├── LICENSE ├── PS-HRNet.ipynb ├── README.md ├── README ├── fig.png └── result.png ├── apex └── apex.md ├── cls_hrnet_w32_sgd_lr5e-2_wd1e-4_bs32_x100.yaml ├── config ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ ├── default.cpython-36.pyc │ ├── default.cpython-37.pyc │ ├── models.cpython-36.pyc │ └── models.cpython-37.pyc ├── default.py └── models.py ├── data_loader.py ├── data_loader_v2.py ├── dataset.py ├── dataset_v2.py ├── double_samplers.py ├── lr_scheduler.py ├── prepare.py ├── random_erasing.py ├── re_ranking.py └── triplet_loss.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 | 3 | # Deep-High-Resolution Representation Learning for Cross-Resolution Person Re-identification 4 | Journal of IEEE TIP 5 | 6 | 7 |
8 | 9 | ## Contents :clipboard: 10 | 1. [Introduction](#introduction) 11 | 2. [Usage](#usage) 12 | 3. [Results](#results) 13 | 4. [Acknowledgements](#acknowledgements) 14 | 15 | ## Introduction :bookmark: 16 | 17 | We propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the cross-resolution person re-ID problem. Specifically, in order to restore the resolution of low-resolution images and make reasonable use of different channel information of feature maps, we introduce and innovate VDSR module with channel attention (CA) mechanism, named as VDSR-CA. Then we reform the HRNet by designing a novel representation head to extract discriminating features, named as HRNet-ReID. In addition, a pseudo-siamese framework is constructed to reduce the difference of feature distributions between low-resolution images and high-resolution images. The experimental results on five cross-resolution person datasets verify the effectiveness of our proposed approach. Compared with the state-of-the-art methods, our proposed PS-HRNet improves 3.4%, 6.2%, 2.5%,1.1% and 4.2% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively. 18 | 19 | ## Usage 20 | 21 | We use apex (A PyTorch Extension) a Pytorch extension with NVIDIA-maintained utilities to streamline mixed precision and distributed training. Some of the code here will be included in upstream Pytorch eventually. The intention of Apex is to make up-to-date utilities available to users as quickly as possible.Installation instructions can be found here: https://github.com/NVIDIA/apex#quick-start. 22 | 23 | We display the process of the algorithm as an ipynb file, you can use jupyter notebook to view and run it. 24 | 25 | You may need HRNet-W32-C ImageNet pretrained models or learn more about HRNet: https://github.com/HRNet/HRNet-Image-Classification.git. 26 | 27 | Wanna know more detail of the first phase？ Check this：https://github.com/NUISTGY/Person-re-identification-based-on-HRNet 28 | ## Results :trophy: 29 | 30 |
31 | 32 | 33 | 34 |
35 | 36 | ## Acknowledgements :thumbsup: 37 | 38 | - This code is built on [HRNet-Image-Classification](https://github.com/HRNet/HRNet-Image-Classification) and [Person_reID_baseline_pytorch](https://github.com/layumi/Person_reID_baseline_pytorch). We thank the authors for sharing their codes. To the great spirit of open source! 39 | - Thank [Z.Dong](https://github.com/dzc2000) and [H.Wang](https://github.com/Rockdow), they are the most important contributors to the related work of the experiment. If you have any questions in the process of testing, you can send them by email or pose issues. 40 | - Thanks for the right to use the GPU workstation provided by Nanyang Technological University. 41 | -------------------------------------------------------------------------------- /README/fig.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/README/fig.png -------------------------------------------------------------------------------- /README/result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/README/result.png -------------------------------------------------------------------------------- /apex/apex.md: -------------------------------------------------------------------------------- 1 | https://github.com/NVIDIA/apex.git -------------------------------------------------------------------------------- /cls_hrnet_w32_sgd_lr5e-2_wd1e-4_bs32_x100.yaml: -------------------------------------------------------------------------------- 1 | GPUS: (0,1,2,3) 2 | LOG_DIR: 'log/' 3 | DATA_DIR: '' 4 | OUTPUT_DIR: 'output/' 5 | WORKERS: 4 6 | PRINT_FREQ: 1000 7 | 8 | MODEL: 9 | NAME: cls_hrnet 10 | IMAGE_SIZE: 11 | - 224 12 | - 224 13 | EXTRA: 14 | STAGE1: 15 | NUM_MODULES: 1 16 | NUM_RANCHES: 1 17 | BLOCK: BOTTLENECK 18 | NUM_BLOCKS: 19 | - 4 20 | NUM_CHANNELS: 21 | - 64 22 | FUSE_METHOD: SUM 23 | STAGE2: 24 | NUM_MODULES: 1 25 | NUM_BRANCHES: 2 26 | BLOCK: BASIC 27 | NUM_BLOCKS: 28 | - 4 29 | - 4 30 | NUM_CHANNELS: 31 | - 32 32 | - 64 33 | FUSE_METHOD: SUM 34 | STAGE3: 35 | NUM_MODULES: 4 36 | NUM_BRANCHES: 3 37 | BLOCK: BASIC 38 | NUM_BLOCKS: 39 | - 4 40 | - 4 41 | - 4 42 | NUM_CHANNELS: 43 | - 32 44 | - 64 45 | - 128 46 | FUSE_METHOD: SUM 47 | STAGE4: 48 | NUM_MODULES: 3 49 | NUM_BRANCHES: 4 50 | BLOCK: BASIC 51 | NUM_BLOCKS: 52 | - 4 53 | - 4 54 | - 4 55 | - 4 56 | NUM_CHANNELS: 57 | - 32 58 | - 64 59 | - 128 60 | - 256 61 | FUSE_METHOD: SUM 62 | CUDNN: 63 | BENCHMARK: true 64 | DETERMINISTIC: false 65 | ENABLED: true 66 | DATASET: 67 | DATASET: 'imagenet' 68 | DATA_FORMAT: 'jpg' 69 | ROOT: 'data/imagenet/' 70 | TEST_SET: 'val' 71 | TRAIN_SET: 'train' 72 | TEST: 73 | BATCH_SIZE_PER_GPU: 32 74 | MODEL_FILE: '' 75 | TRAIN: 76 | BATCH_SIZE_PER_GPU: 32 77 | BEGIN_EPOCH: 0 78 | END_EPOCH: 100 79 | RESUME: true 80 | LR_FACTOR: 0.1 81 | LR_STEP: 82 | - 30 83 | - 60 84 | - 90 85 | OPTIMIZER: sgd 86 | LR: 0.05 87 | WD: 0.0001 88 | MOMENTUM: 0.9 89 | NESTEROV: true 90 | SHUFFLE: true 91 | DEBUG: 92 | DEBUG: false 93 | -------------------------------------------------------------------------------- /config/__init__.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------------ 2 | # Copyright (c) Microsoft 3 | # Licensed under the MIT License. 4 | # Written by Bin Xiao (Bin.Xiao@microsoft.com) 5 | # ------------------------------------------------------------------------------ 6 | 7 | from .default import _C as config 8 | from .models import MODEL_EXTRAS 9 | -------------------------------------------------------------------------------- /config/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/config/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /config/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/config/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /config/__pycache__/default.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/config/__pycache__/default.cpython-36.pyc -------------------------------------------------------------------------------- /config/__pycache__/default.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/config/__pycache__/default.cpython-37.pyc -------------------------------------------------------------------------------- /config/__pycache__/models.