask): + assert ( + 2 * self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] + == offset.shape[1] + ) + assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == mask.shape[1] + return dcn_v2_conv( + input, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.deformable_groups, + ) + + +class DCN(DCNv2): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation=1, + deformable_groups=1, + ): + super(DCN, self).__init__( + in_channels, out_channels, kernel_size, stride, padding, dilation, deformable_groups + ) + + channels_ = self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1] + self.conv_offset_mask = nn.Conv2d( + self.in_channels, + channels_, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + bias=True, + ) + self.init_offset() + + def init_offset(self): + self.conv_offset_mask.weight.data.zero_() + self.conv_offset_mask.bias.data.zero_() + + def forward(self, input): + out = self.conv_offset_mask(input) + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + return dcn_v2_conv( + input, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.deformable_groups, + ) + + +class _DCNv2Pooling(Function): + @staticmethod + def forward( + ctx, + input, + rois, + offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=0.0, + ): + ctx.spatial_scale = spatial_scale + ctx.no_trans = int(no_trans) + ctx.output_dim = output_dim + ctx.group_size = group_size + ctx.pooled_size = pooled_size + ctx.part_size = pooled_size if part_size is None else part_size + ctx.sample_per_part = sample_per_part + ctx.trans_std = trans_std + + output, output_count = _backend.dcn_v2_psroi_pooling_forward( + input, + rois, + offset, + ctx.no_trans, + ctx.spatial_scale, + ctx.output_dim, + ctx.group_size, + ctx.pooled_size, + ctx.part_size, + ctx.sample_per_part, + ctx.trans_std, + ) + ctx.save_for_backward(input, rois, offset, output_count) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, rois, offset, output_count = ctx.saved_tensors + grad_input, grad_offset = _backend.dcn_v2_psroi_pooling_backward( + grad_output, + input, + rois, + offset, + output_count, + ctx.no_trans, + ctx.spatial_scale, + ctx.output_dim, + ctx.group_size, + ctx.pooled_size, + ctx.part_size, + ctx.sample_per_part, + ctx.trans_std, + ) + + return grad_input, None, grad_offset, None, None, None, None, None, None, None, None + + +dcn_v2_pooling = _DCNv2Pooling.apply + + +class DCNv2Pooling(nn.Module): + def __init__( + self, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=0.0, + ): + super(DCNv2Pooling, self).__init__() + self.spatial_scale = spatial_scale + self.pooled_size = pooled_size + self.output_dim = output_dim + self.no_trans = no_trans + self.group_size = group_size + self.part_size = pooled_size if part_size is None else part_size + self.sample_per_part = sample_per_part + self.trans_std = trans_std + + def forward(self, input, rois, offset): + assert input.shape[1] == self.output_dim + if self.no_trans: + offset = input.new() + return dcn_v2_pooling( + input, + rois, + offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std, + ) + + +class DCNPooling(DCNv2Pooling): + def __init__( + self, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=0.0, + deform_fc_dim=1024, + ): + super(DCNPooling, self).__init__( + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std, + ) + + self.deform_fc_dim = deform_fc_dim + + if not no_trans: + self.offset_mask_fc = nn.Sequential( + nn.Linear( + self.pooled_size * self.pooled_size * self.output_dim, self.deform_fc_dim + ), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_dim, self.deform_fc_dim), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_dim, self.pooled_size * self.pooled_size * 3), + ) + self.offset_mask_fc[4].weight.data.zero_() + self.offset_mask_fc[4].bias.data.zero_() + + def forward(self, input, rois): + offset = input.new() + + if not self.no_trans: + + # do roi_align first + n = rois.shape[0] + roi = dcn_v2_pooling( + input, + rois, + offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + True, # no trans + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std, + ) + + # build mask and offset + offset_mask = self.offset_mask_fc(roi.view(n, -1)) + offset_mask = offset_mask.view(n, 3, self.pooled_size, self.pooled_size) + o1, o2, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + + # do pooling with offset and mask + return ( + dcn_v2_pooling( + input, + rois, + offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std, + ) + * mask + ) + # only roi_align + return dcn_v2_pooling( + input, + rois, + offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std, + ) diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/dcn_v2_onnx.py b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/dcn_v2_onnx.py new file mode 100644 index 000000000..eb536d1aa --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/dcn_v2_onnx.py @@ -0,0 +1,344 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import math +import torch +from torch import nn +from torch.autograd import Function +from torch.nn.modules.utils import _pair +from torch.autograd.function import once_differentiable +import json + +import _ext as _backend + + +class _DCNv2(Function): + + @staticmethod + def symbolic(g, input, offset_mask, weight, bias, stride, padding, dilation, deformable_groups): + return g.op("Plugin", input, offset_mask, weight, bias, name_s="DCNv2", info_s=json.dumps({ + "dilation": dilation, + "padding": padding, + "stride": stride, + "deformable_groups": deformable_groups + })) + + ############################################### 修改的部分 ############################################################ + # 这里以下的修改，并不是必须的，仅仅是复现DCN的时候，输入改成是input和offset_mask，原始的做法是chunk分割为x, y, mask， + # 然后再cat，再对mask做sigmoid后输入到dcn。这么做效率比较底下。实现DCN的时候输入input和offset_mask，内部做了分割和sigmomid + # 减少数据流转，提高效率，也因此在这里对操作做了修改 + @staticmethod + def forward(ctx, input, offset_mask, weight, bias, + stride, padding, dilation, deformable_groups): + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.kernel_size = _pair(weight.shape[2:4]) + ctx.deformable_groups = deformable_groups + + o1, o2, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + + # @staticmethod + # def forward(ctx, input, offset, mask, weight, bias, + # stride, padding, dilation, deformable_groups): + # ctx.stride = _pair(stride) + # ctx.padding = _pair(padding) + # ctx.dilation = _pair(dilation) + # ctx.kernel_size = _pair(weight.shape[2:4]) + # ctx.deformable_groups = deformable_groups + output = _backend.dcn_v2_forward(input, weight, bias, + offset, mask, + ctx.kernel_size[0], ctx.kernel_size[1], + ctx.stride[0], ctx.stride[1], + ctx.padding[0], ctx.padding[1], + ctx.dilation[0], ctx.dilation[1], + ctx.deformable_groups) + ctx.save_for_backward(input, offset, mask, weight, bias) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input, grad_offset, grad_mask, grad_weight, grad_bias = \ + _backend.dcn_v2_backward(input, weight, + bias, + offset, mask, + grad_output, + ctx.kernel_size[0], ctx.kernel_size[1], + ctx.stride[0], ctx.stride[1], + ctx.padding[0], ctx.padding[1], + ctx.dilation[0], ctx.dilation[1], + ctx.deformable_groups) + + return grad_input, grad_offset, grad_mask, grad_weight, grad_bias,\ + None, None, None, None, + + +dcn_v2_conv = _DCNv2.apply + + +class DCNv2(nn.Module): + + def __init__(self, in_channels, out_channels, + kernel_size, stride=1, padding=1, dilation=1, deformable_groups=1): + super(DCNv2, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.deformable_groups = deformable_groups + + self.weight = nn.Parameter(torch.Tensor( + out_channels, in_channels, *self.kernel_size)) + self.bias = nn.Parameter(torch.Tensor(out_channels)) + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + self.bias.data.zero_() + + def forward(self, input, offset, mask): + assert 2 * self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == \ + offset.shape[1] + assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == \ + mask.shape[1] + return dcn_v2_conv(input, offset, mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.deformable_groups) + + +class DCN(DCNv2): + + def __init__(self, in_channels, out_channels, + kernel_size, stride, padding, + dilation=1, deformable_groups=1): + super(DCN, self).__init__(in_channels, out_channels, + kernel_size, stride, padding, dilation, deformable_groups) + + channels_ = self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1] + self.conv_offset_mask = nn.Conv2d(self.in_channels, + channels_, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + bias=True) + self.init_offset() + + def init_offset(self): + self.conv_offset_mask.weight.data.zero_() + self.conv_offset_mask.bias.data.zero_() + + def forward(self, input): + # out = self.conv_offset_mask(input) + # o1, o2, mask = torch.chunk(out, 3, dim=1) + # offset = torch.cat((o1, o2), dim=1) + # mask = torch.sigmoid(mask) + # return dcn_v2_conv(input, offset, mask, + # self.weight, self.bias, + # self.stride, + # self.padding, + # self.dilation, + # self.deformable_groups) + + ############################################### 修改的部分 ############################################################ + offset_mask = self.conv_offset_mask(input) + #o1, o2, mask = torch.chunk(out, 3, dim=1) + #offset = torch.cat((o1, o2), dim=1) + #mask = torch.sigmoid(mask) + return dcn_v2_conv(input, offset_mask, + self.weight, self.bias, + self.stride, + self.padding, + self.dilation, + self.deformable_groups) + ############################################### ---------- ############################################################ + + + +class _DCNv2Pooling(Function): + @staticmethod + def forward(ctx, input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=.0): + ctx.spatial_scale = spatial_scale + ctx.no_trans = int(no_trans) + ctx.output_dim = output_dim + ctx.group_size = group_size + ctx.pooled_size = pooled_size + ctx.part_size = pooled_size if part_size is None else part_size + ctx.sample_per_part = sample_per_part + ctx.trans_std = trans_std + + output, output_count = \ + _backend.dcn_v2_psroi_pooling_forward(input, rois, offset, + ctx.no_trans, ctx.spatial_scale, + ctx.output_dim, ctx.group_size, + ctx.pooled_size, ctx.part_size, + ctx.sample_per_part, ctx.trans_std) + ctx.save_for_backward(input, rois, offset, output_count) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, rois, offset, output_count = ctx.saved_tensors + grad_input, grad_offset = \ + _backend.dcn_v2_psroi_pooling_backward(grad_output, + input, + rois, + offset, + output_count, + ctx.no_trans, + ctx.spatial_scale, + ctx.output_dim, + ctx.group_size, + ctx.pooled_size, + ctx.part_size, + ctx.sample_per_part, + ctx.trans_std) + + return grad_input, None, grad_offset, \ + None, None, None, None, None, None, None, None + + +dcn_v2_pooling = _DCNv2Pooling.apply + + +class DCNv2Pooling(nn.Module): + + def __init__(self, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=.0): + super(DCNv2Pooling, self).__init__() + self.spatial_scale = spatial_scale + self.pooled_size = pooled_size + self.output_dim = output_dim + self.no_trans = no_trans + self.group_size = group_size + self.part_size = pooled_size if part_size is None else part_size + self.sample_per_part = sample_per_part + self.trans_std = trans_std + + def forward(self, input, rois, offset): + assert input.shape[1] == self.output_dim + if self.no_trans: + offset = input.new() + return dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) + + +class DCNPooling(DCNv2Pooling): + + def __init__(self, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size=1, + part_size=None, + sample_per_part=4, + trans_std=.0, + deform_fc_dim=1024): + super(DCNPooling, self).__init__(spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std) + + self.deform_fc_dim = deform_fc_dim + + if not no_trans: + self.offset_mask_fc = nn.Sequential( + nn.Linear(self.pooled_size * self.pooled_size * + self.output_dim, self.deform_fc_dim), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_dim, self.deform_fc_dim), + nn.ReLU(inplace=True), + nn.Linear(self.deform_fc_dim, self.pooled_size * + self.pooled_size * 3) + ) + self.offset_mask_fc[4].weight.data.zero_() + self.offset_mask_fc[4].bias.data.zero_() + + def forward(self, input, rois): + offset = input.new() + + if not self.no_trans: + + # do roi_align first + n = rois.shape[0] + roi = dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + True, # no trans + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) + + # build mask and offset + offset_mask = self.offset_mask_fc(roi.view(n, -1)) + offset_mask = offset_mask.view( + n, 3, self.pooled_size, self.pooled_size) + o1, o2, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + + # do pooling with offset and mask + return dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) * mask + # only roi_align + return dcn_v2_pooling(input, rois, offset, + self.spatial_scale, + self.pooled_size, + self.output_dim, + self.no_trans, + self.group_size, + self.part_size, + self.sample_per_part, + self.trans_std) diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/make.sh b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/make.sh new file mode 100755 index 000000000..55f5587e6 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/make.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +sudo rm *.so +sudo rm -r build/ +sudo python3 setup.py build develop diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/setup.py b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/setup.py new file mode 100644 index 000000000..ad9814a46 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/setup.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python + +import glob +import os + +import torch +from setuptools import find_packages, setup +from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension + +requirements = ["torch", "torchvision"] + + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "src") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + os.environ["CC"] = "g++" + sources = main_file + source_cpu + extension = CppExtension + extra_compile_args = {"cxx": []} + define_macros = [] + + + if torch.cuda.is_available() and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + # raise NotImplementedError('Cuda is not available') + pass + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + ext_modules = [ + extension( + "_ext", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + return ext_modules + + +setup( + name="DCNv2", + version="0.1", + author="charlesshang", + url="https://github.com/charlesshang/DCNv2", + description="deformable convolutional networks", + packages=find_packages(exclude=("configs", "tests")), + # install_requires=requirements, + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_cpu.cpp b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_cpu.cpp new file mode 100644 index 000000000..6c0e7804d --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_cpu.cpp @@ -0,0 +1,272 @@ +#include +#include "cpu/dcn_v2_im2col_cpu.h" + +#include +//#include + +#include +//#include +//#include + +//extern THCState *state; + +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +at::Tensor +dcn_v2_cpu_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); + // printf("Channels: %d %d\n", channels, channels_kernel); + // printf("Channels: %d %d\n", channels_out, channels_kernel); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({bias.sizes()[0], height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + using scalar_t = float; + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto output_n = output.select(0, b); + + // Do Bias first: + // M,N,K are dims of matrix A and B + // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm) + // (N x 1) (1 x M) + // long m_ = channels_out; + // long n_ = height_out * width_out; + // long k_ = 1; + // THFloatBlas_gemm('t', 'n', n_, m_, k_, 1.0f, + // ones.contiguous().data(), k_, + // bias.contiguous().data(), k_, 0.0f, + // output_n.data(), n_); + + // modulated_deformable_im2col_cpu(input_n.data(), + // offset_n.data(), + // mask_n.data(), + // 1, channels, height, width, + // height_out, width_out, kernel_h, kernel_w, + // pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + // deformable_group, + // columns.data()); + + // //(k * m) x (m * n) + // // Y = WC + // long m = channels_out; + // long n = height_out * width_out; + // long k = channels * kernel_h * kernel_w; + // THFloatBlas_gemm('n', 'n', n, m, k, 1.0f, + // columns.data(), n, + // weight.data(), k, 1.0f, + // output_n.data(), n); + + // torch implementation + auto ones_T = at::transpose(ones.contiguous(), 2, 0); + ones_T = at::mul(ones_T, bias.contiguous()); + ones_T = at::transpose(ones_T, 2, 0); + output_n = at::add(output_n, ones_T); + + modulated_deformable_im2col_cpu(input_n.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + deformable_group, + columns.data_ptr()); + + //(k * m) x (m * n) + // Y = WC + + // torch implementation + auto weight_flat = weight.view({channels_out, channels * kernel_h * kernel_w}); + auto product = at::matmul(weight_flat, columns); + output.select(0, b) = at::add(output_n, product.view({channels_out, height_out, width_out})); + } + return output; +} + +std::vector dcn_v2_cpu_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + + THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); + THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); + + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.type().is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.type().is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.type().is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.type().is_cuda(), "mask must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + auto grad_input = at::zeros_like(input); + auto grad_weight = at::zeros_like(weight); + auto grad_bias = at::zeros_like(bias); + auto grad_offset = at::zeros_like(offset); + auto grad_mask = at::zeros_like(mask); + + using scalar_t = float; + + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto grad_output_n = grad_output.select(0, b); + auto grad_input_n = grad_input.select(0, b); + auto grad_offset_n = grad_offset.select(0, b); + auto grad_mask_n = grad_mask.select(0, b); + + // long m = channels * kernel_h * kernel_w; + // long n = height_out * width_out; + // long k = channels_out; + + // THFloatBlas_gemm('n', 't', n, m, k, 1.0f, + // grad_output_n.data(), n, + // weight.data(), m, 0.0f, + // columns.data(), n); + + // Torch implementation + auto weight_flat = weight.view({channels_out, channels*kernel_h*kernel_w}); + weight_flat = at::transpose(weight_flat, 1, 0); + auto grad_output_n_flat = grad_output_n.view({channels_out, height_out*width_out}); + columns = at::matmul(weight_flat, grad_output_n_flat); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cpu(columns.data(), + input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_offset_n.data(), + grad_mask_n.data()); + // gradient w.r.t. input data + modulated_deformable_col2im_cpu(columns.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_input_n.data()); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and group + modulated_deformable_im2col_cpu(input_n.data(), + offset_n.data(), + mask_n.data(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + columns.data()); + + // long m_ = channels_out; + // long n_ = channels * kernel_h * kernel_w; + // long k_ = height_out * width_out; + + // THFloatBlas_gemm('t', 'n', n_, m_, k_, 1.0f, + // columns.data(), k_, + // grad_output_n.data(), k_, 1.0f, + // grad_weight.data(), n_); + + // gradient w.r.t. bias + // long m_ = channels_out; + // long k__ = height_out * width_out; + // THFloatBlas_gemv('t', k_, m_, 1.0f, + // grad_output_n.data(), k_, + // ones.data(), 1, 1.0f, + // grad_bias.data(), 1); + + // Torch implementation + auto product = at::matmul(grad_output_n_flat, at::transpose(columns, 1, 0)); + grad_weight = at::add(grad_weight, product.view({channels_out, channels, kernel_h, kernel_w})); + + + // Torch implementation + auto ones_flat = ones.view({height_out*width_out}); + product = at::matmul(grad_output_n_flat, ones_flat); + grad_bias = at::add(grad_bias, product); + } + + return { + grad_input, grad_offset, grad_mask, grad_weight, grad_bias + }; +} diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp new file mode 100644 index 000000000..1704a60d1 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp @@ -0,0 +1,395 @@ +#include "dcn_v2_im2col_cpu.h" +#include +#include +#include + +#include +//#include + +#include +//#include +//#include + +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +/*#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + + +float dmcn_im2col_bilinear_cpu(const float *bottom_data, const int data_width, + const int height, const int width, float h, float w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + float lh = h - h_low; + float lw = w - w_low; + float hh = 1 - lh, hw = 1 - lw; + + float v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + float v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + float v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + float v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +float dmcn_get_gradient_weight_cpu(float argmax_h, float argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +float dmcn_get_coordinate_weight_cpu(float argmax_h, float argmax_w, + const int height, const int width, const float *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +void modulated_deformable_im2col_cpu_kernel(const int n, const float *data_im, const float *data_offset, const float *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + float *data_col) +{ + // launch channels * batch_size * height_col * width_col cores + for(int index=0; index(0); + const float h_im = h_in + i * dilation_h + offset_h; + const float w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + // data_col_ptr += batch_size * height_col * width_col; + data_col_ptr += height_col * width_col; + } + } + } +} + +void modulated_deformable_col2im_cpu_kernel(const int n, const float *data_col, const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + float *grad_im) +{ + for(int index = 0; index < n; index++) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + const float cur_inv_h_data = h_in + i * dilation_h + offset_h; + const float cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const float cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + float weight = dmcn_get_gradient_weight_cpu(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + //atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + *(grad_im + cur_bottom_grad_pos) += weight * cur_top_grad; + + } + } + } + } +} + +void modulated_deformable_col2im_coord_cpu_kernel(const int n, const float *data_col, const float *data_im, + const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + float *grad_offset, float *grad_mask) +{ + for(int index = 0; index < n; index++) + { + float val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float inv_h = h_in + i * dilation_h + offset_h; + float inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cpu(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const float weight = dmcn_get_coordinate_weight_cpu( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cpu(const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + modulated_deformable_im2col_cpu_kernel( + num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col); + + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + }*/ + +} + +void modulated_deformable_col2im_cpu(const float* data_col, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* grad_im){ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + modulated_deformable_col2im_cpu_kernel( + num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im); + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + }*/ + +} + +void modulated_deformable_col2im_coord_cpu(const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float* grad_offset, float* grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + modulated_deformable_col2im_coord_cpu_kernel( + num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset, grad_mask); + /*cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + }*/ +} \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_im2col_cpu.h b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_im2col_cpu.h new file mode 100644 index 000000000..bad5c5287 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_im2col_cpu.h @@ -0,0 +1,99 @@ + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.h + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1811.11168 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu + */ + +/***************** Adapted by Charles Shang *********************/ +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +#ifndef DCN_V2_IM2COL_CPU +#define DCN_V2_IM2COL_CPU + +#ifdef __cplusplus +extern "C" +{ +#endif + + void modulated_deformable_im2col_cpu(const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *data_col); + + void modulated_deformable_col2im_cpu(const float *data_col, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *grad_im); + + void modulated_deformable_col2im_coord_cpu(const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float *grad_offset, float *grad_mask); + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp new file mode 100644 index 000000000..6e41aaeda --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp @@ -0,0 +1,426 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ +// modified from the CUDA version for CPU use by Daniel K. Suhendro + +#include +#include +#include + +#include +//#include + +#include +//#include +//#include + +/*#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + +template +T bilinear_interp_cpu( + const T *data, + const T x, + const T y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + T dist_x = static_cast(x - x1); + T dist_y = static_cast(y - y1); + T value11 = data[y1 * width + x1]; + T value12 = data[y2 * width + x1]; + T value21 = data[y1 * width + x2]; + T value22 = data[y2 * width + x2]; + T value = (1 - dist_x) * (1 - dist_y) * value11 + + (1 - dist_x) * dist_y * value12 + + dist_x * (1 - dist_y) * value21 + + dist_x * dist_y * value22; + return value; +} + +template + void DeformablePSROIPoolForwardKernelCpu( + const int count, + const T *bottom_data, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const T *bottom_rois, const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + T *top_data, + T *top_count) +{ + for(int index = 0; index < count; index++) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 + T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + T sum = 0; + int count = 0; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = std::min(std::max(gw, 0), group_size - 1); + gh = std::min(std::max(gh, 0), group_size - 1); + + const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = std::min(std::max(w, T(0.)), width - T(1.)); + h = std::min(std::max(h, T(0.)), height - T(1.)); + int c = (ctop * group_size + gh) * group_size + gw; + T val = bilinear_interp_cpu(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? static_cast(0) : sum / count; + top_count[index] = count; + } +} + +template +void DeformablePSROIPoolBackwardAccKernelCpu( + const int count, + const T *top_diff, + const T *top_count, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + T *bottom_data_diff, T *bottom_trans_diff, + const T *bottom_data, + const T *bottom_rois, + const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + for(int index = 0; index < count; index++) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 + T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + T diff_val = top_diff[index] / top_count[index]; + const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = std::min(std::max(gw, 0), group_size - 1); + gh = std::min(std::max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = std::min(std::max(w, T(0.)), width - T(1.)); + h = std::min(std::max(h, T(0.)), height - T(1.)); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + T dist_x = w - x0, dist_y = h - y0; + T q00 = (1 - dist_x) * (1 - dist_y); + T q01 = (1 - dist_x) * dist_y; + T q10 = dist_x * (1 - dist_y); + T q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + /*atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val);*/ + *(offset_bottom_data_diff + bottom_index_base + y0 * width + x0) += q00 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y1 * width + x0) += q01 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y0 * width + x1) += q10 * diff_val; + *(offset_bottom_data_diff + bottom_index_base + y1 * width + x1) += q11 * diff_val; + + + if (no_trans) + { + continue; + } + T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + /*atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y);*/ + *(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w) += diff_x; + *(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w) += diff_y; + } + } + } +} + +std::tuple +dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + /*AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "rois must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + + auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + //cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (out.numel() == 0) + { + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); + } + + /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512);*/ + + AT_DISPATCH_FLOATING_TYPES(input.type(), "dcn_v2_psroi_pooling_cpu_forward", [&] { + DeformablePSROIPoolForwardKernelCpu( + out_size, + input.contiguous().data(), + spatial_scale, + channels, + height, width, + pooled_height, + pooled_width, + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + output_dim, + group_size, + part_size, + num_classes, + channels_each_class, + out.data(), + top_count.data()); + }); + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); +} + +std::tuple +dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + /*AT_ASSERTM(out_grad.type().is_cuda(), "out_grad must be a CUDA tensor"); + AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.type().is_cuda(), "bbox must be a CUDA tensor"); + AT_ASSERTM(trans.type().is_cuda(), "trans must be a CUDA tensor"); + AT_ASSERTM(top_count.type().is_cuda(), "top_count must be a CUDA tensor");*/ + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); + auto trans_grad = at::zeros_like(trans); + + if (input_grad.numel() == 0) + { + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); + } + + /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + cudaStream_t stream = at::cuda::getCurrentCUDAStream();*/ + + AT_DISPATCH_FLOATING_TYPES(out_grad.type(), "dcn_v2_psroi_pooling_cpu_backward", [&] { + DeformablePSROIPoolBackwardAccKernelCpu( + out_size, + out_grad.contiguous().data(), + top_count.contiguous().data(), + num_bbox, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + output_dim, + input_grad.contiguous().data(), + trans_grad.contiguous().data(), + input.contiguous().data(), + bbox.contiguous().data(), + trans.contiguous().data(), + no_trans, + trans_std, + sample_per_part, + group_size, + part_size, + num_classes, + channels_each_class); + }); + //THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); +} \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/vision.h b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/vision.h new file mode 100644 index 000000000..d5fbf1f07 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cpu/vision.h @@ -0,0 +1,60 @@ +#pragma once +#include + +at::Tensor +dcn_v2_cpu_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group); + +std::vector +dcn_v2_cpu_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group); + + +std::tuple +dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); + +std::tuple +dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_cuda.cu b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_cuda.cu new file mode 100644 index 000000000..22adbb124 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_cuda.cu @@ -0,0 +1,216 @@ +#include +#include "dcn_v2_im2col_cuda.h" + +#include +#include + +#include +#include +#include + +THCState *state = at::globalContext().lazyInitCUDA(); + +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu + +// [batch gemm] +// https://github.com/pytorch/pytorch/blob/master/aten/src/THC/generic/THCTensorMathBlas.cu + + +at::Tensor +dcn_v2_cuda_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + using scalar_t = float; + // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); + AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); + // printf("Channels: %d %d\n", channels, channels_kernel); + // printf("Channels: %d %d\n", channels_out, channels_kernel); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({batch, bias.sizes()[0], height_out, width_out}, input.options()); + auto columns = at::empty({batch, channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::zeros({batch, channels_out, height_out, width_out}, input.options()); + + // Add biases to output tensor + // torch implementation + auto ones_T = at::transpose(ones.contiguous(), 3, 1); + ones_T = at::mul(ones_T, bias.contiguous()); + ones_T = at::transpose(ones_T, 3, 1); + output = at::add(output, ones_T); + + modulated_deformable_im2col_cuda(c10::cuda::getCurrentCUDAStream(), + input.data_ptr(), + offset.data_ptr(), + mask.data_ptr(), + batch, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + deformable_group, + columns.data_ptr()); + + // Scale columns and add to output + // torch implementation + auto weight_flat = weight.view({channels_out, channels * kernel_h * kernel_w}); + auto product = at::matmul(weight_flat, columns); + output = at::add(output, product.view({batch, channels_out, height_out, width_out})); + + return output; +} + +std::vector dcn_v2_cuda_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + + THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); + THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); + + AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor"); + AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor"); + AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor"); + AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); + + AT_ASSERTM(channels == channels_kernel, + "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); + + const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + auto ones = at::ones({height_out, width_out}, input.options()); + auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); + auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); + + auto grad_input = at::zeros_like(input); + auto grad_weight = at::zeros_like(weight); + auto grad_bias = at::zeros_like(bias); + auto grad_offset = at::zeros_like(offset); + auto grad_mask = at::zeros_like(mask); + + using scalar_t = float; + + for (int b = 0; b < batch; b++) + { + auto input_n = input.select(0, b); + auto offset_n = offset.select(0, b); + auto mask_n = mask.select(0, b); + auto grad_output_n = grad_output.select(0, b); + auto grad_input_n = grad_input.select(0, b); + auto grad_offset_n = grad_offset.select(0, b); + auto grad_mask_n = grad_mask.select(0, b); + + // Torch implementation + auto weight_flat = weight.view({channels_out, channels*kernel_h*kernel_w}); + weight_flat = at::transpose(weight_flat, 1, 0); + + auto grad_output_n_flat = grad_output_n.view({channels_out, height_out*width_out}); + columns = at::matmul(weight_flat, grad_output_n_flat); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda(c10::cuda::getCurrentCUDAStream(), + columns.data_ptr(), + input_n.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_offset_n.data_ptr(), + grad_mask_n.data_ptr()); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda(c10::cuda::getCurrentCUDAStream(), + columns.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + grad_input_n.data_ptr()); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and group + modulated_deformable_im2col_cuda(c10::cuda::getCurrentCUDAStream(), + input_n.data_ptr(), + offset_n.data_ptr(), + mask_n.data_ptr(), + 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, + columns.data_ptr()); + + + // Torch implementation + auto product = at::matmul(grad_output_n_flat, at::transpose(columns, 1, 0)); + grad_weight = at::add(grad_weight, product.view({channels_out, channels, kernel_h, kernel_w})); + + // Torch implementation + auto ones_flat = ones.view({height_out*width_out}); + product = at::matmul(grad_output_n_flat, ones_flat); + grad_bias = at::add(grad_bias, product); + + } + + return { + grad_input, grad_offset, grad_mask, grad_weight, grad_bias + }; +} diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu new file mode 100644 index 000000000..4140eacf6 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu @@ -0,0 +1,402 @@ +#include "dcn_v2_im2col_cuda.h" +#include +#include +#include + +#include +#include + +#include +#include +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + + +__device__ float dmcn_im2col_bilinear_cuda(const float *bottom_data, const int data_width, + const int height, const int width, float h, float w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + float lh = h - h_low; + float lw = w - w_low; + float hh = 1 - lh, hw = 1 - lw; + + float v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + float v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + float v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + float v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +__device__ float dmcn_get_gradient_weight_cuda(float argmax_h, float argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +__device__ float dmcn_get_coordinate_weight_cuda(float argmax_h, float argmax_w, + const int height, const int width, const float *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + float weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +__global__ void modulated_deformable_im2col_gpu_kernel(const int n, + const float *data_im, const float *data_offset, const float *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + float *data_col) +{ + // launch channels * batch_size * height_col * width_col cores + CUDA_KERNEL_LOOP(index, n) + { + // NOTE(CharlesShang): different from Dai Jifeng's MXNet implementation, col_buffer is of shape (c*kw*kh, N, oh, ow) + // here columns is of shape (N, c*kw*kh, oh * ow), need to adapt axis + + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + // const int b_col = (index / width_col / height_col) % batch_size; + const int b_col = (index / width_col / height_col / num_channels) % batch_size; + // const int c_im = (index / width_col / height_col) / batch_size; + const int c_im = (index / width_col / height_col) % num_channels; + // const int c_col = c_im * kernel_h * kernel_w; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + // float *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + float *data_col_ptr = data_col + ((b_col * num_channels * kernel_w * kernel_h + c_col) * height_col + h_col) * width_col + w_col; + //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const float *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const float *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + const float *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float val = static_cast(0); + const float h_im = h_in + i * dilation_h + offset_h; + const float w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + // data_col_ptr += batch_size * height_col * width_col; + data_col_ptr += height_col * width_col; + } + } + } +} + +__global__ void modulated_deformable_col2im_gpu_kernel(const int n, + const float *data_col, const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + float *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + const float cur_inv_h_data = h_in + i * dilation_h + offset_h; + const float cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const float cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + float weight = dmcn_get_gradient_weight_cuda(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, + const float *data_col, const float *data_im, + const float *data_offset, const float *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + float *grad_offset, float *grad_mask) +{ + CUDA_KERNEL_LOOP(index, n) + { + float val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const float offset_h = data_offset_ptr[data_offset_h_ptr]; + const float offset_w = data_offset_ptr[data_offset_w_ptr]; + const float mask = data_mask_ptr[data_mask_hw_ptr]; + float inv_h = h_in + i * dilation_h + offset_h; + float inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cuda(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const float weight = dmcn_get_coordinate_weight_cuda( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cuda(cudaStream_t stream, + const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + modulated_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +void modulated_deformable_col2im_cuda(cudaStream_t stream, + const float* data_col, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float* grad_im){ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + modulated_deformable_col2im_gpu_kernel + <<>>( + num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, + const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float* grad_offset, float* grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + modulated_deformable_col2im_coord_gpu_kernel + <<>>( + num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset, grad_mask); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + } +} \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_im2col_cuda.h b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_im2col_cuda.h new file mode 100644 index 000000000..c85683198 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_im2col_cuda.h @@ -0,0 +1,101 @@ + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.h + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1811.11168 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu + */ + +/***************** Adapted by Charles Shang *********************/ + +#ifndef DCN_V2_IM2COL_CUDA +#define DCN_V2_IM2COL_CUDA + +#ifdef __cplusplus +extern "C" +{ +#endif + + void modulated_deformable_im2col_cuda(cudaStream_t stream, + const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *data_col); + + void modulated_deformable_col2im_cuda(cudaStream_t stream, + const float *data_col, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, float *grad_im); + + void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, + const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + float *grad_offset, float *grad_mask); + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu new file mode 100644 index 000000000..019021719 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu @@ -0,0 +1,419 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ + +#include +#include +#include +#include + +#include +#include + +#include +#include +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + +template +__device__ T bilinear_interp_cuda( + const T *data, + const T x, + const T y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + T dist_x = static_cast(x - x1); + T dist_y = static_cast(y - y1); + T value11 = data[y1 * width + x1]; + T value12 = data[y2 * width + x1]; + T value21 = data[y1 * width + x2]; + T value22 = data[y2 * width + x2]; + T value = (1 - dist_x) * (1 - dist_y) * value11 + + (1 - dist_x) * dist_y * value12 + + dist_x * (1 - dist_y) * value21 + + dist_x * dist_y * value22; + return value; +} + +template +__global__ void DeformablePSROIPoolForwardKernelCuda( + const int count, + const T *bottom_data, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const T *bottom_rois, const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + T *top_data, + T *top_count) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + T roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + T sum = 0; + int count = 0; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + T val = bilinear_interp_cuda(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? static_cast(0) : sum / count; + top_count[index] = count; + } +} + +template +__global__ void DeformablePSROIPoolBackwardAccKernelCuda( + const int count, + const T *top_diff, + const T *top_count, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + T *bottom_data_diff, T *bottom_trans_diff, + const T *bottom_data, + const T *bottom_rois, + const T *bottom_trans, + const int no_trans, + const T trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const T *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + T roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + T bin_size_h = roi_height / static_cast(pooled_height); + T bin_size_w = roi_width / static_cast(pooled_width); + + T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); + T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); + + int part_h = floor(static_cast(ph) / pooled_height * part_size); + int part_w = floor(static_cast(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; + T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; + + T wstart = static_cast(pw) * bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + T hstart = static_cast(ph) * bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + T diff_val = top_diff[index] / top_count[index]; + const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor(static_cast(pw) * group_size / pooled_width); + int gh = floor(static_cast(ph) * group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + T w = wstart + iw * sub_bin_size_w; + T h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + T dist_x = w - x0, dist_y = h - y0; + T q00 = (1 - dist_x) * (1 - dist_y); + T q01 = (1 - dist_x) * dist_y; + T q10 = dist_x * (1 - dist_y); + T q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val); + + if (no_trans) + { + continue; + } + T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y); + } + } + } +} + +std::tuple +dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.is_cuda(), "rois must be a CUDA tensor"); + AT_ASSERTM(trans.is_cuda(), "trans must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + + auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); + + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (out.numel() == 0) + { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); + } + + dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + + AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "dcn_v2_psroi_pooling_cuda_forward", [&] { + DeformablePSROIPoolForwardKernelCuda<<>>( + out_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, width, + pooled_height, + pooled_width, + bbox.contiguous().data_ptr(), + trans.contiguous().data_ptr(), + no_trans, + trans_std, + sample_per_part, + output_dim, + group_size, + part_size, + num_classes, + channels_each_class, + out.data_ptr(), + top_count.data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(out, top_count); +} + +std::tuple +dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + AT_ASSERTM(out_grad.is_cuda(), "out_grad must be a CUDA tensor"); + AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(bbox.is_cuda(), "bbox must be a CUDA tensor"); + AT_ASSERTM(trans.is_cuda(), "trans must be a CUDA tensor"); + AT_ASSERTM(top_count.is_cuda(), "top_count must be a CUDA tensor"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + const int num_bbox = bbox.size(0); + + AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); + auto pooled_height = pooled_size; + auto pooled_width = pooled_size; + long out_size = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); + auto trans_grad = at::zeros_like(trans); + + if (input_grad.numel() == 0) + { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); + } + + dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); + dim3 block(512); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES(out_grad.scalar_type(), "dcn_v2_psroi_pooling_cuda_backward", [&] { + DeformablePSROIPoolBackwardAccKernelCuda<<>>( + out_size, + out_grad.contiguous().data_ptr(), + top_count.contiguous().data_ptr(), + num_bbox, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + output_dim, + input_grad.contiguous().data_ptr(), + trans_grad.contiguous().data_ptr(), + input.contiguous().data_ptr(), + bbox.contiguous().data_ptr(), + trans.contiguous().data_ptr(), + no_trans, + trans_std, + sample_per_part, + group_size, + part_size, + num_classes, + channels_each_class); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(input_grad, trans_grad); +} diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/vision.h b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/vision.h new file mode 100644 index 000000000..f3672b11e --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/cuda/vision.h @@ -0,0 +1,60 @@ +#pragma once +#include +#include +at::Tensor +dcn_v2_cuda_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group); + +std::vector +dcn_v2_cuda_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group); + + +std::tuple +dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); + +std::tuple +dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std); \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/dcn_v2.h b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/dcn_v2.h new file mode 100644 index 000000000..de670bf99 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/dcn_v2.h @@ -0,0 +1,190 @@ +#pragma once + +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + +at::Tensor +dcn_v2_forward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const int kernel_h, + const int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int deformable_group) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_cuda_forward(input, weight, bias, offset, mask, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_cpu_forward(input, weight, bias, offset, mask, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); + } +} + +std::vector +dcn_v2_backward(const at::Tensor &input, + const at::Tensor &weight, + const at::Tensor &bias, + const at::Tensor &offset, + const at::Tensor &mask, + const at::Tensor &grad_output, + int kernel_h, int kernel_w, + int stride_h, int stride_w, + int pad_h, int pad_w, + int dilation_h, int dilation_w, + int deformable_group) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_cuda_backward(input, + weight, + bias, + offset, + mask, + grad_output, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_cpu_backward(input, + weight, + bias, + offset, + mask, + grad_output, + kernel_h, kernel_w, + stride_h, stride_w, + pad_h, pad_w, + dilation_h, dilation_w, + deformable_group); + } +} + +std::tuple +dcn_v2_psroi_pooling_forward(const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_psroi_pooling_cuda_forward(input, + bbox, + trans, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_psroi_pooling_cpu_forward(input, + bbox, + trans, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); + } +} + +std::tuple +dcn_v2_psroi_pooling_backward(const at::Tensor &out_grad, + const at::Tensor &input, + const at::Tensor &bbox, + const at::Tensor &trans, + const at::Tensor &top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.type().is_cuda()) + { +#ifdef WITH_CUDA + return dcn_v2_psroi_pooling_cuda_backward(out_grad, + input, + bbox, + trans, + top_count, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + else{ + return dcn_v2_psroi_pooling_cpu_backward(out_grad, + input, + bbox, + trans, + top_count, + no_trans, + spatial_scale, + output_dim, + group_size, + pooled_size, + part_size, + sample_per_part, + trans_std); + } +} \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/vision.cpp b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/vision.cpp new file mode 100644 index 000000000..ff54233e0 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/src/vision.cpp @@ -0,0 +1,9 @@ + +#include "dcn_v2.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("dcn_v2_forward", &dcn_v2_forward, "dcn_v2_forward"); + m.def("dcn_v2_backward", &dcn_v2_backward, "dcn_v2_backward"); + m.def("dcn_v2_psroi_pooling_forward", &dcn_v2_psroi_pooling_forward, "dcn_v2_psroi_pooling_forward"); + m.def("dcn_v2_psroi_pooling_backward", &dcn_v2_psroi_pooling_backward, "dcn_v2_psroi_pooling_backward"); +} diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/testcpu.py b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/testcpu.py new file mode 100644 index 000000000..0a0b36eb9 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/testcpu.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from dcn_v2 import dcn_v2_conv, DCNv2, DCN +from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling + +deformable_groups = 1 +N, inC, inH, inW = 2, 2, 4, 4 +outC = 2 +kH, kW = 3, 3 + + +def conv_identify(weight, bias): + weight.data.zero_() + bias.data.zero_() + o, i, h, w = weight.shape + y = h//2 + x = w//2 + for p in range(i): + for q in range(o): + if p == q: + weight.data[q, p, y, x] = 1.0 + + +def check_zero_offset(): + conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True) + + conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True) + + dcn_v2 = DCNv2(inC, outC, (kH, kW), + stride=1, padding=1, dilation=1, + deformable_groups=deformable_groups) + + conv_offset.weight.data.zero_() + conv_offset.bias.data.zero_() + conv_mask.weight.data.zero_() + conv_mask.bias.data.zero_() + conv_identify(dcn_v2.weight, dcn_v2.bias) + + input = torch.randn(N, inC, inH, inW) + offset = conv_offset(input) + mask = conv_mask(input) + mask = torch.sigmoid(mask) + output = dcn_v2(input, offset, mask) + output *= 2 + d = (input - output).abs().max() + if d < 1e-10: + print('Zero offset passed') + else: + print('Zero offset failed') + print(input) + print(output) + +def check_gradient_dconv(): + + input = torch.rand(N, inC, inH, inW) * 0.01 + input.requires_grad = True + + offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW) * 2 + # offset.data.zero_() + # offset.data -= 0.5 + offset.requires_grad = True + + mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW) + # mask.data.zero_() + mask.requires_grad = True + mask = torch.sigmoid(mask) + + weight = torch.randn(outC, inC, kH, kW) + weight.requires_grad = True + + bias = torch.rand(outC) + bias.requires_grad = True + + stride = 1 + padding = 1 + dilation = 1 + + print('check_gradient_dconv: ', + gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups), + eps=1e-3, atol=1e-4, rtol=1e-2)) + + +def check_pooling_zero_offset(): + + input = torch.randn(2, 16, 64, 64).zero_() + input[0, :, 16:26, 16:26] = 1. + input[1, :, 10:20, 20:30] = 2. + rois = torch.tensor([ + [0, 65, 65, 103, 103], + [1, 81, 41, 119, 79], + ]).float() + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=True, + group_size=1, + trans_std=0.0) + + out = pooling(input, rois, input.new()) + s = ', '.join(['%f' % out[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=False, + group_size=1, + trans_std=0.0) + offset = torch.randn(20, 2, 7, 7).zero_() + dout = dpooling(input, rois, offset) + s = ', '.join(['%f' % dout[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + +def check_gradient_dpooling(): + input = torch.randn(2, 3, 5, 5) * 0.01 + N = 4 + batch_inds = torch.randint(2, (N, 1)).float() + x = torch.rand((N, 1)).float() * 15 + y = torch.rand((N, 1)).float() * 15 + w = torch.rand((N, 1)).float() * 10 + h = torch.rand((N, 1)).float() * 10 + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(N, 2, 3, 3) + input.requires_grad = True + offset.requires_grad = True + + spatial_scale = 1.0 / 4 + pooled_size = 3 + output_dim = 3 + no_trans = 0 + group_size = 1 + trans_std = 0.0 + sample_per_part = 4 + part_size = pooled_size + + print('check_gradient_dpooling:', + gradcheck(dcn_v2_pooling, (input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std), + eps=1e-4)) + + +def example_dconv(): + input = torch.randn(2, 64, 128, 128) + # wrap all things (offset and mask) in DCN + dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, + padding=1, deformable_groups=2) + # print(dcn.weight.shape, input.shape) + output = dcn(input) + targert = output.new(*output.size()) + targert.data.uniform_(-0.01, 0.01) + error = (targert - output).mean() + error.backward() + print(output.shape) + + +def example_dpooling(): + input = torch.randn(2, 32, 64, 64) + batch_inds = torch.randint(2, (20, 1)).float() + x = torch.randint(256, (20, 1)).float() + y = torch.randint(256, (20, 1)).float() + w = torch.randint(64, (20, 1)).float() + h = torch.randint(64, (20, 1)).float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(20, 2, 7, 7) + input.requires_grad = True + offset.requires_grad = True + + # normal roi_align + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=True, + group_size=1, + trans_std=0.1) + + # deformable pooling + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1) + + out = pooling(input, rois, offset) + dout = dpooling(input, rois, offset) + print(out.shape) + print(dout.shape) + + target_out = out.new(*out.size()) + target_out.data.uniform_(-0.01, 0.01) + target_dout = dout.new(*dout.size()) + target_dout.data.uniform_(-0.01, 0.01) + e = (target_out - out).mean() + e.backward() + e = (target_dout - dout).mean() + e.backward() + + +def example_mdpooling(): + input = torch.randn(2, 32, 64, 64) + input.requires_grad = True + batch_inds = torch.randint(2, (20, 1)).float() + x = torch.randint(256, (20, 1)).float() + y = torch.randint(256, (20, 1)).float() + w = torch.randint(64, (20, 1)).float() + h = torch.randint(64, (20, 1)).float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + + # mdformable pooling (V2) + dpooling = DCNPooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1, + deform_fc_dim=1024) + + dout = dpooling(input, rois) + target = dout.new(*dout.size()) + target.data.uniform_(-0.1, 0.1) + error = (target - dout).mean() + error.backward() + print(dout.shape) + + +if __name__ == '__main__': + + example_dconv() + example_dpooling() + example_mdpooling() + + check_pooling_zero_offset() + # zero offset check + if inC == outC: + check_zero_offset() + + check_gradient_dpooling() + check_gradient_dconv() + # """ + # ****** Note: backward is not reentrant error may not be a serious problem, + # ****** since the max error is less than 1e-7, + # ****** Still looking for what trigger this problem + # """ diff --git a/cv/instance_segmentation/yolact/pytorch/external/DCNv2/testcuda.py b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/testcuda.py new file mode 100644 index 000000000..3bd5bd223 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/external/DCNv2/testcuda.