From 14dcae8407c6c6e6a5e6b227f64fd10d4a7eceb5 Mon Sep 17 00:00:00 2001 From: "shengquan.nian" Date: Mon, 13 Feb 2023 14:58:35 +0800 Subject: [PATCH 1/5] add gitee issue and feature yolov3 tf link #I6B4CU add gitee issue and feature yolov3 tf Signed-off-by: shengquan.nian --- cv/detection/yolov3/tensorflow/.gitignore | 2 + cv/detection/yolov3/tensorflow/LICENSE | 21 + cv/detection/yolov3/tensorflow/LICENSE.fuck | 13 + cv/detection/yolov3/tensorflow/README.md | 28 + .../yolov3/tensorflow/README_origin.md | 147 + .../yolov3/tensorflow/convert_weight.py | 93 + .../yolov3/tensorflow/core/__init__.py | 0 .../yolov3/tensorflow/core/backbone.py | 59 + cv/detection/yolov3/tensorflow/core/common.py | 90 + cv/detection/yolov3/tensorflow/core/config.py | 71 + .../yolov3/tensorflow/core/dataset.py | 296 + cv/detection/yolov3/tensorflow/core/utils.py | 212 + cv/detection/yolov3/tensorflow/core/yolov3.py | 261 + .../data/anchors/basline_anchors.txt | 1 + .../tensorflow/data/anchors/coco_anchors.txt | 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index 000000000..deaec135c --- /dev/null +++ b/cv/detection/yolov3/tensorflow/.gitignore @@ -0,0 +1,2 @@ +**/__pycache__/ +**/log/ diff --git a/cv/detection/yolov3/tensorflow/LICENSE b/cv/detection/yolov3/tensorflow/LICENSE new file mode 100644 index 000000000..78bf38e4c --- /dev/null +++ b/cv/detection/yolov3/tensorflow/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 YangYun + +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/detection/yolov3/tensorflow/LICENSE.fuck b/cv/detection/yolov3/tensorflow/LICENSE.fuck new file mode 100644 index 000000000..e6f18f4fe --- /dev/null +++ b/cv/detection/yolov3/tensorflow/LICENSE.fuck @@ -0,0 +1,13 @@ + DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE + Version 1, JUNE 2019 + +Copyright (C) 2019 YunYang1994 + +Everyone is permitted to copy and distribute verbatim or modified +copies of this license document, and changing it is allowed as long +as the name is changed. + + DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE + TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION + + 0. You just DO WHAT THE FUCK YOU WANT TO. DON'T ASK ME, JUST DO IT. diff --git a/cv/detection/yolov3/tensorflow/README.md b/cv/detection/yolov3/tensorflow/README.md new file mode 100644 index 000000000..bc6211f3a --- /dev/null +++ b/cv/detection/yolov3/tensorflow/README.md @@ -0,0 +1,28 @@ +## Prepare +``` +bash init_tf.sh +``` + +## Download dataset and checkpoint +``` +# download dataset +mkdir -p VOC +cd VOC +wget http://10.150.9.95/swapp/datasets/cv/detection/VOC_07_12.tgz +tar -zxvf VOC_07_12.tgz +rm -rf VOC_07_12.tgz +cd .. + +# download checkpoint +mkdir checkpoint +cd checkpoint +wget http://10.150.9.95/swapp/datasets/cv/detection/yolov3_coco_demo.ckpt.tar.gz +tar -zxvf yolov3_coco_demo.ckpt.tar.gz +rm -rf yolov3_coco_demo.ckpt.tar.gz +cd .. +``` + +## Run training +``` +bash ./run_training.sh +``` diff --git a/cv/detection/yolov3/tensorflow/README_origin.md b/cv/detection/yolov3/tensorflow/README_origin.md new file mode 100644 index 000000000..ca726055e --- /dev/null +++ b/cv/detection/yolov3/tensorflow/README_origin.md @@ -0,0 +1,147 @@ +## Run inference on BI +```bashrc +bash ./run_inference.sh +``` + +## Run training on BI +```bashrc +bash ./run_training.sh +``` + +## 🆕 Are you looking for a new YOLOv3 implemented by TF2.0 ? + +>If you hate the fucking tensorflow1.x very much, no worries! I have implemented **a new YOLOv3 repo with TF2.0**, and also made a chinese blog on how to implement YOLOv3 object detector from scratch.
+[code](https://github.com/YunYang1994/TensorFlow2.0-Examples/tree/master/4-Object_Detection/YOLOV3) | [blog](https://yunyang1994.gitee.io/2018/12/28/YOLOv3/) | [issue](https://github.com/YunYang1994/tensorflow-yolov3/issues/39) + +## part 1. Quick start +1. Clone this file +```bashrc +$ git clone https://github.com/YunYang1994/tensorflow-yolov3.git +``` +2. You are supposed to install some dependencies before getting out hands with these codes. +```bashrc +$ cd tensorflow-yolov3 +$ pip install -r ./docs/requirements.txt +``` +3. Exporting loaded COCO weights as TF checkpoint(`yolov3_coco.ckpt`)【[BaiduCloud](https://pan.baidu.com/s/11mwiUy8KotjUVQXqkGGPFQ&shfl=sharepset)】 +```bashrc +$ cd checkpoint +$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz +$ tar -xvf yolov3_coco.tar.gz +$ cd .. +$ python convert_weight.py +$ python freeze_graph.py +``` +4. Then you will get some `.pb` files in the root path., and run the demo script +```bashrc +$ python image_demo.py +$ python video_demo.py # if use camera, set video_path = 0 +``` +

+ + +

+ +## part 2. Train your own dataset +Two files are required as follows: + +- [`dataset.txt`](https://raw.githubusercontent.com/YunYang1994/tensorflow-yolov3/master/data/dataset/voc_train.txt): + +``` +xxx/xxx.jpg 18.19,6.32,424.13,421.83,20 323.86,2.65,640.0,421.94,20 +xxx/xxx.jpg 48,240,195,371,11 8,12,352,498,14 +# image_path x_min, y_min, x_max, y_max, class_id x_min, y_min ,..., class_id +# make sure that x_max < width and y_max < height +``` + +- [`class.names`](https://github.com/YunYang1994/tensorflow-yolov3/blob/master/data/classes/coco.names): + +``` +person +bicycle +car +... +toothbrush +``` + +### 2.1 Train on VOC dataset +Download VOC PASCAL trainval and test data +```bashrc +$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar +$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar +$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar +``` +Extract all of these tars into one directory and rename them, which should have the following basic structure. + +```bashrc + +VOC # path: /home/yang/dataset/VOC +├── test +| └──VOCdevkit +| └──VOC2007 (from VOCtest_06-Nov-2007.tar) +└── train + └──VOCdevkit + └──VOC2007 (from VOCtrainval_06-Nov-2007.tar) + └──VOC2012 (from VOCtrainval_11-May-2012.tar) + +$ python scripts/voc_annotation.py --data_path /home/yang/test/VOC +``` +Then edit your `./core/config.py` to make some necessary configurations + +```bashrc +__C.YOLO.CLASSES = "./data/classes/voc.names" +__C.TRAIN.ANNOT_PATH = "./data/dataset/voc_train.txt" +__C.TEST.ANNOT_PATH = "./data/dataset/voc_test.txt" +``` +Here are two kinds of training method: + +##### (1) train from scratch: + +```bashrc +$ python train.py +$ tensorboard --logdir ./data +``` +##### (2) train from COCO weights(recommend): + +```bashrc +$ cd checkpoint +$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz +$ tar -xvf yolov3_coco.tar.gz +$ cd .. +$ python convert_weight.py --train_from_coco +$ python train.py +``` +### 2.2 Evaluate on VOC dataset + +``` +$ python evaluate.py +$ cd mAP +$ python main.py -na +``` + +the mAP on the VOC2012 dataset: + +

+ + +

+ + +## part 3. Stargazers over time + +[![Stargazers over time](https://starcharts.herokuapp.com/YunYang1994/tensorflow-yolov3.svg)](https://starcharts.herokuapp.com/YunYang1994/tensorflow-yolov3) + +## part 4. Other Implementations + +[-**`YOLOv3目标检测有了TensorFlow实现，可用自己的数据来训练`**](https://mp.weixin.qq.com/s/cq7g1-4oFTftLbmKcpi_aQ)
+ +[-**`Stronger-yolo`**](https://github.com/Stinky-Tofu/Stronger-yolo)
+ +[- **`Implementing YOLO v3 in Tensorflow (TF-Slim)`**](https://itnext.io/implementing-yolo-v3-in-tensorflow-tf-slim-c3c55ff59dbe) + +[- **`YOLOv3_TensorFlow`**](https://github.com/wizyoung/YOLOv3_TensorFlow) + +[- **`Object Detection using YOLOv2 on Pascal VOC2012`**](https://fairyonice.github.io/Part_1_Object_Detection_with_Yolo_for_VOC_2014_data_anchor_box_clustering.html) + +[-**`Understanding YOLO`**](https://hackernoon.com/understanding-yolo-f5a74bbc7967) + diff --git a/cv/detection/yolov3/tensorflow/convert_weight.py b/cv/detection/yolov3/tensorflow/convert_weight.py new file mode 100644 index 000000000..910524ce5 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/convert_weight.py @@ -0,0 +1,93 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : convert_weight.py +# Author : YunYang1994 +# Created date: 2019-02-28 13:51:31 +# Description : +# +#================================================================ + +import argparse +import tensorflow as tf +from core.yolov3 import YOLOV3 +from core.config import cfg +parser = argparse.ArgumentParser() +parser.add_argument("--train_from_coco", action='store_true') +flag = parser.parse_args() + +org_weights_path = cfg.YOLO.ORIGINAL_WEIGHT +cur_weights_path = cfg.YOLO.DEMO_WEIGHT +preserve_cur_names = ['conv_sbbox', 'conv_mbbox', 'conv_lbbox'] +preserve_org_names = ['Conv_6', 'Conv_14', 'Conv_22'] + + +org_weights_mess = [] +tf.Graph().as_default() +load = tf.train.import_meta_graph(org_weights_path + '.meta') +with tf.Session() as sess: + load.restore(sess, org_weights_path) + for var in tf.global_variables(): + var_name = var.op.name + var_name_mess = str(var_name).split('/') + var_shape = var.shape + if flag.train_from_coco: + if (var_name_mess[-1] not in ['weights', 'gamma', 'beta', 'moving_mean', 'moving_variance']) or \ + (var_name_mess[1] == 'yolo-v3' and (var_name_mess[-2] in preserve_org_names)): continue + org_weights_mess.append([var_name, var_shape]) + print("=> " + str(var_name).ljust(50), var_shape) +print() +tf.reset_default_graph() + +cur_weights_mess = [] +tf.Graph().as_default() +with tf.name_scope('input'): + input_data = tf.placeholder(dtype=tf.float32, shape=(1, 416, 416, 3), name='input_data') + training = tf.placeholder(dtype=tf.bool, name='trainable') +model = YOLOV3(input_data, training) +for var in tf.global_variables(): + var_name = var.op.name + var_name_mess = str(var_name).split('/') + var_shape = var.shape + print(var_name_mess[0]) + if flag.train_from_coco: + if var_name_mess[0] in preserve_cur_names: continue + cur_weights_mess.append([var_name, var_shape]) + print("=> " + str(var_name).ljust(50), var_shape) + +org_weights_num = len(org_weights_mess) +cur_weights_num = len(cur_weights_mess) +if cur_weights_num != org_weights_num: + raise RuntimeError + +print('=> Number of weights that will rename:\t%d' % cur_weights_num) +cur_to_org_dict = {} +for index in range(org_weights_num): + org_name, org_shape = org_weights_mess[index] + cur_name, cur_shape = cur_weights_mess[index] + if cur_shape != org_shape: + print(org_weights_mess[index]) + print(cur_weights_mess[index]) + raise RuntimeError + cur_to_org_dict[cur_name] = org_name + print("=> " + str(cur_name).ljust(50) + ' : ' + org_name) + +with tf.name_scope('load_save'): + name_to_var_dict = {var.op.name: var for var in tf.global_variables()} + restore_dict = {cur_to_org_dict[cur_name]: name_to_var_dict[cur_name] for cur_name in cur_to_org_dict} + load = tf.train.Saver(restore_dict) + save = tf.train.Saver(tf.global_variables()) + for var in tf.global_variables(): + print("=> " + var.op.name) + +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + print('=> Restoring weights from:\t %s' % org_weights_path) + load.restore(sess, org_weights_path) + save.save(sess, cur_weights_path) +tf.reset_default_graph() + + diff --git a/cv/detection/yolov3/tensorflow/core/__init__.py b/cv/detection/yolov3/tensorflow/core/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/detection/yolov3/tensorflow/core/backbone.py b/cv/detection/yolov3/tensorflow/core/backbone.py new file mode 100644 index 000000000..0ce12deb4 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/core/backbone.py @@ -0,0 +1,59 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : backbone.py +# Author : YunYang1994 +# Created date: 2019-02-17 11:03:35 +# Description : +# +#================================================================ + +import core.common as common +import tensorflow.compat.v1 as tf + + +def darknet53(input_data, trainable): + + with tf.variable_scope('darknet'): + + input_data = common.convolutional(input_data, filters_shape=(3, 3, 3, 32), trainable=trainable, name='conv0') + input_data = common.convolutional(input_data, filters_shape=(3, 3, 32, 64), + trainable=trainable, name='conv1', downsample=True) + + for i in range(1): + input_data = common.residual_block(input_data, 64, 32, 64, trainable=trainable, name='residual%d' %(i+0)) + + input_data = common.convolutional(input_data, filters_shape=(3, 3, 64, 128), + trainable=trainable, name='conv4', downsample=True) + + for i in range(2): + input_data = common.residual_block(input_data, 128, 64, 128, trainable=trainable, name='residual%d' %(i+1)) + + input_data = common.convolutional(input_data, filters_shape=(3, 3, 128, 256), + trainable=trainable, name='conv9', downsample=True) + + for i in range(8): + input_data = common.residual_block(input_data, 256, 128, 256, trainable=trainable, name='residual%d' %(i+3)) + + route_1 = input_data + input_data = common.convolutional(input_data, filters_shape=(3, 3, 256, 512), + trainable=trainable, name='conv26', downsample=True) + + for i in range(8): + input_data = common.residual_block(input_data, 512, 256, 512, trainable=trainable, name='residual%d' %(i+11)) + + route_2 = input_data + input_data = common.convolutional(input_data, filters_shape=(3, 3, 512, 1024), + trainable=trainable, name='conv43', downsample=True) + + for i in range(4): + input_data = common.residual_block(input_data, 1024, 512, 1024, trainable=trainable, name='residual%d' %(i+19)) + + return route_1, route_2, input_data + + + + diff --git a/cv/detection/yolov3/tensorflow/core/common.py b/cv/detection/yolov3/tensorflow/core/common.py new file mode 100644 index 000000000..05fcac1cf --- /dev/null +++ b/cv/detection/yolov3/tensorflow/core/common.py @@ -0,0 +1,90 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : common.py +# Author : YunYang1994 +# Created date: 2019-02-28 09:56:29 +# Description : +# +#================================================================ + +import tensorflow.compat.v1 as tf + + +def convolutional(input_data, filters_shape, trainable, name, downsample=False, activate=True, bn=True): + + with tf.variable_scope(name): + if downsample: + pad_h, pad_w = (filters_shape[0] - 2) // 2 + 1, (filters_shape[1] - 2) // 2 + 1 + paddings = tf.constant([[0, 0], [pad_h, pad_h], [pad_w, pad_w], [0, 0]]) + input_data = tf.pad(input_data, paddings, 'CONSTANT') + strides = (1, 2, 2, 1) + padding = 'VALID' + else: + strides = (1, 1, 1, 1) + padding = "SAME" + + weight = tf.get_variable(name='weight', dtype=tf.float32, trainable=True, + shape=filters_shape, initializer=tf.random_normal_initializer(stddev=0.01)) + conv = tf.nn.conv2d(input=input_data, filter=weight, strides=strides, padding=padding) + + if bn: + conv = tf.layers.batch_normalization(conv, beta_initializer=tf.zeros_initializer(), + gamma_initializer=tf.ones_initializer(), + moving_mean_initializer=tf.zeros_initializer(), + moving_variance_initializer=tf.ones_initializer(), training=trainable) + else: + bias = tf.get_variable(name='bias', shape=filters_shape[-1], trainable=True, + dtype=tf.float32, initializer=tf.constant_initializer(0.0)) + conv = tf.nn.bias_add(conv, bias) + + if activate == True: conv = tf.nn.leaky_relu(conv, alpha=0.1) + + return conv + + +def residual_block(input_data, input_channel, filter_num1, filter_num2, trainable, name): + + short_cut = input_data + + with tf.variable_scope(name): + input_data = convolutional(input_data, filters_shape=(1, 1, input_channel, filter_num1), + trainable=trainable, name='conv1') + input_data = convolutional(input_data, filters_shape=(3, 3, filter_num1, filter_num2), + trainable=trainable, name='conv2') + + residual_output = input_data + short_cut + + return residual_output + + + +def route(name, previous_output, current_output): + + with tf.variable_scope(name): + output = tf.concat([current_output, previous_output], axis=-1) + + return output + + +def upsample(input_data, name, method="deconv"): + assert method in ["resize", "deconv"] + + if method == "resize": + with tf.variable_scope(name): + input_shape = tf.shape(input_data) + output = tf.image.resize_nearest_neighbor(input_data, (input_shape[1] * 2, input_shape[2] * 2)) + + if method == "deconv": + # replace resize_nearest_neighbor with conv2d_transpose To support TensorRT optimization + numm_filter = input_data.shape.as_list()[-1] + output = tf.layers.conv2d_transpose(input_data, numm_filter, kernel_size=2, padding='same', + strides=(2,2), kernel_initializer=tf.random_normal_initializer()) + + return output + + + diff --git a/cv/detection/yolov3/tensorflow/core/config.py b/cv/detection/yolov3/tensorflow/core/config.py new file mode 100644 index 000000000..483d6f48d --- /dev/null +++ b/cv/detection/yolov3/tensorflow/core/config.py @@ -0,0 +1,71 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : config.py +# Author : YunYang1994 +# Created date: 2019-02-28 13:06:54 +# Description : +# +#================================================================ + +from easydict import EasyDict as edict + + +__C = edict() +# Consumers can get config by: from config import cfg + +cfg = __C + +# YOLO options +__C.YOLO = edict() + +# Set the class name +__C.YOLO.CLASSES = "./data/classes/voc.names" +__C.YOLO.ANCHORS = "./data/anchors/basline_anchors.txt" +__C.YOLO.MOVING_AVE_DECAY = 0.9995 +__C.YOLO.STRIDES = [8, 16, 32] +__C.YOLO.ANCHOR_PER_SCALE = 3 +__C.YOLO.IOU_LOSS_THRESH = 0.5 +__C.YOLO.UPSAMPLE_METHOD = "resize" +__C.YOLO.ORIGINAL_WEIGHT = "./checkpoint/yolov3_coco.ckpt" +__C.YOLO.DEMO_WEIGHT = "./checkpoint/yolov3_coco_demo.ckpt" + +# Train options +__C.TRAIN = edict() + +__C.TRAIN.ANNOT_PATH = "./data/dataset/voc_train.txt" +__C.TRAIN.BATCH_SIZE = 6 +__C.TRAIN.INPUT_SIZE = [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] +__C.TRAIN.DATA_AUG = True +__C.TRAIN.LEARN_RATE_INIT = 1e-4 +__C.TRAIN.LEARN_RATE_END = 1e-6 +__C.TRAIN.WARMUP_EPOCHS = 2 +__C.TRAIN.FISRT_STAGE_EPOCHS = 4 +__C.TRAIN.SECOND_STAGE_EPOCHS = 4 +__C.TRAIN.INITIAL_WEIGHT = "./checkpoint/yolov3_coco_demo.ckpt" + + + +# TEST options +__C.TEST = edict() + +__C.TEST.ANNOT_PATH = "./data/dataset/voc_test.txt" +__C.TEST.BATCH_SIZE = 8 +__C.TEST.INPUT_SIZE = 544 +__C.TEST.DATA_AUG = False +__C.TEST.WRITE_IMAGE = False +__C.TEST.WRITE_IMAGE_PATH = "./data/detection/" +__C.TEST.WRITE_IMAGE_SHOW_LABEL = True +__C.TEST.WEIGHT_FILE = "./model_inference/yolov3_test_loss=6.7481.ckpt-8" +__C.TEST.SHOW_LABEL = True +__C.TEST.SCORE_THRESHOLD = 0.3 +__C.TEST.IOU_THRESHOLD = 0.45 + + + + + + diff --git a/cv/detection/yolov3/tensorflow/core/dataset.py b/cv/detection/yolov3/tensorflow/core/dataset.py new file mode 100644 index 000000000..1238cf175 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/core/dataset.py @@ -0,0 +1,296 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : dataset.py +# Author : YunYang1994 +# Created date: 2019-03-15 18:05:03 +# Description : +# +#================================================================ + +import os +import cv2 +import random +import numpy as np +import tensorflow.compat.v1 as tf +import core.utils as utils +from core.config import cfg + +def set_seed(seed=42): + random.seed(seed) + np.random.seed(seed) + tf.set_random_seed(seed) + +class Dataset(object): + """implement Dataset here""" + def __init__(self, dataset_type): + set_seed() + self.annot_path = cfg.TRAIN.ANNOT_PATH if dataset_type == 'train' else cfg.TEST.ANNOT_PATH + self.input_sizes = cfg.TRAIN.INPUT_SIZE if dataset_type == 'train' else cfg.TEST.INPUT_SIZE + self.batch_size = cfg.TRAIN.BATCH_SIZE if dataset_type == 'train' else cfg.TEST.BATCH_SIZE + self.data_aug = cfg.TRAIN.DATA_AUG if dataset_type == 'train' else cfg.TEST.DATA_AUG + + self.train_input_sizes = cfg.TRAIN.INPUT_SIZE + self.strides = np.array(cfg.YOLO.STRIDES) + self.classes = utils.read_class_names(cfg.YOLO.CLASSES) + self.num_classes = len(self.classes) + self.anchors = np.array(utils.get_anchors(cfg.YOLO.ANCHORS)) + self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE + self.max_bbox_per_scale = 150 + + self.annotations = self.load_annotations(dataset_type) + self.num_samples = len(self.annotations) + self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) + self.batch_count = 0 + + + def load_annotations(self, dataset_type): + with open(self.annot_path, 'r') as f: + txt = f.readlines() + annotations = [line.strip() for line in txt if len(line.strip().split()[1:]) != 0] + np.random.shuffle(annotations) + return annotations + + def __iter__(self): + return self + + def __next__(self): + + with tf.device('/cpu:0'): + self.train_input_size = random.choice(self.train_input_sizes) + self.train_output_sizes = self.train_input_size // self.strides + + batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3)) + + batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0], + self.anchor_per_scale, 5 + self.num_classes)) + batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1], + self.anchor_per_scale, 5 + self.num_classes)) + batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2], + self.anchor_per_scale, 5 + self.num_classes)) + + batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4)) + batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4)) + batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4)) + + num = 0 + if self.batch_count < self.num_batchs: + while num < self.batch_size: + index = self.batch_count * self.batch_size + num + if index >= self.num_samples: index -= self.num_samples + annotation = self.annotations[index] + image, bboxes = self.parse_annotation(annotation) + label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes) + + batch_image[num, :, :, :] = image + batch_label_sbbox[num, :, :, :, :] = label_sbbox + batch_label_mbbox[num, :, :, :, :] = label_mbbox + batch_label_lbbox[num, :, :, :, :] = label_lbbox + batch_sbboxes[num, :, :] = sbboxes + batch_mbboxes[num, :, :] = mbboxes + batch_lbboxes[num, :, :] = lbboxes + num += 1 + self.batch_count += 1 + return batch_image, batch_label_sbbox, batch_label_mbbox, batch_label_lbbox, \ + batch_sbboxes, batch_mbboxes, batch_lbboxes + else: + self.batch_count = 0 + np.random.shuffle(self.annotations) + raise StopIteration + + def random_horizontal_flip(self, image, bboxes): + + if random.random() < 0.5: + _, w, _ = image.shape + image = image[:, ::-1, :] + bboxes[:, [0,2]] = w - bboxes[:, [2,0]] + + return image, bboxes + + def random_crop(self, image, bboxes): + + if random.random() < 0.5: + h, w, _ = image.shape + max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1) + + max_l_trans = max_bbox[0] + max_u_trans = max_bbox[1] + max_r_trans = w - max_bbox[2] + max_d_trans = h - max_bbox[3] + + crop_xmin = max(0, int(max_bbox[0] - random.uniform(0, max_l_trans))) + crop_ymin = max(0, int(max_bbox[1] - random.uniform(0, max_u_trans))) + crop_xmax = max(w, int(max_bbox[2] + random.uniform(0, max_r_trans))) + crop_ymax = max(h, int(max_bbox[3] + random.uniform(0, max_d_trans))) + + image = image[crop_ymin : crop_ymax, crop_xmin : crop_xmax] + + bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin + bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin + + return image, bboxes + + def random_translate(self, image, bboxes): + + if random.random() < 0.5: + h, w, _ = image.shape + max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1) + + max_l_trans = max_bbox[0] + max_u_trans = max_bbox[1] + max_r_trans = w - max_bbox[2] + max_d_trans = h - max_bbox[3] + + tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) + ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) + + M = np.array([[1, 0, tx], [0, 1, ty]]) + image = cv2.warpAffine(image, M, (w, h)) + + bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx + bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty + + return image, bboxes + + def parse_annotation(self, annotation): + + line = annotation.split() + image_path = line[0] + if not os.path.exists(image_path): + raise KeyError("%s does not exist ... " %image_path) + image = np.array(cv2.imread(image_path)) + bboxes = np.array([list(map(lambda x: int(float(x)), box.split(','))) for box in line[1:]]) + + if self.data_aug: + image, bboxes = self.random_horizontal_flip(np.copy(image), np.copy(bboxes)) + image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) + image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes)) + + image, bboxes = utils.image_preporcess(np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes)) + + updated_bb = [] + for bb in bboxes: + x1, y1, x2, y2, cls_label = bb + + if x2 <= x1 or y2 <= y1: + # dont use such boxes as this may cause nan loss. + continue + + x1 = int(np.clip(x1, 0, image.shape[1])) + y1 = int(np.clip(y1, 0, image.shape[0])) + x2 = int(np.clip(x2, 0, image.shape[1])) + y2 = int(np.clip(y2, 0, image.shape[0])) + # clipping coordinates between 0 to image dimensions as negative values + # or values greater than image dimensions may cause nan loss. + updated_bb.append([x1, y1, x2, y2, cls_label]) + + return image, np.array(updated_bb) + + def bbox_iou(self, boxes1, boxes2): + + boxes1 = np.array(boxes1) + boxes2 = np.array(boxes2) + + boxes1_area = boxes1[..., 2] * boxes1[..., 3] + boxes2_area = boxes2[..., 2] * boxes2[..., 3] + + boxes1 = np.concatenate([boxes1[..., :2] - boxes1[..., 2:] * 0.5, + boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) + boxes2 = np.concatenate([boxes2[..., :2] - boxes2[..., 2:] * 0.5, + boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) + + left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) + right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) + + inter_section = np.maximum(right_down - left_up, 0.0) + inter_area = inter_section[..., 0] * inter_section[..., 1] + union_area = boxes1_area + boxes2_area - inter_area + + return inter_area / (union_area + 1e-6) + # added 1e-6 in denominator to avoid generation of inf, which may cause nan loss + + + def preprocess_true_boxes(self, bboxes): + + label = [np.zeros((self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale, + 5 + self.num_classes)) for i in range(3)] + bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(3)] + bbox_count = np.zeros((3,)) + + for bbox in bboxes: + bbox_coor = bbox[:4] + bbox_class_ind = bbox[4] + + onehot = np.zeros(self.num_classes, dtype=np.float) + onehot[bbox_class_ind] = 1.0 + uniform_distribution = np.full(self.num_classes, 1.0 / self.num_classes) + deta = 0.01 + smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution + + bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2]], axis=-1) + bbox_xywh_scaled = 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] + + iou = [] + exist_positive = False + for i in range(3): + anchors_xywh = np.zeros((self.anchor_per_scale, 4)) + anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 + anchors_xywh[:, 2:4] = self.anchors[i] + + iou_scale = self.bbox_iou(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh) + iou.append(iou_scale) + iou_mask = iou_scale > 0.3 + + if np.any(iou_mask): + xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + xind = np.clip(xind, 0, self.train_output_sizes[i] - 1) + yind = np.clip(yind, 0, self.train_output_sizes[i] - 1) + # This will mitigate errors generated when the location computed by this is more the grid cell location. + # e.g. For 52x52 grid cells possible values of xind and yind are in range [0-51] including both. + # But sometimes the coomputation makes it 52 and then it will try to find that location in label array + # which is not present and throws error during training. + + label[i][yind, xind, iou_mask, :] = 0 + label[i][yind, xind, iou_mask, 0:4] = bbox_xywh + label[i][yind, xind, iou_mask, 4:5] = 1.0 + label[i][yind, xind, iou_mask, 5:] = smooth_onehot + + bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) + bboxes_xywh[i][bbox_ind, :4] = bbox_xywh + bbox_count[i] += 1 + + exist_positive = True + + if not exist_positive: + best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) + best_detect = int(best_anchor_ind / self.anchor_per_scale) + best_anchor = int(best_anchor_ind % self.anchor_per_scale) + xind, yind = np.floor(bbox_xywh_scaled[best_detect, 0:2]).astype(np.int32) + xind = np.clip(xind, 0, self.train_output_sizes[i] - 1) + yind = np.clip(yind, 0, self.train_output_sizes[i] - 1) + # This will mitigate errors generated when the location computed by this is more the grid cell location. + # e.g. For 52x52 grid cells possible values of xind and yind are in range [0-51] including both. + # But sometimes the coomputation makes it 52 and then it will try to find that location in label array + # which is not present and throws error during training. + + label[best_detect][yind, xind, best_anchor, :] = 0 + label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh + label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 + label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot + + bbox_ind = int(bbox_count[best_detect] % self.max_bbox_per_scale) + bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh + bbox_count[best_detect] += 1 + label_sbbox, label_mbbox, label_lbbox = label + sbboxes, mbboxes, lbboxes = bboxes_xywh + return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes + + def __len__(self): + return self.num_batchs + + + + diff --git a/cv/detection/yolov3/tensorflow/core/utils.py b/cv/detection/yolov3/tensorflow/core/utils.py new file mode 100644 index 000000000..b7f2d0f43 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/core/utils.py @@ -0,0 +1,212 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : utils.py +# Author : YunYang1994 +# Created date: 2019-02-28 13:14:19 +# Description : +# +#================================================================ + +import cv2 +import random +import colorsys +import numpy as np +import tensorflow.compat.v1 as tf +from core.config import cfg + +def read_class_names(class_file_name): + '''loads class name from a file''' + names = {} + with open(class_file_name, 'r') as data: + for ID, name in enumerate(data): + names[ID] = name.strip('\n') + return names + + +def get_anchors(anchors_path): + '''loads the anchors from a file''' + with open(anchors_path) as f: + anchors = f.readline() + anchors = np.array(anchors.split(','), dtype=np.float32) + return anchors.reshape(3, 3, 2) + + +def image_preporcess(image, target_size, gt_boxes=None): + + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32) + + ih, iw = target_size + h, w, _ = image.shape + + scale = min(iw/w, ih/h) + nw, nh = int(scale * w), int(scale * h) + image_resized = cv2.resize(image, (nw, nh)) + + image_paded = np.full(shape=[ih, iw, 3], fill_value=128.0) + dw, dh = (iw - nw) // 2, (ih-nh) // 2 + image_paded[dh:nh+dh, dw:nw+dw, :] = image_resized + image_paded = image_paded / 255. + + if gt_boxes is None: + return image_paded + + else: + gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw + gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh + return image_paded, gt_boxes + + +def draw_bbox(image, bboxes, classes=read_class_names(cfg.YOLO.CLASSES), show_label=True): + """ + bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates. + """ + + num_classes = len(classes) + image_h, image_w, _ = image.shape + hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)] + colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) + colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors)) + + random.seed(0) + random.shuffle(colors) + random.seed(None) + + for i, bbox in enumerate(bboxes): + coor = np.array(bbox[:4], dtype=np.int32) + fontScale = 0.5 + score = bbox[4] + class_ind = int(bbox[5]) + bbox_color = colors[class_ind] + bbox_thick = int(0.6 * (image_h + image_w) / 600) + c1, c2 = (coor[0], coor[1]), (coor[2], coor[3]) + cv2.rectangle(image, c1, c2, bbox_color, bbox_thick) + + if show_label: + bbox_mess = '%s: %.2f' % (classes[class_ind], score) + t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0] + cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1) # filled + + cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX, + fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA) + + return image + + + +def bboxes_iou(boxes1, boxes2): + + boxes1 = np.array(boxes1) + boxes2 = np.array(boxes2) + + boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1]) + boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1]) + + left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) + right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) + + inter_section = np.maximum(right_down - left_up, 0.0) + inter_area = inter_section[..., 0] * inter_section[..., 1] + union_area = boxes1_area + boxes2_area - inter_area + ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps) + + return ious + + + +def read_pb_return_tensors(graph, pb_file, return_elements): + + with tf.gfile.FastGFile(pb_file, 'rb') as f: + frozen_graph_def = tf.GraphDef() + frozen_graph_def.ParseFromString(f.read()) + + with graph.as_default(): + return_elements = tf.import_graph_def(frozen_graph_def, + return_elements=return_elements) + return return_elements + + +def nms(bboxes, iou_threshold, sigma=0.3, method='nms'): + """ + :param bboxes: (xmin, ymin, xmax, ymax, score, class) + + Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf + https://github.com/bharatsingh430/soft-nms + """ + classes_in_img = list(set(bboxes[:, 5])) + best_bboxes = [] + + for cls in classes_in_img: + cls_mask = (bboxes[:, 5] == cls) + cls_bboxes = bboxes[cls_mask] + + while len(cls_bboxes) > 0: + max_ind = np.argmax(cls_bboxes[:, 4]) + best_bbox = cls_bboxes[max_ind] + best_bboxes.append(best_bbox) + cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]]) + iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4]) + weight = np.ones((len(iou),), dtype=np.float32) + + assert method in ['nms', 'soft-nms'] + + if method == 'nms': + iou_mask = iou > iou_threshold + weight[iou_mask] = 0.0 + + if method == 'soft-nms': + weight = np.exp(-(1.0 * iou ** 2 / sigma)) + + cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight + score_mask = cls_bboxes[:, 4] > 0. + cls_bboxes = cls_bboxes[score_mask] + + return best_bboxes + + +def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold): + + valid_scale=[0, np.inf] + pred_bbox = np.array(pred_bbox) + + pred_xywh = pred_bbox[:, 0:4] + pred_conf = pred_bbox[:, 4] + pred_prob = pred_bbox[:, 5:] + + # # (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) + pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5, + pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1) + # # (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) + org_h, org_w = org_img_shape + resize_ratio = min(input_size / org_w, input_size / org_h) + + dw = (input_size - resize_ratio * org_w) / 2 + dh = (input_size - resize_ratio * org_h) / 2 + + pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio + pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio + + # # (3) clip some boxes those are out of range + pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]), + np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1) + invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3])) + pred_coor[invalid_mask] = 0 + + # # (4) discard some invalid boxes + bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1)) + scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1])) + + # # (5) discard some boxes with low scores + classes = np.argmax(pred_prob, axis=-1) + scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes] + score_mask = scores > score_threshold + mask = np.logical_and(scale_mask, score_mask) + coors, scores, classes = pred_coor[mask], scores[mask], classes[mask] + + return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1) + + + diff --git a/cv/detection/yolov3/tensorflow/core/yolov3.py b/cv/detection/yolov3/tensorflow/core/yolov3.py new file mode 100644 index 000000000..1d75165ee --- /dev/null +++ b/cv/detection/yolov3/tensorflow/core/yolov3.py @@ -0,0 +1,261 @@ +#! /usr/bin/env python +# coding=utf-8 +#================================================================ +# Copyright (C) 2019 * Ltd. All rights reserved. +# +# Editor : VIM +# File name : yolov3.py +# Author : YunYang1994 +# Created date: 2019-02-28 10:47:03 +# Description : +# +#================================================================ + +import numpy as np +import tensorflow.compat.v1 as tf +import core.utils as utils +import core.common as common +import core.backbone as backbone +from core.config import cfg + +tf.disable_v2_behavior() + +class YOLOV3(object): + """Implement tensoflow yolov3 here""" + def __init__(self, input_data, trainable): + + self.trainable = trainable + self.classes = utils.read_class_names(cfg.YOLO.CLASSES) + self.num_class = len(self.classes) + self.strides = np.array(cfg.YOLO.STRIDES) + self.anchors = utils.get_anchors(cfg.YOLO.ANCHORS) + self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE + self.iou_loss_thresh = cfg.YOLO.IOU_LOSS_THRESH + self.upsample_method = cfg.YOLO.UPSAMPLE_METHOD + + try: + self.conv_lbbox, self.conv_mbbox, self.conv_sbbox = self.__build_nework(input_data) + except: + raise NotImplementedError("Can not build up yolov3 network!") + + with tf.variable_scope('pred_sbbox'): + self.pred_sbbox = self.decode(self.conv_sbbox, self.anchors[0], self.strides[0]) + + with tf.variable_scope('pred_mbbox'): + self.pred_mbbox = self.decode(self.conv_mbbox, self.anchors[1], self.strides[1]) + + with tf.variable_scope('pred_lbbox'): + self.pred_lbbox = self.decode(self.conv_lbbox, self.anchors[2], self.strides[2]) + + def __build_nework(self, input_data): + + route_1, route_2, input_data = backbone.darknet53(input_data, self.trainable) + + input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv52') + input_data = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, 'conv53') + input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv54') + input_data = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, 'conv55') + input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv56') + + conv_lobj_branch = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, name='conv_lobj_branch') + conv_lbbox = common.convolutional(conv_lobj_branch, (1, 1, 1024, 3*(self.num_class + 5)), + trainable=self.trainable, name='conv_lbbox', activate=False, bn=False) + + input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv57') + input_data = common.upsample(input_data, name='upsample0', method=self.upsample_method) + + with tf.variable_scope('route_1'): + input_data = tf.concat([input_data, route_2], axis=-1) + + input_data = common.convolutional(input_data, (1, 1, 768, 256), self.trainable, 'conv58') + input_data = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, 'conv59') + input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv60') + input_data = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, 'conv61') + input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv62') + + conv_mobj_branch = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, name='conv_mobj_branch' ) + conv_mbbox = common.convolutional(conv_mobj_branch, (1, 1, 512, 3*(self.num_class + 5)), + trainable=self.trainable, name='conv_mbbox', activate=False, bn=False) + + input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv63') + input_data = common.upsample(input_data, name='upsample1', method=self.upsample_method) + + with tf.variable_scope('route_2'): + input_data = tf.concat([input_data, route_1], axis=-1) + + input_data = common.convolutional(input_data, (1, 1, 384, 128), self.trainable, 'conv64') + input_data = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, 'conv65') + input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv66') + input_data = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, 'conv67') + input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv68') + + conv_sobj_branch = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, name='conv_sobj_branch') + conv_sbbox = common.convolutional(conv_sobj_branch, (1, 1, 256, 3*(self.num_class + 5)), + trainable=self.trainable, name='conv_sbbox', activate=False, bn=False) + + return conv_lbbox, conv_mbbox, conv_sbbox + + def decode(self, conv_output, anchors, stride): + """ + return tensor of shape [batch_size, output_size, output_size, anchor_per_scale, 5 + num_classes] + contains (x, y, w, h, score, probability) + """ + + conv_shape = tf.shape(conv_output) + batch_size = conv_shape[0] + output_size = conv_shape[1] + anchor_per_scale = len(anchors) + + conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, anchor_per_scale, 5 + self.num_class)) + + conv_raw_dxdy = conv_output[:, :, :, :, 0:2] + conv_raw_dwdh = conv_output[:, :, :, :, 2:4] + conv_raw_conf = conv_output[:, :, :, :, 4:5] + conv_raw_prob = conv_output[:, :, :, :, 5: ] + + y = tf.tile(tf.range(output_size, dtype=tf.int32)[:, tf.newaxis], [1, output_size]) + x = tf.tile(tf.range(output_size, dtype=tf.int32)[tf.newaxis, :], [output_size, 1]) + + xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1) + xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, anchor_per_scale, 1]) + xy_grid = tf.cast(xy_grid, tf.float32) + + pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * stride + pred_wh = (tf.exp(conv_raw_dwdh) * anchors) * stride + pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1) + + pred_conf = tf.sigmoid(conv_raw_conf) + pred_prob = tf.sigmoid(conv_raw_prob) + + return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1) + + def focal(self, target, actual, alpha=1, gamma=2): + focal_loss = alpha * tf.pow(tf.abs(target - actual), gamma) + return focal_loss + + def bbox_giou(self, boxes1, boxes2): + + boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5, + boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) + boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5, + boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) + + boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]), + tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis=-1) + boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]), + tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis=-1) + + boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1]) + boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1]) + + left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2]) + right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:]) + + inter_section = tf.maximum(right_down - left_up, 0.0) + inter_area = inter_section[..., 0] * inter_section[..., 1] + union_area = boxes1_area + boxes2_area - inter_area + iou = inter_area / (union_area + 1e-6) + # added 1e-6 in denominator to avoid generation of inf, which may cause nan loss + + enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2]) + enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:]) + enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0) + enclose_area = enclose[..., 0] * enclose[..., 1] + giou = iou - 1.0 * (enclose_area - union_area) / (enclose_area + 1e-6) + # added 1e-6 in denominator to avoid generation of inf, which may cause nan loss + + return giou + + def bbox_iou(self, boxes1, boxes2): + + boxes1_area = boxes1[..., 2] * boxes1[..., 3] + boxes2_area = boxes2[..., 2] * boxes2[..., 3] + + boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5, + boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) + boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5, + boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) + + left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2]) + right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:]) + + inter_section = tf.maximum(right_down - left_up, 0.0) + inter_area = inter_section[..., 0] * inter_section[..., 1] + union_area = boxes1_area + boxes2_area - inter_area + iou = 1.0 * inter_area / union_area + + return iou + + def loss_layer(self, conv, pred, label, bboxes, anchors, stride): + + conv_shape = tf.shape(conv) + batch_size = conv_shape[0] + output_size = conv_shape[1] + input_size = stride * output_size + conv = tf.reshape(conv, (batch_size, output_size, output_size, + self.anchor_per_scale, 5 + self.num_class)) + conv_raw_conf = conv[:, :, :, :, 4:5] + conv_raw_prob = conv[:, :, :, :, 5:] + + pred_xywh = pred[:, :, :, :, 0:4] + pred_conf = pred[:, :, :, :, 4:5] + + label_xywh = label[:, :, :, :, 0:4] + respond_bbox = label[:, :, :, :, 4:5] + label_prob = label[:, :, :, :, 5:] + + giou = tf.expand_dims(self.bbox_giou(pred_xywh, label_xywh), axis=-1) + input_size = tf.cast(input_size, tf.float32) + + bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2) + giou_loss = respond_bbox * bbox_loss_scale * (1- giou) + + iou = self.bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :]) + max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1) + + respond_bgd = (1.0 - respond_bbox) * tf.cast( max_iou < self.iou_loss_thresh, tf.float32 ) + + conf_focal = self.focal(respond_bbox, pred_conf) + + conf_loss = conf_focal * ( + respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf) + + + respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf) + ) + + prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob) + + giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1,2,3,4])) + conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1,2,3,4])) + prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1,2,3,4])) + + return giou_loss, conf_loss, prob_loss + + + + def compute_loss(self, label_sbbox, label_mbbox, label_lbbox, true_sbbox, true_mbbox, true_lbbox): + + with tf.name_scope('smaller_box_loss'): + loss_sbbox = self.loss_layer(self.conv_sbbox, self.pred_sbbox, label_sbbox, true_sbbox, + anchors = self.anchors[0], stride = self.strides[0]) + + with tf.name_scope('medium_box_loss'): + loss_mbbox = self.loss_layer(self.conv_mbbox, self.pred_mbbox, label_mbbox, true_mbbox, + anchors = self.anchors[1], stride = self.strides[1]) + + with tf.name_scope('bigger_box_loss'): + loss_lbbox = self.loss_layer(self.conv_lbbox, self.pred_lbbox, label_lbbox, true_lbbox, + anchors = self.anchors[2], stride = self.strides[2]) + + with tf.name_scope('giou_loss'): + giou_loss = loss_sbbox[0] + loss_mbbox[0] + loss_lbbox[0] + + with tf.name_scope('conf_loss'): + conf_loss = loss_sbbox[1] + loss_mbbox[1] + loss_lbbox[1] + + with tf.name_scope('prob_loss'): + prob_loss = loss_sbbox[2] + loss_mbbox[2] + loss_lbbox[2] + + return giou_loss, conf_loss, prob_loss + + diff --git a/cv/detection/yolov3/tensorflow/data/anchors/basline_anchors.txt b/cv/detection/yolov3/tensorflow/data/anchors/basline_anchors.txt new file mode 100644 index 000000000..ee6e756ec --- /dev/null +++ b/cv/detection/yolov3/tensorflow/data/anchors/basline_anchors.txt @@ -0,0 +1 @@ +1.25,1.625, 2.0,3.75, 4.125,2.875, 1.875,3.8125, 3.875,2.8125, 3.6875,7.4375, 3.625,2.8125, 4.875,6.1875, 11.65625,10.1875 diff --git a/cv/detection/yolov3/tensorflow/data/anchors/coco_anchors.txt b/cv/detection/yolov3/tensorflow/data/anchors/coco_anchors.txt new file mode 100644 index 000000000..80785b05f --- /dev/null +++ b/cv/detection/yolov3/tensorflow/data/anchors/coco_anchors.txt @@ -0,0 +1 @@ +10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 diff --git a/cv/detection/yolov3/tensorflow/data/classes/coco.names b/cv/detection/yolov3/tensorflow/data/classes/coco.names new file mode 100644 index 000000000..ca76c80b5 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/data/classes/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/detection/yolov3/tensorflow/data/classes/voc.names b/cv/detection/yolov3/tensorflow/data/classes/voc.names new file mode 100644 index 000000000..1168c3999 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/data/classes/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 \ No newline at end of file diff --git a/cv/detection/yolov3/tensorflow/data/dataset/voc_test.txt b/cv/detection/yolov3/tensorflow/data/dataset/voc_test.txt new file mode 100644 index 000000000..e0d2e385a --- /dev/null +++ b/cv/detection/yolov3/tensorflow/data/dataset/voc_test.txt @@ -0,0 +1,4952 @@ +./VOC/test/VOCdevkit/VOC2007/JPEGImages/000001.jpg 48,240,195,371,11 8,12,352,498,14 +./VOC/test/VOCdevkit/VOC2007/JPEGImages/000002.jpg 139,200,207,301,18 +./VOC/test/VOCdevkit/VOC2007/JPEGImages/000003.jpg 123,155,215,195,17 239,156,307,205,8 +./VOC/test/VOCdevkit/VOC2007/JPEGImages/000004.jpg 13,311,84,362,6 362,330,500,389,6 235,328,334,375,6 175,327,252,364,6 139,320,189,359,6 108,325,150,353,6 84,323,121,350,6 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2022 Iluvatar CoreX. All rights reserved. +# Copyright Declaration: This software, including all of its code and documentation, +# except for the third-party software it contains, is a copyrighted work of Shanghai Iluvatar CoreX +# Semiconductor Co., Ltd. and its affiliates ("Iluvatar CoreX") in accordance with the PRC Copyright +# Law and relevant international treaties, and all rights contained therein are enjoyed by Iluvatar +# CoreX. No user of this software shall have any right, ownership or interest in this software and +# any use of this software shall be in compliance with the terms and conditions of the End User +# License Agreement. + + +import os +import time + +import torch +import torchvision +from .utils import presets_classification as presets + +""" +Examples: + +>>> dataset_train, dataset_val = load_data(train_dir, val_dir, args) +""" + + +def get_datasets(traindir, + valdir, + resize_size=256, + crop_size=224, + auto_augment_policy=None, + random_erase_prob=0.): + # Data loading code + print("Loading data") + print("Loading training data") + dataset = torchvision.datasets.ImageFolder( + traindir, + presets.ClassificationPresetTrain(crop_size=crop_size, auto_augment_policy=auto_augment_policy, + random_erase_prob=random_erase_prob)) + + print("Loading validation data") + dataset_test = torchvision.datasets.ImageFolder( + valdir, + presets.ClassificationPresetEval(crop_size=crop_size, resize_size=resize_size)) + + return dataset, dataset_test + + +def get_input_size(model): + biger_input_size_models = ['inception'] + resize_size = 256 + crop_size = 224 + for bi_model in biger_input_size_models: + if bi_model in model: + resize_size = 342 + crop_size = 299 + + return resize_size, crop_size + + +def load_data(train_dir, val_dir, args): + auto_augment_policy = getattr(args, "auto_augment", None) + random_erase_prob = getattr(args, "random_erase", 0.0) + resize_size, crop_size = get_input_size(args.model) + dataset, dataset_test = get_datasets(train_dir, val_dir, + auto_augment_policy=auto_augment_policy, + random_erase_prob=random_erase_prob, + resize_size=resize_size, + crop_size=crop_size) + if args.distributed: + train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) + test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test) + else: + train_sampler = torch.utils.data.RandomSampler(dataset) + test_sampler = torch.utils.data.SequentialSampler(dataset_test) + + return dataset, dataset_test, train_sampler, test_sampler + + +def _create_torch_dataloader(train_dir, val_dir, args): + dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir, args) + data_loader = torch.utils.data.DataLoader( + dataset, batch_size=args.batch_size, + sampler=train_sampler, num_workers=args.workers, pin_memory=True) + + data_loader_test = torch.utils.data.DataLoader( + dataset_test, batch_size=args.batch_size, + sampler=test_sampler, num_workers=args.workers, pin_memory=True) + + return data_loader, data_loader_test + + +def _create_dali_dataloader(train_dir, val_dir, args): + from .dali_classification import get_imagenet_iter_dali + device = torch.cuda.current_device() + _, crop_size = get_input_size(args.model) + data_loader = get_imagenet_iter_dali('train', train_dir, args.batch_size, + num_threads=args.workers, + device_id=device, + size=crop_size) + data_loader_test = get_imagenet_iter_dali('val', train_dir, args.batch_size, + num_threads=args.workers, + device_id=device, + size=crop_size) + + return data_loader, data_loader_test + + +def create_dataloader(train_dir, val_dir, args): + print("Creating data loaders") + if args.dali: + train_dir = os.path.dirname(train_dir) + val_dir = os.path.dirname(val_dir) + return _create_dali_dataloader(train_dir, val_dir, args) + return _create_torch_dataloader(train_dir, val_dir, args) diff --git a/cv/detection/yolov3/tensorflow/dataloader/dali_classification.py b/cv/detection/yolov3/tensorflow/dataloader/dali_classification.py new file mode 100644 index 000000000..4c92283b2 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/dali_classification.py @@ -0,0 +1,121 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright Declaration: This software, including all of its code and documentation, +# except for the third-party software it contains, is a copyrighted work of Shanghai Iluvatar CoreX +# Semiconductor Co., Ltd. and its affiliates ("Iluvatar CoreX") in accordance with the PRC Copyright +# Law and relevant international treaties, and all rights contained therein are enjoyed by Iluvatar +# CoreX. No user of this software shall have any right, ownership or interest in this software and +# any use of this software shall be in compliance with the terms and conditions of the End User +# License Agreement. + + +import os + +import nvidia.dali.ops as ops +import nvidia.dali.types as types +from nvidia.dali.pipeline import Pipeline +from nvidia.dali.plugin.pytorch import DALIClassificationIterator, DALIGenericIterator + +class HybridTrainPipe(Pipeline): + def __init__(self, batch_size, num_threads, device_id, data_dir, size): + super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id) + self.input = ops.FileReader(file_root=data_dir, random_shuffle=True) + self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB) + self.res = ops.RandomResizedCrop(device="gpu", size=size, random_area=[0.08, 1.25]) + self.cmnp = ops.CropMirrorNormalize(device="gpu", + output_dtype=types.FLOAT, + output_layout=types.NCHW, + image_type=types.RGB, + mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], + std=[0.229 * 255, 0.224 * 255, 0.225 * 255]) + + def define_graph(self): + self.jpegs, self.labels = self.input(name="Reader") + + images = self.decode(self.jpegs) + images = self.res(images.gpu()) + output = self.cmnp(images) + return [output, self.labels] + + +class HybridValPipe(Pipeline): + def __init__(self, batch_size, num_threads, device_id, data_dir, size): + super(HybridValPipe, self).__init__(batch_size, num_threads, device_id) + self.input = ops.FileReader(file_root=data_dir, random_shuffle=False) + self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB) + self.res = ops.Resize(device="gpu", resize_x=size, resize_y=size) + self.cmnp = ops.CropMirrorNormalize(device="gpu", + output_dtype=types.FLOAT, + output_layout=types.NCHW, + crop=(size, size), + image_type=types.RGB, + mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], + std=[0.229 * 255, 0.224 * 255, 0.225 * 255]) + + def define_graph(self): + self.jpegs, self.labels = self.input(name="Reader") + + images = self.decode(self.jpegs) + images = self.res(images.gpu()) + output = self.cmnp(images) + return [output, self.labels] + + +def get_imagenet_iter_dali(type, image_dir, batch_size, num_threads, device_id, size): + if type == 'train': + pip_train = HybridTrainPipe(batch_size=batch_size, num_threads=num_threads, device_id=device_id, + data_dir = os.path.join(image_dir, "train"), + size=size) + pip_train.build() + dali_iter_train = DALIClassificationIterator(pip_train, size=pip_train.epoch_size("Reader")) + return dali_iter_train + elif type == 'val': + pip_val = HybridValPipe(batch_size=batch_size, num_threads=num_threads, device_id=device_id, + data_dir = os.path.join(image_dir, "val"), + size=size) + pip_val.build() + dali_iter_val = DALIClassificationIterator(pip_val, size=pip_val.epoch_size("Reader")) + return dali_iter_val + + +def main(arguments): + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', help='directory to save data to', type=str, default='classification data') + args = parser.parse_args(arguments) + + train_loader = get_imagenet_iter_dali(type='train', image_dir=args.data_dir, + batch_size=256, + num_threads=4, size=224, device_id=3) + + val_loader = get_imagenet_iter_dali(type="val", image_dir=args.data_dir, + batch_size=256, + num_threads=4, size=224, device_id=3) + + print('start dali train dataloader.') + start = time.time() + for epoch in range(20): + for i, data in enumerate(train_loader): + images = data[0]["data"].cuda(non_blocking=True) + labels = data[0]["label"].squeeze().long().cuda(non_blocking=True) + + # WARN: Very important + train_loader.reset() + print("Epoch", epoch) + print('dali iterate time: %fs' % (time.time() - start)) + print('end dali train dataloader.') + + + print('start dali val dataloader.') + start = time.time() + for i, data in enumerate(val_loader): + images = data[0]["data"].cuda(non_blocking=True) + print(images.shape) + labels = data[0]["label"].squeeze().long().cuda(non_blocking=True) + print(labels.shape) + print('dali iterate time: %fs' % (time.time() - start)) + print('end dali val dataloader.') + + +if __name__ == '__main__': + import os, time, sys + import argparse + sys.exit(main(sys.argv[1:])) \ No newline at end of file diff --git a/cv/detection/yolov3/tensorflow/dataloader/detection.py b/cv/detection/yolov3/tensorflow/dataloader/detection.py new file mode 100644 index 000000000..ecd66e196 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/detection.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright Declaration: This software, including all of its code and documentation, +# except for the third-party software it contains, is a copyrighted work of Shanghai Iluvatar CoreX +# Semiconductor Co., Ltd. and its affiliates ("Iluvatar CoreX") in accordance with the PRC Copyright +# Law and relevant international treaties, and all rights contained therein are enjoyed by Iluvatar +# CoreX. No user of this software shall have any right, ownership or interest in this software and +# any use of this software shall be in compliance with the terms and conditions of the End User +# License Agreement. + + +from .utils.coco_utils import get_coco, get_coco_kp +from .utils import presets_detection as presets +from .utils.pascal_voc import get_voc + +""" +Examples: + +>>> dataset_train, num_classes = get_dataset("voc", "train", "/path/to/VOC2012_sample") +>>> dataset_val, _ = get_dataset("voc", "val", "/path/to/VOC2012_sample") +""" + + +def get_transform(train, data_augmentation="ssd"): + return presets.DetectionPresetTrain(data_augmentation) if train else presets.DetectionPresetEval() + + +def get_dataset(name, image_set, data_path): + transform = get_transform(image_set.lower() == "train") + paths = { + "coco": (data_path, get_coco, 91), + "coco_kp": (data_path, get_coco_kp, 2), + "voc": (data_path, get_voc, 21) + } + p, ds_fn, num_classes = paths[name] + + ds = ds_fn(p, image_set=image_set, transforms=transform) + return ds, num_classes \ No newline at end of file diff --git a/cv/detection/yolov3/tensorflow/dataloader/segmentation.py b/cv/detection/yolov3/tensorflow/dataloader/segmentation.py new file mode 100644 index 000000000..7c2f2b6ee --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/segmentation.py @@ -0,0 +1,46 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright Declaration: This software, including all of its code and documentation, +# except for the third-party software it contains, is a copyrighted work of Shanghai Iluvatar CoreX +# Semiconductor Co., Ltd. and its affiliates ("Iluvatar CoreX") in accordance with the PRC Copyright +# Law and relevant international treaties, and all rights contained therein are enjoyed by Iluvatar +# CoreX. No user of this software shall have any right, ownership or interest in this software and +# any use of this software shall be in compliance with the terms and conditions of the End User +# License Agreement. + + +import torchvision + +from .utils.coco_seg_utils import get_coco +from .utils import presets_segmentation as presets +from .utils.camvid import get_camvid + +""" +Examples: + +>>> dataset_train, num_classes = get_dataset("/path/to/CamVid11", "camvid", "train") +>>> dataset_val, _ = get_dataset("/path/to/CamVid11", "camvid", "val") + +""" + + +def get_transform(train): + base_size = 520 + crop_size = 480 + return presets.SegmentationPresetTrain(base_size, crop_size) if train else presets.SegmentationPresetEval(base_size) + + +def get_dataset(dir_path, name, image_set): + transform = get_transform(image_set == 'train') + # name = 'camvid' + def sbd(*args, **kwargs): + return torchvision.datasets.SBDataset(*args, mode='segmentation', **kwargs) + paths = { + "voc": (dir_path, torchvision.datasets.VOCSegmentation, 21), + "voc_aug": (dir_path, sbd, 21), + "coco": (dir_path, get_coco, 21), + "camvid": (dir_path, get_camvid, 12) + } + p, ds_fn, num_classes = paths[name] + + ds = ds_fn(p, image_set=image_set, transforms=transform) + return ds, num_classes \ No newline at end of file diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/__init__.py b/cv/detection/yolov3/tensorflow/dataloader/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/camvid.py b/cv/detection/yolov3/tensorflow/dataloader/utils/camvid.py new file mode 100644 index 000000000..0f5487480 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/camvid.py @@ -0,0 +1,191 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright Declaration: This software, including all of its code and documentation, +# except for the third-party software it contains, is a copyrighted work of Shanghai Iluvatar CoreX +# Semiconductor Co., Ltd. and its affiliates ("Iluvatar CoreX") in accordance with the PRC Copyright +# Law and relevant international treaties, and all rights contained therein are enjoyed by Iluvatar +# CoreX. No user of this software shall have any right, ownership or interest in this software and +# any use of this software shall be in compliance with the terms and conditions of the End User +# License Agreement. + + +from collections import OrderedDict +import cv2 +from functools import lru_cache +import numpy as np +import os +import os.path as osp +from PIL import Image + +from .pascal_voc import BaseDataset + + +class SemanticSeg(BaseDataset): + + def __init__(self, + data_dir, + anno_dir, + data_suffix='.png', + anno_suffix='.png', + split=None, + classes=None, + label_rgb=False, + **kwargs): + super().__init__(**kwargs) + self.data_dir = data_dir + self.anno_dir = anno_dir + self.data_suffix = data_suffix + self.anno_suffix = anno_suffix + self.label_rgb = label_rgb + if classes is not None: + self.CLASSES = classes + self.COLORS = np.random.randint(0, 255, size=(len(self.CLASSES), 3)) + self.annotations = SemanticSeg.load_annotations(data_dir, + anno_dir, + data_suffix, + anno_suffix, + split) + self.image_ids = list(self.annotations.keys()) + + @staticmethod + def load_annotations(data_dir, anno_dir, data_suffix, anno_suffix, split): + """Load annotation from directory. + + Returns + ------- + dict[dict] + All image info of dataset. + """ + + img_infos = dict() + image_id = 0 + # 如果提供了 split file, 则根据 split file 中的文件名得到数据集的图片和标注 + if split is not None: + with open(split) as f: + for line in f: + img_name = line.strip() + img_file = osp.join(data_dir, img_name + data_suffix) + img_info = dict(file_path=img_file) + if anno_dir is not None: + anno_file = osp.join(anno_dir, img_name + anno_suffix) + img_info['anno_path'] = anno_file + img_infos[image_id] = img_info + image_id += 1 + else: + for img in scandir(data_dir, data_suffix): + img_file = osp.join(data_dir, img) + img_info = dict(file_path=img_file) + if anno_dir is not None: + anno_file = osp.join(anno_dir, + img.replace(data_suffix, anno_suffix)) + img_info['anno_path'] = anno_file + img_infos[image_id] = img_info + image_id += 1 + + return img_infos + + def get_data(self, idx): + image_id = self.image_ids[idx] + img = self._read_image(image_id) + mask = self._read_mask(image_id) + return img, mask + + def get_img_info(self, image_id): + return self.annotations[image_id] + + @lru_cache(maxsize=None) + def _read_image(self, image_id): + img_info = self.get_img_info(image_id) + image = Image.open(img_info['file_path']) + return image + + @lru_cache(maxsize=None) + def _read_mask(self, image_id): + img_info = self.get_img_info(image_id) + anno_path = img_info['anno_path'] + # TODO: 当 mask 是 RGB 格式时， 通过 self.COLORS 中 class_id 和 RGB 值 + # 的映射关系， 将 mask 转换为灰度图， 其中每个像素值都对应一个 class_id + if self.label_rgb: + # mask = cv2.imread(anno_path) + # mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB) + raise NotImplementedError + else: + mask = Image.open(anno_path) + return mask + + @lru_cache(maxsize=None) + def get_class_name(self, class_id): + class_dict = {class_name: i for i, class_name in enumerate(self.CLASSES)} + class_id_dict = {cls_id: name for name, cls_id in class_dict.items()} + return class_id_dict[class_id] + + @lru_cache(maxsize=None) + def get_class_color(self, class_id): + if not isinstance(class_id, str): + class_name = self.get_class_name(class_id) + else: + class_name = class_id + CLASS_COLOR = OrderedDict(zip(self.CLASSES, self.COLORS)) + return CLASS_COLOR[class_name] + + +def scandir(dir_path, suffix=None): + file_paths = [] + for parent, _, fns in sorted(os.walk(dir_path)): + # 将 dir_path 从 parent 中去除， 使 parent 为相对路径 + parent = parent[len(dir_path):] + for fn in sorted(fns): + if fn.endswith(suffix): + # file 在 dir_path 中的相对路径 + path = os.path.join(parent, fn) + file_paths.append(path) + return file_paths + + + +class CamVid(SemanticSeg): + """CamVid dataset with 32 classes. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.CLASSES = ('Animal', 'Archway', 'Bicyclist', 'Bridge', + 'Building', 'Car', 'CartLuggagePram', 'Child', + 'Column_Pole', 'Fence', 'LaneMkgsDriv', 'LaneMkgsNonDriv', + 'Misc_Text', 'MotorcycleScooter', 'OtherMoving', 'ParkingBlock', + 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk', + 'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone', + 'TrafficLight', 'Train', 'Tree', 'Truck_Bus', + 'Tunnel', 'VegetationMisc', 'Void', 'Wall') + + self.COLORS = ([64,128,64], [192,0,128], [0,128,192], [0,128,64], + [128,0,0], [64,0,128], [64,0,192], [192,128,64], + [192,192,128], [64,64,128], [128,0,192], [192,0,64], + [128,128,64], [192,0,192], [128,64,64], [64,192,128], + [64,64,0], [128,64,128], [128,128,192], [0,0,192], + [192,128,128], [128,128,128], [64,128,192], [0,0,64], + [0,64,64], [192,64,128], [128,128,0], [192,128,192], + [64,0,64], [192,192,0], [0,0,0], [64,192,0]) + + + +class CamVid11(SemanticSeg): + """CamVid dataset with 11 classes. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + # 如果将 `Unlabelled` (对应 CamVid 中的 `Void` 类) 也算作一类的话, 则总共有 12 类. + self.CLASSES = ('Sky', 'Building', 'Pole', 'Road', + 'Pavement', 'Tree', 'SignSymbol', 'Fence', + 'Car', 'Pedestrian', 'Bicyclist', 'Unlabelled') + + self.COLORS = ([128,128,128], [128,0,0], [192,192,128], [128,64,128], + [60,40,222], [128,128,0], [192,128,128], [64,64,128], + [64,0,128], [64,64,0], [0,128,192], [0,0,0]) + + +def get_camvid(root, image_set, transforms): + data_dir = os.path.join(root, image_set) + anno_dir = os.path.join(root, image_set + "annot") + return CamVid11(data_dir=data_dir, anno_dir=anno_dir, transform=transforms) + diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/coco_seg_utils.py b/cv/detection/yolov3/tensorflow/dataloader/utils/coco_seg_utils.py new file mode 100644 index 000000000..bab58447b --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/coco_seg_utils.py @@ -0,0 +1,115 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +import copy +import torch +import torch.utils.data +import torchvision +from PIL import Image + +import os + +from pycocotools import mask as coco_mask + +from .transforms_seg import Compose + + +class FilterAndRemapCocoCategories(object): + def __init__(self, categories, remap=True): + self.categories = categories + self.remap = remap + + def __call__(self, image, anno): + anno = [obj for obj in anno if obj["category_id"] in self.categories] + if not self.remap: + return image, anno + anno = copy.deepcopy(anno) + for obj in anno: + obj["category_id"] = self.categories.index(obj["category_id"]) + return image, anno + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +class ConvertCocoPolysToMask(object): + def __call__(self, image, anno): + w, h = image.size + segmentations = [obj["segmentation"] for obj in anno] + cats = [obj["category_id"] for obj in anno] + if segmentations: + masks = convert_coco_poly_to_mask(segmentations, h, w) + cats = torch.as_tensor(cats, dtype=masks.dtype) + # merge all instance masks into a single segmentation map + # with its corresponding categories + target, _ = (masks * cats[:, None, None]).max(dim=0) + # discard overlapping instances + target[masks.sum(0) > 1] = 255 + else: + target = torch.zeros((h, w), dtype=torch.uint8) + target = Image.fromarray(target.numpy()) + return image, target + + +def _coco_remove_images_without_annotations(dataset, cat_list=None): + def _has_valid_annotation(anno): + # if it's empty, there is no annotation + if len(anno) == 0: + return False + # if more than 1k pixels occupied in the image + return sum(obj["area"] for obj in anno) > 1000 + + assert isinstance(dataset, torchvision.datasets.CocoDetection) + ids = [] + for ds_idx, img_id in enumerate(dataset.ids): + ann_ids = dataset.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = dataset.coco.loadAnns(ann_ids) + if cat_list: + anno = [obj for obj in anno if obj["category_id"] in cat_list] + if _has_valid_annotation(anno): + ids.append(ds_idx) + + dataset = torch.utils.data.Subset(dataset, ids) + return dataset + + +def get_coco(root, image_set, transforms): + PATHS = { + "train": ("train2017", os.path.join("annotations", "instances_train2017.json")), + "val": ("val2017", os.path.join("annotations", "instances_val2017.json")), + # "train": ("val2017", os.path.join("annotations", "instances_val2017.json")) + } + CAT_LIST = [0, 5, 2, 16, 9, 44, 6, 3, 17, 62, 21, 67, 18, 19, 4, + 1, 64, 20, 63, 7, 72] + + transforms = Compose([ + FilterAndRemapCocoCategories(CAT_LIST, remap=True), + ConvertCocoPolysToMask(), + transforms + ]) + + img_folder, ann_file = PATHS[image_set] + img_folder = os.path.join(root, img_folder) + ann_file = os.path.join(root, ann_file) + + dataset = torchvision.datasets.CocoDetection(img_folder, ann_file, transforms=transforms) + + if image_set == "train": + dataset = _coco_remove_images_without_annotations(dataset, CAT_LIST) + + return dataset diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/coco_utils.py b/cv/detection/yolov3/tensorflow/dataloader/utils/coco_utils.py new file mode 100644 index 000000000..cef31f4a8 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/coco_utils.py @@ -0,0 +1,255 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +import copy +import os +from PIL import Image + +import torch +import torch.utils.data +import torchvision + +from pycocotools import mask as coco_mask +from pycocotools.coco import COCO + +from . import transforms_det as T + +class FilterAndRemapCocoCategories(object): + def __init__(self, categories, remap=True): + self.categories = categories + self.remap = remap + + def __call__(self, image, target): + anno = target["annotations"] + anno = [obj for obj in anno if obj["category_id"] in self.categories] + if not self.remap: + target["annotations"] = anno + return image, target + anno = copy.deepcopy(anno) + for obj in anno: + obj["category_id"] = self.categories.index(obj["category_id"]) + target["annotations"] = anno + return image, target + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +class ConvertCocoPolysToMask(object): + def __call__(self, image, target): + w, h = image.size + + image_id = target["image_id"] + image_id = torch.tensor([image_id]) + + anno = target["annotations"] + + anno = [obj for obj in anno if obj['iscrowd'] == 0] + + boxes = [obj["bbox"] for obj in anno] + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2].clamp_(min=0, max=w) + boxes[:, 1::2].clamp_(min=0, max=h) + + classes = [obj["category_id"] for obj in anno] + classes = torch.tensor(classes, dtype=torch.int64) + + segmentations = [obj["segmentation"] for obj in anno] + masks = convert_coco_poly_to_mask(segmentations, h, w) + + keypoints = None + if anno and "keypoints" in anno[0]: + keypoints = [obj["keypoints"] for obj in anno] + keypoints = torch.as_tensor(keypoints, dtype=torch.float32) + num_keypoints = keypoints.shape[0] + if num_keypoints: + keypoints = keypoints.view(num_keypoints, -1, 3) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + boxes = boxes[keep] + classes = classes[keep] + masks = masks[keep] + if keypoints is not None: + keypoints = keypoints[keep] + + target = {} + target["boxes"] = boxes + target["labels"] = classes + target["masks"] = masks + target["image_id"] = image_id + if keypoints is not None: + target["keypoints"] = keypoints + + # for conversion to coco api + area = torch.tensor([obj["area"] for obj in anno]) + iscrowd = torch.tensor([obj["iscrowd"] for obj in anno]) + target["area"] = area + target["iscrowd"] = iscrowd + + return image, target + + +def _coco_remove_images_without_annotations(dataset, cat_list=None): + def _has_only_empty_bbox(anno): + return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno) + + def _count_visible_keypoints(anno): + return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno) + + min_keypoints_per_image = 10 + + def _has_valid_annotation(anno): + # if it's empty, there is no annotation + if len(anno) == 0: + return False + # if all boxes have close to zero area, there is no annotation + if _has_only_empty_bbox(anno): + return False + # keypoints task have a slight different critera for considering + # if an annotation is valid + if "keypoints" not in anno[0]: + return True + # for keypoint detection tasks, only consider valid images those + # containing at least min_keypoints_per_image + if _count_visible_keypoints(anno) >= min_keypoints_per_image: + return True + return False + + assert isinstance(dataset, torchvision.datasets.CocoDetection) + ids = [] + for ds_idx, img_id in enumerate(dataset.ids): + ann_ids = dataset.