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/config/__pycache__/models.cpython-36.pyc -------------------------------------------------------------------------------- /config/__pycache__/models.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification/e6abdaa97700bc16164acb2f05e71cd92caf3289/config/__pycache__/models.cpython-37.pyc -------------------------------------------------------------------------------- /config/default.py: -------------------------------------------------------------------------------- 1 | 2 | # ------------------------------------------------------------------------------ 3 | # Copyright (c) Microsoft 4 | # Licensed under the MIT License. 5 | # Written by Bin Xiao (Bin.Xiao@microsoft.com) 6 | # Modified by Ke Sun (sunk@mail.ustc.edu.cn) 7 | # ------------------------------------------------------------------------------ 8 | 9 | from __future__ import absolute_import 10 | from __future__ import division 11 | from __future__ import print_function 12 | 13 | import os 14 | 15 | from yacs.config import CfgNode as CN 16 | 17 | 18 | _C = CN() 19 | 20 | _C.OUTPUT_DIR = '' 21 | _C.LOG_DIR = '' 22 | _C.DATA_DIR = '' 23 | _C.GPUS = (0,) 24 | _C.WORKERS = 4 25 | _C.PRINT_FREQ = 20 26 | _C.AUTO_RESUME = False 27 | _C.PIN_MEMORY = True 28 | _C.RANK = 0 29 | 30 | # Cudnn related params 31 | _C.CUDNN = CN() 32 | _C.CUDNN.BENCHMARK = True 33 | _C.CUDNN.DETERMINISTIC = False 34 | _C.CUDNN.ENABLED = True 35 | 36 | # common params for NETWORK 37 | _C.MODEL = CN() 38 | _C.MODEL.NAME = 'cls_hrnet' 39 | _C.MODEL.INIT_WEIGHTS = True 40 | _C.MODEL.PRETRAINED = '' 41 | _C.MODEL.NUM_JOINTS = 17 42 | _C.MODEL.NUM_CLASSES = 1000 43 | _C.MODEL.TAG_PER_JOINT = True 44 | _C.MODEL.TARGET_TYPE = 'gaussian' 45 | _C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 46 | _C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 47 | _C.MODEL.SIGMA = 2 48 | _C.MODEL.EXTRA = CN(new_allowed=True) 49 | 50 | _C.LOSS = CN() 51 | _C.LOSS.USE_OHKM = False 52 | _C.LOSS.TOPK = 8 53 | _C.LOSS.USE_TARGET_WEIGHT = True 54 | _C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False 55 | 56 | # DATASET related params 57 | _C.DATASET = CN() 58 | _C.DATASET.ROOT = '' 59 | _C.DATASET.DATASET = 'mpii' 60 | _C.DATASET.TRAIN_SET = 'train' 61 | _C.DATASET.TEST_SET = 'valid' 62 | _C.DATASET.DATA_FORMAT = 'jpg' 63 | _C.DATASET.HYBRID_JOINTS_TYPE = '' 64 | _C.DATASET.SELECT_DATA = False 65 | 66 | # training data augmentation 67 | _C.DATASET.FLIP = True 68 | _C.DATASET.SCALE_FACTOR = 0.25 69 | _C.DATASET.ROT_FACTOR = 30 70 | _C.DATASET.PROB_HALF_BODY = 0.0 71 | _C.DATASET.NUM_JOINTS_HALF_BODY = 8 72 | _C.DATASET.COLOR_RGB = False 73 | 74 | # train 75 | _C.TRAIN = CN() 76 | 77 | _C.TRAIN.LR_FACTOR = 0.1 78 | _C.TRAIN.LR_STEP = [90, 110] 79 | _C.TRAIN.LR = 0.001 80 | 81 | _C.TRAIN.OPTIMIZER = 'adam' 82 | _C.TRAIN.MOMENTUM = 0.9 83 | _C.TRAIN.WD = 0.0001 84 | _C.TRAIN.NESTEROV = False 85 | _C.TRAIN.GAMMA1 = 0.99 86 | _C.TRAIN.GAMMA2 = 0.0 87 | 88 | _C.TRAIN.BEGIN_EPOCH = 0 89 | _C.TRAIN.END_EPOCH = 140 90 | 91 | _C.TRAIN.RESUME = False 92 | _C.TRAIN.CHECKPOINT = '' 93 | 94 | _C.TRAIN.BATCH_SIZE_PER_GPU = 32 95 | _C.TRAIN.SHUFFLE = True 96 | 97 | # testing 98 | _C.TEST = CN() 99 | 100 | # size of images for each device 101 | _C.TEST.BATCH_SIZE_PER_GPU = 32 102 | # Test Model Epoch 103 | _C.TEST.FLIP_TEST = False 104 | _C.TEST.POST_PROCESS = False 105 | _C.TEST.SHIFT_HEATMAP = False 106 | 107 | _C.TEST.USE_GT_BBOX = False 108 | 109 | # nms 110 | _C.TEST.IMAGE_THRE = 0.1 111 | _C.TEST.NMS_THRE = 0.6 112 | _C.TEST.SOFT_NMS = False 113 | _C.TEST.OKS_THRE = 0.5 114 | _C.TEST.IN_VIS_THRE = 0.0 115 | _C.TEST.COCO_BBOX_FILE = '' 116 | _C.TEST.BBOX_THRE = 1.0 117 | _C.TEST.MODEL_FILE = '' 118 | 119 | # debug 120 | _C.DEBUG = CN() 121 | _C.DEBUG.DEBUG = False 122 | _C.DEBUG.SAVE_BATCH_IMAGES_GT = False 123 | _C.DEBUG.SAVE_BATCH_IMAGES_PRED = False 124 | _C.DEBUG.SAVE_HEATMAPS_GT = False 125 | _C.DEBUG.SAVE_HEATMAPS_PRED = False 126 | 127 | 128 | if __name__ == '__main__': 129 | import sys 130 | with open(sys.argv[1], 'w') as f: 131 | print(_C, file=f) 132 | 133 | -------------------------------------------------------------------------------- /config/models.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------------ 2 | # Copyright (c) Microsoft 3 | # Licensed under the MIT License. 4 | # Create by Bin Xiao (Bin.Xiao@microsoft.com) 5 | # Modified by Ke Sun (sunk@mail.ustc.edu.cn) 6 | # ------------------------------------------------------------------------------ 7 | 8 | from __future__ import absolute_import 9 | from __future__ import division 10 | from __future__ import print_function 11 | 12 | from yacs.config import CfgNode as CN 13 | 14 | # high_resoluton_net related params for classification 15 | POSE_HIGH_RESOLUTION_NET = CN() 16 | POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*'] 17 | POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64 18 | POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1 19 | POSE_HIGH_RESOLUTION_NET.WITH_HEAD = True 20 | 21 | POSE_HIGH_RESOLUTION_NET.STAGE2 = CN() 22 | POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1 23 | POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2 24 | POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4] 25 | POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64] 26 | POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC' 27 | POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM' 28 | 29 | POSE_HIGH_RESOLUTION_NET.STAGE3 = CN() 30 | POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1 31 | POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3 32 | POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4] 33 | POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128] 34 | POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC' 35 | POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM' 36 | 37 | POSE_HIGH_RESOLUTION_NET.STAGE4 = CN() 38 | POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1 39 | POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4 40 | POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] 41 | POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] 42 | POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC' 43 | POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM' 44 | 45 | MODEL_EXTRAS = { 46 | 'cls_hrnet': POSE_HIGH_RESOLUTION_NET, 47 | } 48 | -------------------------------------------------------------------------------- /data_loader.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import print_function 3 | from __future__ import division 4 | 5 | import os 6 | from PIL import Image 7 | import numpy as np 8 | import os.path as osp 9 | import io 10 | import random 11 | import torch 12 | from torch.utils.data import Dataset 13 | from torchvision import transforms 14 | from torchvision.transforms import * 15 | from torchvision.transforms import functional as F 16 | def read_image(img_path): 17 | """Keep reading image until succeed. 