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from dcn_v2 import dcn_v2_conv, DCNv2, DCN +from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling + +deformable_groups = 1 +N, inC, inH, inW = 2, 2, 4, 4 +outC = 2 +kH, kW = 3, 3 + + +def conv_identify(weight, bias): + weight.data.zero_() + bias.data.zero_() + o, i, h, w = weight.shape + y = h//2 + x = w//2 + for p in range(i): + for q in range(o): + if p == q: + weight.data[q, p, y, x] = 1.0 + + +def check_zero_offset(): + conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True).cuda() + + conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW, + kernel_size=(kH, kW), + stride=(1, 1), + padding=(1, 1), + bias=True).cuda() + + dcn_v2 = DCNv2(inC, outC, (kH, kW), + stride=1, padding=1, dilation=1, + deformable_groups=deformable_groups).cuda() + + conv_offset.weight.data.zero_() + conv_offset.bias.data.zero_() + conv_mask.weight.data.zero_() + conv_mask.bias.data.zero_() + conv_identify(dcn_v2.weight, dcn_v2.bias) + + input = torch.randn(N, inC, inH, inW).cuda() + offset = conv_offset(input) + mask = conv_mask(input) + mask = torch.sigmoid(mask) + output = dcn_v2(input, offset, mask) + output *= 2 + d = (input - output).abs().max() + if d < 1e-10: + print('Zero offset passed') + else: + print('Zero offset failed') + print(input) + print(output) + +def check_gradient_dconv(): + + input = torch.rand(N, inC, inH, inW).cuda() * 0.01 + input.requires_grad = True + + offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW).cuda() * 2 + # offset.data.zero_() + # offset.data -= 0.5 + offset.requires_grad = True + + mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW).cuda() + # mask.data.zero_() + mask.requires_grad = True + mask = torch.sigmoid(mask) + + weight = torch.randn(outC, inC, kH, kW).cuda() + weight.requires_grad = True + + bias = torch.rand(outC).cuda() + bias.requires_grad = True + + stride = 1 + padding = 1 + dilation = 1 + + print('check_gradient_dconv: ', + gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias, + stride, padding, dilation, deformable_groups), + eps=1e-3, atol=1e-4, rtol=1e-2)) + + +def check_pooling_zero_offset(): + + input = torch.randn(2, 16, 64, 64).cuda().zero_() + input[0, :, 16:26, 16:26] = 1. + input[1, :, 10:20, 20:30] = 2. + rois = torch.tensor([ + [0, 65, 65, 103, 103], + [1, 81, 41, 119, 79], + ]).cuda().float() + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=True, + group_size=1, + trans_std=0.0).cuda() + + out = pooling(input, rois, input.new()) + s = ', '.join(['%f' % out[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=16, + no_trans=False, + group_size=1, + trans_std=0.0).cuda() + offset = torch.randn(20, 2, 7, 7).cuda().zero_() + dout = dpooling(input, rois, offset) + s = ', '.join(['%f' % dout[i, :, :, :].mean().item() + for i in range(rois.shape[0])]) + print(s) + + +def check_gradient_dpooling(): + input = torch.randn(2, 3, 5, 5).cuda() * 0.01 + N = 4 + batch_inds = torch.randint(2, (N, 1)).cuda().float() + x = torch.rand((N, 1)).cuda().float() * 15 + y = torch.rand((N, 1)).cuda().float() * 15 + w = torch.rand((N, 1)).cuda().float() * 10 + h = torch.rand((N, 1)).cuda().float() * 10 + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(N, 2, 3, 3).cuda() + input.requires_grad = True + offset.requires_grad = True + + spatial_scale = 1.0 / 4 + pooled_size = 3 + output_dim = 3 + no_trans = 0 + group_size = 1 + trans_std = 0.0 + sample_per_part = 4 + part_size = pooled_size + + print('check_gradient_dpooling:', + gradcheck(dcn_v2_pooling, (input, rois, offset, + spatial_scale, + pooled_size, + output_dim, + no_trans, + group_size, + part_size, + sample_per_part, + trans_std), + eps=1e-4)) + + +def example_dconv(): + input = torch.randn(2, 64, 128, 128).cuda() + # wrap all things (offset and mask) in DCN + dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, + padding=1, deformable_groups=2).cuda() + # print(dcn.weight.shape, input.shape) + output = dcn(input) + targert = output.new(*output.size()) + targert.data.uniform_(-0.01, 0.01) + error = (targert - output).mean() + error.backward() + print(output.shape) + + +def example_dpooling(): + input = torch.randn(2, 32, 64, 64).cuda() + batch_inds = torch.randint(2, (20, 1)).cuda().float() + x = torch.randint(256, (20, 1)).cuda().float() + y = torch.randint(256, (20, 1)).cuda().float() + w = torch.randint(64, (20, 1)).cuda().float() + h = torch.randint(64, (20, 1)).cuda().float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + offset = torch.randn(20, 2, 7, 7).cuda() + input.requires_grad = True + offset.requires_grad = True + + # normal roi_align + pooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=True, + group_size=1, + trans_std=0.1).cuda() + + # deformable pooling + dpooling = DCNv2Pooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1).cuda() + + out = pooling(input, rois, offset) + dout = dpooling(input, rois, offset) + print(out.shape) + print(dout.shape) + + target_out = out.new(*out.size()) + target_out.data.uniform_(-0.01, 0.01) + target_dout = dout.new(*dout.size()) + target_dout.data.uniform_(-0.01, 0.01) + e = (target_out - out).mean() + e.backward() + e = (target_dout - dout).mean() + e.backward() + + +def example_mdpooling(): + input = torch.randn(2, 32, 64, 64).cuda() + input.requires_grad = True + batch_inds = torch.randint(2, (20, 1)).cuda().float() + x = torch.randint(256, (20, 1)).cuda().float() + y = torch.randint(256, (20, 1)).cuda().float() + w = torch.randint(64, (20, 1)).cuda().float() + h = torch.randint(64, (20, 1)).cuda().float() + rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) + + # mdformable pooling (V2) + dpooling = DCNPooling(spatial_scale=1.0 / 4, + pooled_size=7, + output_dim=32, + no_trans=False, + group_size=1, + trans_std=0.1, + deform_fc_dim=1024).cuda() + + dout = dpooling(input, rois) + target = dout.new(*dout.size()) + target.data.uniform_(-0.1, 0.1) + error = (target - dout).mean() + error.backward() + print(dout.shape) + + +if __name__ == '__main__': + + example_dconv() + example_dpooling() + example_mdpooling() + + check_pooling_zero_offset() + # zero offset check + if inC == outC: + check_zero_offset() + + check_gradient_dpooling() + check_gradient_dconv() + # """ + # ****** Note: backward is not reentrant error may not be a serious problem, + # ****** since the max error is less than 1e-7, + # ****** Still looking for what trigger this problem + # """ diff --git a/cv/instance_segmentation/yolact/pytorch/layers/__init__.py b/cv/instance_segmentation/yolact/pytorch/layers/__init__.py new file mode 100644 index 000000000..53a3f4b51 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/__init__.py @@ -0,0 +1,2 @@ +from .functions import * +from .modules import * diff --git a/cv/instance_segmentation/yolact/pytorch/layers/box_utils.py b/cv/instance_segmentation/yolact/pytorch/layers/box_utils.py new file mode 100644 index 000000000..d792f06a0 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/box_utils.py @@ -0,0 +1,389 @@ +# -*- coding: utf-8 -*- +import torch +from utils import timer + +from data import cfg + +@torch.jit.script +def point_form(boxes): + """ Convert prior_boxes to (xmin, ymin, xmax, ymax) + representation for comparison to point form ground truth data. + Args: + boxes: (tensor) center-size default boxes from priorbox layers. + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat((boxes[:, :2] - boxes[:, 2:]/2, # xmin, ymin + boxes[:, :2] + boxes[:, 2:]/2), 1) # xmax, ymax + + +@torch.jit.script +def center_size(boxes): + """ Convert prior_boxes to (cx, cy, w, h) + representation for comparison to center-size form ground truth data. + Args: + boxes: (tensor) point_form boxes + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat(( (boxes[:, 2:] + boxes[:, :2])/2, # cx, cy + boxes[:, 2:] - boxes[:, :2] ), 1) # w, h + +@torch.jit.script +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [n,A,4]. + box_b: (tensor) bounding boxes, Shape: [n,B,4]. + Return: + (tensor) intersection area, Shape: [n,A,B]. + """ + n = box_a.size(0) + A = box_a.size(1) + B = box_b.size(1) + max_xy = torch.min(box_a[:, :, 2:].unsqueeze(2).expand(n, A, B, 2), + box_b[:, :, 2:].unsqueeze(1).expand(n, A, B, 2)) + min_xy = torch.max(box_a[:, :, :2].unsqueeze(2).expand(n, A, B, 2), + box_b[:, :, :2].unsqueeze(1).expand(n, A, B, 2)) + return torch.clamp(max_xy - min_xy, min=0).prod(3) # inter + + +def jaccard(box_a, box_b, iscrowd:bool=False): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. If iscrowd=True, put the crowd in box_b. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + use_batch = True + if box_a.dim() == 2: + use_batch = False + box_a = box_a[None, ...] + box_b = box_b[None, ...] + + inter = intersect(box_a, box_b) + area_a = ((box_a[:, :, 2]-box_a[:, :, 0]) * + (box_a[:, :, 3]-box_a[:, :, 1])).unsqueeze(2).expand_as(inter) # [A,B] + area_b = ((box_b[:, :, 2]-box_b[:, :, 0]) * + (box_b[:, :, 3]-box_b[:, :, 1])).unsqueeze(1).expand_as(inter) # [A,B] + union = area_a + area_b - inter + + out = inter / area_a if iscrowd else inter / union + return out if use_batch else out.squeeze(0) + +def elemwise_box_iou(box_a, box_b): + """ Does the same as above but instead of pairwise, elementwise along the inner dimension. """ + max_xy = torch.min(box_a[:, 2:], box_b[:, 2:]) + min_xy = torch.max(box_a[:, :2], box_b[:, :2]) + inter = torch.clamp((max_xy - min_xy), min=0) + inter = inter[:, 0] * inter[:, 1] + + area_a = (box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1]) + area_b = (box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1]) + + union = area_a + area_b - inter + union = torch.clamp(union, min=0.1) + + # Return value is [n] for inputs [n, 4] + return torch.clamp(inter / union, max=1) + +def mask_iou(masks_a, masks_b, iscrowd=False): + """ + Computes the pariwise mask IoU between two sets of masks of size [a, h, w] and [b, h, w]. + The output is of size [a, b]. + + Wait I thought this was "box_utils", why am I putting this in here? + """ + + masks_a = masks_a.view(masks_a.size(0), -1) + masks_b = masks_b.view(masks_b.size(0), -1) + + intersection = masks_a @ masks_b.t() + area_a = masks_a.sum(dim=1).unsqueeze(1) + area_b = masks_b.sum(dim=1).unsqueeze(0) + + return intersection / (area_a + area_b - intersection) if not iscrowd else intersection / area_a + +def elemwise_mask_iou(masks_a, masks_b): + """ Does the same as above but instead of pairwise, elementwise along the outer dimension. """ + masks_a = masks_a.view(-1, masks_a.size(-1)) + masks_b = masks_b.view(-1, masks_b.size(-1)) + + intersection = (masks_a * masks_b).sum(dim=0) + area_a = masks_a.sum(dim=0) + area_b = masks_b.sum(dim=0) + + # Return value is [n] for inputs [h, w, n] + return torch.clamp(intersection / torch.clamp(area_a + area_b - intersection, min=0.1), max=1) + + + +def change(gt, priors): + """ + Compute the d_change metric proposed in Box2Pix: + https://lmb.informatik.uni-freiburg.de/Publications/2018/UB18/paper-box2pix.pdf + + Input should be in point form (xmin, ymin, xmax, ymax). + + Output is of shape [num_gt, num_priors] + Note this returns -change so it can be a drop in replacement for + """ + num_priors = priors.size(0) + num_gt = gt.size(0) + + gt_w = (gt[:, 2] - gt[:, 0])[:, None].expand(num_gt, num_priors) + gt_h = (gt[:, 3] - gt[:, 1])[:, None].expand(num_gt, num_priors) + + gt_mat = gt[:, None, :].expand(num_gt, num_priors, 4) + pr_mat = priors[None, :, :].expand(num_gt, num_priors, 4) + + diff = gt_mat - pr_mat + diff[:, :, 0] /= gt_w + diff[:, :, 2] /= gt_w + diff[:, :, 1] /= gt_h + diff[:, :, 3] /= gt_h + + return -torch.sqrt( (diff ** 2).sum(dim=2) ) + + + + +def match(pos_thresh, neg_thresh, truths, priors, labels, crowd_boxes, loc_t, conf_t, idx_t, idx, loc_data): + """Match each prior box with the ground truth box of the highest jaccard + overlap, encode the bounding boxes, then return the matched indices + corresponding to both confidence and location preds. + Args: + pos_thresh: (float) IoU > pos_thresh ==> positive. + neg_thresh: (float) IoU < neg_thresh ==> negative. + truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors]. + priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4]. + labels: (tensor) All the class labels for the image, Shape: [num_obj]. + crowd_boxes: (tensor) All the crowd box annotations or None if there are none. + loc_t: (tensor) Tensor to be filled w/ endcoded location targets. + conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. Note: -1 means neutral. + idx_t: (tensor) Tensor to be filled w/ the index of the matched gt box for each prior. + idx: (int) current batch index. + loc_data: (tensor) The predicted bbox regression coordinates for this batch. + Return: + The matched indices corresponding to 1)location and 2)confidence preds. + """ + decoded_priors = decode(loc_data, priors, cfg.use_yolo_regressors) if cfg.use_prediction_matching else point_form(priors) + + # Size [num_objects, num_priors] + overlaps = jaccard(truths, decoded_priors) if not cfg.use_change_matching else change(truths, decoded_priors) + + # Size [num_priors] best ground truth for each prior + best_truth_overlap, best_truth_idx = overlaps.max(0) + + # We want to ensure that each gt gets used at least once so that we don't + # waste any training data. In order to do that, find the max overlap anchor + # with each gt, and force that anchor to use that gt. + for _ in range(overlaps.size(0)): + # Find j, the gt with the highest overlap with a prior + # In effect, this will loop through overlaps.size(0) in a "smart" order, + # always choosing the highest overlap first. + best_prior_overlap, best_prior_idx = overlaps.max(1) + j = best_prior_overlap.max(0)[1] + + # Find i, the highest overlap anchor with this gt + i = best_prior_idx[j] + + # Set all other overlaps with i to be -1 so that no other gt uses it + overlaps[:, i] = -1 + # Set all other overlaps with j to be -1 so that this loop never uses j again + overlaps[j, :] = -1 + + # Overwrite i's score to be 2 so it doesn't get thresholded ever + best_truth_overlap[i] = 2 + # Set the gt to be used for i to be j, overwriting whatever was there + best_truth_idx[i] = j + + matches = truths[best_truth_idx] # Shape: [num_priors,4] + conf = labels[best_truth_idx] + 1 # Shape: [num_priors] + + conf[best_truth_overlap < pos_thresh] = -1 # label as neutral + conf[best_truth_overlap < neg_thresh] = 0 # label as background + + # Deal with crowd annotations for COCO + if crowd_boxes is not None and cfg.crowd_iou_threshold < 1: + # Size [num_priors, num_crowds] + crowd_overlaps = jaccard(decoded_priors, crowd_boxes, iscrowd=True) + # Size [num_priors] + best_crowd_overlap, best_crowd_idx = crowd_overlaps.max(1) + # Set non-positives with crowd iou of over the threshold to be neutral. + conf[(conf <= 0) & (best_crowd_overlap > cfg.crowd_iou_threshold)] = -1 + + loc = encode(matches, priors, cfg.use_yolo_regressors) + loc_t[idx] = loc # [num_priors,4] encoded offsets to learn + conf_t[idx] = conf # [num_priors] top class label for each prior + idx_t[idx] = best_truth_idx # [num_priors] indices for lookup + +@torch.jit.script +def encode(matched, priors, use_yolo_regressors:bool=False): + """ + Encode bboxes matched with each prior into the format + produced by the network. See decode for more details on + this format. Note that encode(decode(x, p), p) = x. + + Args: + - matched: A tensor of bboxes in point form with shape [num_priors, 4] + - priors: The tensor of all priors with shape [num_priors, 4] + Return: A tensor with encoded relative coordinates in the format + outputted by the network (see decode). Size: [num_priors, 4] + """ + + if use_yolo_regressors: + # Exactly the reverse of what we did in decode + # In fact encode(decode(x, p), p) should be x + boxes = center_size(matched) + + loc = torch.cat(( + boxes[:, :2] - priors[:, :2], + torch.log(boxes[:, 2:] / priors[:, 2:]) + ), 1) + else: + variances = [0.1, 0.2] + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + loc = torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + return loc + +@torch.jit.script +def decode(loc, priors, use_yolo_regressors:bool=False): + """ + Decode predicted bbox coordinates using the same scheme + employed by Yolov2: https://arxiv.org/pdf/1612.08242.pdf + + b_x = (sigmoid(pred_x) - .5) / conv_w + prior_x + b_y = (sigmoid(pred_y) - .5) / conv_h + prior_y + b_w = prior_w * exp(loc_w) + b_h = prior_h * exp(loc_h) + + Note that loc is inputed as [(s(x)-.5)/conv_w, (s(y)-.5)/conv_h, w, h] + while priors are inputed as [x, y, w, h] where each coordinate + is relative to size of the image (even sigmoid(x)). We do this + in the network by dividing by the 'cell size', which is just + the size of the convouts. + + Also note that prior_x and prior_y are center coordinates which + is why we have to subtract .5 from sigmoid(pred_x and pred_y). + + Args: + - loc: The predicted bounding boxes of size [num_priors, 4] + - priors: The priorbox coords with size [num_priors, 4] + + Returns: A tensor of decoded relative coordinates in point form + form with size [num_priors, 4] + """ + + if use_yolo_regressors: + # Decoded boxes in center-size notation + boxes = torch.cat(( + loc[:, :2] + priors[:, :2], + priors[:, 2:] * torch.exp(loc[:, 2:]) + ), 1) + + boxes = point_form(boxes) + else: + variances = [0.1, 0.2] + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + + return boxes + + + +def log_sum_exp(x): + """Utility function for computing log_sum_exp while determining + This will be used to determine unaveraged confidence loss across + all examples in a batch. + Args: + x (Variable(tensor)): conf_preds from conf layers + """ + x_max = x.data.max() + return torch.log(torch.sum(torch.exp(x-x_max), 1)) + x_max + + +@torch.jit.script +def sanitize_coordinates(_x1, _x2, img_size:int, padding:int=0, cast:bool=True): + """ + Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, and x2 <= image_size. + Also converts from relative to absolute coordinates and casts the results to long tensors. + + If cast is false, the result won't be cast to longs. + Warning: this does things in-place behind the scenes so copy if necessary. + """ + _x1 = _x1 * img_size + _x2 = _x2 * img_size + if cast: + _x1 = _x1.long() + _x2 = _x2.long() + x1 = torch.min(_x1, _x2) + x2 = torch.max(_x1, _x2) + x1 = torch.clamp(x1-padding, min=0) + x2 = torch.clamp(x2+padding, max=img_size) + + return x1, x2 + + +@torch.jit.script +def crop(masks, boxes, padding:int=1): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. + Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [h, w, n] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + h, w, n = masks.size() + x1, x2 = sanitize_coordinates(boxes[:, 0], boxes[:, 2], w, padding, cast=False) + y1, y2 = sanitize_coordinates(boxes[:, 1], boxes[:, 3], h, padding, cast=False) + + rows = torch.arange(w, device=masks.device, dtype=x1.dtype).view(1, -1, 1).expand(h, w, n) + cols = torch.arange(h, device=masks.device, dtype=x1.dtype).view(-1, 1, 1).expand(h, w, n) + + masks_left = rows >= x1.view(1, 1, -1) + masks_right = rows < x2.view(1, 1, -1) + masks_up = cols >= y1.view(1, 1, -1) + masks_down = cols < y2.view(1, 1, -1) + + crop_mask = masks_left * masks_right * masks_up * masks_down + + return masks * crop_mask.float() + + +def index2d(src, idx): + """ + Indexes a tensor by a 2d index. + + In effect, this does + out[i, j] = src[i, idx[i, j]] + + Both src and idx should have the same size. + """ + + offs = torch.arange(idx.size(0), device=idx.device)[:, None].expand_as(idx) + idx = idx + offs * idx.size(1) + + return src.view(-1)[idx.view(-1)].view(idx.size()) diff --git a/cv/instance_segmentation/yolact/pytorch/layers/functions/__init__.py b/cv/instance_segmentation/yolact/pytorch/layers/functions/__init__.py new file mode 100644 index 000000000..56ef07f46 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/functions/__init__.py @@ -0,0 +1,4 @@ +from .detection import Detect + + +__all__ = ['Detect'] diff --git a/cv/instance_segmentation/yolact/pytorch/layers/functions/detection.py b/cv/instance_segmentation/yolact/pytorch/layers/functions/detection.py new file mode 100644 index 000000000..4e5fd068c --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/functions/detection.py @@ -0,0 +1,228 @@ +import torch +import torch.nn.functional as F +from ..box_utils import decode, jaccard, index2d +from utils import timer + +from data import cfg, mask_type + +import numpy as np + + +class Detect(object): + """At test time, Detect is the final layer of SSD. Decode location preds, + apply non-maximum suppression to location predictions based on conf + scores and threshold to a top_k number of output predictions for both + confidence score and locations, as the predicted masks. + """ + # TODO: Refactor this whole class away. It needs to go. + + def __init__(self, num_classes, bkg_label, top_k, conf_thresh, nms_thresh): + self.num_classes = num_classes + self.background_label = bkg_label + self.top_k = top_k + # Parameters used in nms. + self.nms_thresh = nms_thresh + if nms_thresh <= 0: + raise ValueError('nms_threshold must be non negative.') + self.conf_thresh = conf_thresh + + self.use_cross_class_nms = False + self.use_fast_nms = False + + def __call__(self, predictions, net): + """ + Args: + loc_data: (tensor) Loc preds from loc layers + Shape: [batch, num_priors, 4] + conf_data: (tensor) Shape: Conf preds from conf layers + Shape: [batch, num_priors, num_classes] + mask_data: (tensor) Mask preds from mask layers + Shape: [batch, num_priors, mask_dim] + prior_data: (tensor) Prior boxes and variances from priorbox layers + Shape: [num_priors, 4] + proto_data: (tensor) If using mask_type.lincomb, the prototype masks + Shape: [batch, mask_h, mask_w, mask_dim] + + Returns: + output of shape (batch_size, top_k, 1 + 1 + 4 + mask_dim) + These outputs are in the order: class idx, confidence, bbox coords, and mask. + + Note that the outputs are sorted only if cross_class_nms is False + """ + + loc_data = predictions['loc'] + conf_data = predictions['conf'] + mask_data = predictions['mask'] + prior_data = predictions['priors'] + + proto_data = predictions['proto'] if 'proto' in predictions else None + inst_data = predictions['inst'] if 'inst' in predictions else None + + out = [] + + with timer.env('Detect'): + batch_size = loc_data.size(0) + num_priors = prior_data.size(0) + + conf_preds = conf_data.view(batch_size, num_priors, self.num_classes).transpose(2, 1).contiguous() + + for batch_idx in range(batch_size): + decoded_boxes = decode(loc_data[batch_idx], prior_data) + result = self.detect(batch_idx, conf_preds, decoded_boxes, mask_data, inst_data) + + if result is not None and proto_data is not None: + result['proto'] = proto_data[batch_idx] + + out.append({'detection': result, 'net': net}) + + return out + + + def detect(self, batch_idx, conf_preds, decoded_boxes, mask_data, inst_data): + """ Perform nms for only the max scoring class that isn't background (class 0) """ + cur_scores = conf_preds[batch_idx, 1:, :] + conf_scores, _ = torch.max(cur_scores, dim=0) + + keep = (conf_scores > self.conf_thresh) + scores = cur_scores[:, keep] + boxes = decoded_boxes[keep, :] + masks = mask_data[batch_idx, keep, :] + + if inst_data is not None: + inst = inst_data[batch_idx, keep, :] + + if scores.size(1) == 0: + return None + + if self.use_fast_nms: + if self.use_cross_class_nms: + boxes, masks, classes, scores = self.cc_fast_nms(boxes, masks, scores, self.nms_thresh, self.top_k) + else: + boxes, masks, classes, scores = self.fast_nms(boxes, masks, scores, self.nms_thresh, self.top_k) + else: + boxes, masks, classes, scores = self.traditional_nms(boxes, masks, scores, self.nms_thresh, self.conf_thresh) + + if self.use_cross_class_nms: + print('Warning: Cross Class Traditional NMS is not implemented.') + + return {'box': boxes, 'mask': masks, 'class': classes, 'score': scores} + + + def cc_fast_nms(self, boxes, masks, scores, iou_threshold:float=0.5, top_k:int=200): + # Collapse all the classes into 1 + scores, classes = scores.max(dim=0) + + _, idx = scores.sort(0, descending=True) + idx = idx[:top_k] + + boxes_idx = boxes[idx] + + # Compute the pairwise IoU between the boxes + iou = jaccard(boxes_idx, boxes_idx) + + # Zero out the lower triangle of the cosine similarity matrix and diagonal + iou.triu_(diagonal=1) + + # Now that everything in the diagonal and below is zeroed out, if we take the max + # of the IoU matrix along the columns, each column will represent the maximum IoU + # between this element and every element with a higher score than this element. + iou_max, _ = torch.max(iou, dim=0) + + # Now just filter out the ones greater than the threshold, i.e., only keep boxes that + # don't have a higher scoring box that would supress it in normal NMS. + idx_out = idx[iou_max <= iou_threshold] + + return boxes[idx_out], masks[idx_out], classes[idx_out], scores[idx_out] + + def fast_nms(self, boxes, masks, scores, iou_threshold:float=0.5, top_k:int=200, second_threshold:bool=False): + scores, idx = scores.sort(1, descending=True) + + idx = idx[:, :top_k].contiguous() + scores = scores[:, :top_k] + + num_classes, num_dets = idx.size() + + boxes = boxes[idx.view(-1), :].view(num_classes, num_dets, 4) + masks = masks[idx.view(-1), :].view(num_classes, num_dets, -1) + + iou = jaccard(boxes, boxes) + iou.triu_(diagonal=1) + iou_max, _ = iou.max(dim=1) + + # Now just filter out the ones higher than the threshold + keep = (iou_max <= iou_threshold) + + # We should also only keep detections over the confidence threshold, but at the cost of + # maxing out your detection count for every image, you can just not do that. Because we + # have such a minimal amount of computation per detection (matrix mulitplication only), + # this increase doesn't affect us much (+0.2 mAP for 34 -> 33 fps), so we leave it out. + # However, when you implement this in your method, you should do this second threshold. + if second_threshold: + keep *= (scores > self.conf_thresh) + + # Assign each kept detection to its corresponding class + classes = torch.arange(num_classes, device=boxes.device)[:, None].expand_as(keep) + classes = classes[keep] + + boxes = boxes[keep] + masks = masks[keep] + scores = scores[keep] + + # Only keep the top cfg.max_num_detections highest scores across all classes + scores, idx = scores.sort(0, descending=True) + idx = idx[:cfg.max_num_detections] + scores = scores[:cfg.max_num_detections] + + classes = classes[idx] + boxes = boxes[idx] + masks = masks[idx] + + return boxes, masks, classes, scores + + def traditional_nms(self, boxes, masks, scores, iou_threshold=0.5, conf_thresh=0.05): + import pyximport + pyximport.install(setup_args={"include_dirs":np.get_include()}, reload_support=True) + + from utils.cython_nms import nms as cnms + + num_classes = scores.size(0) + + idx_lst = [] + cls_lst = [] + scr_lst = [] + + # Multiplying by max_size is necessary because of how cnms computes its area and intersections + boxes = boxes * cfg.max_size + + for _cls in range(num_classes): + cls_scores = scores[_cls, :] + conf_mask = cls_scores > conf_thresh + idx = torch.arange(cls_scores.size(0), device=boxes.device) + + cls_scores = cls_scores[conf_mask] + idx = idx[conf_mask] + + if cls_scores.size(0) == 0: + continue + + preds = torch.cat([boxes[conf_mask], cls_scores[:, None]], dim=1).cpu().numpy() + keep = cnms(preds, iou_threshold) + keep = torch.Tensor(keep, device=boxes.device).long() + + idx_lst.append(idx[keep]) + cls_lst.append(keep * 0 + _cls) + scr_lst.append(cls_scores[keep]) + + idx = torch.cat(idx_lst, dim=0) + classes = torch.cat(cls_lst, dim=0) + scores = torch.cat(scr_lst, dim=0) + + scores, idx2 = scores.sort(0, descending=True) + idx2 = idx2[:cfg.max_num_detections] + scores = scores[:cfg.max_num_detections] + + idx = idx[idx2] + classes = classes[idx2] + + # Undo the multiplication above + return boxes[idx] / cfg.max_size, masks[idx], classes, scores diff --git a/cv/instance_segmentation/yolact/pytorch/layers/interpolate.py b/cv/instance_segmentation/yolact/pytorch/layers/interpolate.py new file mode 100644 index 000000000..71419a80c --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/interpolate.py @@ -0,0 +1,17 @@ +import torch.nn as nn +import torch.nn.functional as F + +class InterpolateModule(nn.Module): + """ + This is a module version of F.interpolate (rip nn.Upsampling). + Any arguments you give it just get passed along for the ride. + """ + + def __init__(self, *args, **kwdargs): + super().