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = dataset.coco.loadAnns(ann_ids) + if cat_list: + anno = [obj for obj in anno if obj["category_id"] in cat_list] + if _has_valid_annotation(anno): + ids.append(ds_idx) + + dataset = torch.utils.data.Subset(dataset, ids) + return dataset + + +def convert_to_coco_api(ds): + coco_ds = COCO() + # annotation IDs need to start at 1, not 0, see torchvision issue #1530 + ann_id = 1 + dataset = {'images': [], 'categories': [], 'annotations': []} + categories = set() + for img_idx in range(len(ds)): + # find better way to get target + # targets = ds.get_annotations(img_idx) + img, targets = ds[img_idx] + image_id = targets["image_id"].item() + img_dict = {} + img_dict['id'] = image_id + img_dict['height'] = img.shape[-2] + img_dict['width'] = img.shape[-1] + dataset['images'].append(img_dict) + bboxes = targets["boxes"] + bboxes[:, 2:] -= bboxes[:, :2] + bboxes = bboxes.tolist() + labels = targets['labels'].tolist() + areas = targets['area'].tolist() + iscrowd = targets['iscrowd'].tolist() + if 'masks' in targets: + masks = targets['masks'] + # make masks Fortran contiguous for coco_mask + masks = masks.permute(0, 2, 1).contiguous().permute(0, 2, 1) + if 'keypoints' in targets: + keypoints = targets['keypoints'] + keypoints = keypoints.reshape(keypoints.shape[0], -1).tolist() + num_objs = len(bboxes) + for i in range(num_objs): + ann = {} + ann['image_id'] = image_id + ann['bbox'] = bboxes[i] + ann['category_id'] = labels[i] + categories.add(labels[i]) + ann['area'] = areas[i] + ann['iscrowd'] = iscrowd[i] + ann['id'] = ann_id + if 'masks' in targets: + ann["segmentation"] = coco_mask.encode(masks[i].numpy()) + if 'keypoints' in targets: + ann['keypoints'] = keypoints[i] + ann['num_keypoints'] = sum(k != 0 for k in keypoints[i][2::3]) + dataset['annotations'].append(ann) + ann_id += 1 + dataset['categories'] = [{'id': i} for i in sorted(categories)] + coco_ds.dataset = dataset + coco_ds.createIndex() + return coco_ds + + +def get_coco_api_from_dataset(dataset): + for _ in range(10): + if isinstance(dataset, torchvision.datasets.CocoDetection): + break + if isinstance(dataset, torch.utils.data.Subset): + dataset = dataset.dataset + if isinstance(dataset, torchvision.datasets.CocoDetection): + return dataset.coco + return convert_to_coco_api(dataset) + + +class CocoDetection(torchvision.datasets.CocoDetection): + def __init__(self, img_folder, ann_file, transforms): + super(CocoDetection, self).__init__(img_folder, ann_file) + self._transforms = transforms + + def __getitem__(self, idx): + img, target = super(CocoDetection, self).__getitem__(idx) + image_id = self.ids[idx] + target = dict(image_id=image_id, annotations=target) + if self._transforms is not None: + img, target = self._transforms(img, target) + return img, target + + +def get_coco(root, image_set, transforms, mode='instances'): + anno_file_template = "{}_{}2017.json" + PATHS = { + "train": ("train2017", os.path.join("annotations", anno_file_template.format(mode, "train"))), + "val": ("val2017", os.path.join("annotations", anno_file_template.format(mode, "val"))), + # "train": ("val2017", os.path.join("annotations", anno_file_template.format(mode, "val"))) + } + + t = [ConvertCocoPolysToMask()] + + if transforms is not None: + t.append(transforms) + transforms = T.Compose(t) + + img_folder, ann_file = PATHS[image_set] + img_folder = os.path.join(root, img_folder) + ann_file = os.path.join(root, ann_file) + + dataset = CocoDetection(img_folder, ann_file, transforms=transforms) + + if image_set == "train": + dataset = _coco_remove_images_without_annotations(dataset) + + # dataset = torch.common_utils.data.Subset(dataset, [i for i in range(500)]) + + return dataset + + +def get_coco_kp(root, image_set, transforms): + return get_coco(root, image_set, transforms, mode="person_keypoints") diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/functional.py b/cv/detection/yolov3/tensorflow/dataloader/utils/functional.py new file mode 100644 index 000000000..ea5212135 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/functional.py @@ -0,0 +1,1324 @@ +import math +import numbers +import warnings +from enum import Enum + +import numpy as np +from PIL import Image + +import torch +from torch import Tensor +from typing import List, Tuple, Any, Optional + +try: + import accimage +except ImportError: + accimage = None + +from . import functional_pil as F_pil +from . import functional_tensor as F_t + + +class InterpolationMode(Enum): + """Interpolation modes + """ + NEAREST = "nearest" + BILINEAR = "bilinear" + BICUBIC = "bicubic" + # For PIL compatibility + BOX = "box" + HAMMING = "hamming" + LANCZOS = "lanczos" + + +# TODO: Once torchscript supports Enums with staticmethod +# this can be put into InterpolationMode as staticmethod +def _interpolation_modes_from_int(i: int) -> InterpolationMode: + inverse_modes_mapping = { + 0: InterpolationMode.NEAREST, + 2: InterpolationMode.BILINEAR, + 3: InterpolationMode.BICUBIC, + 4: InterpolationMode.BOX, + 5: InterpolationMode.HAMMING, + 1: InterpolationMode.LANCZOS, + } + return inverse_modes_mapping[i] + + +pil_modes_mapping = { + InterpolationMode.NEAREST: 0, + InterpolationMode.BILINEAR: 2, + InterpolationMode.BICUBIC: 3, + InterpolationMode.BOX: 4, + InterpolationMode.HAMMING: 5, + InterpolationMode.LANCZOS: 1, +} + +_is_pil_image = F_pil._is_pil_image +_parse_fill = F_pil._parse_fill + + +def _get_image_size(img: Tensor) -> List[int]: + """Returns image size as [w, h] + """ + if isinstance(img, torch.Tensor): + return F_t._get_image_size(img) + + return F_pil._get_image_size(img) + + +def _get_image_num_channels(img: Tensor) -> int: + """Returns number of image channels + """ + if isinstance(img, torch.Tensor): + return F_t._get_image_num_channels(img) + + return F_pil._get_image_num_channels(img) + + +@torch.jit.unused +def _is_numpy(img: Any) -> bool: + return isinstance(img, np.ndarray) + + +@torch.jit.unused +def _is_numpy_image(img: Any) -> bool: + return img.ndim in {2, 3} + + +def to_tensor(pic): + """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. + This function does not support torchscript. + + See :class:`~torchvision.transforms.ToTensor` for more details. + + Args: + pic (PIL Image or numpy.ndarray): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + if not(F_pil._is_pil_image(pic) or _is_numpy(pic)): + raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic))) + + if _is_numpy(pic) and not _is_numpy_image(pic): + raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim)) + + default_float_dtype = torch.get_default_dtype() + + if isinstance(pic, np.ndarray): + # handle numpy array + if pic.ndim == 2: + pic = pic[:, :, None] + + img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous() + # backward compatibility + if isinstance(img, torch.ByteTensor): + return img.to(dtype=default_float_dtype).div(255) + else: + return img + + if accimage is not None and isinstance(pic, accimage.Image): + nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=default_float_dtype) + pic.copyto(nppic) + return torch.from_numpy(nppic) + + # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + elif pic.mode == 'F': + img = torch.from_numpy(np.array(pic, np.float32, copy=False)) + elif pic.mode == '1': + img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False)) + else: + img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes())) + + img = img.view(pic.size[1], pic.size[0], len(pic.getbands())) + # put it from HWC to CHW format + img = img.permute((2, 0, 1)).contiguous() + if isinstance(img, torch.ByteTensor): + return img.to(dtype=default_float_dtype).div(255) + else: + return img + + +def pil_to_tensor(pic): + """Convert a ``PIL Image`` to a tensor of the same type. + This function does not support torchscript. + + See :class:`~torchvision.transforms.PILToTensor` for more details. + + Args: + pic (PIL Image): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + if not F_pil._is_pil_image(pic): + raise TypeError('pic should be PIL Image. Got {}'.format(type(pic))) + + if accimage is not None and isinstance(pic, accimage.Image): + # accimage format is always uint8 internally, so always return uint8 here + nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.uint8) + pic.copyto(nppic) + return torch.as_tensor(nppic) + + # handle PIL Image + img = torch.as_tensor(np.asarray(pic)) + img = img.view(pic.size[1], pic.size[0], len(pic.getbands())) + # put it from HWC to CHW format + img = img.permute((2, 0, 1)) + return img + + +def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor: + """Convert a tensor image to the given ``dtype`` and scale the values accordingly + This function does not support PIL Image. + + Args: + image (torch.Tensor): Image to be converted + dtype (torch.dtype): Desired data type of the output + + Returns: + Tensor: Converted image + + .. note:: + + When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly. + If converted back and forth, this mismatch has no effect. + + Raises: + RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as + well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to + overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range + of the integer ``dtype``. + """ + if not isinstance(image, torch.Tensor): + raise TypeError('Input img should be Tensor Image') + + return F_t.convert_image_dtype(image, dtype) + + +def to_pil_image(pic, mode=None): + """Convert a tensor or an ndarray to PIL Image. This function does not support torchscript. + + See :class:`~torchvision.transforms.ToPILImage` for more details. + + Args: + pic (Tensor or numpy.ndarray): Image to be converted to PIL Image. + mode (`PIL.Image mode`_): color space and pixel depth of input data (optional). + + .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes + + Returns: + PIL Image: Image converted to PIL Image. + """ + if not(isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)): + raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic))) + + elif isinstance(pic, torch.Tensor): + if pic.ndimension() not in {2, 3}: + raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndimension())) + + elif pic.ndimension() == 2: + # if 2D image, add channel dimension (CHW) + pic = pic.unsqueeze(0) + + # check number of channels + if pic.shape[-3] > 4: + raise ValueError('pic should not have > 4 channels. Got {} channels.'.format(pic.shape[-3])) + + elif isinstance(pic, np.ndarray): + if pic.ndim not in {2, 3}: + raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim)) + + elif pic.ndim == 2: + # if 2D image, add channel dimension (HWC) + pic = np.expand_dims(pic, 2) + + # check number of channels + if pic.shape[-1] > 4: + raise ValueError('pic should not have > 4 channels. Got {} channels.'.format(pic.shape[-1])) + + npimg = pic + if isinstance(pic, torch.Tensor): + if pic.is_floating_point() and mode != 'F': + pic = pic.mul(255).byte() + npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0)) + + if not isinstance(npimg, np.ndarray): + raise TypeError('Input pic must be a torch.Tensor or NumPy ndarray, ' + + 'not {}'.format(type(npimg))) + + if npimg.shape[2] == 1: + expected_mode = None + npimg = npimg[:, :, 0] + if npimg.dtype == np.uint8: + expected_mode = 'L' + elif npimg.dtype == np.int16: + expected_mode = 'I;16' + elif npimg.dtype == np.int32: + expected_mode = 'I' + elif npimg.dtype == np.float32: + expected_mode = 'F' + if mode is not None and mode != expected_mode: + raise ValueError("Incorrect mode ({}) supplied for input type {}. Should be {}" + .format(mode, np.dtype, expected_mode)) + mode = expected_mode + + elif npimg.shape[2] == 2: + permitted_2_channel_modes = ['LA'] + if mode is not None and mode not in permitted_2_channel_modes: + raise ValueError("Only modes {} are supported for 2D inputs".format(permitted_2_channel_modes)) + + if mode is None and npimg.dtype == np.uint8: + mode = 'LA' + + elif npimg.shape[2] == 4: + permitted_4_channel_modes = ['RGBA', 'CMYK', 'RGBX'] + if mode is not None and mode not in permitted_4_channel_modes: + raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes)) + + if mode is None and npimg.dtype == np.uint8: + mode = 'RGBA' + else: + permitted_3_channel_modes = ['RGB', 'YCbCr', 'HSV'] + if mode is not None and mode not in permitted_3_channel_modes: + raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes)) + if mode is None and npimg.dtype == np.uint8: + mode = 'RGB' + + if mode is None: + raise TypeError('Input type {} is not supported'.format(npimg.dtype)) + + return Image.fromarray(npimg, mode=mode) + + +def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor: + """Normalize a tensor image with mean and standard deviation. + This transform does not support PIL Image. + + .. note:: + This transform acts out of place by default, i.e., it does not mutates the input tensor. + + See :class:`~torchvision.transforms.Normalize` for more details. + + Args: + tensor (Tensor): Tensor image of size (C, H, W) or (B, C, H, W) to be normalized. + mean (sequence): Sequence of means for each channel. + std (sequence): Sequence of standard deviations for each channel. + inplace(bool,optional): Bool to make this operation inplace. + + Returns: + Tensor: Normalized Tensor image. + """ + if not isinstance(tensor, torch.Tensor): + raise TypeError('Input tensor should be a torch tensor. Got {}.'.format(type(tensor))) + + if tensor.ndim < 3: + raise ValueError('Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = ' + '{}.'.format(tensor.size())) + + if not inplace: + tensor = tensor.clone() + + dtype = tensor.dtype + mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device) + std = torch.as_tensor(std, dtype=dtype, device=tensor.device) + if (std == 0).any(): + raise ValueError('std evaluated to zero after conversion to {}, leading to division by zero.'.format(dtype)) + if mean.ndim == 1: + mean = mean.view(-1, 1, 1) + if std.ndim == 1: + std = std.view(-1, 1, 1) + tensor.sub_(mean).div_(std) + return tensor + + +def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR) -> Tensor: + r"""Resize the input image to the given size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + Args: + img (PIL Image or Tensor): Image to be resized. + size (sequence or int): Desired output size. If size is a sequence like + (h, w), the output size will be matched to this. If size is an int, + the smaller edge of the image will be matched to this number maintaining + the aspect ratio. i.e, if height > width, then image will be rescaled to + :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`. + In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. + Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable. + + Returns: + PIL Image or Tensor: Resized image. + """ + # Backward compatibility with integer value + if isinstance(interpolation, int): + warnings.warn( + "Argument interpolation should be of type InterpolationMode instead of int. " + "Please, use InterpolationMode enum." + ) + interpolation = _interpolation_modes_from_int(interpolation) + + if not isinstance(interpolation, InterpolationMode): + raise TypeError("Argument interpolation should be a InterpolationMode") + + if not isinstance(img, torch.Tensor): + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.resize(img, size=size, interpolation=pil_interpolation) + + return F_t.resize(img, size=size, interpolation=interpolation.value) + + +def scale(*args, **kwargs): + warnings.warn("The use of the transforms.Scale transform is deprecated, " + + "please use transforms.Resize instead.") + return resize(*args, **kwargs) + + +def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor: + r"""Pad the given image on all sides with the given "pad" value. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric, + at most 3 leading dimensions for mode edge, + and an arbitrary number of leading dimensions for mode constant + + Args: + img (PIL Image or Tensor): Image to be padded. + padding (int or sequence): Padding on each border. If a single int is provided this + is used to pad all borders. If sequence of length 2 is provided this is the padding + on left/right and top/bottom respectively. If a sequence of length 4 is provided + this is the padding for the left, top, right and bottom borders respectively. + In torchscript mode padding as single int is not supported, use a sequence of length 1: ``[padding, ]``. + fill (number or str or tuple): Pixel fill value for constant fill. Default is 0. + If a tuple of length 3, it is used to fill R, G, B channels respectively. + This value is only used when the padding_mode is constant. + Only number is supported for torch Tensor. + Only int or str or tuple value is supported for PIL Image. + padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. + + - constant: pads with a constant value, this value is specified with fill + + - edge: pads with the last value on the edge of the image, + if input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2 + + - reflect: pads with reflection of image (without repeating the last value on the edge) + + padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode + will result in [3, 2, 1, 2, 3, 4, 3, 2] + + - symmetric: pads with reflection of image (repeating the last value on the edge) + + padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode + will result in [2, 1, 1, 2, 3, 4, 4, 3] + + Returns: + PIL Image or Tensor: Padded image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) + + return F_t.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) + + +def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor: + """Crop the given image at specified location and output size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + Args: + img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. + top (int): Vertical component of the top left corner of the crop box. + left (int): Horizontal component of the top left corner of the crop box. + height (int): Height of the crop box. + width (int): Width of the crop box. + + Returns: + PIL Image or Tensor: Cropped image. + """ + + if not isinstance(img, torch.Tensor): + return F_pil.crop(img, top, left, height, width) + + return F_t.crop(img, top, left, height, width) + + +def center_crop(img: Tensor, output_size: List[int]) -> Tensor: + """Crops the given image at the center. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + img (PIL Image or Tensor): Image to be cropped. + output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, + it is used for both directions. + + Returns: + PIL Image or Tensor: Cropped image. + """ + if isinstance(output_size, numbers.Number): + output_size = (int(output_size), int(output_size)) + elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: + output_size = (output_size[0], output_size[0]) + + image_width, image_height = _get_image_size(img) + crop_height, crop_width = output_size + + if crop_width > image_width or crop_height > image_height: + padding_ltrb = [ + (crop_width - image_width) // 2 if crop_width > image_width else 0, + (crop_height - image_height) // 2 if crop_height > image_height else 0, + (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, + (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, + ] + img = pad(img, padding_ltrb, fill=0) # PIL uses fill value 0 + image_width, image_height = _get_image_size(img) + if crop_width == image_width and crop_height == image_height: + return img + + crop_top = int(round((image_height - crop_height) / 2.)) + crop_left = int(round((image_width - crop_width) / 2.)) + return crop(img, crop_top, crop_left, crop_height, crop_width) + + +def resized_crop( + img: Tensor, top: int, left: int, height: int, width: int, size: List[int], + interpolation: InterpolationMode = InterpolationMode.BILINEAR +) -> Tensor: + """Crop the given image and resize it to desired size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + Notably used in :class:`~torchvision.transforms.RandomResizedCrop`. + + Args: + img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. + top (int): Vertical component of the top left corner of the crop box. + left (int): Horizontal component of the top left corner of the crop box. + height (int): Height of the crop box. + width (int): Width of the crop box. + size (sequence or int): Desired output size. Same semantics as ``resize``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. + Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable. + + Returns: + PIL Image or Tensor: Cropped image. + """ + img = crop(img, top, left, height, width) + img = resize(img, size, interpolation) + return img + + +def hflip(img: Tensor) -> Tensor: + """Horizontally flip the given image. + + Args: + img (PIL Image or Tensor): Image to be flipped. If img + is a Tensor, it is expected to be in [..., H, W] format, + where ... means it can have an arbitrary number of leading + dimensions. + + Returns: + PIL Image or Tensor: Horizontally flipped image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.hflip(img) + + return F_t.hflip(img) + + +def _get_perspective_coeffs( + startpoints: List[List[int]], endpoints: List[List[int]] +) -> List[float]: + """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms. + + In Perspective Transform each pixel (x, y) in the original image gets transformed as, + (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) ) + + Args: + startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the original image. + endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image. + + Returns: + octuple (a, b, c, d, e, f, g, h) for transforming each pixel. + """ + a_matrix = torch.zeros(2 * len(startpoints), 8, dtype=torch.float) + + for i, (p1, p2) in enumerate(zip(endpoints, startpoints)): + a_matrix[2 * i, :] = torch.tensor([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]]) + a_matrix[2 * i + 1, :] = torch.tensor([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]]) + + b_matrix = torch.tensor(startpoints, dtype=torch.float).view(8) + res = torch.lstsq(b_matrix, a_matrix)[0] + + output: List[float] = res.squeeze(1).tolist() + return output + + +def perspective( + img: Tensor, + startpoints: List[List[int]], + endpoints: List[List[int]], + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + fill: Optional[List[float]] = None +) -> Tensor: + """Perform perspective transform of the given image. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): Image to be transformed. + startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the original image. + endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + In torchscript mode single int/float value is not supported, please use a sequence + of length 1: ``[value, ]``. + If input is PIL Image, the options is only available for ``Pillow>=5.0.0``. + + Returns: + PIL Image or Tensor: transformed Image. + """ + + coeffs = _get_perspective_coeffs(startpoints, endpoints) + + # Backward compatibility with integer value + if isinstance(interpolation, int): + warnings.warn( + "Argument interpolation should be of type InterpolationMode instead of int. " + "Please, use InterpolationMode enum." + ) + interpolation = _interpolation_modes_from_int(interpolation) + + if not isinstance(interpolation, InterpolationMode): + raise TypeError("Argument interpolation should be a InterpolationMode") + + if not isinstance(img, torch.Tensor): + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill) + + return F_t.perspective(img, coeffs, interpolation=interpolation.value, fill=fill) + + +def vflip(img: Tensor) -> Tensor: + """Vertically flip the given image. + + Args: + img (PIL Image or Tensor): Image to be flipped. If img + is a Tensor, it is expected to be in [..., H, W] format, + where ... means it can have an arbitrary number of leading + dimensions. + + Returns: + PIL Image or Tensor: Vertically flipped image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.vflip(img) + + return F_t.vflip(img) + + +def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + """Crop the given image into four corners and the central crop. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + .. Note:: + This transform returns a tuple of images and there may be a + mismatch in the number of inputs and targets your ``Dataset`` returns. + + Args: + img (PIL Image or Tensor): Image to be cropped. + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + + Returns: + tuple: tuple (tl, tr, bl, br, center) + Corresponding top left, top right, bottom left, bottom right and center crop. + """ + if isinstance(size, numbers.Number): + size = (int(size), int(size)) + elif isinstance(size, (tuple, list)) and len(size) == 1: + size = (size[0], size[0]) + + if len(size) != 2: + raise ValueError("Please provide only two dimensions (h, w) for size.") + + image_width, image_height = _get_image_size(img) + crop_height, crop_width = size + if crop_width > image_width or crop_height > image_height: + msg = "Requested crop size {} is bigger than input size {}" + raise ValueError(msg.format(size, (image_height, image_width))) + + tl = crop(img, 0, 0, crop_height, crop_width) + tr = crop(img, 0, image_width - crop_width, crop_height, crop_width) + bl = crop(img, image_height - crop_height, 0, crop_height, crop_width) + br = crop(img, image_height - crop_height, image_width - crop_width, crop_height, crop_width) + + center = center_crop(img, [crop_height, crop_width]) + + return tl, tr, bl, br, center + + +def ten_crop(img: Tensor, size: List[int], vertical_flip: bool = False) -> List[Tensor]: + """Generate ten cropped images from the given image. + Crop the given image into four corners and the central crop plus the + flipped version of these (horizontal flipping is used by default). + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + .. Note:: + This transform returns a tuple of images and there may be a + mismatch in the number of inputs and targets your ``Dataset`` returns. + + Args: + img (PIL Image or Tensor): Image to be cropped. + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + vertical_flip (bool): Use vertical flipping instead of horizontal + + Returns: + tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip) + Corresponding top left, top right, bottom left, bottom right and + center crop and same for the flipped image. + """ + if isinstance(size, numbers.Number): + size = (int(size), int(size)) + elif isinstance(size, (tuple, list)) and len(size) == 1: + size = (size[0], size[0]) + + if len(size) != 2: + raise ValueError("Please provide only two dimensions (h, w) for size.") + + first_five = five_crop(img, size) + + if vertical_flip: + img = vflip(img) + else: + img = hflip(img) + + second_five = five_crop(img, size) + return first_five + second_five + + +def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor: + """Adjust brightness of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + brightness_factor (float): How much to adjust the brightness. Can be + any non negative number. 0 gives a black image, 1 gives the + original image while 2 increases the brightness by a factor of 2. + + Returns: + PIL Image or Tensor: Brightness adjusted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.adjust_brightness(img, brightness_factor) + + return F_t.adjust_brightness(img, brightness_factor) + + +def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor: + """Adjust contrast of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + contrast_factor (float): How much to adjust the contrast. Can be any + non negative number. 0 gives a solid gray image, 1 gives the + original image while 2 increases the contrast by a factor of 2. + + Returns: + PIL Image or Tensor: Contrast adjusted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.adjust_contrast(img, contrast_factor) + + return F_t.adjust_contrast(img, contrast_factor) + + +def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor: + """Adjust color saturation of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + saturation_factor (float): How much to adjust the saturation. 0 will + give a black and white image, 1 will give the original image while + 2 will enhance the saturation by a factor of 2. + + Returns: + PIL Image or Tensor: Saturation adjusted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.adjust_saturation(img, saturation_factor) + + return F_t.adjust_saturation(img, saturation_factor) + + +def adjust_hue(img: Tensor, hue_factor: float) -> Tensor: + """Adjust hue of an image. + + The image hue is adjusted by converting the image to HSV and + cyclically shifting the intensities in the hue channel (H). + The image is then converted back to original image mode. + + `hue_factor` is the amount of shift in H channel and must be in the + interval `[-0.5, 0.5]`. + + See `Hue`_ for more details. + + .. _Hue: https://en.wikipedia.org/wiki/Hue + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image mode "1", "L", "I", "F" and modes with transparency (alpha channel) are not supported. + hue_factor (float): How much to shift the hue channel. Should be in + [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in + HSV space in positive and negative direction respectively. + 0 means no shift. Therefore, both -0.5 and 0.5 will give an image + with complementary colors while 0 gives the original image. + + Returns: + PIL Image or Tensor: Hue adjusted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.adjust_hue(img, hue_factor) + + return F_t.adjust_hue(img, hue_factor) + + +def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor: + r"""Perform gamma correction on an image. + + Also known as Power Law Transform. Intensities in RGB mode are adjusted + based on the following equation: + + .. math:: + I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma} + + See `Gamma Correction`_ for more details. + + .. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction + + Args: + img (PIL Image or Tensor): PIL Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, modes with transparency (alpha channel) are not supported. + gamma (float): Non negative real number, same as :math:`\gamma` in the equation. + gamma larger than 1 make the shadows darker, + while gamma smaller than 1 make dark regions lighter. + gain (float): The constant multiplier. + Returns: + PIL Image or Tensor: Gamma correction adjusted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.adjust_gamma(img, gamma, gain) + + return F_t.adjust_gamma(img, gamma, gain) + + +def _get_inverse_affine_matrix( + center: List[float], angle: float, translate: List[float], scale: float, shear: List[float] +) -> List[float]: + # Helper method to compute inverse matrix for affine transformation + + # As it is explained in PIL.Image.rotate + # We need compute INVERSE of affine transformation matrix: M = T * C * RSS * C^-1 + # where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1] + # C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1] + # RSS is rotation with scale and shear matrix + # RSS(a, s, (sx, sy)) = + # = R(a) * S(s) * SHy(sy) * SHx(sx) + # = [ s*cos(a - sy)/cos(sy), s*(-cos(a - sy)*tan(x)/cos(y) - sin(a)), 0 ] + # [ s*sin(a + sy)/cos(sy), s*(-sin(a - sy)*tan(x)/cos(y) + cos(a)), 0 ] + # [ 0 , 0 , 1 ] + # + # where R is a rotation matrix, S is a scaling matrix, and SHx and SHy are the shears: + # SHx(s) = [1, -tan(s)] and SHy(s) = [1 , 0] + # [0, 1 ] [-tan(s), 1] + # + # Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1 + + rot = math.radians(angle) + sx, sy = [math.radians(s) for s in shear] + + cx, cy = center + tx, ty = translate + + # RSS without scaling + a = math.cos(rot - sy) / math.cos(sy) + b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot) + c = math.sin(rot - sy) / math.cos(sy) + d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot) + + # Inverted rotation matrix with scale and shear + # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1 + matrix = [d, -b, 0.0, -c, a, 0.0] + matrix = [x / scale for x in matrix] + + # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1 + matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty) + matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty) + + # Apply center translation: C * RSS^-1 * C^-1 * T^-1 + matrix[2] += cx + matrix[5] += cy + + return matrix + + +def rotate( + img: Tensor, angle: float, interpolation: InterpolationMode = InterpolationMode.NEAREST, + expand: bool = False, center: Optional[List[int]] = None, + fill: Optional[List[float]] = None, resample: Optional[int] = None +) -> Tensor: + """Rotate the image by angle. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): image to be rotated. + angle (number): rotation angle value in degrees, counter-clockwise. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable. + expand (bool, optional): Optional expansion flag. + If true, expands the output image to make it large enough to hold the entire rotated image. + If false or omitted, make the output image the same size as the input image. + Note that the expand flag assumes rotation around the center and no translation. + center (sequence, optional): Optional center of rotation. Origin is the upper left corner. + Default is the center of the image. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + In torchscript mode single int/float value is not supported, please use a sequence + of length 1: ``[value, ]``. + If input is PIL Image, the options is only available for ``Pillow>=5.2.0``. + + Returns: + PIL Image or Tensor: Rotated image. + + .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters + + """ + if resample is not None: + warnings.warn( + "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead" + ) + interpolation = _interpolation_modes_from_int(resample) + + # Backward compatibility with integer value + if isinstance(interpolation, int): + warnings.warn( + "Argument interpolation should be of type InterpolationMode instead of int. " + "Please, use InterpolationMode enum." + ) + interpolation = _interpolation_modes_from_int(interpolation) + + if not isinstance(angle, (int, float)): + raise TypeError("Argument angle should be int or float") + + if center is not None and not isinstance(center, (list, tuple)): + raise TypeError("Argument center should be a sequence") + + if not isinstance(interpolation, InterpolationMode): + raise TypeError("Argument interpolation should be a InterpolationMode") + + if not isinstance(img, torch.Tensor): + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.rotate(img, angle=angle, interpolation=pil_interpolation, expand=expand, center=center, fill=fill) + + center_f = [0.0, 0.0] + if center is not None: + img_size = _get_image_size(img) + # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. + center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, img_size)] + + # due to current incoherence of rotation angle direction between affine and rotate implementations + # we need to set -angle. + matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0]) + return F_t.rotate(img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill) + + +def affine( + img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float], + interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[List[float]] = None, + resample: Optional[int] = None, fillcolor: Optional[List[float]] = None +) -> Tensor: + """Apply affine transformation on the image keeping image center invariant. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): image to transform. + angle (number): rotation angle in degrees between -180 and 180, clockwise direction. + translate (sequence of integers): horizontal and vertical translations (post-rotation translation) + scale (float): overall scale + shear (float or sequence): shear angle value in degrees between -180 to 180, clockwise direction. + If a sequence is specified, the first value corresponds to a shear parallel to the x axis, while + the second value corresponds to a shear parallel to the y axis. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + In torchscript mode single int/float value is not supported, please use a sequence + of length 1: ``[value, ]``. + If input is PIL Image, the options is only available for ``Pillow>=5.0.0``. + fillcolor (sequence, int, float): deprecated argument and will be removed since v0.10.0. + Please use the ``fill`` parameter instead. + resample (int, optional): deprecated argument and will be removed since v0.10.0. + Please use the ``interpolation`` parameter instead. + + Returns: + PIL Image or Tensor: Transformed image. + """ + if resample is not None: + warnings.warn( + "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead" + ) + interpolation = _interpolation_modes_from_int(resample) + + # Backward compatibility with integer value + if isinstance(interpolation, int): + warnings.warn( + "Argument interpolation should be of type InterpolationMode instead of int. " + "Please, use InterpolationMode enum." + ) + interpolation = _interpolation_modes_from_int(interpolation) + + if fillcolor is not None: + warnings.warn( + "Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead" + ) + fill = fillcolor + + if not isinstance(angle, (int, float)): + raise TypeError("Argument angle should be int or float") + + if not isinstance(translate, (list, tuple)): + raise TypeError("Argument translate should be a sequence") + + if len(translate) != 2: + raise ValueError("Argument translate should be a sequence of length 2") + + if scale <= 0.0: + raise ValueError("Argument scale should be positive") + + if not isinstance(shear, (numbers.Number, (list, tuple))): + raise TypeError("Shear should be either a single value or a sequence of two values") + + if not isinstance(interpolation, InterpolationMode): + raise TypeError("Argument interpolation should be a InterpolationMode") + + if isinstance(angle, int): + angle = float(angle) + + if isinstance(translate, tuple): + translate = list(translate) + + if isinstance(shear, numbers.Number): + shear = [shear, 0.0] + + if isinstance(shear, tuple): + shear = list(shear) + + if len(shear) == 1: + shear = [shear[0], shear[0]] + + if len(shear) != 2: + raise ValueError("Shear should be a sequence containing two values. Got {}".format(shear)) + + img_size = _get_image_size(img) + if not isinstance(img, torch.Tensor): + # center = (img_size[0] * 0.5 + 0.5, img_size[1] * 0.5 + 0.5) + # it is visually better to estimate the center without 0.5 offset + # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine + center = [img_size[0] * 0.5, img_size[1] * 0.5] + matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear) + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.affine(img, matrix=matrix, interpolation=pil_interpolation, fill=fill) + + translate_f = [1.0 * t for t in translate] + matrix = _get_inverse_affine_matrix([0.0, 0.0], angle, translate_f, scale, shear) + return F_t.affine(img, matrix=matrix, interpolation=interpolation.value, fill=fill) + + +@torch.jit.unused +def to_grayscale(img, num_output_channels=1): + """Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image. + This transform does not support torch Tensor. + + Args: + img (PIL Image): PIL Image to be converted to grayscale. + num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default is 1. + + Returns: + PIL Image: Grayscale version of the image. + if num_output_channels = 1 : returned image is single channel + + if num_output_channels = 3 : returned image is 3 channel with r = g = b + """ + if isinstance(img, Image.Image): + return F_pil.to_grayscale(img, num_output_channels) + + raise TypeError("Input should be PIL Image") + + +def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor: + """Convert RGB image to grayscale version of image. + If the image is torch Tensor, it is expected + to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions + + Note: + Please, note that this method supports only RGB images as input. For inputs in other color spaces, + please, consider using meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image. + + Args: + img (PIL Image or Tensor): RGB Image to be converted to grayscale. + num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1. + + Returns: + PIL Image or Tensor: Grayscale version of the image. + if num_output_channels = 1 : returned image is single channel + + if num_output_channels = 3 : returned image is 3 channel with r = g = b + """ + if not isinstance(img, torch.Tensor): + return F_pil.to_grayscale(img, num_output_channels) + + return F_t.rgb_to_grayscale(img, num_output_channels) + + +def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor: + """ Erase the input Tensor Image with given value. + This transform does not support PIL Image. + + Args: + img (Tensor Image): Tensor image of size (C, H, W) to be erased + i (int): i in (i,j) i.e coordinates of the upper left corner. + j (int): j in (i,j) i.e coordinates of the upper left corner. + h (int): Height of the erased region. + w (int): Width of the erased region. + v: Erasing value. + inplace(bool, optional): For in-place operations. By default is set False. + + Returns: + Tensor Image: Erased image. + """ + if not isinstance(img, torch.Tensor): + raise TypeError('img should be Tensor Image. Got {}'.format(type(img))) + + if not inplace: + img = img.clone() + + img[..., i:i + h, j:j + w] = v + return img + + +def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor: + """Performs Gaussian blurring on the image by given kernel. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): Image to be blurred + kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers + like ``(kx, ky)`` or a single integer for square kernels. + In torchscript mode kernel_size as single int is not supported, use a sequence of length 1: ``[ksize, ]``. + sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a + sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the + same sigma in both X/Y directions. If None, then it is computed using + ``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``. + Default, None. In torchscript mode sigma as single float is + not supported, use a sequence of length 1: ``[sigma, ]``. + + Returns: + PIL Image or Tensor: Gaussian Blurred version of the image. + """ + if not isinstance(kernel_size, (int, list, tuple)): + raise TypeError('kernel_size should be int or a sequence of integers. Got {}'.format(type(kernel_size))) + if isinstance(kernel_size, int): + kernel_size = [kernel_size, kernel_size] + if len(kernel_size) != 2: + raise ValueError('If kernel_size is a sequence its length should be 2. Got {}'.format(len(kernel_size))) + for ksize in kernel_size: + if ksize % 2 == 0 or ksize < 0: + raise ValueError('kernel_size should have odd and positive integers. Got {}'.format(kernel_size)) + + if sigma is None: + sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size] + + if sigma is not None and not isinstance(sigma, (int, float, list, tuple)): + raise TypeError('sigma should be either float or sequence of floats. Got {}'.format(type(sigma))) + if isinstance(sigma, (int, float)): + sigma = [float(sigma), float(sigma)] + if isinstance(sigma, (list, tuple)) and len(sigma) == 1: + sigma = [sigma[0], sigma[0]] + if len(sigma) != 2: + raise ValueError('If sigma is a sequence, its length should be 2. Got {}'.format(len(sigma))) + for s in sigma: + if s <= 0.: + raise ValueError('sigma should have positive values. Got {}'.format(sigma)) + + t_img = img + if not isinstance(img, torch.Tensor): + if not F_pil._is_pil_image(img): + raise TypeError('img should be PIL Image or Tensor. Got {}'.format(type(img))) + + t_img = to_tensor(img) + + output = F_t.gaussian_blur(t_img, kernel_size, sigma) + + if not isinstance(img, torch.Tensor): + output = to_pil_image(output) + return output + + +def invert(img: Tensor) -> Tensor: + """Invert the colors of an RGB/grayscale image. + + Args: + img (PIL Image or Tensor): Image to have its colors inverted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Returns: + PIL Image or Tensor: Color inverted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.invert(img) + + return F_t.invert(img) + + +def posterize(img: Tensor, bits: int) -> Tensor: + """Posterize an image by reducing the number of bits for each color channel. + + Args: + img (PIL Image or Tensor): Image to have its colors posterized. + If img is torch Tensor, it should be of type torch.uint8 and + it is expected to be in [..., 1 or 3, H, W] format, where ... means + it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + bits (int): The number of bits to keep for each channel (0-8). + Returns: + PIL Image or Tensor: Posterized image. + """ + if not (0 <= bits <= 8): + raise ValueError('The number if bits should be between 0 and 8. Got {}'.format(bits)) + + if not isinstance(img, torch.Tensor): + return F_pil.posterize(img, bits) + + return F_t.posterize(img, bits) + + +def solarize(img: Tensor, threshold: float) -> Tensor: + """Solarize an RGB/grayscale image by inverting all pixel values above a threshold. + + Args: + img (PIL Image or Tensor): Image to have its colors inverted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + threshold (float): All pixels equal or above this value are inverted. + Returns: + PIL Image or Tensor: Solarized image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.solarize(img, threshold) + + return F_t.solarize(img, threshold) + + +def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor: + """Adjust the sharpness of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + sharpness_factor (float): How much to adjust the sharpness. Can be + any non negative number. 0 gives a blurred image, 1 gives the + original image while 2 increases the sharpness by a factor of 2. + + Returns: + PIL Image or Tensor: Sharpness adjusted image. + """ + if not isinstance(img, torch.Tensor): + return F_pil.adjust_sharpness(img, sharpness_factor) + + return F_t.adjust_sharpness(img, sharpness_factor) + + +def autocontrast(img: Tensor) -> Tensor: + """Maximize contrast of an image by remapping its + pixels per channel so that the lowest becomes black and the lightest + becomes white. + + Args: + img (PIL Image or Tensor): Image on which autocontrast is applied. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Returns: + PIL Image or Tensor: An image that was autocontrasted. + """ + if not isinstance(img, torch.Tensor): + return F_pil.autocontrast(img) + + return F_t.autocontrast(img) + + +def equalize(img: Tensor) -> Tensor: + """Equalize the histogram of an image by applying + a non-linear mapping to the input in order to create a uniform + distribution of grayscale values in the output. + + Args: + img (PIL Image or Tensor): Image on which equalize is applied. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". + + Returns: + PIL Image or Tensor: An image that was equalized. + """ + if not isinstance(img, torch.Tensor): + return F_pil.equalize(img) + + return F_t.equalize(img) diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/functional_pil.py b/cv/detection/yolov3/tensorflow/dataloader/utils/functional_pil.py new file mode 100644 index 000000000..6999a2acf --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/functional_pil.py @@ -0,0 +1,349 @@ +import numbers +from typing import Any, List, Sequence + +import numpy as np +import torch +from PIL import Image, ImageOps, ImageEnhance, ImageFilter, __version__ as PILLOW_VERSION + +try: + import accimage +except ImportError: + accimage = None + + +@torch.jit.unused +def _is_pil_image(img: Any) -> bool: + if accimage is not None: + return isinstance(img, (Image.Image, accimage.Image)) + else: + return isinstance(img, Image.Image) + + +@torch.jit.unused +def _get_image_size(img: Any) -> List[int]: + if _is_pil_image(img): + return img.size + raise TypeError("Unexpected type {}".format(type(img))) + + +@torch.jit.unused +def _get_image_num_channels(img: Any) -> int: + if _is_pil_image(img): + return 1 if img.mode == 'L' else 3 + raise TypeError("Unexpected type {}".format(type(img))) + + +@torch.jit.unused +def hflip(img): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + return img.transpose(Image.FLIP_LEFT_RIGHT) + + +@torch.jit.unused +def vflip(img): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + return img.transpose(Image.FLIP_TOP_BOTTOM) + + +@torch.jit.unused +def adjust_brightness(img, brightness_factor): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Brightness(img) + img = enhancer.enhance(brightness_factor) + return img + + +@torch.jit.unused +def adjust_contrast(img, contrast_factor): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Contrast(img) + img = enhancer.enhance(contrast_factor) + return img + + +@torch.jit.unused +def adjust_saturation(img, saturation_factor): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Color(img) + img = enhancer.enhance(saturation_factor) + return img + + +@torch.jit.unused +def adjust_hue(img, hue_factor): + if not(-0.5 <= hue_factor <= 0.5): + raise ValueError('hue_factor ({}) is not in [-0.5, 0.5].'