18 | This can avoid IOError incurred by heavy IO process.""" 19 | got_img = False 20 | if not osp.exists(img_path): 21 | raise IOError("{} does not exist".format(img_path)) 22 | while not got_img: 23 | try: 24 | img = Image.open(img_path).convert('RGB') 25 | got_img = True 26 | except IOError: 27 | print("IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.".format(img_path)) 28 | pass 29 | return img 30 | 31 | class ImageDatasettest(Dataset): 32 | """Image Person ReID Dataset""" 33 | def __init__(self, dataset, transform=None): 34 | self.dataset = dataset 35 | self.transform = transform 36 | 37 | def __len__(self): 38 | return len(self.dataset) 39 | 40 | def __getitem__(self, index): 41 | img_path, pid, camid = self.dataset[index] 42 | img = read_image(img_path) 43 | # print(img_path) 44 | if self.transform is not None: 45 | img = self.transform(img) 46 | 47 | return img, pid, camid 48 | 49 | 50 | class ImageDatasettrain(Dataset): 51 | """Image Person ReID Dataset""" 52 | def __init__(self, dataset, height, width): 53 | self.dataset = dataset 54 | self.height=height 55 | self.width=width 56 | def __len__(self): 57 | return len(self.dataset) 58 | 59 | def __getitem__(self, index): 60 | img_path, pid, _ = self.dataset[index] 61 | img1 = read_image(img_path) 62 | path2=img_path.split('/')[0].replace('mlr_duke','duke') 63 | #path2=img_path.split('/')[0].replace('mlr_cuhk03','cuhk03') 64 | #path2=img_path.split('/')[0].replace('mlr_market1501','market1501') 65 | img2=read_image(path2) 66 | 67 | width, height = img1.size 68 | resolution=(width*1.0)/self.width 69 | Random2DT=Random2DTranslation(self.height,self.width) 70 | RandomHor=RandomHorizontalFlip2() 71 | Pad = transforms.Pad(10) 72 | Resize = transforms.Resize([256,128]) 73 | toten=ToTensor() 74 | normm=Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 75 | 76 | img1 = Resize(img1) 77 | img2 = Resize(img2) 78 | img1,img2=RandomHor(img1,img2) 79 | img1 = Pad(img1) 80 | img2 = Pad(img2) 81 | img1,img2=Random2DT.twoimages(img1,img2) 82 | img1,img2=toten(img1),toten(img2) 83 | img1,img2=normm(img1),normm(img2) 84 | return img1, img2, pid ,img_path ,path2 85 | 86 | 87 | 88 | class RandomHorizontalFlip2(object): 89 | 90 | def __init__(self, p=0.5): 91 | self.p = p 92 | 93 | def __call__(self, img1,img2): 94 | 95 | if random.random() < self.p: 96 | return F.hflip(img1),F.hflip(img2) 97 | return img1,img2 98 | 99 | 100 | 101 | class Random2DTranslation(object): 102 | """ 103 | With a probability, first increase image size to (1 + 1/8), and then perform random crop. 104 | 105 | Args: 106 | - height (int): target height. 107 | - width (int): target width. 108 | - p (float): probability of performing this transformation. Default: 0.5. 109 | """ 110 | def __init__(self, height, width, p=1, interpolation=Image.BILINEAR): 111 | self.height = height 112 | self.width = width 113 | self.p = p 114 | self.interpolation = interpolation 115 | 116 | def __call__(self, img): 117 | """ 118 | Args: 119 | - img (PIL Image): Image to be cropped. 120 | """ 121 | #resized_img = img.resize((new_width, new_height), self.interpolation) 122 | x_maxrange = 20 123 | y_maxrange = 20 124 | x1 = int(round(random.uniform(0, x_maxrange))) 125 | y1 = int(round(random.uniform(0, y_maxrange))) 126 | croped_img = img.crop((x1, y1, x1 + self.width, y1 + self.height)) 127 | return croped_img 128 | 129 | def twoimages(self, img1,img2): 130 | """ 131 | Args: 132 | - img (PIL Image): Image to be cropped. 133 | """ 134 | x_maxrange = 20 135 | y_maxrange = 20 136 | x1 = int(round(random.uniform(0, x_maxrange))) 137 | y1 = int(round(random.uniform(0, y_maxrange))) 138 | croped_img1 = img1.crop((x1, y1, x1 + self.width, y1 + self.height)) 139 | croped_img2 = img2.crop((x1, y1, x1 + self.width, y1 + self.height)) 140 | return croped_img1, croped_img2 141 | -------------------------------------------------------------------------------- /data_loader_v2.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import print_function 3 | from __future__ import division 4 | 5 | import os 6 | from PIL import Image 7 | import numpy as np 8 | import os.path as osp 9 | import io 10 | import random 11 | import torch 12 | from torch.utils.data import Dataset 13 | from torchvision import transforms 14 | from torchvision.transforms import * 15 | from torchvision.transforms import functional as F 16 | def read_image(img_path): 17 | """Keep reading image until succeed. 18 | This can avoid IOError incurred by heavy IO process.""" 19 | got_img = False 20 | if not osp.exists(img_path): 21 | raise IOError("{} does not exist".format(img_path)) 22 | while not got_img: 23 | try: 24 | img = Image.open(img_path).convert('RGB') 25 | got_img = True 26 | except IOError: 27 | print("IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.".format(img_path)) 28 | pass 29 | return img 30 | 31 | class ImageDatasettest(Dataset): 32 | """Image Person ReID Dataset""" 33 | def __init__(self, dataset, transform=None): 34 | self.dataset = dataset 35 | self.transform = transform 36 | 37 | def __len__(self): 38 | return len(self.dataset) 39 | 40 | def __getitem__(self, index): 41 | img_path, pid, camid = self.dataset[index] 42 | img = read_image(img_path) 43 | # print(img_path) 44 | if self.transform is not None: 45 | img = self.transform(img) 46 | 47 | return img, pid, camid 48 | 49 | 50 | class ImageDatasettrain(Dataset): 51 | """Image Person ReID Dataset""" 52 | def __init__(self, dataset, height, width): 53 | self.dataset = dataset 54 | self.height=height 55 | self.width=width 56 | def __len__(self): 57 | return len(self.dataset) 58 | 59 | def __getitem__(self, index): 60 | img_path, pid, _ = self.dataset[index] 61 | img1 = read_image(img_path) 62 | path2=img_path.split('/')[0].replace('mlr_duke','duke') 63 | #path2=img_path.split('/')[0].replace('mlr_cuhk03','cuhk03') 64 | #path2=img_path.split('/')[0].replace('mlr_market1501','market1501') 65 | #path2=img_path.split('/')[0].replace('mlr_vp_v1','vp') 66 | #path2=path2.replace('bmp','png') 67 | #path2=img_path.split('/')[0].replace('mlr_caviar','caviar') 68 | img2=read_image(path2) 69 | 70 | width, height = img1.size 71 | resolution=(width*1.0)/self.width 72 | Random2DT=Random2DTranslation(self.height,self.width) 73 | RandomHor=RandomHorizontalFlip2() 74 | Pad = transforms.Pad(10) 75 | Resize = transforms.Resize([256,128]) 76 | toten=ToTensor() 77 | normm=Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 78 | 79 | img1 = Resize(img1) 80 | img2 = Resize(img2) 81 | img1,img2=RandomHor(img1,img2) 82 | img1 = Pad(img1) 83 | img2 = Pad(img2) 84 | img1,img2=Random2DT.twoimages(img1,img2) 85 | img1,img2=toten(img1),toten(img2) 86 | img1,img2=normm(img1),normm(img2) 87 | return img1, img2, pid ,img_path ,path2 88 | 89 | 90 | 91 | class RandomHorizontalFlip2(object): 92 | 93 | def __init__(self, p=0.5): 94 | self.p = p 95 | 96 | def __call__(self, img1,img2): 97 | 98 | if random.random() < self.p: 99 | return F.hflip(img1),F.hflip(img2) 100 | return img1,img2 101 | 102 | 103 | 104 | class Random2DTranslation(object): 105 | """ 106 | With a probability, first increase image size to (1 + 1/8), and then perform random crop. 107 | 108 | Args: 109 | - height (int): target height. 