__init__() + + self.args = args + self.kwdargs = kwdargs + + def forward(self, x): + return F.interpolate(x, *self.args, **self.kwdargs) diff --git a/cv/instance_segmentation/yolact/pytorch/layers/modules/__init__.py b/cv/instance_segmentation/yolact/pytorch/layers/modules/__init__.py new file mode 100644 index 000000000..cf24bddbf --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/modules/__init__.py @@ -0,0 +1,3 @@ +from .multibox_loss import MultiBoxLoss + +__all__ = ['MultiBoxLoss'] diff --git a/cv/instance_segmentation/yolact/pytorch/layers/modules/multibox_loss.py b/cv/instance_segmentation/yolact/pytorch/layers/modules/multibox_loss.py new file mode 100644 index 000000000..ddf3904ce --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/modules/multibox_loss.py @@ -0,0 +1,694 @@ +# -*- coding: utf-8 -*- +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable +from ..box_utils import match, log_sum_exp, decode, center_size, crop, elemwise_mask_iou, elemwise_box_iou + +from data import cfg, mask_type, activation_func + +class MultiBoxLoss(nn.Module): + """SSD Weighted Loss Function + Compute Targets: + 1) Produce Confidence Target Indices by matching ground truth boxes + with (default) 'priorboxes' that have jaccard index > threshold parameter + (default threshold: 0.5). + 2) Produce localization target by 'encoding' variance into offsets of ground + truth boxes and their matched 'priorboxes'. + 3) Hard negative mining to filter the excessive number of negative examples + that comes with using a large number of default bounding boxes. + (default negative:positive ratio 3:1) + Objective Loss: + L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N + Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss + weighted by α which is set to 1 by cross val. + Args: + c: class confidences, + l: predicted boxes, + g: ground truth boxes + N: number of matched default boxes + See: https://arxiv.org/pdf/1512.02325.pdf for more details. + """ + + def __init__(self, num_classes, pos_threshold, neg_threshold, negpos_ratio): + super(MultiBoxLoss, self).__init__() + self.num_classes = num_classes + + self.pos_threshold = pos_threshold + self.neg_threshold = neg_threshold + self.negpos_ratio = negpos_ratio + + # If you output a proto mask with this area, your l1 loss will be l1_alpha + # Note that the area is relative (so 1 would be the entire image) + self.l1_expected_area = 20*20/70/70 + self.l1_alpha = 0.1 + + if cfg.use_class_balanced_conf: + self.class_instances = None + self.total_instances = 0 + + def forward(self, net, predictions, targets, masks, num_crowds): + """Multibox Loss + Args: + predictions (tuple): A tuple containing loc preds, conf preds, + mask preds, and prior boxes from SSD net. + loc shape: torch.size(batch_size,num_priors,4) + conf shape: torch.size(batch_size,num_priors,num_classes) + masks shape: torch.size(batch_size,num_priors,mask_dim) + priors shape: torch.size(num_priors,4) + proto* shape: torch.size(batch_size,mask_h,mask_w,mask_dim) + + targets (list): Ground truth boxes and labels for a batch, + shape: [batch_size][num_objs,5] (last idx is the label). + + masks (list): Ground truth masks for each object in each image, + shape: [batch_size][num_objs,im_height,im_width] + + num_crowds (list): Number of crowd annotations per batch. The crowd + annotations should be the last num_crowds elements of targets and masks. + + * Only if mask_type == lincomb + """ + + loc_data = predictions['loc'] + conf_data = predictions['conf'] + mask_data = predictions['mask'] + priors = predictions['priors'] + + if cfg.mask_type == mask_type.lincomb: + proto_data = predictions['proto'] + + score_data = predictions['score'] if cfg.use_mask_scoring else None + inst_data = predictions['inst'] if cfg.use_instance_coeff else None + + labels = [None] * len(targets) # Used in sem segm loss + + batch_size = loc_data.size(0) + num_priors = priors.size(0) + num_classes = self.num_classes + + # Match priors (default boxes) and ground truth boxes + # These tensors will be created with the same device as loc_data + loc_t = loc_data.new(batch_size, num_priors, 4) + gt_box_t = loc_data.new(batch_size, num_priors, 4) + conf_t = loc_data.new(batch_size, num_priors).long() + idx_t = loc_data.new(batch_size, num_priors).long() + + if cfg.use_class_existence_loss: + class_existence_t = loc_data.new(batch_size, num_classes-1) + + for idx in range(batch_size): + truths = targets[idx][:, :-1].data + labels[idx] = targets[idx][:, -1].data.long() + + if cfg.use_class_existence_loss: + # Construct a one-hot vector for each object and collapse it into an existence vector with max + # Also it's fine to include the crowd annotations here + class_existence_t[idx, :] = torch.eye(num_classes-1, device=conf_t.get_device())[labels[idx]].max(dim=0)[0] + + # Split the crowd annotations because they come bundled in + cur_crowds = num_crowds[idx] + if cur_crowds > 0: + split = lambda x: (x[-cur_crowds:], x[:-cur_crowds]) + crowd_boxes, truths = split(truths) + + # We don't use the crowd labels or masks + _, labels[idx] = split(labels[idx]) + _, masks[idx] = split(masks[idx]) + else: + crowd_boxes = None + + + match(self.pos_threshold, self.neg_threshold, + truths, priors.data, labels[idx], crowd_boxes, + loc_t, conf_t, idx_t, idx, loc_data[idx]) + + gt_box_t[idx, :, :] = truths[idx_t[idx]] + + # wrap targets + loc_t = Variable(loc_t, requires_grad=False) + conf_t = Variable(conf_t, requires_grad=False) + idx_t = Variable(idx_t, requires_grad=False) + + pos = conf_t > 0 + num_pos = pos.sum(dim=1, keepdim=True) + + # Shape: [batch,num_priors,4] + pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data) + + losses = {} + + # Localization Loss (Smooth L1) + if cfg.train_boxes: + loc_p = loc_data[pos_idx].view(-1, 4) + loc_t = loc_t[pos_idx].view(-1, 4) + losses['B'] = F.smooth_l1_loss(loc_p, loc_t, reduction='sum') * cfg.bbox_alpha + + if cfg.train_masks: + if cfg.mask_type == mask_type.direct: + if cfg.use_gt_bboxes: + pos_masks = [] + for idx in range(batch_size): + pos_masks.append(masks[idx][idx_t[idx, pos[idx]]]) + masks_t = torch.cat(pos_masks, 0) + masks_p = mask_data[pos, :].view(-1, cfg.mask_dim) + losses['M'] = F.binary_cross_entropy(torch.clamp(masks_p, 0, 1), masks_t, reduction='sum') * cfg.mask_alpha + else: + losses['M'] = self.direct_mask_loss(pos_idx, idx_t, loc_data, mask_data, priors, masks) + elif cfg.mask_type == mask_type.lincomb: + ret = self.lincomb_mask_loss(pos, idx_t, loc_data, mask_data, priors, proto_data, masks, gt_box_t, score_data, inst_data, labels) + if cfg.use_maskiou: + loss, maskiou_targets = ret + else: + loss = ret + losses.update(loss) + + if cfg.mask_proto_loss is not None: + if cfg.mask_proto_loss == 'l1': + losses['P'] = torch.mean(torch.abs(proto_data)) / self.l1_expected_area * self.l1_alpha + elif cfg.mask_proto_loss == 'disj': + losses['P'] = -torch.mean(torch.max(F.log_softmax(proto_data, dim=-1), dim=-1)[0]) + + # Confidence loss + if cfg.use_focal_loss: + if cfg.use_sigmoid_focal_loss: + losses['C'] = self.focal_conf_sigmoid_loss(conf_data, conf_t) + elif cfg.use_objectness_score: + losses['C'] = self.focal_conf_objectness_loss(conf_data, conf_t) + else: + losses['C'] = self.focal_conf_loss(conf_data, conf_t) + else: + if cfg.use_objectness_score: + losses['C'] = self.conf_objectness_loss(conf_data, conf_t, batch_size, loc_p, loc_t, priors) + else: + losses['C'] = self.ohem_conf_loss(conf_data, conf_t, pos, batch_size) + + # Mask IoU Loss + if cfg.use_maskiou and maskiou_targets is not None: + losses['I'] = self.mask_iou_loss(net, maskiou_targets) + + # These losses also don't depend on anchors + if cfg.use_class_existence_loss: + losses['E'] = self.class_existence_loss(predictions['classes'], class_existence_t) + if cfg.use_semantic_segmentation_loss: + losses['S'] = self.semantic_segmentation_loss(predictions['segm'], masks, labels) + + # Divide all losses by the number of positives. + # Don't do it for loss[P] because that doesn't depend on the anchors. + total_num_pos = num_pos.data.sum().float() + for k in losses: + if k not in ('P', 'E', 'S'): + losses[k] /= total_num_pos + else: + losses[k] /= batch_size + + # Loss Key: + # - B: Box Localization Loss + # - C: Class Confidence Loss + # - M: Mask Loss + # - P: Prototype Loss + # - D: Coefficient Diversity Loss + # - E: Class Existence Loss + # - S: Semantic Segmentation Loss + return losses + + def class_existence_loss(self, class_data, class_existence_t): + return cfg.class_existence_alpha * F.binary_cross_entropy_with_logits(class_data, class_existence_t, reduction='sum') + + def semantic_segmentation_loss(self, segment_data, mask_t, class_t, interpolation_mode='bilinear'): + # Note num_classes here is without the background class so cfg.num_classes-1 + batch_size, num_classes, mask_h, mask_w = segment_data.size() + loss_s = 0 + + for idx in range(batch_size): + cur_segment = segment_data[idx] + cur_class_t = class_t[idx] + + with torch.no_grad(): + downsampled_masks = F.interpolate(mask_t[idx].unsqueeze(0), (mask_h, mask_w), + mode=interpolation_mode, align_corners=False).squeeze(0) + downsampled_masks = downsampled_masks.gt(0.5).float() + + # Construct Semantic Segmentation + segment_t = torch.zeros_like(cur_segment, requires_grad=False) + for obj_idx in range(downsampled_masks.size(0)): + segment_t[cur_class_t[obj_idx]] = torch.max(segment_t[cur_class_t[obj_idx]], downsampled_masks[obj_idx]) + + loss_s += F.binary_cross_entropy_with_logits(cur_segment, segment_t, reduction='sum') + + return loss_s / mask_h / mask_w * cfg.semantic_segmentation_alpha + + + def ohem_conf_loss(self, conf_data, conf_t, pos, num): + # Compute max conf across batch for hard negative mining + batch_conf = conf_data.view(-1, self.num_classes) + if cfg.ohem_use_most_confident: + # i.e. max(softmax) along classes > 0 + batch_conf = F.softmax(batch_conf, dim=1) + loss_c, _ = batch_conf[:, 1:].max(dim=1) + else: + # i.e. -softmax(class 0 confidence) + loss_c = log_sum_exp(batch_conf) - batch_conf[:, 0] + + # Hard Negative Mining + loss_c = loss_c.view(num, -1) + loss_c[pos] = 0 # filter out pos boxes + loss_c[conf_t < 0] = 0 # filter out neutrals (conf_t = -1) + _, loss_idx = loss_c.sort(1, descending=True) + _, idx_rank = loss_idx.sort(1) + num_pos = pos.long().sum(1, keepdim=True) + num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1) + neg = idx_rank < num_neg.expand_as(idx_rank) + + # Just in case there aren't enough negatives, don't start using positives as negatives + neg[pos] = 0 + neg[conf_t < 0] = 0 # Filter out neutrals + + # Confidence Loss Including Positive and Negative Examples + pos_idx = pos.unsqueeze(2).expand_as(conf_data) + neg_idx = neg.unsqueeze(2).expand_as(conf_data) + conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1, self.num_classes) + targets_weighted = conf_t[(pos+neg).gt(0)] + loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='none') + + if cfg.use_class_balanced_conf: + # Lazy initialization + if self.class_instances is None: + self.class_instances = torch.zeros(self.num_classes, device=targets_weighted.device) + + classes, counts = targets_weighted.unique(return_counts=True) + + for _cls, _cnt in zip(classes.cpu().numpy(), counts.cpu().numpy()): + self.class_instances[_cls] += _cnt + + self.total_instances += targets_weighted.size(0) + + weighting = 1 - (self.class_instances[targets_weighted] / self.total_instances) + weighting = torch.clamp(weighting, min=1/self.num_classes) + + # If you do the math, the average weight of self.class_instances is this + avg_weight = (self.num_classes - 1) / self.num_classes + + loss_c = (loss_c * weighting).sum() / avg_weight + else: + loss_c = loss_c.sum() + + return cfg.conf_alpha * loss_c + + def focal_conf_loss(self, conf_data, conf_t): + """ + Focal loss as described in https://arxiv.org/pdf/1708.02002.pdf + Adapted from https://github.com/clcarwin/focal_loss_pytorch/blob/master/focalloss.py + Note that this uses softmax and not the original sigmoid from the paper. + """ + conf_t = conf_t.view(-1) # [batch_size*num_priors] + conf_data = conf_data.view(-1, conf_data.size(-1)) # [batch_size*num_priors, num_classes] + + # Ignore neutral samples (class < 0) + keep = (conf_t >= 0).float() + conf_t[conf_t < 0] = 0 # so that gather doesn't drum up a fuss + + logpt = F.log_softmax(conf_data, dim=-1) + logpt = logpt.gather(1, conf_t.unsqueeze(-1)) + logpt = logpt.view(-1) + pt = logpt.exp() + + # I adapted the alpha_t calculation here from + # https://github.com/pytorch/pytorch/blob/master/modules/detectron/softmax_focal_loss_op.cu + # You'd think you want all the alphas to sum to one, but in the original implementation they + # just give background an alpha of 1-alpha and each forground an alpha of alpha. + background = (conf_t == 0).float() + at = (1 - cfg.focal_loss_alpha) * background + cfg.focal_loss_alpha * (1 - background) + + loss = -at * (1 - pt) ** cfg.focal_loss_gamma * logpt + + # See comment above for keep + return cfg.conf_alpha * (loss * keep).sum() + + def focal_conf_sigmoid_loss(self, conf_data, conf_t): + """ + Focal loss but using sigmoid like the original paper. + Note: To make things mesh easier, the network still predicts 81 class confidences in this mode. + Because retinanet originally only predicts 80, we simply just don't use conf_data[..., 0] + """ + num_classes = conf_data.size(-1) + + conf_t = conf_t.view(-1) # [batch_size*num_priors] + conf_data = conf_data.view(-1, num_classes) # [batch_size*num_priors, num_classes] + + # Ignore neutral samples (class < 0) + keep = (conf_t >= 0).float() + conf_t[conf_t < 0] = 0 # can't mask with -1, so filter that out + + # Compute a one-hot embedding of conf_t + # From https://github.com/kuangliu/pytorch-retinanet/blob/master/utils.py + conf_one_t = torch.eye(num_classes, device=conf_t.get_device())[conf_t] + conf_pm_t = conf_one_t * 2 - 1 # -1 if background, +1 if forground for specific class + + logpt = F.logsigmoid(conf_data * conf_pm_t) # note: 1 - sigmoid(x) = sigmoid(-x) + pt = logpt.exp() + + at = cfg.focal_loss_alpha * conf_one_t + (1 - cfg.focal_loss_alpha) * (1 - conf_one_t) + at[..., 0] = 0 # Set alpha for the background class to 0 because sigmoid focal loss doesn't use it + + loss = -at * (1 - pt) ** cfg.focal_loss_gamma * logpt + loss = keep * loss.sum(dim=-1) + + return cfg.conf_alpha * loss.sum() + + def focal_conf_objectness_loss(self, conf_data, conf_t): + """ + Instead of using softmax, use class[0] to be the objectness score and do sigmoid focal loss on that. + Then for the rest of the classes, softmax them and apply CE for only the positive examples. + + If class[0] = 1 implies forground and class[0] = 0 implies background then you achieve something + similar during test-time to softmax by setting class[1:] = softmax(class[1:]) * class[0] and invert class[0]. + """ + + conf_t = conf_t.view(-1) # [batch_size*num_priors] + conf_data = conf_data.view(-1, conf_data.size(-1)) # [batch_size*num_priors, num_classes] + + # Ignore neutral samples (class < 0) + keep = (conf_t >= 0).float() + conf_t[conf_t < 0] = 0 # so that gather doesn't drum up a fuss + + background = (conf_t == 0).float() + at = (1 - cfg.focal_loss_alpha) * background + cfg.focal_loss_alpha * (1 - background) + + logpt = F.logsigmoid(conf_data[:, 0]) * (1 - background) + F.logsigmoid(-conf_data[:, 0]) * background + pt = logpt.exp() + + obj_loss = -at * (1 - pt) ** cfg.focal_loss_gamma * logpt + + # All that was the objectiveness loss--now time for the class confidence loss + pos_mask = conf_t > 0 + conf_data_pos = (conf_data[:, 1:])[pos_mask] # Now this has just 80 classes + conf_t_pos = conf_t[pos_mask] - 1 # So subtract 1 here + + class_loss = F.cross_entropy(conf_data_pos, conf_t_pos, reduction='sum') + + return cfg.conf_alpha * (class_loss + (obj_loss * keep).sum()) + + def conf_objectness_loss(self, conf_data, conf_t, batch_size, loc_p, loc_t, priors): + """ + Instead of using softmax, use class[0] to be p(obj) * p(IoU) as in YOLO. + Then for the rest of the classes, softmax them and apply CE for only the positive examples. + """ + + conf_t = conf_t.view(-1) # [batch_size*num_priors] + conf_data = conf_data.view(-1, conf_data.size(-1)) # [batch_size*num_priors, num_classes] + + pos_mask = (conf_t > 0) + neg_mask = (conf_t == 0) + + obj_data = conf_data[:, 0] + obj_data_pos = obj_data[pos_mask] + obj_data_neg = obj_data[neg_mask] + + # Don't be confused, this is just binary cross entropy similified + obj_neg_loss = - F.logsigmoid(-obj_data_neg).sum() + + with torch.no_grad(): + pos_priors = priors.unsqueeze(0).expand(batch_size, -1, -1).reshape(-1, 4)[pos_mask, :] + + boxes_pred = decode(loc_p, pos_priors, cfg.use_yolo_regressors) + boxes_targ = decode(loc_t, pos_priors, cfg.use_yolo_regressors) + + iou_targets = elemwise_box_iou(boxes_pred, boxes_targ) + + obj_pos_loss = - iou_targets * F.logsigmoid(obj_data_pos) - (1 - iou_targets) * F.logsigmoid(-obj_data_pos) + obj_pos_loss = obj_pos_loss.sum() + + # All that was the objectiveness loss--now time for the class confidence loss + conf_data_pos = (conf_data[:, 1:])[pos_mask] # Now this has just 80 classes + conf_t_pos = conf_t[pos_mask] - 1 # So subtract 1 here + + class_loss = F.cross_entropy(conf_data_pos, conf_t_pos, reduction='sum') + + return cfg.conf_alpha * (class_loss + obj_pos_loss + obj_neg_loss) + + + def direct_mask_loss(self, pos_idx, idx_t, loc_data, mask_data, priors, masks): + """ Crops the gt masks using the predicted bboxes, scales them down, and outputs the BCE loss. """ + loss_m = 0 + for idx in range(mask_data.size(0)): + with torch.no_grad(): + cur_pos_idx = pos_idx[idx, :, :] + cur_pos_idx_squeezed = cur_pos_idx[:, 1] + + # Shape: [num_priors, 4], decoded predicted bboxes + pos_bboxes = decode(loc_data[idx, :, :], priors.data, cfg.use_yolo_regressors) + pos_bboxes = pos_bboxes[cur_pos_idx].view(-1, 4).clamp(0, 1) + pos_lookup = idx_t[idx, cur_pos_idx_squeezed] + + cur_masks = masks[idx] + pos_masks = cur_masks[pos_lookup, :, :] + + # Convert bboxes to absolute coordinates + num_pos, img_height, img_width = pos_masks.size() + + # Take care of all the bad behavior that can be caused by out of bounds coordinates + x1, x2 = sanitize_coordinates(pos_bboxes[:, 0], pos_bboxes[:, 2], img_width) + y1, y2 = sanitize_coordinates(pos_bboxes[:, 1], pos_bboxes[:, 3], img_height) + + # Crop each gt mask with the predicted bbox and rescale to the predicted mask size + # Note that each bounding box crop is a different size so I don't think we can vectorize this + scaled_masks = [] + for jdx in range(num_pos): + tmp_mask = pos_masks[jdx, y1[jdx]:y2[jdx], x1[jdx]:x2[jdx]] + + # Restore any dimensions we've left out because our bbox was 1px wide + while tmp_mask.dim() < 2: + tmp_mask = tmp_mask.unsqueeze(0) + + new_mask = F.adaptive_avg_pool2d(tmp_mask.unsqueeze(0), cfg.mask_size) + scaled_masks.append(new_mask.view(1, -1)) + + mask_t = torch.cat(scaled_masks, 0).gt(0.5).float() # Threshold downsampled mask + + pos_mask_data = mask_data[idx, cur_pos_idx_squeezed, :] + loss_m += F.binary_cross_entropy(torch.clamp(pos_mask_data, 0, 1), mask_t, reduction='sum') * cfg.mask_alpha + + return loss_m + + + def coeff_diversity_loss(self, coeffs, instance_t): + """ + coeffs should be size [num_pos, num_coeffs] + instance_t should be size [num_pos] and be values from 0 to num_instances-1 + """ + num_pos = coeffs.size(0) + instance_t = instance_t.view(-1) # juuuust to make sure + + coeffs_norm = F.normalize(coeffs, dim=1) + cos_sim = coeffs_norm @ coeffs_norm.t() + + inst_eq = (instance_t[:, None].expand_as(cos_sim) == instance_t[None, :].expand_as(cos_sim)).float() + + # Rescale to be between 0 and 1 + cos_sim = (cos_sim + 1) / 2 + + # If they're the same instance, use cosine distance, else use cosine similarity + loss = (1 - cos_sim) * inst_eq + cos_sim * (1 - inst_eq) + + # Only divide by num_pos once because we're summing over a num_pos x num_pos tensor + # and all the losses will be divided by num_pos at the end, so just one extra time. + return cfg.mask_proto_coeff_diversity_alpha * loss.sum() / num_pos + + + def lincomb_mask_loss(self, pos, idx_t, loc_data, mask_data, priors, proto_data, masks, gt_box_t, score_data, inst_data, labels, interpolation_mode='bilinear'): + mask_h = proto_data.size(1) + mask_w = proto_data.size(2) + + process_gt_bboxes = cfg.mask_proto_normalize_emulate_roi_pooling or cfg.mask_proto_crop + + if cfg.mask_proto_remove_empty_masks: + # Make sure to store a copy of this because we edit it to get rid of all-zero masks + pos = pos.clone() + + loss_m = 0 + loss_d = 0 # Coefficient diversity loss + + maskiou_t_list = [] + maskiou_net_input_list = [] + label_t_list = [] + + for idx in range(mask_data.size(0)): + with torch.no_grad(): + downsampled_masks = F.interpolate(masks[idx].unsqueeze(0), (mask_h, mask_w), + mode=interpolation_mode, align_corners=False).squeeze(0) + downsampled_masks = downsampled_masks.permute(1, 2, 0).contiguous() + + if cfg.mask_proto_binarize_downsampled_gt: + downsampled_masks = downsampled_masks.gt(0.5).float() + + if cfg.mask_proto_remove_empty_masks: + # Get rid of gt masks that are so small they get downsampled away + very_small_masks = (downsampled_masks.sum(dim=(0,1)) <= 0.0001) + for i in range(very_small_masks.size(0)): + if very_small_masks[i]: + pos[idx, idx_t[idx] == i] = 0 + + if cfg.mask_proto_reweight_mask_loss: + # Ensure that the gt is binary + if not cfg.mask_proto_binarize_downsampled_gt: + bin_gt = downsampled_masks.gt(0.5).float() + else: + bin_gt = downsampled_masks + + gt_foreground_norm = bin_gt / (torch.sum(bin_gt, dim=(0,1), keepdim=True) + 0.0001) + gt_background_norm = (1-bin_gt) / (torch.sum(1-bin_gt, dim=(0,1), keepdim=True) + 0.0001) + + mask_reweighting = gt_foreground_norm * cfg.mask_proto_reweight_coeff + gt_background_norm + mask_reweighting *= mask_h * mask_w + + cur_pos = pos[idx] + pos_idx_t = idx_t[idx, cur_pos] + + if process_gt_bboxes: + # Note: this is in point-form + if cfg.mask_proto_crop_with_pred_box: + pos_gt_box_t = decode(loc_data[idx, :, :], priors.data, cfg.use_yolo_regressors)[cur_pos] + else: + pos_gt_box_t = gt_box_t[idx, cur_pos] + + if pos_idx_t.size(0) == 0: + continue + + proto_masks = proto_data[idx] + proto_coef = mask_data[idx, cur_pos, :] + if cfg.use_mask_scoring: + mask_scores = score_data[idx, cur_pos, :] + + if cfg.mask_proto_coeff_diversity_loss: + if inst_data is not None: + div_coeffs = inst_data[idx, cur_pos, :] + else: + div_coeffs = proto_coef + + loss_d += self.coeff_diversity_loss(div_coeffs, pos_idx_t) + + # If we have over the allowed number of masks, select a random sample + old_num_pos = proto_coef.size(0) + if old_num_pos > cfg.masks_to_train: + perm = torch.randperm(proto_coef.size(0)) + select = perm[:cfg.masks_to_train] + + proto_coef = proto_coef[select, :] + pos_idx_t = pos_idx_t[select] + + if process_gt_bboxes: + pos_gt_box_t = pos_gt_box_t[select, :] + if cfg.use_mask_scoring: + mask_scores = mask_scores[select, :] + + num_pos = proto_coef.size(0) + mask_t = downsampled_masks[:, :, pos_idx_t] + label_t = labels[idx][pos_idx_t] + + # Size: [mask_h, mask_w, num_pos] + pred_masks = proto_masks @ proto_coef.t() + pred_masks = cfg.mask_proto_mask_activation(pred_masks) + + if cfg.mask_proto_double_loss: + if cfg.mask_proto_mask_activation == activation_func.sigmoid: + pre_loss = F.binary_cross_entropy(torch.clamp(pred_masks, 0, 1), mask_t, reduction='sum') + else: + pre_loss = F.smooth_l1_loss(pred_masks, mask_t, reduction='sum') + + loss_m += cfg.mask_proto_double_loss_alpha * pre_loss + + if cfg.mask_proto_crop: + pred_masks = crop(pred_masks, pos_gt_box_t) + + if cfg.mask_proto_mask_activation == activation_func.sigmoid: + pre_loss = F.binary_cross_entropy(torch.clamp(pred_masks, 0, 1), mask_t, reduction='none') + else: + pre_loss = F.smooth_l1_loss(pred_masks, mask_t, reduction='none') + + if cfg.mask_proto_normalize_mask_loss_by_sqrt_area: + gt_area = torch.sum(mask_t, dim=(0, 1), keepdim=True) + pre_loss = pre_loss / (torch.sqrt(gt_area) + 0.0001) + + if cfg.mask_proto_reweight_mask_loss: + pre_loss = pre_loss * mask_reweighting[:, :, pos_idx_t] + + if cfg.mask_proto_normalize_emulate_roi_pooling: + weight = mask_h * mask_w if cfg.mask_proto_crop else 1 + pos_gt_csize = center_size(pos_gt_box_t) + gt_box_width = pos_gt_csize[:, 2] * mask_w + gt_box_height = pos_gt_csize[:, 3] * mask_h + pre_loss = pre_loss.sum(dim=(0, 1)) / gt_box_width / gt_box_height * weight + + # If the number of masks were limited scale the loss accordingly + if old_num_pos > num_pos: + pre_loss *= old_num_pos / num_pos + + loss_m += torch.sum(pre_loss) + + if cfg.use_maskiou: + if cfg.discard_mask_area > 0: + gt_mask_area = torch.sum(mask_t, dim=(0, 1)) + select = gt_mask_area > cfg.discard_mask_area + + if torch.sum(select) < 1: + continue + + pos_gt_box_t = pos_gt_box_t[select, :] + pred_masks = pred_masks[:, :, select] + mask_t = mask_t[:, :, select] + label_t = label_t[select] + + maskiou_net_input = pred_masks.permute(2, 0, 1).contiguous().unsqueeze(1) + pred_masks = pred_masks.gt(0.5).float() + maskiou_t = self._mask_iou(pred_masks, mask_t) + + maskiou_net_input_list.append(maskiou_net_input) + maskiou_t_list.append(maskiou_t) + label_t_list.append(label_t) + + losses = {'M': loss_m * cfg.mask_alpha / mask_h / mask_w} + + if cfg.mask_proto_coeff_diversity_loss: + losses['D'] = loss_d + + if cfg.use_maskiou: + # discard_mask_area discarded every mask in the batch, so nothing to do here + if len(maskiou_t_list) == 0: + return losses, None + + maskiou_t = torch.cat(maskiou_t_list) + label_t = torch.cat(label_t_list) + maskiou_net_input = torch.cat(maskiou_net_input_list) + + num_samples = maskiou_t.size(0) + if cfg.maskious_to_train > 0 and num_samples > cfg.maskious_to_train: + perm = torch.randperm(num_samples) + select = perm[:cfg.masks_to_train] + maskiou_t = maskiou_t[select] + label_t = label_t[select] + maskiou_net_input = maskiou_net_input[select] + + return losses, [maskiou_net_input, maskiou_t, label_t] + + return losses + + def _mask_iou(self, mask1, mask2): + intersection = torch.sum(mask1*mask2, dim=(0, 1)) + area1 = torch.sum(mask1, dim=(0, 1)) + area2 = torch.sum(mask2, dim=(0, 1)) + union = (area1 + area2) - intersection + ret = intersection / union + return ret + + def mask_iou_loss(self, net, maskiou_targets): + maskiou_net_input, maskiou_t, label_t = maskiou_targets + + maskiou_p = net.maskiou_net(maskiou_net_input) + + label_t = label_t[:, None] + maskiou_p = torch.gather(maskiou_p, dim=1, index=label_t).view(-1) + + loss_i = F.smooth_l1_loss(maskiou_p, maskiou_t, reduction='sum') + + return loss_i * cfg.maskiou_alpha diff --git a/cv/instance_segmentation/yolact/pytorch/layers/output_utils.py b/cv/instance_segmentation/yolact/pytorch/layers/output_utils.py new file mode 100644 index 000000000..27efac935 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/layers/output_utils.py @@ -0,0 +1,188 @@ +""" Contains functions used to sanitize and prepare the output of Yolact. """ + + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import cv2 + +from data import cfg, mask_type, MEANS, STD, activation_func +from utils.augmentations import Resize +from utils import timer +from .box_utils import crop, sanitize_coordinates + +def postprocess(det_output, w, h, batch_idx=0, interpolation_mode='bilinear', + visualize_lincomb=False, crop_masks=True, score_threshold=0): + """ + Postprocesses the output of Yolact on testing mode into a format that makes sense, + accounting for all the possible configuration settings. + + Args: + - det_output: The lost of dicts that Detect outputs. + - w: The real with of the image. + - h: The real height of the image. + - batch_idx: If you have multiple images for this batch, the image's index in the batch. + - interpolation_mode: Can be 'nearest' | 'area' | 'bilinear' (see torch.nn.functional.interpolate) + + Returns 4 torch Tensors (in the following order): + - classes [num_det]: The class idx for each detection. + - scores [num_det]: The confidence score for each detection. + - boxes [num_det, 4]: The bounding box for each detection in absolute point form. + - masks [num_det, h, w]: Full image masks for each detection. + """ + + dets = det_output[batch_idx] + net = dets['net'] + dets = dets['detection'] + + if dets is None: + return [torch.Tensor()] * 4 # Warning, this is 4 copies of the same thing + + if score_threshold > 0: + keep = dets['score'] > score_threshold + + for k in dets: + if k != 'proto': + dets[k] = dets[k][keep] + + if dets['score'].size(0) == 0: + return [torch.Tensor()] * 4 + + # Actually extract everything from dets now + classes = dets['class'] + boxes = dets['box'] + scores = dets['score'] + masks = dets['mask'] + + if cfg.mask_type == mask_type.lincomb and cfg.eval_mask_branch: + # At this points masks is only the coefficients + proto_data = dets['proto'] + + # Test flag, do not upvote + if cfg.mask_proto_debug: + np.save('scripts/proto.npy', proto_data.cpu().numpy()) + + if visualize_lincomb: + display_lincomb(proto_data, masks) + + masks = proto_data @ masks.t() + masks = cfg.mask_proto_mask_activation(masks) + + # Crop masks before upsampling because you know why + if crop_masks: + masks = crop(masks, boxes) + + # Permute into the correct output shape [num_dets, proto_h, proto_w] + masks = masks.permute(2, 0, 1).contiguous() + + if cfg.use_maskiou: + with timer.env('maskiou_net'): + with torch.no_grad(): + maskiou_p = net.maskiou_net(masks.unsqueeze(1)) + maskiou_p = torch.gather(maskiou_p, dim=1, index=classes.unsqueeze(1)).squeeze(1) + if cfg.rescore_mask: + if cfg.rescore_bbox: + scores = scores * maskiou_p + else: + scores = [scores, scores * maskiou_p] + + # Scale masks up to the full image + masks = F.interpolate(masks.unsqueeze(0), (h, w), mode=interpolation_mode, align_corners=False).squeeze(0) + + # Binarize the masks + masks.gt_(0.5) + + + boxes[:, 0], boxes[:, 2] = sanitize_coordinates(boxes[:, 0], boxes[:, 2], w, cast=False) + boxes[:, 1], boxes[:, 3] = sanitize_coordinates(boxes[:, 1], boxes[:, 3], h, cast=False) + boxes = boxes.long() + + if cfg.mask_type == mask_type.direct and cfg.eval_mask_branch: + # Upscale masks + full_masks = torch.zeros(masks.size(0), h, w) + + for jdx in range(masks.size(0)): + x1, y1, x2, y2 = boxes[jdx, :] + + mask_w = x2 - x1 + mask_h = y2 - y1 + + # Just in case + if mask_w * mask_h <= 0 or mask_w < 0: + continue + + mask = masks[jdx, :].view(1, 1, cfg.mask_size, cfg.mask_size) + mask = F.interpolate(mask, (mask_h, mask_w), mode=interpolation_mode, align_corners=False) + mask = mask.gt(0.5).float() + full_masks[jdx, y1:y2, x1:x2] = mask + + masks = full_masks + + return classes, scores, boxes, masks + + + + + +def undo_image_transformation(img, w, h): + """ + Takes a transformed image tensor and returns a numpy ndarray that is untransformed. + Arguments w and h are the original height and width of the image. + """ + img_numpy = img.permute(1, 2, 0).cpu().numpy() + img_numpy = img_numpy[:, :, (2, 1, 0)] # To BRG + + if cfg.backbone.transform.normalize: + img_numpy = (img_numpy * np.array(STD) + np.array(MEANS)) / 255.0 + elif cfg.backbone.transform.subtract_means: + img_numpy = (img_numpy / 255.0 + np.array(MEANS) / 255.0).astype(np.float32) + + img_numpy = img_numpy[:, :, (2, 1, 0)] # To RGB + img_numpy = np.clip(img_numpy, 0, 1) + + return cv2.resize(img_numpy, (w,h)) + + +def display_lincomb(proto_data, masks): + out_masks = torch.matmul(proto_data, masks.t()) + # out_masks = cfg.mask_proto_mask_activation(out_masks) + + for kdx in range(1): + jdx = kdx + 0 + import matplotlib.pyplot as plt + coeffs = masks[jdx, :].cpu().numpy() + idx = np.argsort(-np.abs(coeffs)) + # plt.bar(list(range(idx.shape[0])), coeffs[idx]) + # plt.show() + + coeffs_sort = coeffs[idx] + arr_h, arr_w = (4,8) + proto_h, proto_w, _ = proto_data.size() + arr_img = np.zeros([proto_h*arr_h, proto_w*arr_w]) + arr_run = np.zeros([proto_h*arr_h, proto_w*arr_w]) + test = torch.sum(proto_data, -1).cpu().numpy() + + for y in range(arr_h): + for x in range(arr_w): + i = arr_w * y + x + + if i == 0: + running_total = proto_data[:, :, idx[i]].cpu().numpy() * coeffs_sort[i] + else: + running_total += proto_data[:, :, idx[i]].cpu().numpy() * coeffs_sort[i] + + running_total_nonlin = running_total + if cfg.mask_proto_mask_activation == activation_func.sigmoid: + running_total_nonlin = (1/(1+np.exp(-running_total_nonlin))) + + arr_img[y*proto_h:(y+1)*proto_h, x*proto_w:(x+1)*proto_w] = (proto_data[:, :, idx[i]] / torch.max(proto_data[:, :, idx[i]])).cpu().numpy() * coeffs_sort[i] + arr_run[y*proto_h:(y+1)*proto_h, x*proto_w:(x+1)*proto_w] = (running_total_nonlin > 0.5).astype(np.float) + plt.imshow(arr_img) + plt.show() + # plt.imshow(arr_run) + # plt.show() + # plt.imshow(test) + # plt.show() + plt.imshow(out_masks[:, :, jdx].cpu().numpy()) + plt.show() diff --git a/cv/instance_segmentation/yolact/pytorch/run_coco_eval.py b/cv/instance_segmentation/yolact/pytorch/run_coco_eval.py new file mode 100644 index 000000000..27cb4d603 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/run_coco_eval.py @@ -0,0 +1,49 @@ +""" +Runs the coco-supplied cocoeval script to evaluate detections +outputted by using the output_coco_json flag in eval.py. +""" + + +import argparse + +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +parser = argparse.ArgumentParser(description='COCO Detections Evaluator') +parser.add_argument('--bbox_det_file', default='results/bbox_detections.json', type=str) +parser.add_argument('--mask_det_file', default='results/mask_detections.json', type=str) +parser.add_argument('--gt_ann_file', default='data/coco/annotations/instances_val2017.json', type=str) +parser.add_argument('--eval_type', default='both', choices=['bbox', 'mask', 'both'], type=str) +args = parser.parse_args() + + + +if __name__ == '__main__': + + eval_bbox = (args.eval_type in ('bbox', 'both')) + eval_mask = (args.eval_type in ('mask', 'both')) + + print('Loading annotations...') + gt_annotations = COCO(args.gt_ann_file) + if eval_bbox: + bbox_dets = gt_annotations.loadRes(args.bbox_det_file) + if eval_mask: + mask_dets = gt_annotations.loadRes(args.mask_det_file) + + if eval_bbox: + print('\nEvaluating BBoxes:') + bbox_eval = COCOeval(gt_annotations, bbox_dets, 'bbox') + bbox_eval.evaluate() + bbox_eval.accumulate() + bbox_eval.summarize() + + if eval_mask: + print('\nEvaluating Masks:') + bbox_eval = COCOeval(gt_annotations, mask_dets, 'segm') + bbox_eval.evaluate() + bbox_eval.accumulate() + bbox_eval.summarize() + + + diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/augment_bbox.py b/cv/instance_segmentation/yolact/pytorch/scripts/augment_bbox.py new file mode 100644 index 000000000..40c7b8b32 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/augment_bbox.py @@ -0,0 +1,171 @@ + +import os.path as osp +import json, pickle +import sys +from math import sqrt +from itertools import product +import torch +from numpy import random + +import numpy as np + + +max_image_size = 550 +augment_idx = 0 +dump_file = 'weights/bboxes_aug.pkl' +box_file = 'weights/bboxes.pkl' + +def augment_boxes(bboxes): + bboxes_rel = [] + for box in bboxes: + bboxes_rel.append(prep_box(box)) + bboxes_rel = np.concatenate(bboxes_rel, axis=0) + + with open(dump_file, 'wb') as f: + pickle.dump(bboxes_rel, f) + +def prep_box(box_list): + global augment_idx + boxes = np.array([box_list[2:]], dtype=np.float32) + + # Image width and height + width, height = box_list[:2] + + # To point form + boxes[:, 2:] += boxes[:, :2] + + + # Expand + ratio = random.uniform(1, 4) + left = random.uniform(0, width*ratio - width) + top = random.uniform(0, height*ratio - height) + + height *= ratio + width *= ratio + + boxes[:, :2] += (int(left), int(top)) + boxes[:, 2:] += (int(left), int(top)) + + + # RandomSampleCrop + height, width, boxes = random_sample_crop(height, width, boxes) + + + # RandomMirror + if random.randint(0, 2): + boxes[:, 0::2] = width - boxes[:, 2::-2] + + + # Resize + boxes[:, [0, 2]] *= (max_image_size / width) + boxes[:, [1, 3]] *= (max_image_size / height) + width = height = max_image_size + + + # ToPercentCoords + boxes[:, [0, 2]] /= width + boxes[:, [1, 3]] /= height + + if augment_idx % 50000 == 0: + print('Current idx: %d' % augment_idx) + + augment_idx += 1 + + return boxes + + + + +sample_options = ( + # using entire original input image + None, + # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9 + (0.1, None), + (0.3, None), + (0.7, None), + (0.9, None), + # randomly sample a patch + (None, None), +) + +def intersect(box_a, box_b): + max_xy = np.minimum(box_a[:, 2:], box_b[2:]) + min_xy = np.maximum(box_a[:, :2], box_b[:2]) + inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf) + return inter[:, 0] * inter[:, 1] + + +def jaccard_numpy(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: Multiple bounding boxes, Shape: [num_boxes,4] + box_b: Single bounding box, Shape: [4] + Return: + jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])) # [A,B] + area_b = ((box_b[2]-box_b[0]) * + (box_b[3]-box_b[1])) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + + +def random_sample_crop(height, width, boxes=None): + global sample_options + + while True: + # randomly choose a mode + mode = random.choice(sample_options) + if mode is None: + return height, width, boxes + + min_iou, max_iou = mode + if min_iou is None: + min_iou = float('-inf') + if max_iou is None: + max_iou = float('inf') + + for _ in range(50): + w = random.uniform(0.3 * width, width) + h = random.uniform(0.3 * height, height) + + if h / w < 0.5 or h / w > 2: + continue + + left = random.uniform(0, width - w) + top = random.uniform(0, height - h) + + rect = np.array([int(left), int(top), int(left+w), int(top+h)]) + overlap = jaccard_numpy(boxes, rect) + if overlap.min() < min_iou and max_iou < overlap.max(): + continue + + centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0 + + m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1]) + m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1]) + mask = m1 * m2 + + if not mask.any(): + continue + + current_boxes = boxes[mask, :].copy() + current_boxes[:, :2] = np.maximum(current_boxes[:, :2], rect[:2]) + current_boxes[:, :2] -= rect[:2] + current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:], rect[2:]) + current_boxes[:, 2:] -= rect[:2] + + return h, w, current_boxes + + +if __name__ == '__main__': + + with open(box_file, 'rb') as f: + bboxes = pickle.load(f) + + augment_boxes(bboxes) diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/bbox_recall.py b/cv/instance_segmentation/yolact/pytorch/scripts/bbox_recall.py new file mode 100644 index 000000000..3fe26259e --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/bbox_recall.py @@ -0,0 +1,181 @@ +""" +This script compiles all the bounding boxes in the training data and +clusters them for each convout resolution on which they're used. + +Run this script from the Yolact root directory. +""" + +import os.path as osp +import json, pickle +import sys +from math import sqrt +from itertools import product +import torch +import random + +import numpy as np + +dump_file = 'weights/bboxes.pkl' +aug_file = 'weights/bboxes_aug.pkl' + +use_augmented_boxes = True + + +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [A,4]. + box_b: (tensor) bounding boxes, Shape: [B,4]. + Return: + (tensor) intersection area, Shape: [A,B]. + """ + A = box_a.size(0) + B = box_b.size(0) + max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), + box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) + min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), + box_b[:, :2].unsqueeze(0).expand(A, B, 2)) + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + + +def jaccard(box_a, box_b, iscrowd=False): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. If iscrowd=True, put the crowd in box_b. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] + area_b = ((box_b[:, 2]-box_b[:, 0]) * + (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] + union = area_a + area_b - inter + + if iscrowd: + return inter / area_a + else: + return inter / union # [A,B] + +# Also convert to point form +def to_relative(bboxes): + return np.concatenate((bboxes[:, 2:4] / bboxes[:, :2], (bboxes[:, 2:4] + bboxes[:, 4:]) / bboxes[:, :2]), axis=1) + + +def make_priors(conv_size, scales, aspect_ratios): + prior_data = [] + conv_h = conv_size[0] + conv_w = conv_size[1] + + # Iteration order is important (it has to sync up with the convout) + for j, i in product(range(conv_h), range(conv_w)): + x = (i + 0.5) / conv_w + y = (j + 0.5) / conv_h + + for scale, ars in zip(scales, aspect_ratios): + for ar in ars: + w = scale * ar / conv_w + h = scale / ar / conv_h + + # Point form + prior_data += [x - w/2, y - h/2, x + w/2, y + h/2] + + return np.array(prior_data).reshape(-1, 4) + +# fixed_ssd_config +# scales = [[3.5, 4.95], [3.6, 4.90], [3.3, 4.02], [2.7, 3.10], [2.1, 2.37], [2.1, 2.37], [1.8, 1.92]] +# aspect_ratios = [ [[1, sqrt(2), 1/sqrt(2), sqrt(3), 1/sqrt(3)][:n], [1]] for n in [3, 5, 5, 5, 3, 3, 3] ] +# conv_sizes = [(35, 35), (18, 18), (9, 9), (5, 5), (3, 3), (2, 2)] + +scales = [[1.68, 2.91], + [2.95, 2.22, 0.84], + [2.23, 2.17, 3.12], + [0.76, 1.94, 2.72], + [2.10, 2.65], + [1.80, 1.92]] +aspect_ratios = [[[0.72, 0.96], [0.68, 1.17]], + [[1.28, 0.66], [0.63, 1.23], [0.89, 1.40]], + [[2.05, 1.24], [0.57, 0.83], [0.61, 1.15]], + [[1.00, 2.21], [0.47, 1.60], [1.44, 0.79]], + [[1.00, 1.41, 0.71, 1.73, 0.58], [1.08]], + [[1.00, 1.41, 0.71, 1.73, 0.58], [1.00]]] +conv_sizes = [(35, 35), (18, 18), (9, 9), (5, 5), (3, 3), (2, 2)] + +# yrm33_config +# scales = [ [5.3] ] * 5 +# aspect_ratios = [ [[1, 1/sqrt(2), sqrt(2)]] ]*5 +# conv_sizes = [(136, 136), (67, 67), (33, 33), (16, 16), (8, 8)] + + +SMALL = 0 +MEDIUM = 1 +LARGE = 2 + +if __name__ == '__main__': + + with open(dump_file, 'rb') as f: + bboxes = pickle.load(f) + + sizes = [] + smalls = [] + for i in range(len(bboxes)): + area = bboxes[i][4] * bboxes[i][5] + if area < 32 ** 2: + sizes.append(SMALL) + smalls.append(area) + elif area < 96 ** 2: + sizes.append(MEDIUM) + else: + sizes.append(LARGE) + + # Each box is in the form [im_w, im_h, pos_x, pos_y, size_x, size_y] + + if use_augmented_boxes: + with open(aug_file, 'rb') as f: + bboxes_rel = pickle.load(f) + else: + bboxes_rel = to_relative(np.array(bboxes)) + + + with torch.no_grad(): + sizes = torch.Tensor(sizes) + + anchors = [make_priors(cs, s, ar) for cs, s, ar in zip(conv_sizes, scales, aspect_ratios)] + anchors = np.concatenate(anchors, axis=0) + anchors = torch.Tensor(anchors).cuda() + + bboxes_rel = torch.Tensor(bboxes_rel).cuda() + perGTAnchorMax = torch.zeros(bboxes_rel.shape[0]).cuda() + + chunk_size = 1000 + for i in range((bboxes_rel.size(0) // chunk_size) + 1): + start = i * chunk_size + end = min((i + 1) * chunk_size, bboxes_rel.size(0)) + + ious = jaccard(bboxes_rel[start:end, :], anchors) + maxes, maxidx = torch.max(ious, dim=1) + + perGTAnchorMax[start:end] = maxes + + + hits = (perGTAnchorMax > 0.5).float() + + print('Total recall: %.2f' % (torch.sum(hits) / hits.size(0) * 100)) + print() + + for i, metric in zip(range(3), ('small', 'medium', 'large')): + _hits = hits[sizes == i] + _size = (1 if _hits.size(0) == 0 else _hits.size(0)) + print(metric + ' recall: %.2f' % ((torch.sum(_hits) / _size) * 100)) + + + diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/cluster_bbox_sizes.py b/cv/instance_segmentation/yolact/pytorch/scripts/cluster_bbox_sizes.py new file mode 100644 index 000000000..91285ce9d --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/cluster_bbox_sizes.py @@ -0,0 +1,69 @@ +""" +This script compiles all the bounding boxes in the training data and +clusters them for each convout resolution on which they're used. + +Run this script from the Yolact root directory. +""" + +import os.path as osp +import json, pickle +import sys + +import numpy as np +import sklearn.cluster as cluster + +dump_file = 'weights/bboxes.pkl' +max_size = 550 + +num_scale_clusters = 5 +num_aspect_ratio_clusters = 3 + +def to_relative(bboxes): + return bboxes[:, 2:4] / bboxes[:, :2] + +def process(bboxes): + return to_relative(bboxes) * max_size + +if __name__ == '__main__': + + with open(dump_file, 'rb') as f: + bboxes = pickle.load(f) + + bboxes = np.array(bboxes) + bboxes = process(bboxes) + bboxes = bboxes[(bboxes[:, 0] > 1) * (bboxes[:, 1] > 1)] + + scale = np.sqrt(bboxes[:, 0] * bboxes[:, 1]).reshape(-1, 1) + + clusterer = cluster.KMeans(num_scale_clusters, random_state=99, n_jobs=4) + assignments = clusterer.fit_predict(scale) + counts = np.bincount(assignments) + + cluster_centers = clusterer.cluster_centers_ + + center_indices = list(range(num_scale_clusters)) + center_indices.sort(key=lambda x: cluster_centers[x, 0]) + + for idx in center_indices: + center = cluster_centers[idx, 0] + boxes_for_center = bboxes[assignments == idx] + aspect_ratios = (boxes_for_center[:,0] / boxes_for_center[:,1]).reshape(-1, 1) + + c = cluster.KMeans(num_aspect_ratio_clusters, random_state=idx, n_jobs=4) + ca = c.fit_predict(aspect_ratios) + cc = np.bincount(ca) + + c = list(c.cluster_centers_.reshape(-1)) + cidx = list(range(num_aspect_ratio_clusters)) + cidx.sort(key=lambda x: -cc[x]) + + # import code + # code.interact(local=locals()) + + print('%.3f (%d) aspect ratios:' % (center, counts[idx])) + for idx in cidx: + print('\t%.2f (%d)' % (c[idx], cc[idx])) + print() + # exit() + + diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/compute_masks.py b/cv/instance_segmentation/yolact/pytorch/scripts/compute_masks.py new file mode 100644 index 000000000..866741800 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/compute_masks.py @@ -0,0 +1,93 @@ +import numpy as np +import matplotlib.pyplot as plt +import cv2 +import torch +import torch.nn.functional as F + +COLORS = ((255, 0, 0, 128), (0, 255, 0, 128), (0, 0, 255, 128), + (0, 255, 255, 128), (255, 0, 255, 128), (255, 255, 0, 128)) + +def mask_iou(mask1, mask2): + """ + Inputs inputs are matricies of size _ x N. Output is size _1 x _2. + Note: if iscrowd is True, then mask2 should be the crowd. + """ + intersection = torch.matmul(mask1, mask2.t()) + area1 = torch.sum(mask1, dim=1).view(1, -1) + area2 = torch.sum(mask2, dim=1).view(1, -1) + union = (area1.t() + area2) - intersection + + return intersection / union + +def paint_mask(img_numpy, mask, color): + h, w, _ = img_numpy.shape + img_numpy = img_numpy.copy() + + mask = np.tile(mask.reshape(h, w, 1), (1, 1, 3)) + color_np = np.array(color[:3]).reshape(1, 1, 3) + color_np = np.tile(color_np, (h, w, 1)) + mask_color = mask * color_np + + mask_alpha = 0.3 + + # Blend image and mask + image_crop = img_numpy * mask + img_numpy *= (1-mask) + img_numpy += image_crop * (1-mask_alpha) + mask_color * mask_alpha + + return img_numpy + +# Inverse sigmoid +def logit(x): + return np.log(x / (1-x + 0.0001) + 0.0001) + +def sigmoid(x): + return 1 / (1 + np.exp(-x)) + +img_fmt = '../data/coco/images/%012d.jpg' +with open('info.txt', 'r') as f: + img_id = int(f.read()) + +img = plt.imread(img_fmt % img_id).astype(np.float32) +h, w, _ = img.shape + +gt_masks = np.load('gt.npy').astype(np.float32).transpose(1, 2, 0) +proto_masks = np.load('proto.npy').astype(np.float32) + +proto_masks = torch.Tensor(proto_masks).permute(2, 0, 1).contiguous().unsqueeze(0) +proto_masks = F.interpolate(proto_masks, (h, w), mode='bilinear', align_corners=False).squeeze(0) +proto_masks = proto_masks.permute(1, 2, 0).numpy() + +# # A x = b +ls_A = proto_masks.reshape(-1, proto_masks.shape[-1]) +ls_b = gt_masks.reshape(-1, gt_masks.shape[-1]) + +# x is size [256, num_gt] +x = np.linalg.lstsq(ls_A, ls_b, rcond=None)[0] + +approximated_masks = (np.matmul(proto_masks, x) > 0.5).astype(np.float32) + +num_gt = approximated_masks.shape[2] +ious = mask_iou(torch.Tensor(approximated_masks.reshape(-1, num_gt).T), + torch.Tensor(gt_masks.reshape(-1, num_gt).T)) + +ious = [int(ious[i, i].item() * 100) for i in range(num_gt)] +ious.sort(key=lambda x: -x) + +print(ious) + +gt_img = img.copy() + +for i in range(num_gt): + gt_img = paint_mask(gt_img, gt_masks[:, :, i], COLORS[i % len(COLORS)]) + +plt.imshow(gt_img / 255) +plt.title('GT') +plt.show() + +for i in range(num_gt): + img = paint_mask(img, approximated_masks[:, :, i], COLORS[i % len(COLORS)]) + +plt.imshow(img / 255) +plt.title('Approximated') +plt.show() diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/convert_darknet.py b/cv/instance_segmentation/yolact/pytorch/scripts/convert_darknet.py new file mode 100644 index 000000000..b6b5cf4c2 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/convert_darknet.py @@ -0,0 +1,48 @@ +from backbone import DarkNetBackbone +import h5py +import torch + +f = h5py.File('darknet53.h5', 'r') +m = f['model_weights'] + +yolo_keys = list(m.keys()) +yolo_keys = [x for x in yolo_keys if len(m[x].keys()) > 0] +yolo_keys.sort() + +sd = DarkNetBackbone().state_dict() + +sd_keys = list(sd.keys()) +sd_keys.sort() + +# Note this won't work if there are 10 elements in some list but whatever that doesn't happen +layer_keys = list(set(['.'.join(x.split('.')[:-2]) for x in sd_keys])) +layer_keys.sort() + +# print([x for x in sd_keys if x.startswith(layer_keys[0])]) + +mapping = { + '.0.weight' : ('conv2d_%d', 'kernel:0'), + '.1.bias' : ('batch_normalization_%d', 'beta:0'), + '.1.weight' : ('batch_normalization_%d', 'gamma:0'), + '.1.running_var' : ('batch_normalization_%d', 'moving_variance:0'), + '.1.running_mean': ('batch_normalization_%d', 'moving_mean:0'), + '.1.num_batches_tracked': None, +} + +for i, layer_key in zip(range(1, len(layer_keys) + 1), layer_keys): + # This is pretty inefficient but I don't care + for weight_key in [x for x in sd_keys if x.startswith(layer_key)]: + diff = weight_key[len(layer_key):] + + if mapping[diff] is not None: + yolo_key = mapping[diff][0] % i + sub_key = mapping[diff][1] + + yolo_weight = torch.Tensor(m[yolo_key][yolo_key][sub_key].value) + if (len(yolo_weight.size()) == 4): + yolo_weight = yolo_weight.permute(3, 2, 0, 1).contiguous() + + sd[weight_key] = yolo_weight + +torch.save(sd, 'weights/darknet53.pth') + diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/convert_sbd.py b/cv/instance_segmentation/yolact/pytorch/scripts/convert_sbd.py new file mode 100644 index 000000000..61f049a36 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/convert_sbd.py @@ -0,0 +1,88 @@ +import scipy.io, scipy.ndimage +import os.path, json +import pycocotools.mask +import numpy as np + +def mask2bbox(mask): + rows = np.any(mask, axis=1) + cols = np.any(mask, axis=0) + rmin, rmax = np.where(rows)[0][[0, -1]] + cmin, cmax = np.where(cols)[0][[0, -1]] + + return cmin, rmin, cmax - cmin, rmax - rmin + + + +inst_path = './inst/' +img_path = './img/' +img_name_fmt = '%s.jpg' +ann_name_fmt = '%s.mat' + +image_id = 1 +ann_id = 1 + +types = ['train', 'val'] + +for t in types: + with open('%s.txt' % t, 'r') as f: + names = f.read().strip().split('\n') + + images = [] + annotations = [] + + for name in names: + img_name = img_name_fmt % name + + ann_path = os.path.join(inst_path, ann_name_fmt % name) + ann = scipy.io.loadmat(ann_path)['GTinst'][0][0] + + classes = [int(x[0]) for x in ann[2]] + seg = ann[0] + + for idx in range(len(classes)): + mask = (seg == (idx + 1)).astype(np.float) + + rle = pycocotools.mask.encode(np.asfortranarray(mask.astype(np.uint8))) + rle['counts'] = rle['counts'].decode('ascii') + + annotations.append({ + 'id': ann_id, + 'image_id': image_id, + 'category_id': classes[idx], + 'segmentation': rle, + 'area': float(mask.sum()), + 'bbox': [int(x) for x in mask2bbox(mask)], + 'iscrowd': 0 + }) + + ann_id += 1 + + img_name = img_name_fmt % name + img = scipy.ndimage.imread(os.path.join(img_path, img_name)) + + images.append({ + 'id': image_id, + 'width': img.shape[1], + 'height': img.shape[0], + 'file_name': img_name + }) + + image_id += 1 + + info = { + 'year': 2012, + 'version': 1, + 'description': 'Pascal SBD', + } + + categories = [{'id': x+1} for x in range(20)] + + with open('pascal_sbd_%s.json' % t, 'w') as f: + json.dump({ + 'info': info, + 'images': images, + 'annotations': annotations, + 'licenses': {}, + 'categories': categories + }, f) + diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/eval.sh b/cv/instance_segmentation/yolact/pytorch/scripts/eval.sh new file mode 100644 index 000000000..fbd5a429d --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/eval.