.format(hue_factor)) + + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + input_mode = img.mode + if input_mode in {'L', '1', 'I', 'F'}: + return img + + h, s, v = img.convert('HSV').split() + + np_h = np.array(h, dtype=np.uint8) + # uint8 addition take cares of rotation across boundaries + with np.errstate(over='ignore'): + np_h += np.uint8(hue_factor * 255) + h = Image.fromarray(np_h, 'L') + + img = Image.merge('HSV', (h, s, v)).convert(input_mode) + return img + + +@torch.jit.unused +def adjust_gamma(img, gamma, gain=1): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + if gamma < 0: + raise ValueError('Gamma should be a non-negative real number') + + input_mode = img.mode + img = img.convert('RGB') + gamma_map = [(255 + 1 - 1e-3) * gain * pow(ele / 255., gamma) for ele in range(256)] * 3 + img = img.point(gamma_map) # use PIL's point-function to accelerate this part + + img = img.convert(input_mode) + return img + + +@torch.jit.unused +def pad(img, padding, fill=0, padding_mode="constant"): + if not _is_pil_image(img): + raise TypeError("img should be PIL Image. Got {}".format(type(img))) + + if not isinstance(padding, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate padding arg") + if not isinstance(fill, (numbers.Number, str, tuple)): + raise TypeError("Got inappropriate fill arg") + if not isinstance(padding_mode, str): + raise TypeError("Got inappropriate padding_mode arg") + + if isinstance(padding, list): + padding = tuple(padding) + + if isinstance(padding, tuple) and len(padding) not in [1, 2, 4]: + raise ValueError("Padding must be an int or a 1, 2, or 4 element tuple, not a " + + "{} element tuple".format(len(padding))) + + if isinstance(padding, tuple) and len(padding) == 1: + # Compatibility with `functional_tensor.pad` + padding = padding[0] + + if padding_mode not in ["constant", "edge", "reflect", "symmetric"]: + raise ValueError("Padding mode should be either constant, edge, reflect or symmetric") + + if padding_mode == "constant": + opts = _parse_fill(fill, img, "2.3.0", name="fill") + if img.mode == "P": + palette = img.getpalette() + image = ImageOps.expand(img, border=padding, **opts) + image.putpalette(palette) + return image + + return ImageOps.expand(img, border=padding, **opts) + else: + if isinstance(padding, int): + pad_left = pad_right = pad_top = pad_bottom = padding + if isinstance(padding, tuple) and len(padding) == 2: + pad_left = pad_right = padding[0] + pad_top = pad_bottom = padding[1] + if isinstance(padding, tuple) and len(padding) == 4: + pad_left = padding[0] + pad_top = padding[1] + pad_right = padding[2] + pad_bottom = padding[3] + + p = [pad_left, pad_top, pad_right, pad_bottom] + cropping = -np.minimum(p, 0) + + if cropping.any(): + crop_left, crop_top, crop_right, crop_bottom = cropping + img = img.crop((crop_left, crop_top, img.width - crop_right, img.height - crop_bottom)) + + pad_left, pad_top, pad_right, pad_bottom = np.maximum(p, 0) + + if img.mode == 'P': + palette = img.getpalette() + img = np.asarray(img) + img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode) + img = Image.fromarray(img) + img.putpalette(palette) + return img + + img = np.asarray(img) + # RGB image + if len(img.shape) == 3: + img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), padding_mode) + # Grayscale image + if len(img.shape) == 2: + img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode) + + return Image.fromarray(img) + + +@torch.jit.unused +def crop(img: Image.Image, top: int, left: int, height: int, width: int) -> Image.Image: + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + return img.crop((left, top, left + width, top + height)) + + +@torch.jit.unused +def resize(img, size, interpolation=Image.BILINEAR): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + if not (isinstance(size, int) or (isinstance(size, Sequence) and len(size) in (1, 2))): + raise TypeError('Got inappropriate size arg: {}'.format(size)) + + if isinstance(size, int) or len(size) == 1: + if isinstance(size, Sequence): + size = size[0] + w, h = img.size + if (w <= h and w == size) or (h <= w and h == size): + return img + if w < h: + ow = size + oh = int(size * h / w) + return img.resize((ow, oh), interpolation) + else: + oh = size + ow = int(size * w / h) + return img.resize((ow, oh), interpolation) + else: + return img.resize(size[::-1], interpolation) + + +@torch.jit.unused +def _parse_fill(fill, img, min_pil_version, name="fillcolor"): + # Process fill color for affine transforms + major_found, minor_found = (int(v) for v in PILLOW_VERSION.split('.')[:2]) + major_required, minor_required = (int(v) for v in min_pil_version.split('.')[:2]) + if major_found < major_required or (major_found == major_required and minor_found < minor_required): + if fill is None: + return {} + else: + msg = ("The option to fill background area of the transformed image, " + "requires pillow>={}") + raise RuntimeError(msg.format(min_pil_version)) + + num_bands = len(img.getbands()) + if fill is None: + fill = 0 + if isinstance(fill, (int, float)) and num_bands > 1: + fill = tuple([fill] * num_bands) + if isinstance(fill, (list, tuple)): + if len(fill) != num_bands: + msg = ("The number of elements in 'fill' does not match the number of " + "bands of the image ({} != {})") + raise ValueError(msg.format(len(fill), num_bands)) + + fill = tuple(fill) + + return {name: fill} + + +@torch.jit.unused +def affine(img, matrix, interpolation=0, fill=None): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + output_size = img.size + opts = _parse_fill(fill, img, '5.0.0') + return img.transform(output_size, Image.AFFINE, matrix, interpolation, **opts) + + +@torch.jit.unused +def rotate(img, angle, interpolation=0, expand=False, center=None, fill=None): + if not _is_pil_image(img): + raise TypeError("img should be PIL Image. Got {}".format(type(img))) + + opts = _parse_fill(fill, img, '5.2.0') + return img.rotate(angle, interpolation, expand, center, **opts) + + +@torch.jit.unused +def perspective(img, perspective_coeffs, interpolation=Image.BICUBIC, fill=None): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + opts = _parse_fill(fill, img, '5.0.0') + + return img.transform(img.size, Image.PERSPECTIVE, perspective_coeffs, interpolation, **opts) + + +@torch.jit.unused +def to_grayscale(img, num_output_channels): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + if num_output_channels == 1: + img = img.convert('L') + elif num_output_channels == 3: + img = img.convert('L') + np_img = np.array(img, dtype=np.uint8) + np_img = np.dstack([np_img, np_img, np_img]) + img = Image.fromarray(np_img, 'RGB') + else: + raise ValueError('num_output_channels should be either 1 or 3') + + return img + + +@torch.jit.unused +def invert(img): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + return ImageOps.invert(img) + + +@torch.jit.unused +def posterize(img, bits): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + return ImageOps.posterize(img, bits) + + +@torch.jit.unused +def solarize(img, threshold): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + return ImageOps.solarize(img, threshold) + + +@torch.jit.unused +def adjust_sharpness(img, sharpness_factor): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Sharpness(img) + img = enhancer.enhance(sharpness_factor) + return img + + +@torch.jit.unused +def autocontrast(img): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + return ImageOps.autocontrast(img) + + +@torch.jit.unused +def equalize(img): + if not _is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + return ImageOps.equalize(img) diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/functional_tensor.py b/cv/detection/yolov3/tensorflow/dataloader/utils/functional_tensor.py new file mode 100644 index 000000000..69445e6a2 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/functional_tensor.py @@ -0,0 +1,920 @@ +import warnings + +import torch +from torch import Tensor +from torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad +from torch.jit.annotations import BroadcastingList2 +from typing import Optional, Tuple, List + + +def _is_tensor_a_torch_image(x: Tensor) -> bool: + return x.ndim >= 2 + + +def _assert_image_tensor(img): + if not _is_tensor_a_torch_image(img): + raise TypeError("Tensor is not a torch image.") + + +def _get_image_size(img: Tensor) -> List[int]: + # Returns (w, h) of tensor image + _assert_image_tensor(img) + return [img.shape[-1], img.shape[-2]] + + +def _get_image_num_channels(img: Tensor) -> int: + if img.ndim == 2: + return 1 + elif img.ndim > 2: + return img.shape[-3] + + raise TypeError("Input ndim should be 2 or more. Got {}".format(img.ndim)) + + +def _max_value(dtype: torch.dtype) -> float: + # TODO: replace this method with torch.iinfo when it gets torchscript support. + # https://github.com/pytorch/pytorch/issues/41492 + + a = torch.tensor(2, dtype=dtype) + signed = 1 if torch.tensor(0, dtype=dtype).is_signed() else 0 + bits = 1 + max_value = torch.tensor(-signed, dtype=torch.long) + while True: + next_value = a.pow(bits - signed).sub(1) + if next_value > max_value: + max_value = next_value + bits *= 2 + else: + break + return max_value.item() + + +def _assert_channels(img: Tensor, permitted: List[int]) -> None: + c = _get_image_num_channels(img) + if c not in permitted: + raise TypeError("Input image tensor permitted channel values are {}, but found {}".format(permitted, c)) + + +def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor: + if image.dtype == dtype: + return image + + if image.is_floating_point(): + + # TODO: replace with dtype.is_floating_point when torchscript supports it + if torch.tensor(0, dtype=dtype).is_floating_point(): + return image.to(dtype) + + # float to int + if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or ( + image.dtype == torch.float64 and dtype == torch.int64 + ): + msg = f"The cast from {image.dtype} to {dtype} cannot be performed safely." + raise RuntimeError(msg) + + # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321 + # For data in the range 0-1, (float * 255).to(uint) is only 255 + # when float is exactly 1.0. + # `max + 1 - epsilon` provides more evenly distributed mapping of + # ranges of floats to ints. + eps = 1e-3 + max_val = _max_value(dtype) + result = image.mul(max_val + 1.0 - eps) + return result.to(dtype) + else: + input_max = _max_value(image.dtype) + + # int to float + # TODO: replace with dtype.is_floating_point when torchscript supports it + if torch.tensor(0, dtype=dtype).is_floating_point(): + image = image.to(dtype) + return image / input_max + + output_max = _max_value(dtype) + + # int to int + if input_max > output_max: + # factor should be forced to int for torch jit script + # otherwise factor is a float and image // factor can produce different results + factor = int((input_max + 1) // (output_max + 1)) + image = image // factor + return image.to(dtype) + else: + # factor should be forced to int for torch jit script + # otherwise factor is a float and image * factor can produce different results + factor = int((output_max + 1) // (input_max + 1)) + image = image.to(dtype) + return image * factor + + +def vflip(img: Tensor) -> Tensor: + _assert_image_tensor(img) + + return img.flip(-2) + + +def hflip(img: Tensor) -> Tensor: + _assert_image_tensor(img) + + return img.flip(-1) + + +def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor: + _assert_image_tensor(img) + + return img[..., top:top + height, left:left + width] + + +def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor: + if img.ndim < 3: + raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim)) + _assert_channels(img, [3]) + + if num_output_channels not in (1, 3): + raise ValueError('num_output_channels should be either 1 or 3') + + r, g, b = img.unbind(dim=-3) + # This implementation closely follows the TF one: + # https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/ops/image_ops_impl.py#L2105-L2138 + l_img = (0.2989 * r + 0.587 * g + 0.114 * b).to(img.dtype) + l_img = l_img.unsqueeze(dim=-3) + + if num_output_channels == 3: + return l_img.expand(img.shape) + + return l_img + + +def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor: + if brightness_factor < 0: + raise ValueError('brightness_factor ({}) is not non-negative.'.format(brightness_factor)) + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + + return _blend(img, torch.zeros_like(img), brightness_factor) + + +def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor: + if contrast_factor < 0: + raise ValueError('contrast_factor ({}) is not non-negative.'.format(contrast_factor)) + + _assert_image_tensor(img) + + _assert_channels(img, [3]) + + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True) + + return _blend(img, mean, contrast_factor) + + +def adjust_hue(img: Tensor, hue_factor: float) -> Tensor: + if not (-0.5 <= hue_factor <= 0.5): + raise ValueError('hue_factor ({}) is not in [-0.5, 0.5].'.format(hue_factor)) + + if not (isinstance(img, torch.Tensor)): + raise TypeError('Input img should be Tensor image') + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + if _get_image_num_channels(img) == 1: # Match PIL behaviour + return img + + orig_dtype = img.dtype + if img.dtype == torch.uint8: + img = img.to(dtype=torch.float32) / 255.0 + + img = _rgb2hsv(img) + h, s, v = img.unbind(dim=-3) + h = (h + hue_factor) % 1.0 + img = torch.stack((h, s, v), dim=-3) + img_hue_adj = _hsv2rgb(img) + + if orig_dtype == torch.uint8: + img_hue_adj = (img_hue_adj * 255.0).to(dtype=orig_dtype) + + return img_hue_adj + + +def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor: + if saturation_factor < 0: + raise ValueError('saturation_factor ({}) is not non-negative.'.format(saturation_factor)) + + _assert_image_tensor(img) + + _assert_channels(img, [3]) + + return _blend(img, rgb_to_grayscale(img), saturation_factor) + + +def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor: + if not isinstance(img, torch.Tensor): + raise TypeError('Input img should be a Tensor.') + + _assert_channels(img, [1, 3]) + + if gamma < 0: + raise ValueError('Gamma should be a non-negative real number') + + result = img + dtype = img.dtype + if not torch.is_floating_point(img): + result = convert_image_dtype(result, torch.float32) + + result = (gain * result ** gamma).clamp(0, 1) + + result = convert_image_dtype(result, dtype) + result = result.to(dtype) + return result + + +def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor: + """DEPRECATED + """ + warnings.warn( + "This method is deprecated and will be removed in future releases. " + "Please, use ``F.center_crop`` instead." + ) + + _assert_image_tensor(img) + + _, image_width, image_height = img.size() + crop_height, crop_width = output_size + # crop_top = int(round((image_height - crop_height) / 2.)) + # Result can be different between python func and scripted func + # Temporary workaround: + crop_top = int((image_height - crop_height + 1) * 0.5) + # crop_left = int(round((image_width - crop_width) / 2.)) + # Result can be different between python func and scripted func + # Temporary workaround: + crop_left = int((image_width - crop_width + 1) * 0.5) + + return crop(img, crop_top, crop_left, crop_height, crop_width) + + +def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]: + """DEPRECATED + """ + warnings.warn( + "This method is deprecated and will be removed in future releases. " + "Please, use ``F.five_crop`` instead." + ) + + _assert_image_tensor(img) + + assert len(size) == 2, "Please provide only two dimensions (h, w) for size." + + _, image_width, image_height = img.size() + crop_height, crop_width = size + if crop_width > image_width or crop_height > image_height: + msg = "Requested crop size {} is bigger than input size {}" + raise ValueError(msg.format(size, (image_height, image_width))) + + tl = crop(img, 0, 0, crop_width, crop_height) + tr = crop(img, image_width - crop_width, 0, image_width, crop_height) + bl = crop(img, 0, image_height - crop_height, crop_width, image_height) + br = crop(img, image_width - crop_width, image_height - crop_height, image_width, image_height) + center = center_crop(img, (crop_height, crop_width)) + + return [tl, tr, bl, br, center] + + +def ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = False) -> List[Tensor]: + """DEPRECATED + """ + warnings.warn( + "This method is deprecated and will be removed in future releases. " + "Please, use ``F.ten_crop`` instead." + ) + + _assert_image_tensor(img) + + assert len(size) == 2, "Please provide only two dimensions (h, w) for size." + first_five = five_crop(img, size) + + if vertical_flip: + img = vflip(img) + else: + img = hflip(img) + + second_five = five_crop(img, size) + + return first_five + second_five + + +def _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor: + ratio = float(ratio) + bound = 1.0 if img1.is_floating_point() else 255.0 + return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype) + + +def _rgb2hsv(img): + r, g, b = img.unbind(dim=-3) + + # Implementation is based on https://github.com/python-pillow/Pillow/blob/4174d4267616897df3746d315d5a2d0f82c656ee/ + # src/libImaging/Convert.c#L330 + maxc = torch.max(img, dim=-3).values + minc = torch.min(img, dim=-3).values + + # The algorithm erases S and H channel where `maxc = minc`. This avoids NaN + # from happening in the results, because + # + S channel has division by `maxc`, which is zero only if `maxc = minc` + # + H channel has division by `(maxc - minc)`. + # + # Instead of overwriting NaN afterwards, we just prevent it from occuring so + # we don't need to deal with it in case we save the NaN in a buffer in + # backprop, if it is ever supported, but it doesn't hurt to do so. + eqc = maxc == minc + + cr = maxc - minc + # Since `eqc => cr = 0`, replacing denominator with 1 when `eqc` is fine. + ones = torch.ones_like(maxc) + s = cr / torch.where(eqc, ones, maxc) + # Note that `eqc => maxc = minc = r = g = b`. So the following calculation + # of `h` would reduce to `bc - gc + 2 + rc - bc + 4 + rc - bc = 6` so it + # would not matter what values `rc`, `gc`, and `bc` have here, and thus + # replacing denominator with 1 when `eqc` is fine. + cr_divisor = torch.where(eqc, ones, cr) + rc = (maxc - r) / cr_divisor + gc = (maxc - g) / cr_divisor + bc = (maxc - b) / cr_divisor + + hr = (maxc == r) * (bc - gc) + hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc) + hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc) + h = (hr + hg + hb) + h = torch.fmod((h / 6.0 + 1.0), 1.0) + return torch.stack((h, s, maxc), dim=-3) + + +def _hsv2rgb(img): + h, s, v = img.unbind(dim=-3) + i = torch.floor(h * 6.0) + f = (h * 6.0) - i + i = i.to(dtype=torch.int32) + + p = torch.clamp((v * (1.0 - s)), 0.0, 1.0) + q = torch.clamp((v * (1.0 - s * f)), 0.0, 1.0) + t = torch.clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0) + i = i % 6 + + mask = i.unsqueeze(dim=-3) == torch.arange(6, device=i.device).view(-1, 1, 1) + + a1 = torch.stack((v, q, p, p, t, v), dim=-3) + a2 = torch.stack((t, v, v, q, p, p), dim=-3) + a3 = torch.stack((p, p, t, v, v, q), dim=-3) + a4 = torch.stack((a1, a2, a3), dim=-4) + + return torch.einsum("...ijk, ...xijk -> ...xjk", mask.to(dtype=img.dtype), a4) + + +def _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor: + # padding is left, right, top, bottom + + # crop if needed + if padding[0] < 0 or padding[1] < 0 or padding[2] < 0 or padding[3] < 0: + crop_left, crop_right, crop_top, crop_bottom = [-min(x, 0) for x in padding] + img = img[..., crop_top:img.shape[-2] - crop_bottom, crop_left:img.shape[-1] - crop_right] + padding = [max(x, 0) for x in padding] + + in_sizes = img.size() + + x_indices = [i for i in range(in_sizes[-1])] # [0, 1, 2, 3, ...] + left_indices = [i for i in range(padding[0] - 1, -1, -1)] # e.g. [3, 2, 1, 0] + right_indices = [-(i + 1) for i in range(padding[1])] # e.g. [-1, -2, -3] + x_indices = torch.tensor(left_indices + x_indices + right_indices) + + y_indices = [i for i in range(in_sizes[-2])] + top_indices = [i for i in range(padding[2] - 1, -1, -1)] + bottom_indices = [-(i + 1) for i in range(padding[3])] + y_indices = torch.tensor(top_indices + y_indices + bottom_indices) + + ndim = img.ndim + if ndim == 3: + return img[:, y_indices[:, None], x_indices[None, :]] + elif ndim == 4: + return img[:, :, y_indices[:, None], x_indices[None, :]] + else: + raise RuntimeError("Symmetric padding of N-D tensors are not supported yet") + + +def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor: + _assert_image_tensor(img) + + if not isinstance(padding, (int, tuple, list)): + raise TypeError("Got inappropriate padding arg") + if not isinstance(fill, (int, float)): + raise TypeError("Got inappropriate fill arg") + if not isinstance(padding_mode, str): + raise TypeError("Got inappropriate padding_mode arg") + + if isinstance(padding, tuple): + padding = list(padding) + + if isinstance(padding, list) and len(padding) not in [1, 2, 4]: + raise ValueError("Padding must be an int or a 1, 2, or 4 element tuple, not a " + + "{} element tuple".format(len(padding))) + + if padding_mode not in ["constant", "edge", "reflect", "symmetric"]: + raise ValueError("Padding mode should be either constant, edge, reflect or symmetric") + + if isinstance(padding, int): + if torch.jit.is_scripting(): + # This maybe unreachable + raise ValueError("padding can't be an int while torchscripting, set it as a list [value, ]") + pad_left = pad_right = pad_top = pad_bottom = padding + elif len(padding) == 1: + pad_left = pad_right = pad_top = pad_bottom = padding[0] + elif len(padding) == 2: + pad_left = pad_right = padding[0] + pad_top = pad_bottom = padding[1] + else: + pad_left = padding[0] + pad_top = padding[1] + pad_right = padding[2] + pad_bottom = padding[3] + + p = [pad_left, pad_right, pad_top, pad_bottom] + + if padding_mode == "edge": + # remap padding_mode str + padding_mode = "replicate" + elif padding_mode == "symmetric": + # route to another implementation + return _pad_symmetric(img, p) + + need_squeeze = False + if img.ndim < 4: + img = img.unsqueeze(dim=0) + need_squeeze = True + + out_dtype = img.dtype + need_cast = False + if (padding_mode != "constant") and img.dtype not in (torch.float32, torch.float64): + # Here we temporary cast input tensor to float + # until pytorch issue is resolved : + # https://github.com/pytorch/pytorch/issues/40763 + need_cast = True + img = img.to(torch.float32) + + img = torch_pad(img, p, mode=padding_mode, value=float(fill)) + + if need_squeeze: + img = img.squeeze(dim=0) + + if need_cast: + img = img.to(out_dtype) + + return img + + +def resize(img: Tensor, size: List[int], interpolation: str = "bilinear") -> Tensor: + _assert_image_tensor(img) + + if not isinstance(size, (int, tuple, list)): + raise TypeError("Got inappropriate size arg") + if not isinstance(interpolation, str): + raise TypeError("Got inappropriate interpolation arg") + + if interpolation not in ["nearest", "bilinear", "bicubic"]: + raise ValueError("This interpolation mode is unsupported with Tensor input") + + if isinstance(size, tuple): + size = list(size) + + if isinstance(size, list) and len(size) not in [1, 2]: + raise ValueError("Size must be an int or a 1 or 2 element tuple/list, not a " + "{} element tuple/list".format(len(size))) + + w, h = _get_image_size(img) + + if isinstance(size, int): + size_w, size_h = size, size + elif len(size) < 2: + size_w, size_h = size[0], size[0] + else: + size_w, size_h = size[1], size[0] # Convention (h, w) + + if isinstance(size, int) or len(size) < 2: + if w < h: + size_h = int(size_w * h / w) + else: + size_w = int(size_h * w / h) + + if (w <= h and w == size_w) or (h <= w and h == size_h): + return img + + img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64]) + + # Define align_corners to avoid warnings + align_corners = False if interpolation in ["bilinear", "bicubic"] else None + + img = interpolate(img, size=[size_h, size_w], mode=interpolation, align_corners=align_corners) + + if interpolation == "bicubic" and out_dtype == torch.uint8: + img = img.clamp(min=0, max=255) + + img = _cast_squeeze_out(img, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype) + + return img + + +def _assert_grid_transform_inputs( + img: Tensor, + matrix: Optional[List[float]], + interpolation: str, + fill: Optional[List[float]], + supported_interpolation_modes: List[str], + coeffs: Optional[List[float]] = None, +): + + if not (isinstance(img, torch.Tensor)): + raise TypeError("Input img should be Tensor") + + _assert_image_tensor(img) + + if matrix is not None and not isinstance(matrix, list): + raise TypeError("Argument matrix should be a list") + + if matrix is not None and len(matrix) != 6: + raise ValueError("Argument matrix should have 6 float values") + + if coeffs is not None and len(coeffs) != 8: + raise ValueError("Argument coeffs should have 8 float values") + + if fill is not None and not isinstance(fill, (int, float, tuple, list)): + warnings.warn("Argument fill should be either int, float, tuple or list") + + # Check fill + num_channels = _get_image_num_channels(img) + if isinstance(fill, (tuple, list)) and (len(fill) > 1 and len(fill) != num_channels): + msg = ("The number of elements in 'fill' cannot broadcast to match the number of " + "channels of the image ({} != {})") + raise ValueError(msg.format(len(fill), num_channels)) + + if interpolation not in supported_interpolation_modes: + raise ValueError("Interpolation mode '{}' is unsupported with Tensor input".format(interpolation)) + + +def _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]: + need_squeeze = False + # make image NCHW + if img.ndim < 4: + img = img.unsqueeze(dim=0) + need_squeeze = True + + out_dtype = img.dtype + need_cast = False + if out_dtype not in req_dtypes: + need_cast = True + req_dtype = req_dtypes[0] + img = img.to(req_dtype) + return img, need_cast, need_squeeze, out_dtype + + +def _cast_squeeze_out(img: Tensor, need_cast: bool, need_squeeze: bool, out_dtype: torch.dtype): + if need_squeeze: + img = img.squeeze(dim=0) + + if need_cast: + if out_dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): + # it is better to round before cast + img = torch.round(img) + img = img.to(out_dtype) + + return img + + +def _apply_grid_transform(img: Tensor, grid: Tensor, mode: str, fill: Optional[List[float]]) -> Tensor: + + img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [grid.dtype, ]) + + if img.shape[0] > 1: + # Apply same grid to a batch of images + grid = grid.expand(img.shape[0], grid.shape[1], grid.shape[2], grid.shape[3]) + + # Append a dummy mask for customized fill colors, should be faster than grid_sample() twice + if fill is not None: + dummy = torch.ones((img.shape[0], 1, img.shape[2], img.shape[3]), dtype=img.dtype, device=img.device) + img = torch.cat((img, dummy), dim=1) + + img = grid_sample(img, grid, mode=mode, padding_mode="zeros", align_corners=False) + + # Fill with required color + if fill is not None: + mask = img[:, -1:, :, :] # N * 1 * H * W + img = img[:, :-1, :, :] # N * C * H * W + mask = mask.expand_as(img) + len_fill = len(fill) if isinstance(fill, (tuple, list)) else 1 + fill_img = torch.tensor(fill, dtype=img.dtype, device=img.device).view(1, len_fill, 1, 1).expand_as(img) + if mode == 'nearest': + mask = mask < 0.5 + img[mask] = fill_img[mask] + else: # 'bilinear' + img = img * mask + (1.0 - mask) * fill_img + + img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype) + return img + + +def _gen_affine_grid( + theta: Tensor, w: int, h: int, ow: int, oh: int, +) -> Tensor: + # https://github.com/pytorch/pytorch/blob/74b65c32be68b15dc7c9e8bb62459efbfbde33d8/aten/src/ATen/native/ + # AffineGridGenerator.cpp#L18 + # Difference with AffineGridGenerator is that: + # 1) we normalize grid values after applying theta + # 2) we can normalize by other image size, such that it covers "extend" option like in PIL.Image.rotate + + d = 0.5 + base_grid = torch.empty(1, oh, ow, 3, dtype=theta.dtype, device=theta.device) + x_grid = torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow, device=theta.device) + base_grid[..., 0].copy_(x_grid) + y_grid = torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh, device=theta.device).