105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
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# This file is part of COAT, and is distributed under the
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# OSI-approved BSD 3-Clause License. See top-level LICENSE file or
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# https://github.com/Kitware/COAT/blob/master/LICENSE for details.
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import torch
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from utils.transforms import build_transforms
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from utils.utils import create_small_table
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from .cuhk_sysu import CUHKSYSU
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from .prw import PRW
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def print_statistics(dataset):
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"""
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Print dataset statistics.
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"""
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num_imgs = len(dataset.annotations)
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num_boxes = 0
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pid_set = set()
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for anno in dataset.annotations:
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num_boxes += anno["boxes"].shape[0]
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for pid in anno["pids"]:
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pid_set.add(pid)
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statistics = {
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"dataset": dataset.name,
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"split": dataset.split,
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"num_images": num_imgs,
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"num_boxes": num_boxes,
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}
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if dataset.name != "CUHK-SYSU" or dataset.split != "query":
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pid_list = sorted(list(pid_set))
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if dataset.split == "query":
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num_pids, min_pid, max_pid = len(pid_list), min(pid_list), max(pid_list)
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statistics.update(
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{
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"num_labeled_pids": num_pids,
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"min_labeled_pid": int(min_pid),
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"max_labeled_pid": int(max_pid),
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}
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)
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else:
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unlabeled_pid = pid_list[-1]
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pid_list = pid_list[:-1] # remove unlabeled pid
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num_pids, min_pid, max_pid = len(pid_list), min(pid_list), max(pid_list)
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statistics.update(
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{
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"num_labeled_pids": num_pids,
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"min_labeled_pid": int(min_pid),
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"max_labeled_pid": int(max_pid),
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"unlabeled_pid": int(unlabeled_pid),
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}
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)
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print(f"=> {dataset.name}-{dataset.split} loaded:\n" + create_small_table(statistics))
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def build_dataset(dataset_name, root, transforms, split, verbose=True):
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if dataset_name == "CUHK-SYSU":
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dataset = CUHKSYSU(root, transforms, split)
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elif dataset_name == "PRW":
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dataset = PRW(root, transforms, split)
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else:
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raise NotImplementedError(f"Unknow dataset: {dataset_name}")
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if verbose:
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print_statistics(dataset)
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return dataset
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def collate_fn(batch):
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return tuple(zip(*batch))
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def build_train_loader(cfg):
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transforms = build_transforms(cfg, is_train=True)
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dataset = build_dataset(cfg.INPUT.DATASET, cfg.INPUT.DATA_ROOT, transforms, "train")
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return torch.utils.data.DataLoader(
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dataset,
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batch_size=cfg.INPUT.BATCH_SIZE_TRAIN,
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shuffle=True,
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num_workers=cfg.INPUT.NUM_WORKERS_TRAIN,
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pin_memory=True,
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drop_last=True,
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collate_fn=collate_fn,
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)
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def build_test_loader(cfg):
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transforms = build_transforms(cfg, is_train=False)
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gallery_set = build_dataset(cfg.INPUT.DATASET, cfg.INPUT.DATA_ROOT, transforms, "gallery")
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query_set = build_dataset(cfg.INPUT.DATASET, cfg.INPUT.DATA_ROOT, transforms, "query")
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gallery_loader = torch.utils.data.DataLoader(
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gallery_set,
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batch_size=cfg.INPUT.BATCH_SIZE_TEST,
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shuffle=False,
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num_workers=cfg.INPUT.NUM_WORKERS_TEST,
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pin_memory=True,
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collate_fn=collate_fn,
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)
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query_loader = torch.utils.data.DataLoader(
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query_set,
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batch_size=cfg.INPUT.BATCH_SIZE_TEST,
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shuffle=False,
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num_workers=cfg.INPUT.NUM_WORKERS_TEST,
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pin_memory=True,
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collate_fn=collate_fn,
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)
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return gallery_loader, query_loader
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