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH -p GPU-small +#SBATCH -t 2:00:00 +#SBATCH --gres=gpu:p100:1 +#SBATCH --no-requeue + +# Usage: ./eval.sh weights extra_args + +module load python/3.6.4_gcc5_np1.14.5 +module load cuda/9.0 + +cd $SCRATCH/yolact + +python3 eval.py --trained_model=$1 --no_bar $2 > logs/eval/$(basename -- $1).log 2>&1 diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/make_grid.py b/cv/instance_segmentation/yolact/pytorch/scripts/make_grid.py new file mode 100644 index 000000000..046329e18 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/make_grid.py @@ -0,0 +1,204 @@ +import numpy as np +import math, random + +import matplotlib.pyplot as plt +from matplotlib.widgets import Slider, Button + + +fig, ax = plt.subplots() +plt.subplots_adjust(bottom=0.24) +im_handle = None + +save_path = 'grid.npy' + +center_x, center_y = (0.5, 0.5) +grid_w, grid_h = (35, 35) +spacing = 0 +scale = 4 +angle = 0 +grid = None + +all_grids = [] +unique = False + +# A hack +disable_render = False + +def render(): + if disable_render: + return + + x = np.tile(np.array(list(range(grid_w)), dtype=np.float).reshape(1, grid_w), [grid_h, 1]) - grid_w * center_x + y = np.tile(np.array(list(range(grid_h)), dtype=np.float).reshape(grid_h, 1), [1, grid_w]) - grid_h * center_y + + x /= scale + y /= scale + + a1 = angle + math.pi / 3 + a2 = -angle + math.pi / 3 + a3 = angle + + z1 = x * math.sin(a1) + y * math.cos(a1) + z2 = x * math.sin(a2) - y * math.cos(a2) + z3 = x * math.sin(a3) + y * math.cos(a3) + + s1 = np.square(np.sin(z1)) + s2 = np.square(np.sin(z2)) + s3 = np.square(np.sin(z3)) + + line_1 = np.exp(s1 * spacing) * s1 + line_2 = np.exp(s2 * spacing) * s2 + line_3 = np.exp(s3 * spacing) * s3 + + global grid + grid = np.clip(1 - (line_1 + line_2 + line_3) / 3, 0, 1) + + global im_handle + if im_handle is None: + im_handle = plt.imshow(grid) + else: + im_handle.set_data(grid) + fig.canvas.draw_idle() + +def update_scale(val): + global scale + scale = val + + render() + +def update_angle(val): + global angle + angle = val + + render() + +def update_centerx(val): + global center_x + center_x = val + + render() + +def update_centery(val): + global center_y + center_y = val + + render() + +def update_spacing(val): + global spacing + spacing = val + + render() + +def randomize(val): + global center_x, center_y, spacing, scale, angle, disable_render + + center_x, center_y = (random.uniform(0, 1), random.uniform(0, 1)) + spacing = random.uniform(-0.2, 2) + scale = 4 * math.exp(random.uniform(-1, 1)) + angle = random.uniform(-math.pi, math.pi) + + disable_render = True + + scale_slider.set_val(scale) + angle_slider.set_val(angle) + centx_slider.set_val(center_x) + centy_slider.set_val(center_y) + spaci_slider.set_val(spacing) + + disable_render = False + + render() + +def add(val): + all_grids.append(grid) + + global unique + if not unique: + unique = test_uniqueness(np.stack(all_grids)) + + export_len_text.set_text('Num Grids: ' + str(len(all_grids))) + fig.canvas.draw_idle() + +def add_randomize(val): + add(val) + randomize(val) + +def export(val): + np.save(save_path, np.stack(all_grids)) + print('Saved %d grids to "%s"' % (len(all_grids), save_path)) + + global unique + unique = False + all_grids.clear() + + export_len_text.set_text('Num Grids: ' + str(len(all_grids))) + fig.canvas.draw_idle() + +def test_uniqueness(grids): + # Grids shape [ngrids, h, w] + grids = grids.reshape((-1, grid_h, grid_w)) + + for y in range(grid_h): + for x in range(grid_h): + pixel_features = grids[:, y, x] + + # l1 distance for this pixel with every other + l1_dist = np.sum(np.abs(grids - np.tile(pixel_features, grid_h*grid_w).reshape((-1, grid_h, grid_w))), axis=0) + + # Equal if l1 distance is really small. Note that this will include this pixel + num_equal = np.sum((l1_dist < 0.0001).astype(np.int32)) + + if num_equal > 1: + print('Pixel at (%d, %d) has %d other pixel%s with the same representation.' % (x, y, num_equal-1, '' if num_equal==2 else 's')) + return False + + print('Each pixel has a distinct representation.') + return True + + + +render() + +axis = plt.axes([0.22, 0.19, 0.59, 0.03], facecolor='lightgoldenrodyellow') +scale_slider = Slider(axis, 'Scale', 0.1, 20, valinit=scale, valstep=0.1) +scale_slider.on_changed(update_scale) + +axis = plt.axes([0.22, 0.15, 0.59, 0.03], facecolor='lightgoldenrodyellow') +angle_slider = Slider(axis, 'Angle', -math.pi, math.pi, valinit=angle, valstep=0.1) +angle_slider.on_changed(update_angle) + +axis = plt.axes([0.22, 0.11, 0.59, 0.03], facecolor='lightgoldenrodyellow') +centx_slider = Slider(axis, 'Center X', 0, 1, valinit=center_x, valstep=0.05) +centx_slider.on_changed(update_centerx) + +axis = plt.axes([0.22, 0.07, 0.59, 0.03], facecolor='lightgoldenrodyellow') +centy_slider = Slider(axis, 'Center Y', 0, 1, valinit=center_y, valstep=0.05) +centy_slider.on_changed(update_centery) + +axis = plt.axes([0.22, 0.03, 0.59, 0.03], facecolor='lightgoldenrodyellow') +spaci_slider = Slider(axis, 'Spacing', -1, 2, valinit=spacing, valstep=0.05) +spaci_slider.on_changed(update_spacing) + +axis = plt.axes([0.8, 0.54, 0.15, 0.05], facecolor='lightgoldenrodyellow') +rando_button = Button(axis, 'Randomize') +rando_button.on_clicked(randomize) + +axis = plt.axes([0.8, 0.48, 0.15, 0.05], facecolor='lightgoldenrodyellow') +addgr_button = Button(axis, 'Add') +addgr_button.on_clicked(add) + +# Likely not a good way to do this but whatever +export_len_text = plt.text(0, 3, 'Num Grids: 0') + +axis = plt.axes([0.8, 0.42, 0.15, 0.05], facecolor='lightgoldenrodyellow') +addra_button = Button(axis, 'Add / Rand') +addra_button.on_clicked(add_randomize) + +axis = plt.axes([0.8, 0.36, 0.15, 0.05], facecolor='lightgoldenrodyellow') +saveg_button = Button(axis, 'Save') +saveg_button.on_clicked(export) + + + +plt.show() diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/optimize_bboxes.py b/cv/instance_segmentation/yolact/pytorch/scripts/optimize_bboxes.py new file mode 100644 index 000000000..88e33b173 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/optimize_bboxes.py @@ -0,0 +1,204 @@ +""" +Instead of clustering bbox widths and heights, this script +directly optimizes average IoU across the training set given +the specified number of anchor boxes. + +Run this script from the Yolact root directory. +""" + +import pickle +import random +from itertools import product +from math import sqrt + +import numpy as np +import torch +from scipy.optimize import minimize + +dump_file = 'weights/bboxes.pkl' +aug_file = 'weights/bboxes_aug.pkl' + +use_augmented_boxes = True + + +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [A,4]. + box_b: (tensor) bounding boxes, Shape: [B,4]. + Return: + (tensor) intersection area, Shape: [A,B]. + """ + A = box_a.size(0) + B = box_b.size(0) + max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), + box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) + min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), + box_b[:, :2].unsqueeze(0).expand(A, B, 2)) + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + + +def jaccard(box_a, box_b, iscrowd=False): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. If iscrowd=True, put the crowd in box_b. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] + area_b = ((box_b[:, 2]-box_b[:, 0]) * + (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] + union = area_a + area_b - inter + + if iscrowd: + return inter / area_a + else: + return inter / union # [A,B] + +# Also convert to point form +def to_relative(bboxes): + return np.concatenate((bboxes[:, 2:4] / bboxes[:, :2], (bboxes[:, 2:4] + bboxes[:, 4:]) / bboxes[:, :2]), axis=1) + + +def make_priors(conv_size, scales, aspect_ratios): + prior_data = [] + conv_h = conv_size[0] + conv_w = conv_size[1] + + # Iteration order is important (it has to sync up with the convout) + for j, i in product(range(conv_h), range(conv_w)): + x = (i + 0.5) / conv_w + y = (j + 0.5) / conv_h + + for scale, ars in zip(scales, aspect_ratios): + for ar in ars: + w = scale * ar / conv_w + h = scale / ar / conv_h + + # Point form + prior_data += [x - w/2, y - h/2, x + w/2, y + h/2] + return torch.Tensor(prior_data).view(-1, 4).cuda() + + + +scales = [[1.68, 2.91], [2.95, 2.22, 0.84], [2.17, 2.22, 3.22], [0.76, 2.06, 2.81], [5.33, 2.79], [13.69]] +aspect_ratios = [[[0.72, 0.96], [0.68, 1.17]], [[1.30, 0.66], [0.63, 1.23], [0.87, 1.41]], [[1.96, 1.23], [0.58, 0.84], [0.61, 1.15]], [[19.79, 2.21], [0.47, 1.76], [1.38, 0.79]], [[4.79, 17.96], [1.04]], [[14.82]]] +conv_sizes = [(35, 35), (18, 18), (9, 9), (5, 5), (3, 3), (2, 2)] + +optimize_scales = False + +batch_idx = 0 + + +def compute_hits(bboxes, anchors, iou_threshold=0.5): + ious = jaccard(bboxes, anchors) + perGTAnchorMax, _ = torch.max(ious, dim=1) + + return (perGTAnchorMax > iou_threshold) + +def compute_recall(hits, base_hits): + hits = (hits | base_hits).float() + return torch.sum(hits) / hits.size(0) + + +def step(x, x_func, bboxes, base_hits, optim_idx): + # This should set the scale and aspect ratio + x_func(x, scales[optim_idx], aspect_ratios[optim_idx]) + + anchors = make_priors(conv_sizes[optim_idx], scales[optim_idx], aspect_ratios[optim_idx]) + + return -float(compute_recall(compute_hits(bboxes, anchors), base_hits).cpu()) + + +def optimize(full_bboxes, optim_idx, batch_size=5000): + global batch_idx, scales, aspect_ratios, conv_sizes + + start = batch_idx * batch_size + end = min((batch_idx + 1) * batch_size, full_bboxes.size(0)) + + if batch_idx > (full_bboxes.size(0) // batch_size): + batch_idx = 0 + + bboxes = full_bboxes[start:end, :] + + anchor_base = [ + make_priors(conv_sizes[idx], scales[idx], aspect_ratios[idx]) + for idx in range(len(conv_sizes)) if idx != optim_idx] + base_hits = compute_hits(bboxes, torch.cat(anchor_base, dim=0)) + + + def set_x(x, scales, aspect_ratios): + if optimize_scales: + for i in range(len(scales)): + scales[i] = max(x[i], 0) + else: + k = 0 + for i in range(len(aspect_ratios)): + for j in range(len(aspect_ratios[i])): + aspect_ratios[i][j] = x[k] + k += 1 + + + res = minimize(step, x0=scales[optim_idx] if optimize_scales else sum(aspect_ratios[optim_idx], []), method='Powell', + args = (set_x, bboxes, base_hits, optim_idx),) + + +def pretty_str(x:list): + if isinstance(x, list): + return '[' + ', '.join([pretty_str(y) for y in x]) + ']' + elif isinstance(x, np.ndarray): + return pretty_str(list(x)) + else: + return '%.2f' % x + +if __name__ == '__main__': + + if use_augmented_boxes: + with open(aug_file, 'rb') as f: + bboxes = pickle.load(f) + else: + # Load widths and heights from a dump file. Obtain this with + # python3 scripts/save_bboxes.py + with open(dump_file, 'rb') as f: + bboxes = pickle.load(f) + + bboxes = np.array(bboxes) + bboxes = to_relative(bboxes) + + with torch.no_grad(): + bboxes = torch.Tensor(bboxes).cuda() + + def print_out(): + if optimize_scales: + print('Scales: ' + pretty_str(scales)) + else: + print('Aspect Ratios: ' + pretty_str(aspect_ratios)) + + for p in range(10): + print('(Sub Iteration) ', end='') + for i in range(len(conv_sizes)): + print('%d ' % i, end='', flush=True) + optimize(bboxes, i) + print('Done', end='\r') + + print('(Iteration %d) ' % p, end='') + print_out() + print() + + optimize_scales = not optimize_scales + + print('scales = ' + pretty_str(scales)) + print('aspect_ratios = ' + pretty_str(aspect_ratios)) + + diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/parse_eval.py b/cv/instance_segmentation/yolact/pytorch/scripts/parse_eval.py new file mode 100644 index 000000000..153015655 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/parse_eval.py @@ -0,0 +1,49 @@ +import re, sys, os +import matplotlib.pyplot as plt +from matplotlib._color_data import XKCD_COLORS + +with open(sys.argv[1], 'r') as f: + txt = f.read() + +txt, overall = txt.split('overall performance') + +class_names = [] +mAP_overall = [] +mAP_small = [] +mAP_medium = [] +mAP_large = [] + +for class_result in txt.split('evaluate category: ')[1:]: + lines = class_result.split('\n') + class_names.append(lines[0]) + + def grabMAP(string): + return float(string.split('] = ')[1]) * 100 + + mAP_overall.append(grabMAP(lines[ 7])) + mAP_small .append(grabMAP(lines[10])) + mAP_medium .append(grabMAP(lines[11])) + mAP_large .append(grabMAP(lines[12])) + +mAP_map = { + 'small': mAP_small, + 'medium': mAP_medium, + 'large': mAP_large, +} + +if len(sys.argv) > 2: + bars = plt.bar(class_names, mAP_map[sys.argv[2]]) + plt.title(sys.argv[2] + ' mAP per class') +else: + bars = plt.bar(class_names, mAP_overall) + plt.title('overall mAP per class') + +colors = list(XKCD_COLORS.values()) + +for idx, bar in enumerate(bars): + # Mmm pseudorandom colors + char_sum = sum([ord(char) for char in class_names[idx]]) + bar.set_color(colors[char_sum % len(colors)]) + +plt.xticks(rotation='vertical') +plt.show() diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/plot_loss.py b/cv/instance_segmentation/yolact/pytorch/scripts/plot_loss.py new file mode 100644 index 000000000..6ebb56dd9 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/plot_loss.py @@ -0,0 +1,78 @@ +import re, sys, os +import matplotlib.pyplot as plt + +from utils.functions import MovingAverage + +with open(sys.argv[1], 'r') as f: + inp = f.read() + +patterns = { + 'train': re.compile(r'\[\s*(?P\d+)\]\s*(?P\d+) \|\| B: (?P\S+) \| C: (?P\S+) \| M: (?P\S+) \|( S: (?P\S+) \|)? T: (?P\S+)'), + 'val': re.compile(r'\s*(?P[a-z]+) \|\s*(?P\S+)') +} +data = {key: [] for key in patterns} + +for line in inp.split('\n'): + for key, pattern in patterns.items(): + f = pattern.search(line) + + if f is not None: + datum = f.groupdict() + for k, v in datum.items(): + if v is not None: + try: + v = float(v) + except ValueError: + pass + datum[k] = v + + if key == 'val': + datum = (datum, data['train'][-1]) + data[key].append(datum) + break + + +def smoother(y, interval=100): + avg = MovingAverage(interval) + + for i in range(len(y)): + avg.append(y[i]) + y[i] = avg.get_avg() + + return y + +def plot_train(data): + plt.title(os.path.basename(sys.argv[1]) + ' Training Loss') + plt.xlabel('Iteration') + plt.ylabel('Loss') + + loss_names = ['BBox Loss', 'Conf Loss', 'Mask Loss'] + + x = [x['iteration'] for x in data] + plt.plot(x, smoother([y['b'] for y in data])) + plt.plot(x, smoother([y['c'] for y in data])) + plt.plot(x, smoother([y['m'] for y in data])) + + if data[0]['s'] is not None: + plt.plot(x, smoother([y['s'] for y in data])) + loss_names.append('Segmentation Loss') + + plt.legend(loss_names) + plt.show() + +def plot_val(data): + plt.title(os.path.basename(sys.argv[1]) + ' Validation mAP') + plt.xlabel('Epoch') + plt.ylabel('mAP') + + x = [x[1]['epoch'] for x in data if x[0]['type'] == 'box'] + plt.plot(x, [x[0]['all'] for x in data if x[0]['type'] == 'box']) + plt.plot(x, [x[0]['all'] for x in data if x[0]['type'] == 'mask']) + + plt.legend(['BBox mAP', 'Mask mAP']) + plt.show() + +if len(sys.argv) > 2 and sys.argv[2] == 'val': + plot_val(data['val']) +else: + plot_train(data['train']) diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/resume.sh b/cv/instance_segmentation/yolact/pytorch/scripts/resume.sh new file mode 100644 index 000000000..d4857876b --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/resume.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH -p GPU-shared +#SBATCH -t 48:00:00 +#SBATCH --gres=gpu:p100:1 +#SBATCH --no-requeue + +# Usage: ./resume.sh config batch_size resume_file + +module load python/3.6.4_gcc5_np1.14.5 +module load cuda/9.0 + +cd $SCRATCH/yolact + +python3 train.py --config $1 --batch_size $2 --resume=$3 --save_interval 5000 --start_iter=-1 >>logs/$1_log 2>&1 diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/save_bboxes.py b/cv/instance_segmentation/yolact/pytorch/scripts/save_bboxes.py new file mode 100644 index 000000000..63dedb4d5 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/save_bboxes.py @@ -0,0 +1,33 @@ +""" This script transforms and saves bbox coordinates into a pickle object for easy loading. """ + + +import os.path as osp +import json, pickle +import sys + +import numpy as np + +COCO_ROOT = osp.join('.', 'data/coco/') + +annotation_file = 'instances_train2017.json' +annotation_path = osp.join(COCO_ROOT, 'annotations/', annotation_file) + +dump_file = 'weights/bboxes.pkl' + +with open(annotation_path, 'r') as f: + annotations_json = json.load(f) + +annotations = annotations_json['annotations'] +images = annotations_json['images'] +images = {image['id']: image for image in images} +bboxes = [] + +for ann in annotations: + image = images[ann['image_id']] + w,h = (image['width'], image['height']) + + if 'bbox' in ann: + bboxes.append([w, h] + ann['bbox']) + +with open(dump_file, 'wb') as f: + pickle.dump(bboxes, f) diff --git a/cv/instance_segmentation/yolact/pytorch/scripts/unpack_statedict.py b/cv/instance_segmentation/yolact/pytorch/scripts/unpack_statedict.py new file mode 100644 index 000000000..d26d03bc6 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/scripts/unpack_statedict.py @@ -0,0 +1,16 @@ +import torch +import sys, os + +# Usage python scripts/unpack_statedict.py path_to_pth out_folder/ +# Make sure to include that slash after your out folder, since I can't +# be arsed to do path concatenation so I'd rather type out this comment + +print('Loading state dict...') +state = torch.load(sys.argv[1]) + +if not os.path.exists(sys.argv[2]): + os.mkdir(sys.argv[2]) + +print('Saving stuff...') +for key, val in state.items(): + torch.save(val, sys.argv[2] + key) diff --git a/cv/instance_segmentation/yolact/pytorch/train.py b/cv/instance_segmentation/yolact/pytorch/train.py new file mode 100644 index 000000000..5394308f1 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/train.py @@ -0,0 +1,504 @@ +from data import * +from utils.augmentations import SSDAugmentation, BaseTransform +from utils.functions import MovingAverage, SavePath +from utils.logger import Log +from utils import timer +from layers.modules import MultiBoxLoss +from yolact import Yolact +import os +import sys +import time +import math, random +from pathlib import Path +import torch +from torch.autograd import Variable +import torch.nn as nn +import torch.optim as optim +import torch.backends.cudnn as cudnn +import torch.nn.init as init +import torch.utils.data as data +import numpy as np +import argparse +import datetime + +# Oof +import eval as eval_script + +def str2bool(v): + return v.lower() in ("yes", "true", "t", "1") + + +parser = argparse.ArgumentParser( + description='Yolact Training Script') +parser.add_argument('--batch_size', default=8, type=int, + help='Batch size for training') +parser.add_argument('--resume', default=None, type=str, + help='Checkpoint state_dict file to resume training from. If this is "interrupt"'\ + ', the model will resume training from the interrupt file.') +parser.add_argument('--start_iter', default=-1, type=int, + help='Resume training at this iter. If this is -1, the iteration will be'\ + 'determined from the file name.') +parser.add_argument('--num_workers', default=4, type=int, + help='Number of workers used in dataloading') +parser.add_argument('--cuda', default=True, type=str2bool, + help='Use CUDA to train model') +parser.add_argument('--lr', '--learning_rate', default=None, type=float, + help='Initial learning rate. Leave as None to read this from the config.') +parser.add_argument('--momentum', default=None, type=float, + help='Momentum for SGD. Leave as None to read this from the config.') +parser.add_argument('--decay', '--weight_decay', default=None, type=float, + help='Weight decay for SGD. Leave as None to read this from the config.') +parser.add_argument('--gamma', default=None, type=float, + help='For each lr step, what to multiply the lr by. Leave as None to read this from the config.') +parser.add_argument('--save_folder', default='weights/', + help='Directory for saving checkpoint models.') +parser.add_argument('--log_folder', default='logs/', + help='Directory for saving logs.') +parser.add_argument('--config', default=None, + help='The config object to use.') +parser.add_argument('--save_interval', default=10000, type=int, + help='The number of iterations between saving the model.') +parser.add_argument('--validation_size', default=5000, type=int, + help='The number of images to use for validation.') +parser.add_argument('--validation_epoch', default=2, type=int, + help='Output validation information every n iterations. If -1, do no validation.') +parser.add_argument('--keep_latest', dest='keep_latest', action='store_true', + help='Only keep the latest checkpoint instead of each one.') +parser.add_argument('--keep_latest_interval', default=100000, type=int, + help='When --keep_latest is on, don\'t delete the latest file at these intervals. This should be a multiple of save_interval or 0.') +parser.add_argument('--dataset', default=None, type=str, + help='If specified, override the dataset specified in the config with this one (example: coco2017_dataset).') +parser.add_argument('--no_log', dest='log', action='store_false', + help='Don\'t log per iteration information into log_folder.') +parser.add_argument('--log_gpu', dest='log_gpu', action='store_true', + help='Include GPU information in the logs. Nvidia-smi tends to be slow, so set this with caution.') +parser.add_argument('--no_interrupt', dest='interrupt', action='store_false', + help='Don\'t save an interrupt when KeyboardInterrupt is caught.') +parser.add_argument('--batch_alloc', default=None, type=str, + help='If using multiple GPUS, you can set this to be a comma separated list detailing which GPUs should get what local batch size (It should add up to your total batch size).') +parser.add_argument('--no_autoscale', dest='autoscale', action='store_false', + help='YOLACT will automatically scale the lr and the number of iterations depending on the batch size. Set this if you want to disable that.') + +parser.set_defaults(keep_latest=False, log=True, log_gpu=False, interrupt=True, autoscale=True) +args = parser.parse_args() + +if args.config is not None: + set_cfg(args.config) + +if args.dataset is not None: + set_dataset(args.dataset) + +if args.autoscale and args.batch_size != 8: + factor = args.batch_size / 8 + if __name__ == '__main__': + print('Scaling parameters by %.2f to account for a batch size of %d.' % (factor, args.batch_size)) + + cfg.lr *= factor + cfg.max_iter //= factor + cfg.lr_steps = [x // factor for x in cfg.lr_steps] + +# Update training parameters from the config if necessary +def replace(name): + if getattr(args, name) == None: setattr(args, name, getattr(cfg, name)) +replace('lr') +replace('decay') +replace('gamma') +replace('momentum') + +# This is managed by set_lr +cur_lr = args.lr + +if torch.cuda.device_count() == 0: + print('No GPUs detected. Exiting...') + exit(-1) + +if args.batch_size // torch.cuda.device_count() < 6: + if __name__ == '__main__': + print('Per-GPU batch size is less than the recommended limit for batch norm. Disabling batch norm.') + cfg.freeze_bn = True + +loss_types = ['B', 'C', 'M', 'P', 'D', 'E', 'S', 'I'] + +if torch.cuda.is_available(): + if args.cuda: + torch.set_default_tensor_type('torch.cuda.FloatTensor') + if not args.cuda: + print("WARNING: It looks like you have a CUDA device, but aren't " + + "using CUDA.\nRun with --cuda for optimal training speed.") + torch.set_default_tensor_type('torch.FloatTensor') +else: + torch.set_default_tensor_type('torch.FloatTensor') + +class NetLoss(nn.Module): + """ + A wrapper for running the network and computing the loss + This is so we can more efficiently use DataParallel. + """ + + def __init__(self, net:Yolact, criterion:MultiBoxLoss): + super().__init__() + + self.net = net + self.criterion = criterion + + def forward(self, images, targets, masks, num_crowds): + preds = self.net(images) + losses = self.criterion(self.net, preds, targets, masks, num_crowds) + return losses + +class CustomDataParallel(nn.DataParallel): + """ + This is a custom version of DataParallel that works better with our training data. + It should also be faster than the general case. + """ + + def scatter(self, inputs, kwargs, device_ids): + # More like scatter and data prep at the same time. The point is we prep the data in such a way + # that no scatter is necessary, and there's no need to shuffle stuff around different GPUs. + devices = ['cuda:' + str(x) for x in device_ids] + splits = prepare_data(inputs[0], devices, allocation=args.batch_alloc) + + return [[split[device_idx] for split in splits] for device_idx in range(len(devices))], \ + [kwargs] * len(devices) + + def gather(self, outputs, output_device): + out = {} + + for k in outputs[0]: + out[k] = torch.stack([output[k].to(output_device) for output in outputs]) + + return out + +def train(): + if not os.path.exists(args.save_folder): + os.mkdir(args.save_folder) + + dataset = COCODetection(image_path=cfg.dataset.train_images, + info_file=cfg.dataset.train_info, + transform=SSDAugmentation(MEANS)) + + if args.validation_epoch > 0: + setup_eval() + val_dataset = COCODetection(image_path=cfg.dataset.valid_images, + info_file=cfg.dataset.valid_info, + transform=BaseTransform(MEANS)) + + # Parallel wraps the underlying module, but when saving and loading we don't want that + yolact_net = Yolact() + net = yolact_net + net.train() + + if args.log: + log = Log(cfg.name, args.log_folder, dict(args._get_kwargs()), + overwrite=(args.resume is None), log_gpu_stats=args.log_gpu) + + # I don't use the timer during training (I use a different timing method). + # Apparently there's a race condition with multiple GPUs, so disable it just to be safe. + timer.disable_all() + + # Both of these can set args.resume to None, so do them before the check + if args.resume == 'interrupt': + args.resume = SavePath.get_interrupt(args.save_folder) + elif args.resume == 'latest': + args.resume = SavePath.get_latest(args.save_folder, cfg.name) + + if args.resume is not None: + print('Resuming training, loading {}...'.format(args.resume)) + yolact_net.load_weights(args.resume) + + if args.start_iter == -1: + args.start_iter = SavePath.from_str(args.resume).iteration + else: + print('Initializing weights...') + yolact_net.init_weights(backbone_path=args.save_folder + cfg.backbone.path) + + optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, + weight_decay=args.decay) + criterion = MultiBoxLoss(num_classes=cfg.num_classes, + pos_threshold=cfg.positive_iou_threshold, + neg_threshold=cfg.negative_iou_threshold, + negpos_ratio=cfg.ohem_negpos_ratio) + + if args.batch_alloc is not None: + args.batch_alloc = [int(x) for x in args.batch_alloc.split(',')] + if sum(args.batch_alloc) != args.batch_size: + print('Error: Batch allocation (%s) does not sum to batch size (%s).' % (args.batch_alloc, args.batch_size)) + exit(-1) + + net = CustomDataParallel(NetLoss(net, criterion)) + if args.cuda: + net = net.cuda() + + # Initialize everything + if not cfg.freeze_bn: yolact_net.freeze_bn() # Freeze bn so we don't kill our means + yolact_net(torch.zeros(1, 3, cfg.max_size, cfg.max_size).cuda()) + if not cfg.freeze_bn: yolact_net.freeze_bn(True) + + # loss counters + loc_loss = 0 + conf_loss = 0 + iteration = max(args.start_iter, 0) + last_time = time.time() + + epoch_size = len(dataset) // args.batch_size + num_epochs = math.ceil(cfg.max_iter / epoch_size) + + # Which learning rate adjustment step are we on? lr' = lr * gamma ^ step_index + step_index = 0 + + data_loader = data.DataLoader(dataset, args.batch_size, + num_workers=args.num_workers, + shuffle=False, collate_fn=detection_collate, + pin_memory=True) + + + save_path = lambda epoch, iteration: SavePath(cfg.name, epoch, iteration).get_path(root=args.save_folder) + time_avg = MovingAverage() + + global loss_types # Forms the print order + loss_avgs = { k: MovingAverage(100) for k in loss_types } + + print('Begin training!') + print() + # try-except so you can use ctrl+c to save early and stop training + try: + for epoch in range(num_epochs): + # Resume from start_iter + if (epoch+1)*epoch_size < iteration: + continue + + for datum in data_loader: + # Stop if we've reached an epoch if we're resuming from start_iter + if iteration == (epoch+1)*epoch_size: + break + + # Stop at the configured number of iterations even if mid-epoch + if iteration == cfg.max_iter: + break + + # Change a config setting if we've reached the specified iteration + changed = False + for change in cfg.delayed_settings: + if iteration >= change[0]: + changed = True + cfg.replace(change[1]) + + # Reset the loss averages because things might have changed + for avg in loss_avgs: + avg.reset() + + # If a config setting was changed, remove it from the list so we don't keep checking + if changed: + cfg.delayed_settings = [x for x in cfg.delayed_settings if x[0] > iteration] + + # Warm up by linearly interpolating the learning rate from some smaller value + if cfg.lr_warmup_until > 0 and iteration <= cfg.lr_warmup_until: + set_lr(optimizer, (args.lr - cfg.lr_warmup_init) * (iteration / cfg.lr_warmup_until) + cfg.lr_warmup_init) + + # Adjust the learning rate at the given iterations, but also if we resume from past that iteration + while step_index < len(cfg.lr_steps) and iteration >= cfg.lr_steps[step_index]: + step_index += 1 + set_lr(optimizer, args.lr * (args.gamma ** step_index)) + + # Zero the grad to get ready to compute gradients + optimizer.zero_grad() + + # Forward Pass + Compute loss at the same time (see CustomDataParallel and NetLoss) + losses = net(datum) + + losses = { k: (v).mean() for k,v in losses.items() } # Mean here because Dataparallel + loss = sum([losses[k] for k in losses]) + + # no_inf_mean removes some components from the loss, so make sure to backward through all of it + # all_loss = sum([v.mean() for v in losses.values()]) + + # Backprop + loss.backward() # Do this to free up vram even if loss is not finite + if torch.isfinite(loss).item(): + optimizer.step() + + # Add the loss to the moving average for bookkeeping + for k in losses: + loss_avgs[k].add(losses[k].item()) + + cur_time = time.time() + elapsed = cur_time - last_time + last_time = cur_time + + # Exclude graph setup from the timing information + if iteration != args.start_iter: + time_avg.add(elapsed) + + if iteration % 10 == 0: + eta_str = str(datetime.timedelta(seconds=(cfg.max_iter-iteration) * time_avg.get_avg())).split('.')