110 | - width (int): target width. 111 | - p (float): probability of performing this transformation. Default: 0.5. 112 | """ 113 | def __init__(self, height, width, p=1, interpolation=Image.BILINEAR): 114 | self.height = height 115 | self.width = width 116 | self.p = p 117 | self.interpolation = interpolation 118 | 119 | def __call__(self, img): 120 | """ 121 | Args: 122 | - img (PIL Image): Image to be cropped. 123 | """ 124 | #resized_img = img.resize((new_width, new_height), self.interpolation) 125 | x_maxrange = 20 126 | y_maxrange = 20 127 | x1 = int(round(random.uniform(0, x_maxrange))) 128 | y1 = int(round(random.uniform(0, y_maxrange))) 129 | croped_img = img.crop((x1, y1, x1 + self.width, y1 + self.height)) 130 | return croped_img 131 | 132 | def twoimages(self, img1,img2): 133 | """ 134 | Args: 135 | - img (PIL Image): Image to be cropped. 136 | """ 137 | x_maxrange = 20 138 | y_maxrange = 20 139 | x1 = int(round(random.uniform(0, x_maxrange))) 140 | y1 = int(round(random.uniform(0, y_maxrange))) 141 | croped_img1 = img1.crop((x1, y1, x1 + self.width, y1 + self.height)) 142 | croped_img2 = img2.crop((x1, y1, x1 + self.width, y1 + self.height)) 143 | return croped_img1, croped_img2 144 | -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import os 6 | import glob 7 | import re 8 | import sys 9 | import urllib 10 | import tarfile 11 | import zipfile 12 | import os.path as osp 13 | from scipy.io import loadmat 14 | import numpy as np 15 | import h5py 16 | from scipy.misc import imsave 17 | 18 | 19 | class Market1501(object): 20 | 21 | #dataset_dir = 'mlr_market1501' 22 | #dataset_dir = 'mlr_cuhk03' 23 | dataset_dir=r'C:\Users\reID\Desktop\HRNet ReID\mlr_duke' 24 | 25 | def __init__(self, root='data', verbose=True, **kwargs): 26 | super(Market1501, self).__init__() 27 | self.dataset_dir = osp.join(root, self.dataset_dir) 28 | self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train') 29 | self.query_dir = osp.join(self.dataset_dir, 'query') 30 | self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test') 31 | 32 | self._check_before_run() 33 | 34 | train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True) 35 | query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False) 36 | gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False) 37 | num_total_pids = num_train_pids + num_query_pids 38 | num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs 39 | 40 | if verbose: 41 | print("=> Market1501 loaded") 42 | print("Dataset statistics:") 43 | print(" ------------------------------") 44 | print(" subset | # ids | # images") 45 | print(" ------------------------------") 46 | print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs)) 47 | print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs)) 48 | print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs)) 49 | print(" ------------------------------") 50 | print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs)) 51 | print(" ------------------------------") 52 | 53 | self.train = train 54 | self.query = query 55 | self.gallery = gallery 56 | 57 | self.num_train_pids = num_train_pids 58 | self.num_query_pids = num_query_pids 59 | self.num_gallery_pids = num_gallery_pids 60 | 61 | def _check_before_run(self): 62 | """Check if all files are available before going deeper""" 63 | if not osp.exists(self.dataset_dir): 64 | raise RuntimeError("'{}' is not available".format(self.dataset_dir)) 65 | if not osp.exists(self.train_dir): 66 | raise RuntimeError("'{}' is not available".format(self.train_dir)) 67 | if not osp.exists(self.query_dir): 68 | raise RuntimeError("'{}' is not available".format(self.query_dir)) 69 | if not osp.exists(self.gallery_dir): 70 | raise RuntimeError("'{}' is not available".format(self.gallery_dir)) 71 | 72 | def _process_dir(self, dir_path, relabel=False): 73 | img_paths = glob.glob(osp.join(dir_path, '*.jpg')) 74 | pattern = re.compile(r'([-\d]+)_c(\d)') 75 | #pattern = re.compile(r'([-\d]+)c(\d)') 76 | 77 | pid_container = set() 78 | for img_path in img_paths: 79 | pid, _ = map(int, pattern.search(img_path).groups()) 80 | if pid == -1: continue # junk images are just ignored 81 | pid_container.add(pid) 82 | pid2label = {pid:label for label, pid in enumerate(pid_container)} 83 | 84 | dataset = [] 85 | for img_path in img_paths: 86 | pid, camid = map(int, pattern.search(img_path).groups()) 87 | if pid == -1: continue # junk images are just ignored 88 | #assert 0 <= pid <= 1501 # pid == 0 means background 89 | #assert 1 <= camid <= 6 90 | assert 0<=pid<=7140 91 | assert 1<=camid<=8 92 | camid -= 1 # index starts from 0 93 | if relabel: pid = pid2label[pid] 94 | dataset.append((img_path, pid, camid)) 95 | 96 | num_pids = len(pid_container) 97 | num_imgs = len(dataset) 98 | return dataset, num_pids, num_imgs -------------------------------------------------------------------------------- /dataset_v2.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import os 6 | import glob 7 | import re 8 | import sys 9 | import urllib 10 | import tarfile 11 | import zipfile 12 | import os.path as osp 13 | from scipy.io import loadmat 14 | import numpy as np 15 | import h5py 16 | from scipy.misc import imsave 17 | 18 | 19 | class Market1501(object): 20 | 21 | #dataset_dir = 'mlr_market1501' 22 | #dataset_dir=r'C:\Users\reID\Desktop\HRNet ReID\mlr_vp_v1' 23 | #dataset_dir='mlr_caviar' 24 | dataset_dir=r'C:\Users\reID\Desktop\HRNet ReID\mlr_duke' 25 | #dataset_dir='mlr_cuhk03' 26 | 27 | def __init__(self, root='data', verbose=True, **kwargs): 28 | super(Market1501, self).__init__() 29 | self.dataset_dir = osp.join(root, self.dataset_dir) 30 | self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train') 31 | self.query_dir = osp.join(self.dataset_dir, 'query') 32 | self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test') 33 | 34 | self._check_before_run() 35 | 36 | train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True) 37 | query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False) 38 | gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False) 39 | num_total_pids = num_train_pids + num_query_pids 40 | num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs 41 | 42 | if verbose: 43 | print("=> Market1501 loaded") 44 | print("Dataset statistics:") 45 | print(" ------------------------------") 46 | print(" subset | # ids | # images") 47 | print(" ------------------------------") 48 | print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs)) 49 | print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs)) 50 | print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs)) 51 | print(" ------------------------------") 