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + base_grid[..., 2].fill_(1) + + rescaled_theta = theta.transpose(1, 2) / torch.tensor([0.5 * w, 0.5 * h], dtype=theta.dtype, device=theta.device) + output_grid = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta) + return output_grid.view(1, oh, ow, 2) + + +def affine( + img: Tensor, matrix: List[float], interpolation: str = "nearest", fill: Optional[List[float]] = None +) -> Tensor: + _assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"]) + + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3) + shape = img.shape + # grid will be generated on the same device as theta and img + grid = _gen_affine_grid(theta, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2]) + return _apply_grid_transform(img, grid, interpolation, fill=fill) + + +def _compute_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]: + + # Inspired of PIL implementation: + # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054 + + # pts are Top-Left, Top-Right, Bottom-Left, Bottom-Right points. + pts = torch.tensor([ + [-0.5 * w, -0.5 * h, 1.0], + [-0.5 * w, 0.5 * h, 1.0], + [0.5 * w, 0.5 * h, 1.0], + [0.5 * w, -0.5 * h, 1.0], + ]) + theta = torch.tensor(matrix, dtype=torch.float).reshape(1, 2, 3) + new_pts = pts.view(1, 4, 3).bmm(theta.transpose(1, 2)).view(4, 2) + min_vals, _ = new_pts.min(dim=0) + max_vals, _ = new_pts.max(dim=0) + + # Truncate precision to 1e-4 to avoid ceil of Xe-15 to 1.0 + tol = 1e-4 + cmax = torch.ceil((max_vals / tol).trunc_() * tol) + cmin = torch.floor((min_vals / tol).trunc_() * tol) + size = cmax - cmin + return int(size[0]), int(size[1]) + + +def rotate( + img: Tensor, matrix: List[float], interpolation: str = "nearest", + expand: bool = False, fill: Optional[List[float]] = None +) -> Tensor: + _assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"]) + w, h = img.shape[-1], img.shape[-2] + ow, oh = _compute_output_size(matrix, w, h) if expand else (w, h) + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3) + # grid will be generated on the same device as theta and img + grid = _gen_affine_grid(theta, w=w, h=h, ow=ow, oh=oh) + + return _apply_grid_transform(img, grid, interpolation, fill=fill) + + +def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device): + # https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/ + # src/libImaging/Geometry.c#L394 + + # + # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1) + # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1) + # + theta1 = torch.tensor([[ + [coeffs[0], coeffs[1], coeffs[2]], + [coeffs[3], coeffs[4], coeffs[5]] + ]], dtype=dtype, device=device) + theta2 = torch.tensor([[ + [coeffs[6], coeffs[7], 1.0], + [coeffs[6], coeffs[7], 1.0] + ]], dtype=dtype, device=device) + + d = 0.5 + base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device) + x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device) + base_grid[..., 0].copy_(x_grid) + y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + base_grid[..., 2].fill_(1) + + rescaled_theta1 = theta1.transpose(1, 2) / torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device) + output_grid1 = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta1) + output_grid2 = base_grid.view(1, oh * ow, 3).bmm(theta2.transpose(1, 2)) + + output_grid = output_grid1 / output_grid2 - 1.0 + return output_grid.view(1, oh, ow, 2) + + +def perspective( + img: Tensor, perspective_coeffs: List[float], interpolation: str = "bilinear", fill: Optional[List[float]] = None +) -> Tensor: + if not (isinstance(img, torch.Tensor)): + raise TypeError('Input img should be Tensor.') + + _assert_image_tensor(img) + + _assert_grid_transform_inputs( + img, + matrix=None, + interpolation=interpolation, + fill=fill, + supported_interpolation_modes=["nearest", "bilinear"], + coeffs=perspective_coeffs + ) + + ow, oh = img.shape[-1], img.shape[-2] + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=img.device) + return _apply_grid_transform(img, grid, interpolation, fill=fill) + + +def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor: + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + kernel1d = pdf / pdf.sum() + + return kernel1d + + +def _get_gaussian_kernel2d( + kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device +) -> Tensor: + kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0]).to(device, dtype=dtype) + kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1]).to(device, dtype=dtype) + kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :]) + return kernel2d + + +def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Tensor: + if not (isinstance(img, torch.Tensor)): + raise TypeError('img should be Tensor. Got {}'.format(type(img))) + + _assert_image_tensor(img) + + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device) + kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1]) + + img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ]) + + # padding = (left, right, top, bottom) + padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2] + img = torch_pad(img, padding, mode="reflect") + img = conv2d(img, kernel, groups=img.shape[-3]) + + img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype) + return img + + +def invert(img: Tensor) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim)) + + _assert_channels(img, [1, 3]) + + bound = torch.tensor(1 if img.is_floating_point() else 255, dtype=img.dtype, device=img.device) + return bound - img + + +def posterize(img: Tensor, bits: int) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim)) + if img.dtype != torch.uint8: + raise TypeError("Only torch.uint8 image tensors are supported, but found {}".format(img.dtype)) + + _assert_channels(img, [1, 3]) + mask = -int(2**(8 - bits)) # JIT-friendly for: ~(2 ** (8 - bits) - 1) + return img & mask + + +def solarize(img: Tensor, threshold: float) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim)) + + _assert_channels(img, [1, 3]) + + inverted_img = invert(img) + return torch.where(img >= threshold, inverted_img, img) + + +def _blurred_degenerate_image(img: Tensor) -> Tensor: + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + + kernel = torch.ones((3, 3), dtype=dtype, device=img.device) + kernel[1, 1] = 5.0 + kernel /= kernel.sum() + kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1]) + + result_tmp, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ]) + result_tmp = conv2d(result_tmp, kernel, groups=result_tmp.shape[-3]) + result_tmp = _cast_squeeze_out(result_tmp, need_cast, need_squeeze, out_dtype) + + result = img.clone() + result[..., 1:-1, 1:-1] = result_tmp + + return result + + +def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor: + if sharpness_factor < 0: + raise ValueError('sharpness_factor ({}) is not non-negative.'.format(sharpness_factor)) + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + + if img.size(-1) <= 2 or img.size(-2) <= 2: + return img + + return _blend(img, _blurred_degenerate_image(img), sharpness_factor) + + +def autocontrast(img: Tensor) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim)) + + _assert_channels(img, [1, 3]) + + bound = 1.0 if img.is_floating_point() else 255.0 + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + + minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype) + maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype) + eq_idxs = torch.where(minimum == maximum)[0] + minimum[eq_idxs] = 0 + maximum[eq_idxs] = bound + scale = bound / (maximum - minimum) + + return ((img - minimum) * scale).clamp(0, bound).to(img.dtype) + + +def _scale_channel(img_chan): + hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255) + + nonzero_hist = hist[hist != 0] + step = nonzero_hist[:-1].sum() // 255 + if step == 0: + return img_chan + + lut = (torch.cumsum(hist, 0) + (step // 2)) // step + lut = torch.nn.functional.pad(lut, [1, 0])[:-1].clamp(0, 255) + + return lut[img_chan.to(torch.int64)].to(torch.uint8) + + +def _equalize_single_image(img: Tensor) -> Tensor: + return torch.stack([_scale_channel(img[c]) for c in range(img.size(0))]) + + +def equalize(img: Tensor) -> Tensor: + + _assert_image_tensor(img) + + if not (3 <= img.ndim <= 4): + raise TypeError("Input image tensor should have 3 or 4 dimensions, but found {}".format(img.ndim)) + if img.dtype != torch.uint8: + raise TypeError("Only torch.uint8 image tensors are supported, but found {}".format(img.dtype)) + + _assert_channels(img, [1, 3]) + + if img.ndim == 3: + return _equalize_single_image(img) + + return torch.stack([_equalize_single_image(x) for x in img]) diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/misc.py b/cv/detection/yolov3/tensorflow/dataloader/utils/misc.py new file mode 100644 index 000000000..a16b92d0e --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/misc.py @@ -0,0 +1,33 @@ +import os +import sys +import errno +from typing import Any +from PIL import Image + +import torch + + +def mkdir(path): + try: + os.makedirs(path) + except OSError as e: + if e.errno != errno.EEXIST: + raise + +def _is_pil_image(img: Any) -> bool: + try: + import accimage + except ImportError: + accimage = None + if accimage is not None: + return isinstance(img, (Image.Image, accimage.Image)) + else: + return isinstance(img, Image.Image) + +def get_image_size(img): + if isinstance(img, torch.Tensor): + return [img.shape[-1], img.shape[-2]] + + if _is_pil_image(img): + return img.size + raise TypeError("Unexpected type {}".format(type(img))) \ No newline at end of file diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/pascal_voc.py b/cv/detection/yolov3/tensorflow/dataloader/utils/pascal_voc.py new file mode 100644 index 000000000..d60942541 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/pascal_voc.py @@ -0,0 +1,311 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +from collections import OrderedDict +import cv2 +# from functools import lru_cache +import os +import numpy as np +import xml.etree.ElementTree as ET +from PIL import Image + + + +from abc import ABCMeta, abstractmethod + +try: + import torch + from torch.utils.data import Dataset +except: + torch = None + Dataset = object + +# 继承 BaseDataset 的 class 需要实现 get_data, 并填充 image_ids 字段 +class BaseDataset(Dataset, metaclass=ABCMeta): + def __init__(self, transform, **kwargs): + super(BaseDataset, self).__init__() + self.pipeline = transform + self.image_ids = [] + + def __len__(self): + return len(self.image_ids) + + def __getitem__(self, idx): + image, target = self.get_data(idx) + if self.pipeline is not None: + image, target = self.pipeline(image, target) + return image, target + + @abstractmethod + def get_data(self, idx): + pass + + +class PascalVOC(BaseDataset): + ALL_CLASSES = ('__background__', + 'aeroplane', 'bicycle', 'bird', 'boat', + 'bottle', 'bus', 'car', 'cat', 'chair', + 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', + 'sheep', 'sofa', 'train', 'tvmonitor') + + COLORS = ([0, 0, 0], + [128, 0, 0], [0, 128, 0], [128, 128, 0], + [0, 0, 128], [128, 0, 128], [0, 128, 128], + [128, 128, 128], [64, 0, 0], [192, 0, 0], + [64, 128, 0], [192, 128, 0], [64, 0, 128], + [192, 0, 128], [64, 128, 128], [192, 128, 128], + [0, 64, 0], [128, 64, 0], [0, 192, 0], + [128, 192, 0], [0, 64, 128]) + + CLASS_COLOR = OrderedDict(zip(ALL_CLASSES, COLORS)) + + def __init__(self, + data_dir, + split, + image_sets_dir='Main', + classes=None, + keep_difficult=False, + to_torch_tensor=True, + **kwargs): + """Dataset for VOC data. + + Parameters + ---------- + data_dir : str + the root of the VOC2007 or VOC2012 dataset, the directory contains the + following sub-directories: + Annotations, ImageSets, JPEGImages, SegmentationClass, SegmentationObject. + split : str + "train" or "val" + """ + super().__init__(**kwargs) + self.data_dir = data_dir + self.split = split + image_sets_file = os.path.join( + self.data_dir, 'ImageSets', image_sets_dir, '%s.txt' % self.split) + if classes is not None: + self.CLASSES = classes + if '__background__' not in classes: + self.CLASSES.insert(0, '__background__') + else: + self.CLASSES = self.ALL_CLASSES + + self.image_ids = PascalVOC.get_image_ids(image_sets_file) + self.keep_difficult = keep_difficult + self.class_dict = {class_name: i for i, class_name in enumerate(self.CLASSES)} + self.class_id_dict = {cls_id: name for name, cls_id in self.class_dict.items()} + self.to_torch_tensor = to_torch_tensor and torch is not None + + def get_data(self, idx): + image_id = self.image_ids[idx] + bboxes, labels, difficult = self._get_annotation(image_id) + # 忽略掉 difficult 的 object + if not self.keep_difficult: + bboxes = bboxes[difficult==0] + labels = labels[difficult==0] + image = self._read_image(image_id) + img_info = self.get_img_info(idx) + # 因为使用了 lru cache， 为了确保 cache 中的数据不被 pipeline 更改， + # 需要将原始数据的拷贝送入 pipeline + area = (bboxes[:, 2] - bboxes[:, 0]) * (bboxes[:, 3] - bboxes[:, 1]) + image_id = image_id.replace('-', '') + image_id = int(image_id) + + if self.to_torch_tensor: + target = dict(boxes=torch.from_numpy(bboxes), + labels=torch.from_numpy(labels), + image_id=torch.tensor(image_id), + area=torch.from_numpy(area), + iscrowd=torch.tensor([False] * len(bboxes))) + else: + target = dict(boxes=torch.from_numpy(bboxes.copy()), + labels=torch.from_numpy(labels.copy()), + image_id=image_id, + area=area.copy(), + iscrowd=[False] * len(bboxes)) + return image, target + + # 获取索引为 idx 的 sample 的标注信息 + def get_annotation(self, idx): + image_id = self.image_ids[idx] + bboxes, labels, difficult = self._get_annotation(image_id) + anno = dict(image_id=image_id, + bboxes=bboxes, + labels=labels, + difficult=difficult) + return anno + + # @lru_cache(maxsize=None) + def _get_annotation(self, image_id): + annotation_file = os.path.join(self.data_dir, "Annotations", "%s.xml" % image_id) + objects = ET.parse(annotation_file).findall("object") + boxes = [] + labels = [] + difficult = [] + for obj in objects: + class_name = obj.find('name').text.lower().strip() + if class_name not in self.CLASSES: + continue + bbox = obj.find('bndbox') + # VOC dataset format follows Matlab, in which indices start from 0 + x1 = float(bbox.find('xmin').text) - 1 + y1 = float(bbox.find('ymin').text) - 1 + x2 = float(bbox.find('xmax').text) - 1 + y2 = float(bbox.find('ymax').text) - 1 + boxes.append([x1, y1, x2, y2]) + labels.append(self.class_dict[class_name]) + difficult_str = obj.find('difficult').text + difficult.append(int(difficult_str) if difficult_str else 0) + + return (np.array(boxes, dtype=np.float32), + np.array(labels, dtype=np.int64), + np.array(difficult, dtype=np.uint8)) + + @staticmethod + def get_image_ids(image_sets_file): + ids = [] + with open(image_sets_file) as f: + for line in f: + ids.append(line.rstrip()) + return ids + + # @lru_cache(maxsize=None) + def get_img_info(self, idx): + image_id = self.image_ids[idx] + annotation_file = os.path.join(self.data_dir, "Annotations", "%s.xml" % image_id) + anno = ET.parse(annotation_file).getroot() + size = anno.find("size") + im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) + image_path = os.path.join(self.data_dir, "JPEGImages", "%s.jpg" % image_id) + return dict(id=image_id, + file_path=image_path, + height=im_info[0], + width=im_info[1]) + + # @lru_cache(maxsize=None) + def _read_image(self, image_id): + image_file = os.path.join(self.data_dir, "JPEGImages", "%s.jpg" % image_id) + image = Image.open(image_file) + return image + + def get_class_name(self, class_id): + return self.class_id_dict[class_id] + + def get_class_color(self, class_id): + if not isinstance(class_id, str): + class_name = self.get_class_name(class_id) + else: + class_name = class_id + return self.CLASS_COLOR[class_name] + + + +class VOCInstanceSeg(PascalVOC): + IGNORE_IMAGE_IDS = ("2008_005953", "2008_007355") + def __init__(self, **kwargs): + super().__init__(image_sets_dir='Segmentation', **kwargs) + + if "ignore_image_id" in kwargs and (isinstance(kwargs["ignore_image_id"], list) or isinstance(kwargs["ignore_image_id"], tuple)): + self.ignore_ids = kwargs["ignore_image_id"] + else: + self.ignore_ids = self.IGNORE_IMAGE_IDS + + self.image_ids = [id for id in self.image_ids if id not in self.ignore_ids] + + def get_data(self, idx): + image_id = self.image_ids[idx] + bboxes, labels, masks, difficult = self._get_annotation(image_id) + # 忽略掉 difficult 的 object + if not self.keep_difficult: + bboxes = bboxes[difficult==0] + labels = labels[difficult==0] + masks = np.transpose(masks, axes=(2, 0, 1)) + masks = masks[difficult==0] + # masks = np.transpose(masks, axes=(1, 2, 0)) + image = self._read_image(image_id) + img_info = self.get_img_info(idx) + # 因为使用了 lru cache， 为了确保 cache 中的数据不被 pipeline 更改， + # 需要将原始数据的拷贝送入 pipeline + area = (bboxes[:, 2] - bboxes[:, 0]) * (bboxes[:, 3] - bboxes[:, 1]) + image_id = image_id.replace('-', '') + image_id = int(image_id) + if self.to_torch_tensor: + target = dict(boxes=torch.from_numpy(bboxes), + labels=torch.from_numpy(labels), + image_id=torch.tensor(image_id), + area=torch.from_numpy(area), + iscrowd=torch.tensor([False] * len(bboxes)), + masks=torch.from_numpy(masks)) + else: + target = dict(boxes=bboxes.copy(), + labels=labels.copy(), + image_id=image_id, + area=area.copy(), + iscrowd=[False] * len(bboxes), + masks=masks.copy()) + return image, target + + # @lru_cache(maxsize=None) + def _get_annotation(self, image_id): + annotation_file = os.path.join(self.data_dir, "Annotations", "%s.xml" % image_id) + objects = ET.parse(annotation_file).findall("object") + mask = self._read_mask(image_id) + boxes = [] + labels = [] + objmasks = [] + difficult = [] + for obj in objects: + class_name = obj.find('name').text.lower().strip() + if class_name not in self.CLASSES: + continue + bbox = obj.find('bndbox') + # VOC dataset format follows Matlab, in which indices start from 0 + x1 = float(bbox.find('xmin').text) - 1 + y1 = float(bbox.find('ymin').text) - 1 + x2 = float(bbox.find('xmax').text) - 1 + y2 = float(bbox.find('ymax').text) - 1 + boxes.append([x1, y1, x2, y2]) + labels.append(self.class_dict[class_name]) + difficult_str = obj.find('difficult').text + difficult.append(int(difficult_str) if difficult_str else 0) + + class_color = self.get_class_color(class_name) + objmask = self._get_objmask(mask, (x1, y1, x2, y2), class_color) + objmasks.append(objmask) + + if len(objmasks) == 1: + objmasks = np.expand_dims(objmasks[0], axis=-1) + else: + objmasks = np.stack(objmasks, axis=-1) + + # masks 的 shape 为 (height, width, N), 目的是方便复用对 img 的 transform 操作 + return (np.array(boxes, dtype=np.float32), + np.array(labels, dtype=np.int64), + objmasks, + np.array(difficult, dtype=np.uint8)) + + def _read_mask(self, image_id): + mask_file = os.path.join(self.data_dir, "SegmentationClass", "%s.png" % image_id) + mask = cv2.imread(mask_file) + return mask + + def _get_objmask(self, mask, bbox, class_color): + im_heigt, im_width = mask.shape[0], mask.shape[1] + xmin, ymin, xmax, ymax = [int(coor) for coor in bbox] + + objmask = np.zeros((im_heigt, im_width), dtype=np.uint8) + for i in range(ymin, ymax): + for j in range(xmin, xmax): + # mask 为 BGR， class_color 为 RGB + if mask[i,j,0] == class_color[2] and \ + mask[i,j,1] == class_color[1] and \ + mask[i,j,2] == class_color[0]: + objmask[i,j] = 1 + + return objmask + + +def get_voc(root, image_set, transforms, to_torch_tensor=True): + return VOCInstanceSeg(data_dir=root, split=image_set, transform=transforms, to_torch_tensor=to_torch_tensor) \ No newline at end of file diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/presets_classification.py b/cv/detection/yolov3/tensorflow/dataloader/utils/presets_classification.py new file mode 100644 index 000000000..b3f559af4 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/presets_classification.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +from torchvision.transforms import autoaugment, transforms + + +class ClassificationPresetTrain: + def __init__(self, crop_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), hflip_prob=0.5, + auto_augment_policy=None, random_erase_prob=0.0): + trans = [transforms.RandomResizedCrop(crop_size)] + if hflip_prob > 0: + trans.append(transforms.RandomHorizontalFlip(hflip_prob)) + if auto_augment_policy is not None: + aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy) + trans.append(autoaugment.AutoAugment(policy=aa_policy)) + trans.extend([ + transforms.ToTensor(), + transforms.Normalize(mean=mean, std=std), + ]) + if random_erase_prob > 0: + trans.append(transforms.RandomErasing(p=random_erase_prob)) + + self.transforms = transforms.Compose(trans) + + def __call__(self, img): + return self.transforms(img) + + +class ClassificationPresetEval: + def __init__(self, crop_size, resize_size=256, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): + + self.transforms = transforms.Compose([ + transforms.Resize(resize_size), + transforms.CenterCrop(crop_size), + transforms.ToTensor(), + transforms.Normalize(mean=mean, std=std), + ]) + + def __call__(self, img): + return self.transforms(img) diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/presets_detection.py b/cv/detection/yolov3/tensorflow/dataloader/utils/presets_detection.py new file mode 100644 index 000000000..75946f078 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/presets_detection.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +from . import transforms_det as T + + +class DetectionPresetTrain: + def __init__(self, data_augmentation, hflip_prob=0.5, mean=(123., 117., 104.)): + if data_augmentation == 'hflip': + self.transforms = T.Compose([ + T.RandomHorizontalFlip(p=hflip_prob), + T.ToTensor(), + ]) + elif data_augmentation == 'ssd': + self.transforms = T.Compose([ + T.RandomPhotometricDistort(), + T.RandomZoomOut(fill=list(mean)), + T.RandomIoUCrop(), + T.RandomHorizontalFlip(p=hflip_prob), + T.ToTensor(), + ]) + elif data_augmentation == 'ssdlite': + self.transforms = T.Compose([ + T.RandomIoUCrop(), + T.RandomHorizontalFlip(p=hflip_prob), + T.ToTensor(), + ]) + else: + raise ValueError(f'Unknown data augmentation policy "{data_augmentation}"') + + def __call__(self, img, target): + return self.transforms(img, target) + + +class DetectionPresetEval: + def __init__(self): + self.transforms = T.ToTensor() + + def __call__(self, img, target): + return self.transforms(img, target) diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/presets_segmentation.py b/cv/detection/yolov3/tensorflow/dataloader/utils/presets_segmentation.py new file mode 100644 index 000000000..45e0d029b --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/presets_segmentation.