mirror of https://github.com/JDAI-CV/fast-reid.git
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import os.path as osp
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import numpy as np
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import torch.nn.functional as F
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import torch
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import random
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import re
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from PIL import Image
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from .data_utils import read_image
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from torch.utils.data import Dataset
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import torchvision.transforms as T
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class ReidDataset(Dataset):
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"""Image Person ReID Dataset"""
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def __init__(self, img_items, transform=None, relabel=True):
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self.tfms = transform
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self.relabel = relabel
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self.pid2label = None
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if self.relabel:
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self.img_items = []
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pids = set()
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for i, item in enumerate(img_items):
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pid = self.get_pids(item[0], item[1])
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self.img_items.append((item[0], pid, item[2])) # replace pid
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pids.add(pid)
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self.pids = pids
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self.pid2label = dict([(p, i) for i, p in enumerate(self.pids)])
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else:
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self.img_items = img_items
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@property
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def c(self):
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return len(self.pid2label) if self.pid2label is not None else 0
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def __len__(self):
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return len(self.img_items)
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def __getitem__(self, index):
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img_path, pid, camid = self.img_items[index]
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img = read_image(img_path)
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if self.tfms is not None: img = self.tfms(img)
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if self.relabel: pid = self.pid2label[pid]
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return {
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'images': img,
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'targets': pid,
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'camid': camid
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}
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def get_pids(self, file_path, pid):
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""" Suitable for muilti-dataset training """
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if 'cuhk03' in file_path:
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prefix = 'cuhk'
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else:
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prefix = file_path.split('/')[1]
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return prefix + '_' + str(pid)
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