52 | print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs)) 53 | print(" ------------------------------") 54 | 55 | self.train = train 56 | self.query = query 57 | self.gallery = gallery 58 | 59 | self.num_train_pids = num_train_pids 60 | self.num_query_pids = num_query_pids 61 | self.num_gallery_pids = num_gallery_pids 62 | 63 | def _check_before_run(self): 64 | """Check if all files are available before going deeper""" 65 | if not osp.exists(self.dataset_dir): 66 | raise RuntimeError("'{}' is not available".format(self.dataset_dir)) 67 | if not osp.exists(self.train_dir): 68 | raise RuntimeError("'{}' is not available".format(self.train_dir)) 69 | if not osp.exists(self.query_dir): 70 | raise RuntimeError("'{}' is not available".format(self.query_dir)) 71 | if not osp.exists(self.gallery_dir): 72 | raise RuntimeError("'{}' is not available".format(self.gallery_dir)) 73 | 74 | def _process_dir(self, dir_path, relabel=False): 75 | img_paths = glob.glob(osp.join(dir_path, '*.jpg')) 76 | pattern = re.compile(r'([-\d]+)_c(\d)') 77 | #pattern = re.compile(r'([-\d]+)c(\d)') 78 | #pattern = re.compile(r'([-\d]+)') 79 | 80 | pid_container = set() 81 | for img_path in img_paths: 82 | #pid =img_path.split('\\')[-1] 83 | #pid=int(pid[0:4]) 84 | pid, _ = map(int, pattern.search(img_path).groups()) 85 | if pid == -1: continue # junk images are just ignored 86 | pid_container.add(pid) 87 | pid2label = {pid:label for label, pid in enumerate(pid_container)} 88 | 89 | dataset = [] 90 | for img_path in img_paths: 91 | #pid =img_path.split('\\')[-1] 92 | #camid=int(pid[5:7]) 93 | #camid=pid.split('_')[2] 94 | #camid=int(camid) 95 | #print(camid) 96 | #pid=int(pid[0:4]) 97 | #print(pid) 98 | pid, camid = map(int, pattern.search(img_path).groups()) 99 | #if pid == -1: continue # junk images are just ignored 100 | #assert 0 <= pid <= 1501 # pid == 0 means background 101 | #assert 1 <= camid <= 6 102 | #assert 0<=pid<=7140 103 | #assert 1<=camid<=8 104 | camid -= 1 # index starts from 0 105 | if relabel: pid = pid2label[pid] 106 | dataset.append((img_path, pid, camid)) 107 | 108 | num_pids = len(pid_container) 109 | num_imgs = len(dataset) 110 | return dataset, num_pids, num_imgs -------------------------------------------------------------------------------- /double_samplers.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | 4 | from collections import defaultdict 5 | import numpy as np 6 | import copy 7 | import random 8 | 9 | import torch 10 | from torch.utils.data.sampler import Sampler 11 | 12 | 13 | class RandomIdentitySampler(Sampler): 14 | """ 15 | Randomly sample N identities, then for each identity, 16 | randomly sample K instances, therefore batch size is N*K. 17 | 18 | Args: 19 | - data_source (Dataset): dataset to sample from. 20 | - num_instances (int): number of instances per identity in a batch. 21 | - batch_size (int): number of examples in a batch. 22 | """ 23 | def __init__(self, data_source, batch_size, num_instances): 24 | self.data_source = data_source # train_all数据集:751个id 25 | self.batch_size = batch_size # 每个批次图片总数 26 | self.num_instances = num_instances # 每个id选取的图片张数 27 | self.num_pids_per_batch = self.batch_size // self.num_instances # 每个批次包含多少id 28 | self.index_dic = defaultdict(list) # 创建index字典 29 | for index, (_, _, pid ,_ ,_) in enumerate(self.data_source): # pid是list，pid=[0,1,2,3,...,750]作为index_dic的keys 30 | self.index_dic[pid].append(index) # keys对应的value为train_all中每个pid对应的图片序号 31 | self.pids = list(self.index_dic.keys()) 32 | # 计算采样后数据集样本总数：self.length=3004=751*4 33 | self.length = 0 34 | for pid in self.pids: 35 | idxs = self.index_dic[pid] 36 | num = len(idxs) 37 | if num < self.num_instances: 38 | num = self.num_instances 39 | self.length += num - num % self.num_instances 40 | 41 | def __iter__(self): 42 | # __iter__()函数中具体说明了图片采样的过程 43 | batch_idxs_dict = defaultdict(list) 44 | 45 | for pid in self.pids: 46 | idxs = copy.deepcopy(self.index_dic[pid]) 47 | if len(idxs) < self.num_instances: 48 | idxs = np.random.choice(idxs, size=self.num_instances, replace=True) 49 | random.shuffle(idxs) 50 | batch_idxs = [] 51 | for idx in idxs: 52 | batch_idxs.append(idx) 53 | if len(batch_idxs) == self.num_instances: 54 | batch_idxs_dict[pid].append(batch_idxs) 55 | batch_idxs = [] 56 | 57 | avai_pids = copy.deepcopy(self.pids) 58 | final_idxs = [] 59 | 60 | while len(avai_pids) >= self.num_pids_per_batch: 61 | selected_pids = random.sample(avai_pids, self.num_pids_per_batch) 62 | for pid in selected_pids: 63 | batch_idxs = batch_idxs_dict[pid].pop(0) 64 | final_idxs.extend(batch_idxs) 65 | if len(batch_idxs_dict[pid]) == 0: 66 | avai_pids.remove(pid) 67 | 68 | return iter(final_idxs) 69 | 70 | def __len__(self): 71 | # 返回采样处理后数据集包含的图片数：3004 72 | return self.length -------------------------------------------------------------------------------- /lr_scheduler.py: -------------------------------------------------------------------------------- 1 | # encoding: utf-8 2 | """ 3 | @author: sherlock 4 | @contact: sherlockliao01@gmail.com 5 | """ 6 | 7 | from __future__ import absolute_import 8 | from __future__ import division 9 | from __future__ import print_function 10 | from __future__ import unicode_literals 11 | 12 | 13 | class LRScheduler(object): 14 | """Base class of a learning rate scheduler. 15 | 16 | A scheduler returns a new learning rate based on the number of updates that have 17 | been performed. 18 | 19 | Parameters 20 | ---------- 21 | base_lr : float, optional 22 | The initial learning rate. 23 | warmup_epoch: int 24 | number of warmup steps used before this scheduler starts decay 25 | warmup_begin_lr: float 26 | if using warmup, the learning rate from which it starts warming up 27 | warmup_mode: string 28 | warmup can be done in two modes. 29 | 'linear' mode gradually increases lr with each step in equal increments 30 | 'constant' mode keeps lr at warmup_begin_lr for warmup_steps 31 | """ 32 | 33 | def __init__(self, base_lr=0.01, step=(30, 60), factor=0.1, 34 | warmup_epoch=0, warmup_begin_lr=0, warmup_mode='linear'): 35 | self.base_lr = base_lr 36 | self.learning_rate = base_lr 37 | self.step = step 38 | self.factor = factor 39 | assert isinstance(warmup_epoch, int) 40 | self.warmup_epoch = warmup_epoch 41 | 42 | self.warmup_final_lr = base_lr 43 | self.warmup_begin_lr = warmup_begin_lr 44 | if self.warmup_begin_lr > self.warmup_final_lr: 45 | raise ValueError("Base lr has to be higher than warmup_begin_lr") 46 | if self.warmup_epoch < 0: 47 | raise ValueError("Warmup steps has to be positive or 0") 48 | if warmup_mode not in ['linear', 'constant']: 49 | raise ValueError("Supports only linear and constant modes of warmup") 50 | self.warmup_mode = warmup_mode 51 | 52 | def update(self, num_epoch): 53 | if self.warmup_epoch > num_epoch: 54 | # warmup strategy 55 | if self.warmup_mode == 'linear': 56 | self.learning_rate = self.warmup_begin_lr + (self.warmup_final_lr - self.warmup_begin_lr) * \ 57 | num_epoch / self.warmup_epoch 58 | elif self.warmup_mode == 'constant': 59 | self.learning_rate = self.warmup_begin_lr 60 | 61 | else: 62 | count = sum([1 for s in self.step if s <= num_epoch]) 63 | self.learning_rate = self.base_lr * pow(self.factor, count) 64 | return self.learning_rate 65 | -------------------------------------------------------------------------------- /prepare.py: -------------------------------------------------------------------------------- 1 | import os 2 | from shutil import copyfile 3 | 4 | # You only need to change this line to your dataset download path 5 | download_path = r'C:\Users\reID\Desktop\Duke' 6 | 7 | if not os.path.isdir(download_path): 8 | print('please change the download_path') 9 | 10 | save_path = download_path + '/pytorch' 11 | if not os.path.isdir(save_path): 12 | os.mkdir(save_path) 13 | #----------------------------------------- 14 | #query 15 | query_path = download_path + '/query' 16 | query_save_path = download_path + '/pytorch/query' 17 | if not os.path.isdir(query_save_path): 18 | os.mkdir(query_save_path) 19 | 20 | for root, dirs, files in os.walk(query_path, topdown=True): 21 | for name in files: 22 | if not name[-3:]=='bmp': 23 | continue 24 | ID = name.split('_') 25 | src_path = query_path + '/' + name 26 | dst_path = query_save_path + '/' + ID[0] 27 | if not os.path.isdir(dst_path): 28 | os.mkdir(dst_path) 29 | copyfile(src_path, dst_path + '/' + name) 30 | 31 | #----------------------------------------- 32 | #multi-query 33 | query_path = download_path + '/gt_bbox' 34 | query_save_path = download_path + '/pytorch/multi-query' 35 | if not os.path.isdir(query_save_path): 36 | os.mkdir(query_save_path) 37 | 38 | for root, dirs, files in os.walk(query_path, topdown=True): 39 | for name in files: 40 | if not name[-3:]=='bmp': 41 | continue 42 | ID = name.split('_') 43 | src_path = query_path + '/' + name 44 | dst_path = query_save_path + '/' + ID[0] 45 | if not os.path.isdir(dst_path): 46 | os.mkdir(dst_path) 47 | copyfile(src_path, dst_path + '/' + name) 48 | 49 | #----------------------------------------- 50 | #gallery 51 | gallery_path = download_path + '/bounding_box_test' 52 | gallery_save_path = download_path + '/pytorch/gallery' 53 | if not os.path.isdir(gallery_save_path): 54 | os.mkdir(gallery_save_path) 55 | 56 | for root, dirs, files in os.walk(gallery_path, topdown=True): 57 | for name in files: 58 | if not name[-3:]=='bmp': 59 | continue 60 | ID = name.split('_') 61 | src_path = gallery_path + '/' + name 62 | dst_path = gallery_save_path + '/' + ID[0] 63 | if not os.path.isdir(dst_path): 64 | os.mkdir(dst_path) 65 | copyfile(src_path, dst_path + '/' + name) 66 | 67 | #--------------------------------------- 68 | #train_all 69 | train_path = download_path + '/bounding_box_train' 70 | train_save_path = download_path + '/pytorch/train_all' 71 | if not os.path.isdir(train_save_path): 72 | os.mkdir(train_save_path) 73 | 74 | for root, dirs, files in os.walk(train_path, topdown=True): 75 | for name in files: 76 | if not name[-3:]=='bmp': 77 | continue 78 | ID = name.split('_') 79 | src_path = train_path + '/' + name 80 | dst_path = train_save_path + '/' + ID[0] 81 | if not os.path.isdir(dst_path): 82 | os.mkdir(dst_path) 83 | copyfile(src_path, dst_path + '/' + name) 84 | 85 | 86 | #--------------------------------------- 87 | #train_val 88 | train_path = download_path + '/bounding_box_train' 89 | train_save_path = download_path + '/pytorch/train' 90 | val_save_path = download_path + '/pytorch/val' 91 | if not os.path.isdir(train_save_path): 92 | os.mkdir(train_save_path) 93 | os.mkdir(val_save_path) 94 | 95 | for root, dirs, files in os.walk(train_path, topdown=True): 96 | for name in files: 97 | if not name[-3:]=='bmp': 98 | continue 99 | ID = name.split('_') 100 | src_path = train_path + '/' + name 101 | dst_path = train_save_path + '/' + ID[0] 102 | if not os.path.isdir(dst_path): 103 | os.mkdir(dst_path) 104 | dst_path = val_save_path + '/' + ID[0] #first image is used as val image 105 | os.mkdir(dst_path) 106 | copyfile(src_path, dst_path + '/' + name) 107 | -------------------------------------------------------------------------------- /random_erasing.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from torchvision.transforms import * 4 | 5 | from PIL import Image 6 | import random 7 | import math 8 | import numpy as np 9 | import torch 10 | 11 | class RandomErasing(object): 12 | """ Randomly selects a rectangle region in an image and erases its pixels. 13 | 'Random Erasing Data Augmentation' by Zhong et al. 14 | See https://arxiv.org/pdf/1708.04896.pdf 15 | Args: 16 | probability: The probability that the Random Erasing operation will be performed. 17 | sl: Minimum proportion of erased area against input image. 18 | sh: Maximum proportion of erased area against input image. 19 | r1: Minimum aspect ratio of erased area. 20 | mean: Erasing value. 21 | """ 22 | 23 | def __init__(self, probability = 0.5, sl = 0.02, sh = 0.4, r1 = 0.3, mean=[0.485, 0.456, 0.406]): 24 | self.probability = probability 25 | self.mean = mean 26 | self.sl = sl 27 | self.sh = sh 28 | self.r1 = r1 29 | 30 | def __call__(self, img): 31 | 32 | if random.uniform(0, 1) > self.probability: 33 | return img 34 | 35 | for attempt in range(100): 36 | area = img.size()[1] * img.size()[2] 37 | 38 | target_area = random.uniform(self.sl, self.sh) * area 39 | aspect_ratio = random.uniform(self.r1, 1/self.r1) 40 | 41 | h = int(round(math.sqrt(target_area * aspect_ratio))) 42 | w = int(round(math.sqrt(target_area / aspect_ratio))) 43 | 44 | if w < img.size()[2] and h < img.size()[1]: 45 | x1 = random.randint(0, img.size()[1] - h) 46 | y1 = random.randint(0, img.size()[2] - w) 47 | if img.size()[0] == 3: 48 | img[0, x1:x1+h, y1:y1+w] = self.mean[0] 49 | img[1, x1:x1+h, y1:y1+w] = self.mean[1] 50 | img[2, x1:x1+h, y1:y1+w] = self.mean[2] 51 | else: 52 | img[0, x1:x1+h, y1:y1+w] = self.mean[0] 53 | return img 54 | 55 | return img 56 | -------------------------------------------------------------------------------- /re_ranking.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2/python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Mon Jun 26 14:46:56 2017 5 | @author: luohao 6 | Modified by Houjing Huang, 2017-12-22. 7 | - This version accepts distance matrix instead of raw features. 8 | - The difference of `/` division between python 2 and 3 is handled. 9 | - numpy.float16 is replaced by numpy.float32 for numerical precision. 10 | 11 | Modified by Zhedong Zheng, 2018-1-12. 12 | - replace sort with topK, which save about 30s. 13 | """ 14 | 15 | """ 16 | CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017. 