mirror of https://github.com/JDAI-CV/fast-reid.git
126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
# -*- coding: utf-8 -*-
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import os
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import logging
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import json
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import random
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import pandas as pd
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from tabulate import tabulate
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from termcolor import colored
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from fastreid.data.datasets import DATASET_REGISTRY
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from fastreid.data.datasets.bases import ImageDataset
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from fastreid.data.data_utils import read_image
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from fastreid.utils.env import seed_all_rng
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@DATASET_REGISTRY.register()
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class PairDataset(ImageDataset):
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def __init__(self, img_root: str, anno_path: str, transform=None, mode: str = 'train'):
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self._logger = logging.getLogger(__name__)
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assert mode in ('train', 'val', 'test'), self._logger.info(
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'''mode should the one of ('train', 'val', 'test')''')
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self.mode = mode
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if self.mode != 'train':
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self._logger.info('set {} with {} random seed: 12345'.format(self.mode, self.__class__.__name__))
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seed_all_rng(12345)
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self.img_root = img_root
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self.anno_path = anno_path
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self.transform = transform
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all_data = json.load(open(self.anno_path))
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pos_folders = []
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neg_folders = []
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for data in all_data:
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pos_folders.append(data['positive_img_list'])
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neg_folders.append(data['negative_img_list'])
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assert len(pos_folders) == len(neg_folders), self._logger.error('the len of self.pos_foders should be equal to self.pos_foders')
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self.pos_folders = pos_folders
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self.neg_folders = neg_folders
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def __len__(self):
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if self.mode == 'test':
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return len(self.pos_folders) * 10
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return len(self.pos_folders)
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def __getitem__(self, idx):
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if self.mode == 'test':
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idx = int(idx / 10)
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pf = self.pos_folders[idx]
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nf = self.neg_folders[idx]
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label = 1
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if random.random() < 0.5:
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# generate positive pair
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img_path1, img_path2 = random.sample(pf, 2)
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else:
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# generate negative pair
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label = 0
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img_path1, img_path2 = random.choice(pf), random.choice(nf)
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img_path1 = os.path.join(self.img_root, img_path1)
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img_path2 = os.path.join(self.img_root, img_path2)
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img1 = read_image(img_path1)
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img2 = read_image(img_path2)
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if self.transform:
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img1 = self.transform(img1)
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img2 = self.transform(img2)
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return {
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'img1': img1,
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'img2': img2,
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'target': label
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}
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#-------------下面是辅助信息------------------#
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@property
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def num_classes(self):
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return 2
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def get_num_pids(self, data):
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return len(data)
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def get_num_cams(self, data):
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return 1
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def show_train(self):
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num_folders = len(self)
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num_train_images = sum([len(x) for x in self.pos_folders]) + sum([len(x) for x in self.neg_folders])
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headers = ['subset', '# folders', '# images']
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csv_results = [[self.mode, num_folders, num_train_images]]
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# tabulate it
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table = tabulate(
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csv_results,
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tablefmt="pipe",
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headers=headers,
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numalign="left",
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)
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self._logger.info(f"=> Loaded {self.__class__.__name__} in csv format: \n" + colored(table, "cyan"))
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def show_test(self):
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num_folders = len(self)
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num_images = sum([len(x) for x in self.pos_folders]) + sum([len(x) for x in self.neg_folders])
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headers = ['subset', '# folders', '# images']
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csv_results = [[self.mode, num_folders, num_images]]
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# tabulate it
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table = tabulate(
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csv_results,
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tablefmt="pipe",
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headers=headers,
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numalign="left",
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)
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self._logger.info(f"=> Loaded {self.__class__.__name__} in csv format: \n" + colored(table, "cyan"))
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