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +from . import transforms_seg as T + + +class SegmentationPresetTrain: + def __init__(self, base_size, crop_size, hflip_prob=0.5, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): + min_size = int(0.5 * base_size) + max_size = int(2.0 * base_size) + + trans = [T.RandomResize(min_size, max_size)] + if hflip_prob > 0: + trans.append(T.RandomHorizontalFlip(hflip_prob)) + trans.extend([ + T.RandomCrop(crop_size), + T.ToTensor(), + T.Normalize(mean=mean, std=std), + ]) + self.transforms = T.Compose(trans) + + def __call__(self, img, target): + return self.transforms(img, target) + + +class SegmentationPresetEval: + def __init__(self, base_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): + self.transforms = T.Compose([ + T.RandomResize(base_size, base_size), + T.ToTensor(), + T.Normalize(mean=mean, std=std), + ]) + + def __call__(self, img, target): + return self.transforms(img, target) diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/transforms_det.py b/cv/detection/yolov3/tensorflow/dataloader/utils/transforms_det.py new file mode 100644 index 000000000..405e760c0 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/transforms_det.py @@ -0,0 +1,243 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +from typing import List, Tuple, Dict, Optional + +import torch +from torch import nn, Tensor +import torchvision +from torchvision.transforms import transforms as T +from . import functional as F + + +def _flip_coco_person_keypoints(kps, width): + flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] + flipped_data = kps[:, flip_inds] + flipped_data[..., 0] = width - flipped_data[..., 0] + # Maintain COCO convention that if visibility == 0, then x, y = 0 + inds = flipped_data[..., 2] == 0 + flipped_data[inds] = 0 + return flipped_data + + +class Compose(object): + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, image, target): + for t in self.transforms: + image, target = t(image, target) + return image, target + + +class RandomHorizontalFlip(T.RandomHorizontalFlip): + def forward(self, image: Tensor, + target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + if torch.rand(1) < self.p: + image = F.hflip(image) + if target is not None: + width, _ = F._get_image_size(image) + target["boxes"][:, [0, 2]] = width - target["boxes"][:, [2, 0]] + if "masks" in target: + target["masks"] = target["masks"].flip(-1) + if "keypoints" in target: + keypoints = target["keypoints"] + keypoints = _flip_coco_person_keypoints(keypoints, width) + target["keypoints"] = keypoints + return image, target + + +class ToTensor(nn.Module): + def forward(self, image: Tensor, + target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + image = F.to_tensor(image) + return image, target + + +class RandomIoUCrop(nn.Module): + def __init__(self, min_scale: float = 0.3, max_scale: float = 1.0, min_aspect_ratio: float = 0.5, + max_aspect_ratio: float = 2.0, sampler_options: Optional[List[float]] = None, trials: int = 40): + super().__init__() + # Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174 + self.min_scale = min_scale + self.max_scale = max_scale + self.min_aspect_ratio = min_aspect_ratio + self.max_aspect_ratio = max_aspect_ratio + if sampler_options is None: + sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0] + self.options = sampler_options + self.trials = trials + + def forward(self, image: Tensor, + target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + if target is None: + raise ValueError("The targets can't be None for this transform.") + + if isinstance(image, torch.Tensor): + if image.ndimension() not in {2, 3}: + raise ValueError('image should be 2/3 dimensional. Got {} dimensions.'.format(image.ndimension())) + elif image.ndimension() == 2: + image = image.unsqueeze(0) + + orig_w, orig_h = F._get_image_size(image) + + while True: + # sample an option + idx = int(torch.randint(low=0, high=len(self.options), size=(1,))) + min_jaccard_overlap = self.options[idx] + if min_jaccard_overlap >= 1.0: # a value larger than 1 encodes the leave as-is option + return image, target + + for _ in range(self.trials): + # check the aspect ratio limitations + r = self.min_scale + (self.max_scale - self.min_scale) * torch.rand(2) + new_w = int(orig_w * r[0]) + new_h = int(orig_h * r[1]) + aspect_ratio = new_w / new_h + if not (self.min_aspect_ratio <= aspect_ratio <= self.max_aspect_ratio): + continue + + # check for 0 area crops + r = torch.rand(2) + left = int((orig_w - new_w) * r[0]) + top = int((orig_h - new_h) * r[1]) + right = left + new_w + bottom = top + new_h + if left == right or top == bottom: + continue + + # check for any valid boxes with centers within the crop area + cx = 0.5 * (target["boxes"][:, 0] + target["boxes"][:, 2]) + cy = 0.5 * (target["boxes"][:, 1] + target["boxes"][:, 3]) + is_within_crop_area = (left < cx) & (cx < right) & (top < cy) & (cy < bottom) + if not is_within_crop_area.any(): + continue + + # check at least 1 box with jaccard limitations + boxes = target["boxes"][is_within_crop_area] + ious = torchvision.ops.boxes.box_iou(boxes, torch.tensor([[left, top, right, bottom]], + dtype=boxes.dtype, device=boxes.device)) + if ious.max() < min_jaccard_overlap: + continue + + # keep only valid boxes and perform cropping + target["boxes"] = boxes + target["labels"] = target["labels"][is_within_crop_area] + target["boxes"][:, 0::2] -= left + target["boxes"][:, 1::2] -= top + target["boxes"][:, 0::2].clamp_(min=0, max=new_w) + target["boxes"][:, 1::2].clamp_(min=0, max=new_h) + image = F.crop(image, top, left, new_h, new_w) + + return image, target + + +class RandomZoomOut(nn.Module): + def __init__(self, fill: Optional[List[float]] = None, side_range: Tuple[float, float] = (1., 4.), p: float = 0.5): + super().__init__() + if fill is None: + fill = [0., 0., 0.] + self.fill = fill + self.side_range = side_range + if side_range[0] < 1. or side_range[0] > side_range[1]: + raise ValueError("Invalid canvas side range provided {}.".format(side_range)) + self.p = p + + @torch.jit.unused + def _get_fill_value(self, is_pil): + # type: (bool) -> int + # We fake the type to make it work on JIT + return tuple(int(x) for x in self.fill) if is_pil else 0 + + def forward(self, image: Tensor, + target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + if isinstance(image, torch.Tensor): + if image.ndimension() not in {2, 3}: + raise ValueError('image should be 2/3 dimensional. Got {} dimensions.'.format(image.ndimension())) + elif image.ndimension() == 2: + image = image.unsqueeze(0) + + if torch.rand(1) < self.p: + return image, target + + orig_w, orig_h = F._get_image_size(image) + + r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0]) + canvas_width = int(orig_w * r) + canvas_height = int(orig_h * r) + + r = torch.rand(2) + left = int((canvas_width - orig_w) * r[0]) + top = int((canvas_height - orig_h) * r[1]) + right = canvas_width - (left + orig_w) + bottom = canvas_height - (top + orig_h) + + if torch.jit.is_scripting(): + fill = 0 + else: + fill = self._get_fill_value(F._is_pil_image(image)) + + image = F.pad(image, [left, top, right, bottom], fill=fill) + if isinstance(image, torch.Tensor): + v = torch.tensor(self.fill, device=image.device, dtype=image.dtype).view(-1, 1, 1) + image[..., :top, :] = image[..., :, :left] = image[..., (top + orig_h):, :] = \ + image[..., :, (left + orig_w):] = v + + if target is not None: + target["boxes"][:, 0::2] += left + target["boxes"][:, 1::2] += top + + return image, target + + +class RandomPhotometricDistort(nn.Module): + def __init__(self, contrast: Tuple[float] = (0.5, 1.5), saturation: Tuple[float] = (0.5, 1.5), + hue: Tuple[float] = (-0.05, 0.05), brightness: Tuple[float] = (0.875, 1.125), p: float = 0.5): + super().__init__() + self._brightness = T.ColorJitter(brightness=brightness) + self._contrast = T.ColorJitter(contrast=contrast) + self._hue = T.ColorJitter(hue=hue) + self._saturation = T.ColorJitter(saturation=saturation) + self.p = p + + def forward(self, image: Tensor, + target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + if isinstance(image, torch.Tensor): + if image.ndimension() not in {2, 3}: + raise ValueError('image should be 2/3 dimensional. Got {} dimensions.'.format(image.ndimension())) + elif image.ndimension() == 2: + image = image.unsqueeze(0) + + r = torch.rand(7) + + if r[0] < self.p: + image = self._brightness(image) + + contrast_before = r[1] < 0.5 + if contrast_before: + if r[2] < self.p: + image = self._contrast(image) + + if r[3] < self.p: + image = self._saturation(image) + + if r[4] < self.p: + image = self._hue(image) + + if not contrast_before: + if r[5] < self.p: + image = self._contrast(image) + + if r[6] < self.p: + channels = F._get_image_num_channels(image) + permutation = torch.randperm(channels) + + is_pil = F._is_pil_image(image) + if is_pil: + image = F.to_tensor(image) + image = image[..., permutation, :, :] + if is_pil: + image = F.to_pil_image(image) + + return image, target diff --git a/cv/detection/yolov3/tensorflow/dataloader/utils/transforms_seg.py b/cv/detection/yolov3/tensorflow/dataloader/utils/transforms_seg.py new file mode 100644 index 000000000..c091e7b60 --- /dev/null +++ b/cv/detection/yolov3/tensorflow/dataloader/utils/transforms_seg.py @@ -0,0 +1,96 @@ +# Copyright (c) 2022 Iluvatar CoreX. All rights reserved. +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +import numpy as np +from PIL import Image +import random + +import torch +from torchvision import transforms as T +from torchvision.transforms import functional as F + + +def pad_if_smaller(img, size, fill=0): + min_size = min(img.size) + if min_size < size: + ow, oh = img.size + padh = size - oh if oh < size else 0 + padw = size - ow if ow < size else 0 + img = F.pad(img, (0, 0, padw, padh), fill=fill) + return img + + +class Compose(object): + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, image, target): + for t in self.transforms: + image, target = t(image, target) + return image, target + + +class RandomResize(object): + def __init__(self, min_size, max_size=None): + self.min_size = min_size + if max_size is None: + max_size = min_size + self.max_size = max_size + + def __call__(self, image, target): + size = random.randint(self.min_size, self.max_size) + image = F.resize(image, size) + target = F.resize(target, size, interpolation=Image.NEAREST) + return image, target + + +class RandomHorizontalFlip(object): + def __init__(self, flip_prob): + self.flip_prob = flip_prob + + def __call__(self, image, target): + if random.random() < self.flip_prob: + image = F.hflip(image) + target = F.hflip(target) + return image, target + + +class RandomCrop(object): + def __init__(self, size): + self.size = size + + def __call__(self, image, target): + image = pad_if_smaller(image, self.size) + target = pad_if_smaller(target, self.size, fill=255) + crop_params = T.RandomCrop.get_params(image, (self.size, self.size)) + image = F.crop(image, *crop_params) + target = F.crop(target, *crop_params) + return image, target + + +class CenterCrop(object): + def __init__(self, size): + self.size = size + + def __call__(self, image, target): + image = F.center_crop(image, self.size) + target = F.center_crop(target, self.size) + return image, target + + +class ToTensor(object): + def __call__(self, image, target): + image = F.to_tensor(image) + target = torch.as_tensor(np.array(target), dtype=torch.int64) + return image, target + + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, image, target): + image = F.normalize(image, mean=self.mean, std=self.std) + return image, target diff --git a/cv/detection/yolov3/tensorflow/docs/Box-Clustering.ipynb b/cv/detection/yolov3/tensorflow/docs/Box-Clustering.ipynb new file mode 100644 index 000000000..401c7715e --- /dev/null +++ b/cv/detection/yolov3/tensorflow/docs/Box-Clustering.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os, cv2\n", + "%matplotlib inline\n", + "\n", + "LABELS = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', \n", + " 'bus', 'car', 'cat', 'chair', 'cow',\n", + " 'diningtable','dog', 'horse', 'motorbike', 'person',\n", + " 'pottedplant','sheep', 'sofa', 'train', 'tvmonitor']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dowload VOC-dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "train_image_folder = \"../../VOCdevkit/VOC2012/JPEGImages/\"\n", + "train_annot_folder = \"../../VOCdevkit/VOC2012/Annotations/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N train = 17125\n" + ] + } + ], + "source": [ + "import xml.etree.ElementTree as ET\n", + "\n", + "def parse_annotation(ann_dir, img_dir, labels=[]):\n", + " '''\n", + " output:\n", + " - Each element of the train_image is a dictionary containing the annoation infomation of an image.\n", + " - seen_train_labels is the dictionary containing\n", + " (key, value) = (the object class, the number of objects found in the images)\n", + " '''\n", + " all_imgs = []\n", + " seen_labels = {}\n", + " \n", + " for ann in sorted(os.listdir(ann_dir)):\n", + " if \"xml\" not in ann:\n", + " continue\n", + " img = {'object':[]}\n", + "\n", + " tree = ET.parse(ann_dir + ann)\n", + " \n", + " for elem in tree.iter():\n", + " if 'filename' in elem.tag:\n", + " path_to_image = img_dir + elem.text\n", + " img['filename'] = path_to_image\n", + " ## make sure that the image exists:\n", + " if not os.path.exists(path_to_image):\n", + " assert False, \"file does not exist!\\n{}\".format(path_to_image)\n", + " if 'width' in elem.tag:\n", + " img['width'] = int(elem.text)\n", + " if 'height' in elem.tag:\n", + " img['height'] = int(elem.text)\n", + " if 'object' in elem.tag or 'part' in elem.tag:\n", + " obj = {}\n", + " \n", + " for attr in list(elem):\n", + " if 'name' in attr.tag:\n", + " \n", + " obj['name'] = attr.text\n", + " \n", + " if len(labels) > 0 and obj['name'] not in labels:\n", + " break\n", + " else:\n", + " img['object'] += [obj]\n", + " \n", + " \n", + "\n", + " if obj['name'] in seen_labels:\n", + " seen_labels[obj['name']] += 1\n", + " else:\n", + " seen_labels[obj['name']] = 1\n", + " \n", + "\n", + " \n", + " if 'bndbox' in attr.tag:\n", + " for dim in list(attr):\n", + " if 'xmin' in dim.tag:\n", + " obj['xmin'] = int(round(float(dim.text)))\n", + " if 'ymin' in dim.tag:\n", + " obj['ymin'] = int(round(float(dim.text)))\n", + " if 'xmax' in dim.tag:\n", + " obj['xmax'] = int(round(float(dim.text)))\n", + " if 'ymax' in dim.tag:\n", + " obj['ymax'] = int(round(float(dim.text)))\n", + "\n", + " if len(img['object']) > 0:\n", + " all_imgs += [img]\n", + " \n", + " return all_imgs, seen_labels\n", + "\n", + "## Parse annotations \n", + "train_image, seen_train_labels = parse_annotation(train_annot_folder,train_image_folder, labels=LABELS)\n", + "print(\"N train = {}\".format(len(train_image)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Output : train_image\n", + "- train_image是一个字典，它包含了图片以及标注信息" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'filename': '../../VOCdevkit/VOC2012/JPEGImages/2007_000027.jpg',\n", + " 'height': 500,\n", + " 'object': [{'name': 'person',\n", + " 'xmax': 349,\n", + " 'xmin': 174,\n", + " 'ymax': 351,\n", + " 'ymin': 101}],\n", + " 'width': 486},\n", + " {'filename': '../../VOCdevkit/VOC2012/JPEGImages/2007_000032.jpg',\n", + " 'height': 281,\n", + " 'object': [{'name': 'aeroplane',\n", + " 'xmax': 375,\n", + " 'xmin': 104,\n", + " 'ymax': 183,\n", + " 'ymin': 78},\n", + " {'name': 'aeroplane', 'xmax': 197, 'xmin': 133, 'ymax': 123, 'ymin': 88},\n", + " {'name': 'person', 'xmax': 213, 'xmin': 195, 'ymax': 229, 'ymin': 180},\n", + " {'name': 'person', 'xmax': 44, 'xmin': 26, 'ymax': 238, 'ymin': 189}],\n", + " 'width': 500}]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_image[:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualize output : seen_train_labels\n", + "\n", + "- VOC数据集一共有20个类别，下面将这些类别的数量分布情况可视化出来:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_pos = np.arange(len(seen_train_labels))\n", + "fig = plt.figure(figsize=(13,10))\n", + "ax = fig.add_subplot(1,1,1)\n", + "ax.barh(y_pos,list(seen_train_labels.values()))\n", + "ax.set_yticks(y_pos)\n", + "ax.set_yticklabels(list(seen_train_labels.keys()))\n", + "ax.set_title(\"The total number of objects = {} in {} images\".format(\n", + " np.sum(list(seen_train_labels.values())),len(train_image)\n", + "))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## K-means clustering\n", + "\n", + "在论文[YOLO9000:Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) 强烈建议我们使用聚类分析得到先验anchor的尺寸大小，原文这样说到:\n", + "\n", + "
\n", + "Dimension Clusters:\n", + "we encounter two issues with anchor boxes when using them with YOLO.\n", + "The first is that the box dimensions are hand picked. \n", + "the network can learn to adjust the boxes appropriately but if we pick better priors for the network to start with, we can make it easier for the network to learn to predict good detections.\n", + "
\n", + "
\n", + "Instead of choosing priors by hand, we run k-means clustering on the training set bounding boxes to automatically find good priors. If we use standard k-means with Euclidean distance learger boxes generate more error than smaller boxes. However, what we really want are priors that lead to good IOU scores, which is indepedndent of the size of the box. Thus for our distance metric we use 1 - IOU(box,centroid)\n", + "
\n", + "因此，让我们首先为K-means聚类准备要输入数据。 输入数据指的是ground truth bounding box的宽度和高度来作为特征。 考虑到在不同尺度下的场景中，每个boundingbox的尺寸不一。因此，非常有必要来标准化边界框的宽度和高度与图像的宽度和高度。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clustering feature data is ready. shape = (N object, width and height) = (40138, 2)\n" + ] + } + ], + "source": [ + "wh = []\n", + "for anno in train_image:\n", + " aw = float(anno['width']) # width of the original image\n", + " ah = float(anno['height']) # height of the original image\n", + " for obj in anno[\"object\"]:\n", + " w = (obj[\"xmax\"] - obj[\"xmin\"])/aw # make the width range between [0,GRID_W)\n", + " h = (obj[\"ymax\"] - obj[\"ymin\"])/ah # make the width range between [0,GRID_H)\n", + " temp = [w,h]\n", + " wh.append(temp)\n", + "wh = np.array(wh)\n", + "print(\"clustering feature data is ready. shape = (N object, width and height) = {}\".format(wh.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the clustering data\n", + "先来看看归一化后的anchor尺寸分布情况:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.scatter(wh[:,0],wh[:,1],alpha=0.3)\n", + "plt.title(\"Clusters\",fontsize=20)\n", + "plt.xlabel(\"normalized width\",fontsize=20)\n", + "plt.ylabel(\"normalized height\",fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intersection over union\n", + "\n", + "在介绍使用K-means对先验边界框进行聚类时，非常有必要来讨论下iou的概念，因为后面我们会用它来衡量两个boundingbox之间的距离。iou是一种测量在特定数据集中检测相应物体准确度的一个标准。我们可以在很多物体检测挑战中，例如PASCAL VOC challenge中看多很多使用该标准的做法。我们计算两个bounding box的iou时，只需要使用它们的4个位置参数(xmin,ymin, width, height)，这里引用别人一张图:\n", + "\n", + "\"bbx\"\n", + "\n", + "iou的计算公式为: \n", + "$$\\begin{array}{rl}\n", + "IoU &= \\frac{\\textrm{intersection} }{\n", + "\\textrm{union} - \\textrm{intersection}\n", + "}\\\\\n", + "\\textrm{intersection} &= Min(w_1,w_2) Min(h_1,h_2)\\\\\n", + "\\textrm{union} & = w_1 h_1 + w_2 h_2\n", + "\\end{array}$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def iou(box, clusters):\n", + " '''\n", + " :param box: np.array of shape (2,) containing w and h\n", + " :param clusters: np.array of shape (N cluster, 2) \n", + " '''\n", + " x = np.minimum(clusters[:, 0], box[0]) \n", + " y = np.minimum(clusters[:, 1], box[1])\n", + "\n", + " intersection = x * y\n", + " box_area = box[0] * box[1]\n", + " cluster_area = clusters[:, 0] * clusters[:, 1]\n", + "\n", + " iou_ = intersection / (box_area + cluster_area - intersection)\n", + "\n", + " return iou_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The k-means clustering\n", + "\n", + "K-means的聚类方法很简单，它主要包含两个步骤:\n", + "\n", + "\n", + "首先初始化类别数量和聚类中心:\n", + "\n", + "- Step 1: 计算每个boundingbox与所有聚类中心的距离（1-iou)，选择最近的那个聚类中心作为它的类别\n", + "- Step 2: 使用每个类别簇的均值来作为下次迭代计算的类别中心
\n", + "\n", + "重复步骤1和2,直至每个类别的中心位置不再发生变化。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def kmeans(boxes, k, dist=np.median,seed=1):\n", + " \"\"\"\n", + " Calculates k-means clustering with the Intersection over Union (IoU) metric.\n", + " :param boxes: numpy array of shape (r, 2), where r is the number of rows\n", + " :param k: number of clusters\n", + " :param dist: distance function\n", + " :return: numpy array of shape (k, 2)\n", + " \"\"\"\n", + " rows = boxes.shape[0]\n", + "\n", + " distances = np.empty((rows, k)) ## N row x N cluster\n", + " last_clusters = np.zeros((rows,))\n", + "\n", + " np.random.seed(seed)\n", + "\n", + " # initialize the cluster centers to be k items\n", + " clusters = boxes[np.random.choice(rows, k, replace=False)]\n", + "\n", + " while True:\n", + " # Step 1: allocate each item to the closest cluster centers\n", + " for icluster in range(k): # I made change to lars76's code here to make the code faster\n", + " distances[:,icluster] = 1 - iou(clusters[icluster], boxes)\n", + "\n", + " nearest_clusters = np.argmin(distances, axis=1)\n", + "\n", + " if (last_clusters == nearest_clusters).all():\n", + " break\n", + " \n", + " # Step 2: calculate the cluster centers as mean (or median) of all the cases in the clusters.\n", + " for cluster in range(k):\n", + " clusters[cluster] = dist(boxes[nearest_clusters == cluster], axis=0)\n", + "\n", + " last_clusters = nearest_clusters\n", + "\n", + " return clusters,nearest_clusters,distances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The number of Clusters\n", + "\n", + "一般来说，anchor聚类的类别越多，那么yolo算法就越能在不同尺度下与真实框进行回归，但是这样也增加了很多计算量。(这对于一个号称 real-time 目标检测框架来说是极其尴尬的，因此作者也尽量减少boundingbox的数目)。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 clusters: mean IoU = 0.4646\n", + " 3 clusters: mean IoU = 0.5391\n", + " 4 clusters: mean IoU = 0.5801\n", + " 5 clusters: mean IoU = 0.6016\n", + " 6 clusters: mean IoU = 0.6253\n", + " 7 clusters: mean IoU = 0.6434\n", + " 8 clusters: mean IoU = 0.6595\n", + " 9 clusters: mean IoU = 0.6712\n" + ] + } + ], + "source": [ + "kmax = 10\n", + "dist = np.mean\n", + "results = {}\n", + "\n", + "for k in range(2,kmax):\n", + " clusters, nearest_clusters, distances = kmeans(wh,k,seed=2,dist=dist)\n", + " WithinClusterMeanDist = np.mean(distances[np.arange(distances.shape[0]),nearest_clusters])\n", + " result = {\"clusters\": clusters,\n", + " \"nearest_clusters\": nearest_clusters,\n", + " \"distances\": distances,\n", + " \"WithinClusterMeanDist\": WithinClusterMeanDist}\n", + " print(\"{:2.0f} clusters: mean IoU = {:5.4f}\".format(k,1-result[\"WithinClusterMeanDist\"]))\n", + " results[k] = result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "类别的数量越多，每个聚类簇的均值iou就越大，说明聚类簇里的boundingbox愈加紧贴在一起。有时候很难决定类别的数目，这也是k-means的一大痛点！在yolov2论文里设置了5个先验anchor，因此先来看看聚类数目从5到8的效果吧！" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization of k-means results " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_cluster_result(plt,clusters,nearest_clusters,WithinClusterSumDist,wh,k):\n", + " for icluster in np.unique(nearest_clusters):\n", + " pick = nearest_clusters==icluster\n", + " c = current_palette[icluster]\n", + " plt.rc('font', size=8) \n", + " plt.plot(wh[pick,0],wh[pick,1],\"p\",\n", + " color=c,\n", + " alpha=0.5,label=\"cluster = {}, N = {:6.0f}\".format(icluster,np.sum(pick)))\n", + " plt.text(clusters[icluster,0],\n", + " clusters[icluster,1],\n", + " \"c{}\".format(icluster),\n", + " fontsize=20,color=\"red\")\n", + " plt.title(\"Clusters=%d\" %k)\n", + " plt.xlabel(\"width\")\n", + " plt.ylabel(\"height\")\n", + " plt.legend(title=\"Mean IoU = {:5.4f}\".format(WithinClusterSumDist)) \n", + " \n", + "import seaborn as sns\n", + "current_palette = list(sns.xkcd_rgb.values())\n", + "\n", + "figsize = (15,35)\n", + "count =1 \n", + "fig = plt.figure(figsize=figsize)\n", + "for k in range(5,9):\n", + " result = results[k]\n", + " clusters = result[\"clusters\"]\n", + " nearest_clusters = result[\"nearest_clusters\"]\n", + " WithinClusterSumDist = result[\"WithinClusterMeanDist\"]\n", + " \n", + " ax = fig.add_subplot(kmax/2,2,count)\n", + " plot_cluster_result(plt,clusters,nearest_clusters,1 - WithinClusterSumDist,wh,k)\n", + " count += 1\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cv/detection/yolov3/tensorflow/docs/images/.jpg b/cv/detection/yolov3/tensorflow/docs/images/.jpg new file mode 100644 index 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