17 | url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf 18 | Matlab version: https://github.com/zhunzhong07/person-re-ranking 19 | """ 20 | 21 | """ 22 | API 23 | q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery] 24 | q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query] 25 | g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery] 26 | k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3) 27 | Returns: 28 | final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery] 29 | """ 30 | 31 | 32 | import numpy as np 33 | 34 | def k_reciprocal_neigh( initial_rank, i, k1): 35 | forward_k_neigh_index = initial_rank[i,:k1+1] 36 | backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1] 37 | fi = np.where(backward_k_neigh_index==i)[0] 38 | return forward_k_neigh_index[fi] 39 | 40 | def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3): 41 | # The following naming, e.g. gallery_num, is different from outer scope. 42 | # Don't care about it. 43 | original_dist = np.concatenate( 44 | [np.concatenate([q_q_dist, q_g_dist], axis=1), 45 | np.concatenate([q_g_dist.T, g_g_dist], axis=1)], 46 | axis=0) 47 | original_dist = 2. - 2 * original_dist #np.power(original_dist, 2).astype(np.float32) 48 | original_dist = np.transpose(1. * original_dist/np.max(original_dist,axis = 0)) 49 | V = np.zeros_like(original_dist).astype(np.float32) 50 | #initial_rank = np.argsort(original_dist).astype(np.int32) 51 | # top K1+1 52 | initial_rank = np.argpartition( original_dist, range(1,k1+1) ) 53 | 54 | query_num = q_g_dist.shape[0] 55 | all_num = original_dist.shape[0] 56 | 57 | for i in range(all_num): 58 | # k-reciprocal neighbors 59 | k_reciprocal_index = k_reciprocal_neigh( initial_rank, i, k1) 60 | k_reciprocal_expansion_index = k_reciprocal_index 61 | for j in range(len(k_reciprocal_index)): 62 | candidate = k_reciprocal_index[j] 63 | candidate_k_reciprocal_index = k_reciprocal_neigh( initial_rank, candidate, int(np.around(k1/2))) 64 | if len(np.intersect1d(candidate_k_reciprocal_index,k_reciprocal_index))> 2./3*len(candidate_k_reciprocal_index): 65 | k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index,candidate_k_reciprocal_index) 66 | 67 | k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index) 68 | weight = np.exp(-original_dist[i,k_reciprocal_expansion_index]) 69 | V[i,k_reciprocal_expansion_index] = 1.*weight/np.sum(weight) 70 | 71 | original_dist = original_dist[:query_num,] 72 | if k2 != 1: 73 | V_qe = np.zeros_like(V,dtype=np.float32) 74 | for i in range(all_num): 75 | V_qe[i,:] = np.mean(V[initial_rank[i,:k2],:],axis=0) 76 | V = V_qe 77 | del V_qe 78 | del initial_rank 79 | invIndex = [] 80 | for i in range(all_num): 81 | invIndex.append(np.where(V[:,i] != 0)[0]) 82 | 83 | jaccard_dist = np.zeros_like(original_dist,dtype = np.float32) 84 | 85 | for i in range(query_num): 86 | temp_min = np.zeros(shape=[1,all_num],dtype=np.float32) 87 | indNonZero = np.where(V[i,:] != 0)[0] 88 | indImages = [] 89 | indImages = [invIndex[ind] for ind in indNonZero] 90 | for j in range(len(indNonZero)): 91 | temp_min[0,indImages[j]] = temp_min[0,indImages[j]]+ np.minimum(V[i,indNonZero[j]],V[indImages[j],indNonZero[j]]) 92 | jaccard_dist[i] = 1-temp_min/(2.-temp_min) 93 | 94 | final_dist = jaccard_dist*(1-lambda_value) + original_dist*lambda_value 95 | del original_dist 96 | del V 97 | del jaccard_dist 98 | final_dist = final_dist[:query_num,query_num:] 99 | return final_dist 100 | -------------------------------------------------------------------------------- /triplet_loss.py: -------------------------------------------------------------------------------- 1 | # encoding: utf-8 2 | import torch 3 | from typing import Tuple 4 | from torch import nn, Tensor 5 | import torch.nn.functional as F 6 | 7 | def normalize(x, axis=-1): 8 | """Normalizing to unit length along the specified dimension. 9 | Args: 10 | x: pytorch Variable 11 | Returns: 12 | x: pytorch Variable, same shape as input 13 | """ 14 | x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12) 15 | return x 16 | 17 | def euclidean_dist(x, y): 18 | """ 19 | 计算距离矩阵函数 20 | dist=a-b=sqrt(a^2+b^2-2ab) 21 | Args: 22 | x: pytorch Variable, with shape [m, d] 23 | y: pytorch Variable, with shape [n, d] 24 | Returns: 25 | dist: pytorch Variable, with shape [m, n] 26 | """ 27 | m, n = x.size(0), y.size(0) 28 | xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) 29 | yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t() 30 | dist = xx + yy 31 | dist.addmm_(1, -2, x, y.t()) 32 | dist = dist.clamp(min=1e-12).sqrt() # for numerical stability 33 | return dist 34 | 35 | def hard_example_mining(dist_mat, labels, return_inds=False): 36 | """For each anchor, find the hardest positive and negative sample. 37 | Args: 38 | dist_mat: pytorch Variable, pair wise distance between samples, shape [N, N] 39 | labels: pytorch LongTensor, with shape [N] 40 | return_inds: whether to return the indices. Save time if `False`(?) 41 | Returns: 42 | dist_ap: pytorch Variable, distance(anchor, positive); shape [N] 43 | dist_an: pytorch Variable, distance(anchor, negative); shape [N] 44 | p_inds: pytorch LongTensor, with shape [N]; 45 | indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1 46 | n_inds: pytorch LongTensor, with shape [N]; 47 | indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1 48 | NOTE: Only consider the case in which all labels have same num of samples, 49 | thus we can cope with all anchors in parallel. 50 | """ 51 | 52 | assert len(dist_mat.size()) == 2 53 | assert dist_mat.size(0) == dist_mat.size(1) 54 | N = dist_mat.size(0) 55 | 56 | # shape [N, N] 57 | is_pos = labels.expand(N, N).eq(labels.expand(N, N).t()) 58 | is_neg = labels.expand(N, N).ne(labels.expand(N, N).t()) 59 | 60 | # `dist_ap` means distance(anchor, positive) 61 | # both `dist_ap` and `relative_p_inds` with shape [N, 1] 62 | dist_ap, relative_p_inds = torch.max( 63 | dist_mat[is_pos].contiguous().view(N, -1), 1, keepdim=True) 64 | # `dist_an` means distance(anchor, negative) 65 | # both `dist_an` and `relative_n_inds` with shape [N, 1] 66 | dist_an, relative_n_inds = torch.min( 67 | dist_mat[is_neg].contiguous().view(N, -1), 1, keepdim=True) 68 | # shape [N] 69 | dist_ap = dist_ap.squeeze(1) 70 | dist_an = dist_an.squeeze(1) 71 | 72 | if return_inds: 73 | # shape [N, N] 74 | ind = (labels.new().resize_as_(labels) 75 | .copy_(torch.arange(0, N).long()) 76 | .unsqueeze(0).expand(N, N)) 77 | # shape [N, 1] 78 | p_inds = torch.gather( 79 | ind[is_pos].contiguous().view(N, -1), 1, relative_p_inds.data) 80 | n_inds = torch.gather( 81 | ind[is_neg].contiguous().view(N, -1), 1, relative_n_inds.data) 82 | # shape [N] 83 | p_inds = p_inds.squeeze(1) 84 | n_inds = n_inds.squeeze(1) 85 | return dist_ap, dist_an, p_inds, n_inds 86 | 87 | return dist_ap, dist_an 88 | 89 | 90 | class TripletLoss(object): 91 | """Modified from Tong Xiao's open-reid (https://github.com/Cysu/open-reid). 92 | Related Triplet Loss theory can be found in paper 'In Defense of the Triplet 93 | Loss for Person Re-Identification'.""" 