[0] + + total = sum([loss_avgs[k].get_avg() for k in losses]) + loss_labels = sum([[k, loss_avgs[k].get_avg()] for k in loss_types if k in losses], []) + + print(('[%3d] %7d ||' + (' %s: %.3f |' * len(losses)) + ' T: %.3f || ETA: %s || timer: %.3f') + % tuple([epoch, iteration] + loss_labels + [total, eta_str, elapsed]), flush=True) + + if args.log: + precision = 5 + loss_info = {k: round(losses[k].item(), precision) for k in losses} + loss_info['T'] = round(loss.item(), precision) + + if args.log_gpu: + log.log_gpu_stats = (iteration % 10 == 0) # nvidia-smi is sloooow + + log.log('train', loss=loss_info, epoch=epoch, iter=iteration, + lr=round(cur_lr, 10), elapsed=elapsed) + + log.log_gpu_stats = args.log_gpu + + iteration += 1 + + if iteration % args.save_interval == 0 and iteration != args.start_iter: + if args.keep_latest: + latest = SavePath.get_latest(args.save_folder, cfg.name) + + print('Saving state, iter:', iteration) + yolact_net.save_weights(save_path(epoch, iteration)) + + if args.keep_latest and latest is not None: + if args.keep_latest_interval <= 0 or iteration % args.keep_latest_interval != args.save_interval: + print('Deleting old save...') + os.remove(latest) + + # This is done per epoch + if args.validation_epoch > 0: + if epoch % args.validation_epoch == 0 and epoch > 0: + compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None) + + # Compute validation mAP after training is finished + compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None) + except KeyboardInterrupt: + if args.interrupt: + print('Stopping early. Saving network...') + + # Delete previous copy of the interrupted network so we don't spam the weights folder + SavePath.remove_interrupt(args.save_folder) + + yolact_net.save_weights(save_path(epoch, repr(iteration) + '_interrupt')) + exit() + + yolact_net.save_weights(save_path(epoch, iteration)) + + +def set_lr(optimizer, new_lr): + for param_group in optimizer.param_groups: + param_group['lr'] = new_lr + + global cur_lr + cur_lr = new_lr + +def gradinator(x): + x.requires_grad = False + return x + +def prepare_data(datum, devices:list=None, allocation:list=None): + with torch.no_grad(): + if devices is None: + devices = ['cuda:0'] if args.cuda else ['cpu'] + if allocation is None: + allocation = [args.batch_size // len(devices)] * (len(devices) - 1) + allocation.append(args.batch_size - sum(allocation)) # The rest might need more/less + + images, (targets, masks, num_crowds) = datum + + cur_idx = 0 + for device, alloc in zip(devices, allocation): + for _ in range(alloc): + images[cur_idx] = gradinator(images[cur_idx].to(device)) + targets[cur_idx] = gradinator(targets[cur_idx].to(device)) + masks[cur_idx] = gradinator(masks[cur_idx].to(device)) + cur_idx += 1 + + if cfg.preserve_aspect_ratio: + # Choose a random size from the batch + _, h, w = images[random.randint(0, len(images)-1)].size() + + for idx, (image, target, mask, num_crowd) in enumerate(zip(images, targets, masks, num_crowds)): + images[idx], targets[idx], masks[idx], num_crowds[idx] \ + = enforce_size(image, target, mask, num_crowd, w, h) + + cur_idx = 0 + split_images, split_targets, split_masks, split_numcrowds \ + = [[None for alloc in allocation] for _ in range(4)] + + for device_idx, alloc in enumerate(allocation): + split_images[device_idx] = torch.stack(images[cur_idx:cur_idx+alloc], dim=0) + split_targets[device_idx] = targets[cur_idx:cur_idx+alloc] + split_masks[device_idx] = masks[cur_idx:cur_idx+alloc] + split_numcrowds[device_idx] = num_crowds[cur_idx:cur_idx+alloc] + + cur_idx += alloc + + return split_images, split_targets, split_masks, split_numcrowds + +def no_inf_mean(x:torch.Tensor): + """ + Computes the mean of a vector, throwing out all inf values. + If there are no non-inf values, this will return inf (i.e., just the normal mean). + """ + + no_inf = [a for a in x if torch.isfinite(a)] + + if len(no_inf) > 0: + return sum(no_inf) / len(no_inf) + else: + return x.mean() + +def compute_validation_loss(net, data_loader, criterion): + global loss_types + + with torch.no_grad(): + losses = {} + + # Don't switch to eval mode because we want to get losses + iterations = 0 + for datum in data_loader: + images, targets, masks, num_crowds = prepare_data(datum) + out = net(images) + + wrapper = ScatterWrapper(targets, masks, num_crowds) + _losses = criterion(out, wrapper, wrapper.make_mask()) + + for k, v in _losses.items(): + v = v.mean().item() + if k in losses: + losses[k] += v + else: + losses[k] = v + + iterations += 1 + if args.validation_size <= iterations * args.batch_size: + break + + for k in losses: + losses[k] /= iterations + + + loss_labels = sum([[k, losses[k]] for k in loss_types if k in losses], []) + print(('Validation ||' + (' %s: %.3f |' * len(losses)) + ')') % tuple(loss_labels), flush=True) + +def compute_validation_map(epoch, iteration, yolact_net, dataset, log:Log=None): + with torch.no_grad(): + yolact_net.eval() + + start = time.time() + print() + print("Computing validation mAP (this may take a while)...", flush=True) + val_info = eval_script.evaluate(yolact_net, dataset, train_mode=True) + end = time.time() + + if log is not None: + log.log('val', val_info, elapsed=(end - start), epoch=epoch, iter=iteration) + + yolact_net.train() + +def setup_eval(): + eval_script.parse_args(['--no_bar', '--max_images='+str(args.validation_size)]) + +if __name__ == '__main__': + train() diff --git a/cv/instance_segmentation/yolact/pytorch/utils/__init__.py b/cv/instance_segmentation/yolact/pytorch/utils/__init__.py new file mode 100644 index 000000000..a7ecb871a --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/__init__.py @@ -0,0 +1 @@ +from .augmentations import SSDAugmentation \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/utils/augmentations.py b/cv/instance_segmentation/yolact/pytorch/utils/augmentations.py new file mode 100644 index 000000000..cc7a73aa3 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/augmentations.py @@ -0,0 +1,688 @@ +import torch +from torchvision import transforms +import cv2 +import numpy as np +import types +from numpy import random +from math import sqrt + +from data import cfg, MEANS, STD + + +def intersect(box_a, box_b): + max_xy = np.minimum(box_a[:, 2:], box_b[2:]) + min_xy = np.maximum(box_a[:, :2], box_b[:2]) + inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf) + return inter[:, 0] * inter[:, 1] + + +def jaccard_numpy(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: Multiple bounding boxes, Shape: [num_boxes,4] + box_b: Single bounding box, Shape: [4] + Return: + jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])) # [A,B] + area_b = ((box_b[2]-box_b[0]) * + (box_b[3]-box_b[1])) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + + +class Compose(object): + """Composes several augmentations together. + Args: + transforms (List[Transform]): list of transforms to compose. + Example: + >>> augmentations.Compose([ + >>> transforms.CenterCrop(10), + >>> transforms.ToTensor(), + >>> ]) + """ + + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, img, masks=None, boxes=None, labels=None): + for t in self.transforms: + img, masks, boxes, labels = t(img, masks, boxes, labels) + return img, masks, boxes, labels + + +class Lambda(object): + """Applies a lambda as a transform.""" + + def __init__(self, lambd): + assert isinstance(lambd, types.LambdaType) + self.lambd = lambd + + def __call__(self, img, masks=None, boxes=None, labels=None): + return self.lambd(img, masks, boxes, labels) + + +class ConvertFromInts(object): + def __call__(self, image, masks=None, boxes=None, labels=None): + return image.astype(np.float32), masks, boxes, labels + + + +class ToAbsoluteCoords(object): + def __call__(self, image, masks=None, boxes=None, labels=None): + height, width, channels = image.shape + boxes[:, 0] *= width + boxes[:, 2] *= width + boxes[:, 1] *= height + boxes[:, 3] *= height + + return image, masks, boxes, labels + + +class ToPercentCoords(object): + def __call__(self, image, masks=None, boxes=None, labels=None): + height, width, channels = image.shape + boxes[:, 0] /= width + boxes[:, 2] /= width + boxes[:, 1] /= height + boxes[:, 3] /= height + + return image, masks, boxes, labels + + +class Pad(object): + """ + Pads the image to the input width and height, filling the + background with mean and putting the image in the top-left. + + Note: this expects im_w <= width and im_h <= height + """ + def __init__(self, width, height, mean=MEANS, pad_gt=True): + self.mean = mean + self.width = width + self.height = height + self.pad_gt = pad_gt + + def __call__(self, image, masks, boxes=None, labels=None): + im_h, im_w, depth = image.shape + + expand_image = np.zeros( + (self.height, self.width, depth), + dtype=image.dtype) + expand_image[:, :, :] = self.mean + expand_image[:im_h, :im_w] = image + + if self.pad_gt: + expand_masks = np.zeros( + (masks.shape[0], self.height, self.width), + dtype=masks.dtype) + expand_masks[:,:im_h,:im_w] = masks + masks = expand_masks + + return expand_image, masks, boxes, labels + +class Resize(object): + """ If preserve_aspect_ratio is true, this resizes to an approximate area of max_size * max_size """ + + @staticmethod + def calc_size_preserve_ar(img_w, img_h, max_size): + """ I mathed this one out on the piece of paper. Resulting width*height = approx max_size^2 """ + ratio = sqrt(img_w / img_h) + w = max_size * ratio + h = max_size / ratio + return int(w), int(h) + + def __init__(self, resize_gt=True): + self.resize_gt = resize_gt + self.max_size = cfg.max_size + self.preserve_aspect_ratio = cfg.preserve_aspect_ratio + + def __call__(self, image, masks, boxes, labels=None): + img_h, img_w, _ = image.shape + + if self.preserve_aspect_ratio: + width, height = Resize.calc_size_preserve_ar(img_w, img_h, self.max_size) + else: + width, height = self.max_size, self.max_size + + image = cv2.resize(image, (width, height)) + + if self.resize_gt: + # Act like each object is a color channel + masks = masks.transpose((1, 2, 0)) + masks = cv2.resize(masks, (width, height)) + + # OpenCV resizes a (w,h,1) array to (s,s), so fix that + if len(masks.shape) == 2: + masks = np.expand_dims(masks, 0) + else: + masks = masks.transpose((2, 0, 1)) + + # Scale bounding boxes (which are currently absolute coordinates) + boxes[:, [0, 2]] *= (width / img_w) + boxes[:, [1, 3]] *= (height / img_h) + + # Discard boxes that are smaller than we'd like + w = boxes[:, 2] - boxes[:, 0] + h = boxes[:, 3] - boxes[:, 1] + + keep = (w > cfg.discard_box_width) * (h > cfg.discard_box_height) + masks = masks[keep] + boxes = boxes[keep] + labels['labels'] = labels['labels'][keep] + labels['num_crowds'] = (labels['labels'] < 0).sum() + + return image, masks, boxes, labels + + +class RandomSaturation(object): + def __init__(self, lower=0.5, upper=1.5): + self.lower = lower + self.upper = upper + assert self.upper >= self.lower, "contrast upper must be >= lower." + assert self.lower >= 0, "contrast lower must be non-negative." + + def __call__(self, image, masks=None, boxes=None, labels=None): + if random.randint(2): + image[:, :, 1] *= random.uniform(self.lower, self.upper) + + return image, masks, boxes, labels + + +class RandomHue(object): + def __init__(self, delta=18.0): + assert delta >= 0.0 and delta <= 360.0 + self.delta = delta + + def __call__(self, image, masks=None, boxes=None, labels=None): + if random.randint(2): + image[:, :, 0] += random.uniform(-self.delta, self.delta) + image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0 + image[:, :, 0][image[:, :, 0] < 0.0] += 360.0 + return image, masks, boxes, labels + + +class RandomLightingNoise(object): + def __init__(self): + self.perms = ((0, 1, 2), (0, 2, 1), + (1, 0, 2), (1, 2, 0), + (2, 0, 1), (2, 1, 0)) + + def __call__(self, image, masks=None, boxes=None, labels=None): + # Don't shuffle the channels please, why would you do this + + # if random.randint(2): + # swap = self.perms[random.randint(len(self.perms))] + # shuffle = SwapChannels(swap) # shuffle channels + # image = shuffle(image) + return image, masks, boxes, labels + + +class ConvertColor(object): + def __init__(self, current='BGR', transform='HSV'): + self.transform = transform + self.current = current + + def __call__(self, image, masks=None, boxes=None, labels=None): + if self.current == 'BGR' and self.transform == 'HSV': + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + elif self.current == 'HSV' and self.transform == 'BGR': + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + else: + raise NotImplementedError + return image, masks, boxes, labels + + +class RandomContrast(object): + def __init__(self, lower=0.5, upper=1.5): + self.lower = lower + self.upper = upper + assert self.upper >= self.lower, "contrast upper must be >= lower." + assert self.lower >= 0, "contrast lower must be non-negative." + + # expects float image + def __call__(self, image, masks=None, boxes=None, labels=None): + if random.randint(2): + alpha = random.uniform(self.lower, self.upper) + image *= alpha + return image, masks, boxes, labels + + +class RandomBrightness(object): + def __init__(self, delta=32): + assert delta >= 0.0 + assert delta <= 255.0 + self.delta = delta + + def __call__(self, image, masks=None, boxes=None, labels=None): + if random.randint(2): + delta = random.uniform(-self.delta, self.delta) + image += delta + return image, masks, boxes, labels + + +class ToCV2Image(object): + def __call__(self, tensor, masks=None, boxes=None, labels=None): + return tensor.cpu().numpy().astype(np.float32).transpose((1, 2, 0)), masks, boxes, labels + + +class ToTensor(object): + def __call__(self, cvimage, masks=None, boxes=None, labels=None): + return torch.from_numpy(cvimage.astype(np.float32)).permute(2, 0, 1), masks, boxes, labels + + +class RandomSampleCrop(object): + """Crop + Arguments: + img (Image): the image being input during training + boxes (Tensor): the original bounding boxes in pt form + labels (Tensor): the class labels for each bbox + mode (float tuple): the min and max jaccard overlaps + Return: + (img, boxes, classes) + img (Image): the cropped image + boxes (Tensor): the adjusted bounding boxes in pt form + labels (Tensor): the class labels for each bbox + """ + def __init__(self): + self.sample_options = ( + # using entire original input image + None, + # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9 + (0.1, None), + (0.3, None), + (0.7, None), + (0.9, None), + # randomly sample a patch + (None, None), + ) + + def __call__(self, image, masks, boxes=None, labels=None): + height, width, _ = image.shape + while True: + # randomly choose a mode + mode = random.choice(self.sample_options) + if mode is None: + return image, masks, boxes, labels + + min_iou, max_iou = mode + if min_iou is None: + min_iou = float('-inf') + if max_iou is None: + max_iou = float('inf') + + # max trails (50) + for _ in range(50): + current_image = image + + w = random.uniform(0.3 * width, width) + h = random.uniform(0.3 * height, height) + + # aspect ratio constraint b/t .5 & 2 + if h / w < 0.5 or h / w > 2: + continue + + left = random.uniform(width - w) + top = random.uniform(height - h) + + # convert to integer rect x1,y1,x2,y2 + rect = np.array([int(left), int(top), int(left+w), int(top+h)]) + + # calculate IoU (jaccard overlap) b/t the cropped and gt boxes + overlap = jaccard_numpy(boxes, rect) + + # This piece of code is bugged and does nothing: + # https://github.com/amdegroot/ssd.pytorch/issues/68 + # + # However, when I fixed it with overlap.max() < min_iou, + # it cut the mAP in half (after 8k iterations). So it stays. + # + # is min and max overlap constraint satisfied? if not try again + if overlap.min() < min_iou and max_iou < overlap.max(): + continue + + # cut the crop from the image + current_image = current_image[rect[1]:rect[3], rect[0]:rect[2], + :] + + # keep overlap with gt box IF center in sampled patch + centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0 + + # mask in all gt boxes that above and to the left of centers + m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1]) + + # mask in all gt boxes that under and to the right of centers + m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1]) + + # mask in that both m1 and m2 are true + mask = m1 * m2 + + # [0 ... 0 for num_gt and then 1 ... 1 for num_crowds] + num_crowds = labels['num_crowds'] + crowd_mask = np.zeros(mask.shape, dtype=np.int32) + + if num_crowds > 0: + crowd_mask[-num_crowds:] = 1 + + # have any valid boxes? try again if not + # Also make sure you have at least one regular gt + if not mask.any() or np.sum(1-crowd_mask[mask]) == 0: + continue + + # take only the matching gt masks + current_masks = masks[mask, :, :].copy() + + # take only matching gt boxes + current_boxes = boxes[mask, :].copy() + + # take only matching gt labels + labels['labels'] = labels['labels'][mask] + current_labels = labels + + # We now might have fewer crowd annotations + if num_crowds > 0: + labels['num_crowds'] = np.sum(crowd_mask[mask]) + + # should we use the box left and top corner or the crop's + current_boxes[:, :2] = np.maximum(current_boxes[:, :2], + rect[:2]) + # adjust to crop (by substracting crop's left,top) + current_boxes[:, :2] -= rect[:2] + + current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:], + rect[2:]) + # adjust to crop (by substracting crop's left,top) + current_boxes[:, 2:] -= rect[:2] + + # crop the current masks to the same dimensions as the image + current_masks = current_masks[:, rect[1]:rect[3], rect[0]:rect[2]] + + return current_image, current_masks, current_boxes, current_labels + + +class Expand(object): + def __init__(self, mean): + self.mean = mean + + def __call__(self, image, masks, boxes, labels): + if random.randint(2): + return image, masks, boxes, labels + + height, width, depth = image.shape + ratio = random.uniform(1, 4) + left = random.uniform(0, width*ratio - width) + top = random.uniform(0, height*ratio - height) + + expand_image = np.zeros( + (int(height*ratio), int(width*ratio), depth), + dtype=image.dtype) + expand_image[:, :, :] = self.mean + expand_image[int(top):int(top + height), + int(left):int(left + width)] = image + image = expand_image + + expand_masks = np.zeros( + (masks.shape[0], int(height*ratio), int(width*ratio)), + dtype=masks.dtype) + expand_masks[:,int(top):int(top + height), + int(left):int(left + width)] = masks + masks = expand_masks + + boxes = boxes.copy() + boxes[:, :2] += (int(left), int(top)) + boxes[:, 2:] += (int(left), int(top)) + + return image, masks, boxes, labels + + +class RandomMirror(object): + def __call__(self, image, masks, boxes, labels): + _, width, _ = image.shape + if random.randint(2): + image = image[:, ::-1] + masks = masks[:, :, ::-1] + boxes = boxes.copy() + boxes[:, 0::2] = width - boxes[:, 2::-2] + return image, masks, boxes, labels + + +class RandomFlip(object): + def __call__(self, image, masks, boxes, labels): + height , _ , _ = image.shape + if random.randint(2): + image = image[::-1, :] + masks = masks[:, ::-1, :] + boxes = boxes.copy() + boxes[:, 1::2] = height - boxes[:, 3::-2] + return image, masks, boxes, labels + + +class RandomRot90(object): + def __call__(self, image, masks, boxes, labels): + old_height , old_width , _ = image.shape + k = random.randint(4) + image = np.rot90(image,k) + masks = np.array([np.rot90(mask,k) for mask in masks]) + boxes = boxes.copy() + for _ in range(k): + boxes = np.array([[box[1], old_width - 1 - box[2], box[3], old_width - 1 - box[0]] for box in boxes]) + old_width, old_height = old_height, old_width + return image, masks, boxes, labels + + +class SwapChannels(object): + """Transforms a tensorized image by swapping the channels in the order + specified in the swap tuple. + Args: + swaps (int triple): final order of channels + eg: (2, 1, 0) + """ + + def __init__(self, swaps): + self.swaps = swaps + + def __call__(self, image): + """ + Args: + image (Tensor): image tensor to be transformed + Return: + a tensor with channels swapped according to swap + """ + # if torch.is_tensor(image): + # image = image.data.cpu().numpy() + # else: + # image = np.array(image) + image = image[:, :, self.swaps] + return image + + +class PhotometricDistort(object): + def __init__(self): + self.pd = [ + RandomContrast(), + ConvertColor(transform='HSV'), + RandomSaturation(), + RandomHue(), + ConvertColor(current='HSV', transform='BGR'), + RandomContrast() + ] + self.rand_brightness = RandomBrightness() + self.rand_light_noise = RandomLightingNoise() + + def __call__(self, image, masks, boxes, labels): + im = image.copy() + im, masks, boxes, labels = self.rand_brightness(im, masks, boxes, labels) + if random.randint(2): + distort = Compose(self.pd[:-1]) + else: + distort = Compose(self.pd[1:]) + im, masks, boxes, labels = distort(im, masks, boxes, labels) + return self.rand_light_noise(im, masks, boxes, labels) + +class PrepareMasks(object): + """ + Prepares the gt masks for use_gt_bboxes by cropping with the gt box + and downsampling the resulting mask to mask_size, mask_size. This + function doesn't do anything if cfg.use_gt_bboxes is False. + """ + + def __init__(self, mask_size, use_gt_bboxes): + self.mask_size = mask_size + self.use_gt_bboxes = use_gt_bboxes + + def __call__(self, image, masks, boxes, labels=None): + if not self.use_gt_bboxes: + return image, masks, boxes, labels + + height, width, _ = image.shape + + new_masks = np.zeros((masks.shape[0], self.mask_size ** 2)) + + for i in range(len(masks)): + x1, y1, x2, y2 = boxes[i, :] + x1 *= width + x2 *= width + y1 *= height + y2 *= height + x1, y1, x2, y2 = (int(x1), int(y1), int(x2), int(y2)) + + # +1 So that if y1=10.6 and y2=10.9 we still have a bounding box + cropped_mask = masks[i, y1:(y2+1), x1:(x2+1)] + scaled_mask = cv2.resize(cropped_mask, (self.mask_size, self.mask_size)) + + new_masks[i, :] = scaled_mask.reshape(1, -1) + + # Binarize + new_masks[new_masks > 0.5] = 1 + new_masks[new_masks <= 0.5] = 0 + + return image, new_masks, boxes, labels + +class BackboneTransform(object): + """ + Transforms a BRG image made of floats in the range [0, 255] to whatever + input the current backbone network needs. + + transform is a transform config object (see config.py). + in_channel_order is probably 'BGR' but you do you, kid. + """ + def __init__(self, transform, mean, std, in_channel_order): + self.mean = np.array(mean, dtype=np.float32) + self.std = np.array(std, dtype=np.float32) + self.transform = transform + + # Here I use "Algorithms and Coding" to convert string permutations to numbers + self.channel_map = {c: idx for idx, c in enumerate(in_channel_order)} + self.channel_permutation = [self.channel_map[c] for c in transform.channel_order] + + def __call__(self, img, masks=None, boxes=None, labels=None): + + img = img.astype(np.float32) + + if self.transform.normalize: + img = (img - self.mean) / self.std + elif self.transform.subtract_means: + img = (img - self.mean) + elif self.transform.to_float: + img = img / 255 + + img = img[:, :, self.channel_permutation] + + return img.astype(np.float32), masks, boxes, labels + + + + +class BaseTransform(object): + """ Transorm to be used when evaluating. """ + + def __init__(self, mean=MEANS, std=STD): + self.augment = Compose([ + ConvertFromInts(), + Resize(resize_gt=False), + BackboneTransform(cfg.backbone.transform, mean, std, 'BGR') + ]) + + def __call__(self, img, masks=None, boxes=None, labels=None): + return self.augment(img, masks, boxes, labels) + +import torch.nn.functional as F + +class FastBaseTransform(torch.nn.Module): + """ + Transform that does all operations on the GPU for super speed. + This doesn't suppport a lot of config settings and should only be used for production. + Maintain this as necessary. + """ + + def __init__(self): + super().__init__() + + self.mean = torch.Tensor(MEANS).float().cuda()[None, :, None, None] + self.std = torch.Tensor( STD ).float().cuda()[None, :, None, None] + self.transform = cfg.backbone.transform + + def forward(self, img): + self.mean = self.mean.to(img.device) + self.std = self.std.to(img.device) + + # img assumed to be a pytorch BGR image with channel order [n, h, w, c] + if cfg.preserve_aspect_ratio: + _, h, w, _ = img.size() + img_size = Resize.calc_size_preserve_ar(w, h, cfg.max_size) + img_size = (img_size[1], img_size[0]) # Pytorch needs h, w + else: + img_size = (cfg.max_size, cfg.max_size) + + img = img.permute(0, 3, 1, 2).contiguous() + img = F.interpolate(img, img_size, mode='bilinear', align_corners=False) + + if self.transform.normalize: + img = (img - self.mean) / self.std + elif self.transform.subtract_means: + img = (img - self.mean) + elif self.transform.to_float: + img = img / 255 + + if self.transform.channel_order != 'RGB': + raise NotImplementedError + + img = img[:, (2, 1, 0), :, :].contiguous() + + # Return value is in channel order [n, c, h, w] and RGB + return img + +def do_nothing(img=None, masks=None, boxes=None, labels=None): + return img, masks, boxes, labels + + +def enable_if(condition, obj): + return obj if condition else do_nothing + +class SSDAugmentation(object): + """ Transform to be used when training. """ + + def __init__(self, mean=MEANS, std=STD): + self.augment = Compose([ + ConvertFromInts(), + ToAbsoluteCoords(), + enable_if(cfg.augment_photometric_distort, PhotometricDistort()), + enable_if(cfg.augment_expand, Expand(mean)), + enable_if(cfg.augment_random_sample_crop, RandomSampleCrop()), + enable_if(cfg.augment_random_mirror, RandomMirror()), + enable_if(cfg.augment_random_flip, RandomFlip()), + enable_if(cfg.augment_random_flip, RandomRot90()), + Resize(), + enable_if(not cfg.preserve_aspect_ratio, Pad(cfg.max_size, cfg.max_size, mean)), + ToPercentCoords(), + PrepareMasks(cfg.mask_size, cfg.use_gt_bboxes), + BackboneTransform(cfg.backbone.transform, mean, std, 'BGR') + ]) + + def __call__(self, img, masks, boxes, labels): + return self.augment(img, masks, boxes, labels) diff --git a/cv/instance_segmentation/yolact/pytorch/utils/cython_nms.pyx b/cv/instance_segmentation/yolact/pytorch/utils/cython_nms.pyx new file mode 100644 index 000000000..892be872e --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/cython_nms.pyx @@ -0,0 +1,74 @@ +## Note: Figure out the license details later. +# +# Based on: +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +cimport cython +import numpy as np +cimport numpy as np + +cdef inline np.float32_t max(np.float32_t a, np.float32_t b) nogil: + return a if a >= b else b + +cdef inline np.float32_t min(np.float32_t a, np.float32_t b) nogil: + return a if a <= b else b + +@cython.boundscheck(False) +@cython.cdivision(True) +@cython.wraparound(False) +def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float32_t thresh): + cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] + cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] + cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] + + cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) + cdef np.ndarray[np.int64_t, ndim=1] order = scores.argsort()[::-1] + + cdef int ndets = dets.shape[0] + cdef np.ndarray[np.int_t, ndim=1] suppressed = \ + np.zeros((ndets), dtype=np.int) + + # nominal indices + cdef int _i, _j + # sorted indices + cdef int i, j + # temp variables for box i's (the box currently under consideration) + cdef np.float32_t ix1, iy1, ix2, iy2, iarea + # variables for computing overlap with box j (lower scoring box) + cdef np.float32_t xx1, yy1, xx2, yy2 + cdef np.float32_t w, h + cdef np.float32_t inter, ovr + + with nogil: + for _i in range(ndets): + i = order[_i] + if suppressed[i] == 1: + continue + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + for _j in range(_i + 1, ndets): + j = order[_j] + if suppressed[j] == 1: + continue + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0.0, xx2 - xx1 + 1) + h = max(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + if ovr >= thresh: + suppressed[j] = 1 + + return np.where(suppressed == 0)[0] diff --git a/cv/instance_segmentation/yolact/pytorch/utils/functions.py b/cv/instance_segmentation/yolact/pytorch/utils/functions.py new file mode 100644 index 000000000..3b7a4e45a --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/functions.py @@ -0,0 +1,213 @@ +import torch +import torch.nn as nn +import os +import math +from collections import deque +from pathlib import Path +from layers.interpolate import InterpolateModule + +class MovingAverage(): + """ Keeps an average window of the specified number of items. """ + + def __init__(self, max_window_size=1000): + self.max_window_size = max_window_size + self.reset() + + def add(self, elem): + """ Adds an element to the window, removing the earliest element if necessary. """ + if not math.isfinite(elem): + print('Warning: Moving average ignored a value of %f' % elem) + return + + self.window.append(elem) + self.sum += elem + + if len(self.window) > self.max_window_size: + self.sum -= self.window.popleft() + + def append(self, elem): + """ Same as add just more pythonic. """ + self.add(elem) + + def reset(self): + """ Resets the MovingAverage to its initial state. """ + self.window = deque() + self.sum = 0 + + def get_avg(self): + """ Returns the average of the elements in the window. """ + return self.sum / max(len(self.window), 1) + + def __str__(self): + return str(self.get_avg()) + + def __repr__(self): + return repr(self.get_avg()) + + def __len__(self): + return len(self.window) + + +class ProgressBar(): + """ A simple progress bar that just outputs a string. """ + + def __init__(self, length, max_val): + self.max_val = max_val + self.length = length + self.cur_val = 0 + + self.cur_num_bars = -1 + self._update_str() + + def set_val(self, new_val): + self.cur_val = new_val + + if self.cur_val > self.max_val: + self.cur_val = self.max_val + if self.cur_val < 0: + self.cur_val = 0 + + self._update_str() + + def is_finished(self): + return self.cur_val == self.max_val + + def _update_str(self): + num_bars = int(self.length * (self.cur_val / self.max_val)) + + if num_bars != self.cur_num_bars: + self.cur_num_bars = num_bars + self.string = '█' * num_bars + '░' * (self.length - num_bars) + + def __repr__(self): + return self.string + + def __str__(self): + return self.string + + +def init_console(): + """ + Initialize the console to be able to use ANSI escape characters on Windows. + """ + if os.name == 'nt': + from colorama import init + init() + + +class SavePath: + """ + Why is this a class? + Why do I have a class for creating and parsing save paths? + What am I doing with my life? + """ + + def __init__(self, model_name:str, epoch:int, iteration:int): + self.model_name = model_name + self.epoch = epoch + self.iteration = iteration + + def get_path(self, root:str=''): + file_name = self.model_name + '_' + str(self.epoch) + '_' + str(self.iteration) + '.pth' + return os.path.join(root, file_name) + + @staticmethod + def from_str(path:str): + file_name = os.path.basename(path) + + if file_name.endswith('.pth'): + file_name = file_name[:-4] + + params = file_name.split('_') + + if file_name.endswith('interrupt'): + params = params[:-1] + + model_name = '_'.join(params[:-2]) + epoch = params[-2] + iteration = params[-1] + + return SavePath(model_name, int(epoch), int(iteration)) + + @staticmethod + def remove_interrupt(save_folder): + for p in Path(save_folder).glob('*_interrupt.pth'): + p.unlink() + + @staticmethod + def get_interrupt(save_folder): + for p in Path(save_folder).glob('*_interrupt.pth'): + return str(p) + return None + + @staticmethod + def get_latest(save_folder, config): + """ Note: config should be config.name. """ + max_iter = -1 + max_name = None + + for p in Path(save_folder).glob(config + '_*'): + path_name = str(p) + + try: + save = SavePath.from_str(path_name) + except: + continue + + if save.model_name == config and save.iteration > max_iter: + max_iter = save.iteration + max_name = path_name + + return max_name + +def make_net(in_channels, conf, include_last_relu=True): + """ + A helper function to take a config setting and turn it into a network. + Used by protonet and extrahead. Returns (network, out_channels) + """ + def make_layer(layer_cfg): + nonlocal in_channels + + # Possible patterns: + # ( 256, 3, {}) -> conv + # ( 256,-2, {}) -> deconv + # (None,-2, {}) -> bilinear interpolate + # ('cat',[],{}) -> concat the subnetworks in the list + # + # You know it would have probably been simpler just to adopt a 'c' 'd' 'u' naming scheme. + # Whatever, it's too late now. + if isinstance(layer_cfg[0], str): + layer_name = layer_cfg[0] + + if layer_name == 'cat': + nets = [make_net(in_channels, x) for x in layer_cfg[1]] + layer = Concat([net[0] for net in nets], layer_cfg[2]) + num_channels = sum([net[1] for net in nets]) + else: + num_channels = layer_cfg[0] + kernel_size = layer_cfg[1] + + if kernel_size > 0: + layer = nn.Conv2d(in_channels, num_channels, kernel_size, **layer_cfg[2]) + else: + if num_channels is None: + layer = InterpolateModule(scale_factor=-kernel_size, mode='bilinear', align_corners=False, **layer_cfg[2]) + else: + layer = nn.ConvTranspose2d(in_channels, num_channels, -kernel_size, **layer_cfg[2]) + + in_channels = num_channels if num_channels is not None else in_channels + + # Don't return a ReLU layer if we're doing an upsample. This probably doesn't affect anything + # output-wise, but there's no need to go through a ReLU here. + # Commented out for backwards compatibility with previous models + # if num_channels is None: + # return [layer] + # else: + return [layer, nn.ReLU(inplace=True)] + + # Use sum to concat together all the component layer lists + net = sum([make_layer(x) for x in conf], []) + if not include_last_relu: + net = net[:-1] + + return nn.Sequential(*(net)), in_channels \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/utils/logger.py b/cv/instance_segmentation/yolact/pytorch/utils/logger.py new file mode 100644 index 000000000..6d87f576b --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/logger.py @@ -0,0 +1,489 @@ +import os +import json +import time +import sys + +from typing import Union +import datetime + +from collections import defaultdict +import matplotlib.pyplot as plt +import numpy as np + +# Because Python's package heierarchy system sucks +if __name__ == '__main__': + from nvinfo import gpu_info, visible_gpus, nvsmi_available + from functions import MovingAverage +else: + from .nvinfo import gpu_info, visible_gpus, nvsmi_available + from .functions import MovingAverage + +class Log: + """ + A class to log information during training per information and save it out. + It also can include extra debug information like GPU usage / temp automatically. + + Extra args: + - session_data: If you have any data unique to this session, put it here. + - overwrite: Whether or not to overwrite a pre-existing log with this name. + - log_gpu_stats: Whether or not to log gpu information like temp, usage, memory. + Note that this requires nvidia-smi to be present in your PATH. + - log_time: Also log the time in each iteration. + """ + + def __init__(self, log_name:str, log_dir:str='logs/', session_data:dict={}, + overwrite:bool=False, log_gpu_stats:bool=True, log_time:bool=True): + + if log_gpu_stats and not nvsmi_available(): + print('Warning: Log created with log_gpu_stats=True, but nvidia-smi ' \ + 'was not found. Setting log_gpu_stats to False.') + log_gpu_stats = False + + if not os.path.exists(log_dir): + os.makedirs(log_dir) + self.log_path = os.path.join(log_dir, log_name + '.log') + + # if os.path.exists(self.log_path) and overwrite: + # os.unlink(self.log_path) + + if os.path.exists(self.log_path): + # Log already exists, so we're going to add to it. Increment the session counter. + with open(self.log_path, 'r') as f: + for last in f: pass + + if len(last) > 1: + self.session = json.loads(last)['session'] + 1 + else: + self.session = 0 + else: + self.session = 0 + + + self.log_gpu_stats = log_gpu_stats + self.log_time = log_time + + if self.log_gpu_stats: + self.visible_gpus = visible_gpus() + + + self._log_session_header(session_data) + + + def _log_session_header(self, session_data:dict): + """ + Log information that does not change between iterations here. + This is to cut down on the file size so you're not outputing this every iteration. + """ + info = {} + info['type'] = 'session' + info['session'] = self.session + + info['data'] = session_data + + if self.log_gpu_stats: + keys = ['idx', 'name', 'uuid', 'pwr_cap', 'mem_total'] + + gpus = gpu_info() + info['gpus'] = [{k: gpus[i][k] for k in keys} for i in self.visible_gpus] + + if self.log_time: + info['time'] = time.time() + + out = json.dumps(info) + '\n' + + with open(self.log_path, 'a') as f: + f.write(out) + + + def log(self, type:str, data:dict={}, **kwdargs): + """ + Add an iteration to the log with the specified data points. + Type should be the type of information this is (e.g., train, valid, etc.) + + You can either pass data points as kwdargs, or as a dictionary (or both!). + Values should be json-serializable. + """ + info = {} + + info['type'] = type + info['session'] = self.session + + kwdargs.update(data) + info['data'] = kwdargs + + if self.log_gpu_stats: + keys = ['fan_spd', 'temp', 'pwr_used', 'mem_used', 'util'] + + gpus = gpu_info() + info['gpus'] = [{k: gpus[i][k] for k in keys} for i in self.visible_gpus] + + if self.log_time: + info['time'] = time.time() + + + out = json.dumps(info) + '\n' + + with open(self.log_path, 'a') as f: + f.write(out) + + +class LogEntry(): + """ A class that allows you to navigate a dictonary using x.a.b[2].c, etc. """ + + def __init__(self, entry:Union[dict, list]): + self._ = entry + + def __getattr__(self, name): + if name == '_': + return self.__dict__['_'] + + res = self.__dict__['_'][name] + + if type(res) == dict or type(res) == list: + return LogEntry(res) + else: + return res + + def __getitem__(self, name): + return self.__getattr__(name) + + def __len__(self): + return len(self.__dict__['_']) + +class LogVisualizer(): + + COLORS = [ + 'xkcd:azure', + 'xkcd:coral', + 'xkcd:turquoise', + 'xkcd:orchid', + 'xkcd:orange', + + 'xkcd:blue', + 'xkcd:red', + 'xkcd:teal', + 'xkcd:magenta', + 'xkcd:orangered' + ] + + def __init__(self): + self.logs = [] + self.total_logs = [] + self.log_names = [] + + def _decode(self, query:str) -> list: + path, select = (query.split(';') + [''])[:2] + + if select.strip() == '': + select = lambda x, s: True + else: + select = eval('lambda x, s: ' + select) + + if path.strip() == '': + path = lambda x, s: x + else: + path = eval('lambda x, s: ' + path) + + return path, select + + def _follow(self, entry:LogEntry, query:list): + path, select = query + + try: + if select(entry, entry._s): + res = path(entry, entry._s) + + if type(res) == LogEntry: + return res.__dict__['_'] + else: + return res + else: + return None + except (KeyError, IndexError): + return None + + def _color(self, idx:int): + return self.COLORS[idx % len(self.COLORS)] + + def sessions(self, path:str): + """ Prints statistics about the sessions in the file. """ + + if not os.path.exists(path): + print(path + ' doesn\'t exist!') + return + + cur_session = None + cur_time = 0 + last_time = 0 + num_entries = 0 + + def pop_session(): + delta = last_time - cur_time + time_str = str(datetime.timedelta(seconds=delta)).split('.')[0] + print('Session % 3d: % 8d entries | %s elapsed' % (cur_session, num_entries, time_str)) + + with open(path, 'r') as f: + for line in f: + line = line.strip() + if len(line) > 0: + js = json.loads(line) + if js['type'] == 'session': + if cur_session is not None: + pop_session() + cur_time = js['time'] + cur_session = js['session'] + num_entries = 0 + last_time = js['time'] + num_entries += 1 + + pop_session() + + def add(self, path:str, session:Union[int,list]=None): + """ Add a log file to the list of logs being considered. """ + + log = defaultdict(lambda: []) + total_log = [] + + if not os.path.exists(path): + print(path + ' doesn\'t exist!') + return + + session_idx = 0 + ignoring = True + + def valid(idx): + if session is None: + return True + elif type(session) == int: + return (idx == session) + else: + return idx in session + + with open(path, 'r') as f: + for line in f: + line = line.strip() + if len(line) > 0: + js = json.loads(line) + + _type = js['type'] + if _type == 'session': + session_idx = js['session'] + ignoring = not valid(session_idx) + + if not ignoring: + ljs = LogEntry(js) + if _type == 'session': + js['_s'] = ljs + else: + js['_s'] =log['session'][-1] + log[_type].append(ljs) + total_log.append(ljs) + + name = os.path.basename(path) + if session is not None: + name += ' (Session %s)' % session + + self.logs.append(log) + self.total_logs.append(total_log) + self.log_names.append(name) + + def query(self, x:Union[str, list], entry_type:str=None, x_idx:int=None, log_idx:int=None) -> list: + """ + Given a query string (can be already decoded for faster computation), query the entire log + and return all values found by that query. If both log_idx and x_idx is None, this will be + a list of lists in the form [log_idx][result_idx]. If x_idx is not None, then the result + will be a list of [log_idx]. If both are not none, the return value will be a single query + return value. With entry_type=None, this will search the entire log. + """ + + if type(x) is not list: + x = self._decode(x) + + res = [] + + for idx in (range(len(self.logs)) if log_idx is None else [log_idx]): + candidates = [] + log = self.total_logs[idx] if entry_type is None else self.logs[idx][entry_type] + + for entry in log: + candidate = self._follow(entry, x) + if candidate is not None: + candidates.append(candidate) + + if x_idx is not None: + candidates = candidates[x_idx] + res.append(candidates) + + if log_idx is not None: + res = res[0] + return res + + def check(self, entry_type:str, x:str): + """ Checks the log for the valid keys for this input. """ + keys = set() + x = self._decode(x) + + for log in self.logs: + for datum in log[entry_type]: + res = self._follow(datum, x) + + if type(res) == dict: + for key in res.keys(): + keys.add(key) + elif type(res) == list: + keys.add('< %d' % len(res)) + + return list(keys) + + def plot(self, entry_type:str, x:str, y:str, smoothness:int=0): + """ Plot sequential log data. """ + + query_x = self._decode(x) + query_y = self._decode(y) + + for idx, (log, name) in enumerate(zip(self.logs, self.log_names)): + log = log[entry_type] + + if smoothness > 1: + avg = MovingAverage(smoothness) + + _x = [] + _y = [] + + for datum in log: + val_x = self._follow(datum, query_x) + val_y = self._follow(datum, query_y) + + if val_x is not None and val_y is not None: + if smoothness > 1: + avg.append(val_y) + val_y = avg.get_avg() + + if len(avg) < smoothness // 10: + continue + + _x.append(val_x) + _y.append(val_y) + + plt.plot(_x, _y, color=self._color(idx), label=name) + + plt.title(y.replace('x.', entry_type + '.')) + plt.legend() + plt.grid(linestyle=':', linewidth=0.5) + plt.show() + + def bar(self, entry_type:str, x:str, labels:list=None, diff:bool=False, x_idx:int=-1): + """ Plot a bar chart. The result of x should be list or dictionary. """ + + query = self._decode(x) + + data_points = [] + + for idx, (log, name) in enumerate(zip(self.logs, self.log_names)): + log = log[entry_type] + + candidates = [] + + for entry in log: + test = self._follow(entry, query) + + if type(test) == dict: + candidates.append(test) + elif type(test) == list: + candidates.append({idx: v for idx, v in enumerate(test)}) + + if len(candidates) > 0: + data_points.append((name, candidates[x_idx])) + + if len(data_points) == 0: + print('Warning: Nothing to show in bar chart!') + return + + names = [x[0] for x in data_points] + data_points = [x[1] for x in data_points] + + # Construct the labels for the data + if labels is not None: + data_labels = labels + else: + data_labels = set() + for datum in data_points: + for k in datum: + data_labels.add(k) + + data_labels = list(data_labels) + data_labels.sort() + + + data_values = [[(datum[k] if k in datum else None) for k in data_labels] for datum in data_points] + + if diff: + for idx in reversed(range(len(data_values))): + for jdx in range(len(data_labels)): + if data_values[0][jdx] is None or data_values[idx][jdx] is None: + data_values[idx][jdx] = None + else: + data_values[idx][jdx] -= data_values[0][jdx] + + + series_labels = names + + # Plot the graph now + num_bars = len(series_labels) + bar_width = 1 / (num_bars + 1) + + # Set position of bar on X axis + positions = [np.arange(len(data_labels))] + for _ in range(1, num_bars): + positions.append([x + bar_width for x in positions[-1]]) + + # Make the plot + for idx, (series, data, pos) in enumerate(zip(series_labels, data_values, positions)): + plt.bar(pos, data, color=self._color(idx), width=bar_width, edgecolor='white', label=series) + + # Add xticks on the middle of the group bars + plt.title(x.replace('x.', entry_type + '.') + (' diff' if diff else '')) + plt.xticks([r + bar_width for r in range(len(data_labels))], data_labels) + + # Create legend & Show graphic + plt.legend() + plt.show() + + + + def elapsed_time(self, cond1:str='', cond2:str='', legible:bool=True) -> list: + """ + Returns the elapsed time between two entries based on the given conditionals. + If a query isn't specified, the first / last entry will be used. The first query + uses the first value and the second query uses the last value in the results. + + Setting legible to true returns human-readable results, while false returns seconds. + """ + q1 = 'x.time; ' + cond1 + q2 = 'x.time; ' + cond2 + + x1 = self.query(q1, x_idx=0) + x2 = self.query(q2, x_idx=-1) + + diff = (lambda x: str(datetime.timedelta(seconds=x)).split('.')[0]) if legible else lambda x: x + + return [diff(b - a) for a, b in zip(x1, x2)] + + + + + + + + + + + +if __name__ == '__main__': + if len(sys.argv) < 4+1: + print('Usage: python utils/logger.py ') + exit() + + vis = LogVisualizer() + vis.add(sys.argv[1]) + vis.plot(sys.argv[2], sys.argv[3], sys.argv[4]) diff --git a/cv/instance_segmentation/yolact/pytorch/utils/nvinfo.py b/cv/instance_segmentation/yolact/pytorch/utils/nvinfo.py new file mode 100644 index 000000000..98dd5c980 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/nvinfo.py @@ -0,0 +1,63 @@ +# My version of nvgpu because nvgpu didn't have all the information I was looking for. +import re +import subprocess +import shutil +import os + +def gpu_info() -> list: + """ + Returns a dictionary of stats mined from nvidia-smi for each gpu in a list. + Adapted from nvgpu: https://pypi.org/project/nvgpu/, but mine has more info. + """ + gpus = [line for line in _run_cmd(['nvidia-smi', '-L']) if line] + gpu_infos = [re.match('GPU ([0-9]+): ([^(]+) \(UUID: ([^)]+)\)', gpu).groups() for gpu in gpus] + gpu_infos = [dict(zip(['idx', 'name', 'uuid'], info)) for info in gpu_infos] + gpu_count = len(gpus) + + lines = _run_cmd(['nvidia-smi']) + selected_lines = lines[7:7 + 3 * gpu_count] + for i in range(gpu_count): + mem_used, mem_total = [int(m.strip().replace('MiB', '')) for m in + selected_lines[3 * i + 1].split('|')[2].strip().split('/')] + + pw_tmp_info, mem_info, util_info = [x.strip() for x in selected_lines[3 * i + 1].split('|')[1:-1]] + + pw_tmp_info = [x[:-1] for x in pw_tmp_info.split(' ') if len(x) > 0] + fan_speed, temperature, pwr_used, pwr_cap = [int(pw_tmp_info[i]) for i in (0, 1, 3, 5)] + gpu_infos[i]['fan_spd' ] = fan_speed + gpu_infos[i]['temp' ] = temperature + gpu_infos[i]['pwr_used'] = pwr_used + gpu_infos[i]['pwr_cap' ] = pwr_cap + + mem_used, mem_total = [int(x) for x in mem_info.replace('MiB', '').split(' / ')] + gpu_infos[i]['mem_used' ] = mem_used + gpu_infos[i]['mem_total'] = mem_total + + utilization = int(util_info.split(' ')[0][:-1]) + gpu_infos[i]['util'] = utilization + + gpu_infos[i]['idx'] = int(gpu_infos[i]['idx']) + + return gpu_infos + +def nvsmi_available() -> bool: + """ Returns whether or not nvidia-smi is present in this system's PATH. """ + return shutil.which('nvidia-smi') is not None + + +def visible_gpus() -> list: + """ Returns a list of the indexes of all the gpus visible to pytorch. """ + + if 'CUDA_VISIBLE_DEVICES' not in os.environ: + return list(range(len(gpu_info()))) + else: + return [int(x.strip()) for x in os.environ['CUDA_VISIBLE_DEVICES'].split(',')] + + + + +def _run_cmd(cmd:list) -> list: + """ Runs a command and returns a list of output lines. """ + output = subprocess.check_output(cmd) + output = output.decode('UTF-8') + return output.split('\n') \ No newline at end of file diff --git a/cv/instance_segmentation/yolact/pytorch/utils/timer.py b/cv/instance_segmentation/yolact/pytorch/utils/timer.py new file mode 100644 index 000000000..7c1c403dd --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/utils/timer.py @@ -0,0 +1,131 @@ +import time +from collections import defaultdict + +_total_times = defaultdict(lambda: 0) +_start_times = defaultdict(lambda: -1) +_disabled_names = set() +_timer_stack = [] +_running_timer = None +_disable_all = False + +def disable_all(): + global _disable_all + _disable_all = True + +def enable_all(): + global _disable_all + _disable_all = False + +def disable(fn_name): + """ Disables the given function name fom being considered for the average or outputted in print_stats. """ + _disabled_names.add(fn_name) + +def enable(fn_name): + """ Enables function names disabled by disable. """ + _disabled_names.remove(fn_name) + +def reset(): + """ Resets the current timer. Call this at the start of an iteration. """ + global _running_timer + _total_times.clear() + _start_times.clear() + _timer_stack.clear() + _running_timer = None + +def start(fn_name, use_stack=True): + """ + Start timing the specific function. + Note: If use_stack is True, only one timer can be active at a time. + Once you stop this timer, the previous one will start again. + """ + global _running_timer, _disable_all + + if _disable_all: + return + + if use_stack: + if _running_timer is not None: + stop(_running_timer, use_stack=False) + _timer_stack.append(_running_timer) + start(fn_name, use_stack=False) + _running_timer = fn_name + else: + _start_times[fn_name] = time.perf_counter() + +def stop(fn_name=None, use_stack=True): + """ + If use_stack is True, this will stop the currently running timer and restore + the previous timer on the stack if that exists. Note if use_stack is True, + fn_name will be ignored. + + If use_stack is False, this will just stop timing the timer fn_name. + """ + global _running_timer, _disable_all + + if _disable_all: + return + + if use_stack: + if _running_timer is not None: + stop(_running_timer, use_stack=False) + if len(_timer_stack) > 0: + _running_timer = _timer_stack.pop() + start(_running_timer, use_stack=False) + else: + _running_timer = None + else: + print('Warning: timer stopped with no timer running!') + else: + if _start_times[fn_name] > -1: + _total_times[fn_name] += time.perf_counter() - _start_times[fn_name] + else: + print('Warning: timer for %s stopped before starting!' % fn_name) + + +def print_stats(): + """ Prints the current timing information into a table. """ + print() + + all_fn_names = [k for k in _total_times.keys() if k not in _disabled_names] + + max_name_width = max([len(k) for k in all_fn_names] + [4]) + if max_name_width % 2 == 1: max_name_width += 1 + format_str = ' {:>%d} | {:>10.4f} ' % max_name_width + + header = (' {:^%d} | {:^10} ' % max_name_width).format('Name', 'Time (ms)') + print(header) + + sep_idx = header.find('|') + sep_text = ('-' * sep_idx) + '+' + '-' * (len(header)-sep_idx-1) + print(sep_text) + + for name in all_fn_names: + print(format_str.format(name, _total_times[name]*1000)) + + print(sep_text) + print(format_str.format('Total', total_time()*1000)) + print() + +def total_time(): + """ Returns the total amount accumulated across all functions in seconds. """ + return sum([elapsed_time for name, elapsed_time in _total_times.items() if name not in _disabled_names]) + + +class env(): + """ + A class that lets you go: + with timer.env(fn_name): + # (...) + That automatically manages a timer start and stop for you. + """ + + def __init__(self, fn_name, use_stack=True): + self.fn_name = fn_name + self.use_stack = use_stack + + def __enter__(self): + start(self.fn_name, use_stack=self.use_stack) + + def __exit__(self, e, ev, t): + stop(self.fn_name, use_stack=self.use_stack) + diff --git a/cv/instance_segmentation/yolact/pytorch/web/css/index.css b/cv/instance_segmentation/yolact/pytorch/web/css/index.css new file mode 100644 index 000000000..f7715dc2d --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/css/index.css @@ -0,0 +1,63 @@ + +/* +Pallete: + +FFFFFF +D2CBCB +7D8491 +003459 +274C77 +161925 +*/ + +* { box-sizing: border-box; } + +.big { + font-size:72px; + margin-bottom: 20px; +} + +.list_wrapper { + width: 500px; + padding-top: 2px; + padding-bottom: 20px; +} + + +body { + margin:0; + padding:0; + vertical-align: top; + + background-color: #274C77; + color: #ffffff; + font-family: 'Open Sans', sans-serif; + font-size: 24px; + width: 100%; + height: 99vh; + + display: grid; + grid-template-areas: + 'header' + 'main' + 'footer'; + + grid-template-rows: 100px auto 25px; + + text-align: center; +} + +.box { + background-color: #23395B; + border-radius: 10px; +} + +.header { grid-area: header; } +.main { grid-area: main; } +.footer { grid-area: footer; } + +span { + margin:0; + padding:0; + vertical-align: top; +} diff --git a/cv/instance_segmentation/yolact/pytorch/web/css/list.css b/cv/instance_segmentation/yolact/pytorch/web/css/list.css new file mode 100644 index 000000000..d28de2991 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/css/list.css @@ -0,0 +1,28 @@ +ul { + list-style-type: none; + margin: 0; + padding: 0; +} + +li { + /* font: 200 24px/1.5 Helvetica, Verdana, sans-serif; */ + font-size: 22px; +} + +li a { + text-decoration: none; + color: #fff; + display: block; + width: 100%; + + -webkit-transition: font-size 0.2s ease, background-color 0.2s ease; + -moz-transition: font-size 0.2s ease, background-color 0.2s ease; + -o-transition: font-size 0.2s ease, background-color 0.2s ease; + -ms-transition: font-size 0.2s ease, background-color 0.2s ease; + transition: font-size 0.2s ease, background-color 0.2s ease; +} + +li a:hover { + font-size: 30px; + background: rgb(95, 138, 219); +} diff --git a/cv/instance_segmentation/yolact/pytorch/web/css/toggle.css b/cv/instance_segmentation/yolact/pytorch/web/css/toggle.css new file mode 100644 index 000000000..2332167b6 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/css/toggle.css @@ -0,0 +1,56 @@ +.switch { + position: relative; + top: 5; +} + +.switch input {display:none;} + +.slider { + position: relative; + display: inline-block; + width: 60px; + height: 26px; + cursor: pointer; + top: 0; + left: 0; + right: 0; + bottom: 0; + background-color: #ccc; + -webkit-transition: .4s; + transition: .4s; +} + +.slider:before { + position: absolute; + content: ""; + height: 20px; + width: 20px; + left: 3px; + bottom: 3px; + background-color: white; + -webkit-transition: .4s; + transition: .4s; +} + +input:checked + .slider { + background-color: #2196F3; +} + +input:focus + .slider { + box-shadow: 0 0 1px #2196F3; +} + +input:checked + .slider:before { + -webkit-transform: translateX(34px); + -ms-transform: translateX(34px); + transform: translateX(34px); +} + +/* Rounded sliders */ +.slider.round { + border-radius: 34px; +} + +.slider.round:before { + border-radius: 50%; +} diff --git a/cv/instance_segmentation/yolact/pytorch/web/css/viewer.css b/cv/instance_segmentation/yolact/pytorch/web/css/viewer.css new file mode 100644 index 000000000..64ce8a407 --- /dev/null +++ b/cv/instance_segmentation/yolact/pytorch/web/css/viewer.css @@ -0,0 +1,64 @@ + +.info { grid-area: info; } +.image { grid-area: image; } +.controls { grid-area: controls; } + + +#viewer { + display: grid; + grid-template-areas: 'info image controls'; + grid-template-columns: 1fr 2fr 1fr; + grid-gap: 0; +} + +#viewer > div.box { + padding: 10px; + margin: 0 10px 10px 10px; +} + +.image_box { + display: grid; + grid-template-rows: max-content auto; + grid-gap: 10px; +} + +#image_idx, #config_name, .info_value { + color: rgb(152, 160, 175); +} + +.info_section { + text-align: center; + border-bottom: 1px solid #fff; +} + +a { + text-decoration: none; + color: #fff; +} + +a:hover { + color: rgb(152, 160, 175); +} + +.setting { + display: grid; + grid-template-areas: 'label value input'; + grid-template-columns: max-content 30px 1fr; + grid-gap: 20px; + padding: 0 10px 0 10px; + text-align: left; +} +.setting_label { grid-area: label; } +.setting_input { + grid-area: input; +} +.setting_value { + grid-area: value; + color: rgb(152, 160, 175); +} + +.box_title { + width: 100%; + border-bottom: 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