94 | 95 | def __init__(self, margin=None): 96 | self.margin = margin 97 | if margin is not None: 98 | self.ranking_loss = nn.MarginRankingLoss(margin=margin) 99 | else: 100 | self.ranking_loss = nn.SoftMarginLoss() 101 | 102 | def __call__(self, global_feat, labels, normalize_feature=False): 103 | if normalize_feature: 104 | global_feat = normalize(global_feat, axis=-1) 105 | dist_mat = euclidean_dist(global_feat, global_feat) 106 | dist_ap, dist_an = hard_example_mining( 107 | dist_mat, labels) 108 | y = dist_an.new().resize_as_(dist_an).fill_(1) 109 | if self.margin is not None: 110 | loss = self.ranking_loss(dist_an, dist_ap, y) 111 | else: 112 | loss = self.ranking_loss(dist_an - dist_ap, y) 113 | return loss, dist_ap, dist_an 114 | 115 | class CrossEntropyLabelSmooth(nn.Module): 116 | """Cross entropy loss with label smoothing regularizer. 117 | 118 | Reference: 119 | Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016. 120 | Equation: y = (1 - epsilon) * y + epsilon / K. 121 | 122 | Args: 123 | num_classes (int): number of classes. 124 | epsilon (float): weight. 125 | """ 126 | def __init__(self, num_classes, epsilon=0.1, use_gpu=True): 127 | super(CrossEntropyLabelSmooth, self).__init__() 128 | self.num_classes = num_classes 129 | self.epsilon = epsilon 130 | self.use_gpu = use_gpu 131 | self.logsoftmax = nn.LogSoftmax(dim=1) 132 | 133 | def forward(self, inputs, targets): 134 | """ 135 | Args: 136 | inputs: prediction matrix (before softmax) with shape (batch_size, num_classes) 137 | targets: ground truth labels with shape (num_classes) 138 | """ 139 | log_probs = self.logsoftmax(inputs) 140 | targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1) 141 | if self.use_gpu: targets = targets.cuda() 142 | targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes 143 | loss = (- targets * log_probs).mean(0).sum() 144 | return loss 145 | 146 | ############################################ ################################################ 147 | 148 | def get_world_size() -> int: 149 | if not torch.distributed.is_available(): 150 | return 1 151 | if not torch.distributed.is_initialized(): 152 | return 1 153 | return torch.distributed.get_world_size() 154 | 155 | class CircleLoss(object): 156 | def __init__(self): 157 | self._scale = 1 158 | 159 | self.m = 0.25 160 | self.s = 128 161 | 162 | def __call__(self, embedding, targets): 163 | embedding = nn.functional.normalize(embedding, dim=1) 164 | 165 | if get_world_size() > 1: 166 | all_embedding = concat_all_gather(embedding) 167 | all_targets = concat_all_gather(targets) 168 | else: 169 | all_embedding = embedding 170 | all_targets = targets 171 | 172 | dist_mat = torch.matmul(embedding, all_embedding.t()) 173 | 174 | N, M = dist_mat.size() 175 | is_pos = targets.view(N, 1).expand(N, M).eq(all_targets.view(M, 1).expand(M, N).t()) 176 | is_neg = targets.view(N, 1).expand(N, M).ne(all_targets.view(M, 1).expand(M, N).t()) 177 | 178 | s_p = dist_mat[is_pos].contiguous().view(N, -1) 179 | s_n = dist_mat[is_neg].contiguous().view(N, -1) 180 | 181 | alpha_p = F.relu(-s_p.detach() + 1 + self.m) 182 | alpha_n = F.relu(s_n.detach() + self.m) 183 | delta_p = 1 - self.m 184 | delta_n = self.m 185 | 186 | logit_p = - self.s * alpha_p * (s_p - delta_p) 187 | logit_n = self.s * alpha_n * (s_n - delta_n) 188 | 189 | loss = nn.functional.softplus(torch.logsumexp(logit_p, dim=1) + torch.logsumexp(logit_n, dim=1)).mean() 190 | 191 | return loss * self._scale 192 | ##################################################################################################### 193 | 194 | 195 | class HardTripletLoss(nn.Module): 196 | """Triplet loss with hard positive/negative mining. 197 | 198 | Reference: 199 | Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737. 200 | 201 | Imported from ``_. 202 | 203 | Args: 204 | margin (float, optional): margin for triplet. Default is 0.3. 205 | """ 206 | 207 | def __init__(self, margin=0.3): 208 | super(HardTripletLoss, self).__init__() 209 | self.margin = margin 210 | self.ranking_loss = nn.MarginRankingLoss(margin=margin) 211 | 212 | def forward(self, inputs, targets): 213 | """ 214 | Args: 215 | inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim). 216 | targets (torch.LongTensor): ground truth labels with shape (num_classes). 217 | """ 218 | n = inputs.size(0) 219 | 220 | # Compute pairwise distance, replace by the official when merged 221 | dist = torch.pow(inputs, 2).sum(dim=1, keepdim=True).expand(n, n) 222 | dist = dist + dist.t() 223 | dist.addmm_(1, -2, inputs, inputs.t()) 224 | dist = dist.clamp(min=1e-12).sqrt() # for numerical stability 225 | 226 | # For each anchor, find the hardest positive and negative 227 | mask = targets.expand(n, n).eq(targets.expand(n, n).t()) 228 | dist_ap, dist_an = [], [] 229 | for i in range(n): 230 | dist_ap.append(dist[i][mask[i]].max().unsqueeze(0)) 231 | dist_an.append(dist[i][mask[i] == 0].min().unsqueeze(0)) 232 | dist_ap = torch.cat(dist_ap) 233 | dist_an = torch.cat(dist_an) 234 | 235 | # Compute ranking hinge loss 236 | y = torch.ones_like(dist_an) 237 | return self.ranking_loss(dist_an, dist_ap, y) 238 | 239 | ############################################################################################## 240 | 241 | 242 | class CenterLoss(nn.Module): 243 | """Center loss. 244 | Reference: 245 | Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016. 246 | Args: 247 | num_classes (int): number of classes. 248 | feat_dim (int): feature dimension. 249 | """ 250 | 251 | def __init__(self, num_classes=751, feat_dim=6144, use_gpu=True): 252 | super(CenterLoss, self).__init__() 253 | self.num_classes = num_classes 254 | self.feat_dim = feat_dim 255 | self.use_gpu = use_gpu 256 | 257 | if self.use_gpu: 258 | self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda()) 259 | else: 260 | self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim)) 261 | 262 | def forward(self, x, labels): 263 | """ 264 | Args: 265 | x: feature matrix with shape (batch_size, feat_dim). 266 | labels: ground truth labels with shape (num_classes). 267 | """ 268 | assert x.size(0) == labels.size(0), "features.size(0) is not equal to labels.size(0)" 269 | 270 | batch_size = x.size(0) 271 | distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \ 272 | torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t() 273 | distmat.addmm_(1, -2, x, self.centers.t()) 274 | 275 | classes = torch.arange(self.num_classes).long() 276 | if self.use_gpu: classes = classes.cuda() 277 | labels = labels.unsqueeze(1).expand(batch_size, self.num_classes) 278 | mask = labels.eq(classes.expand(batch_size, self.num_classes)) 279 | 280 | dist = distmat * mask.float() 281 | loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size 282 | #dist = [] 283 | #for i in range(batch_size): 284 | # value = distmat[i][mask[i]] 285 | # value = value.clamp(min=1e-12, max=1e+12) # for numerical stability 286 | # dist.append(value) 287 | #dist = torch.cat(dist) 288 | #loss = dist.mean() 289 | return loss --------------------------------------------------------------------------------