mirror of
https://github.com/JDAI-CV/fast-reid.git
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88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
# encoding: utf-8
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"""
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@author: l1aoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import os
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import torch
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from torch.utils.data import DataLoader
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from fastreid.data import samplers
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from fastreid.data.build import fast_batch_collator
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from fastreid.data.common import CommDataset
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from fastreid.data.datasets import DATASET_REGISTRY
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from fastreid.utils import comm
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from .build_transforms import build_transforms
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_root = os.getenv("FASTREID_DATASETS", "datasets")
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def build_cls_train_loader(cfg, mapper=None, **kwargs):
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cfg = cfg.clone()
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train_items = list()
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for d in cfg.DATASETS.NAMES:
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dataset = DATASET_REGISTRY.get(d)(root=_root, **kwargs)
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if comm.is_main_process():
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dataset.show_train()
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train_items.extend(dataset.train)
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if mapper is not None:
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transforms = mapper
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else:
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transforms = build_transforms(cfg, is_train=True)
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train_set = CommDataset(train_items, transforms, relabel=False)
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num_workers = cfg.DATALOADER.NUM_WORKERS
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num_instance = cfg.DATALOADER.NUM_INSTANCE
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mini_batch_size = cfg.SOLVER.IMS_PER_BATCH // comm.get_world_size()
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if cfg.DATALOADER.PK_SAMPLER:
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if cfg.DATALOADER.NAIVE_WAY:
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data_sampler = samplers.NaiveIdentitySampler(train_set.img_items, mini_batch_size, num_instance)
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else:
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data_sampler = samplers.BalancedIdentitySampler(train_set.img_items, mini_batch_size, num_instance)
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else:
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data_sampler = samplers.TrainingSampler(len(train_set))
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batch_sampler = torch.utils.data.sampler.BatchSampler(data_sampler, mini_batch_size, True)
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train_loader = torch.utils.data.DataLoader(
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train_set,
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num_workers=num_workers,
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batch_sampler=batch_sampler,
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collate_fn=fast_batch_collator,
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pin_memory=True,
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)
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return train_loader
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def build_cls_test_loader(cfg, dataset_name, mapper=None, **kwargs):
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cfg = cfg.clone()
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dataset = DATASET_REGISTRY.get(dataset_name)(root=_root, **kwargs)
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if comm.is_main_process():
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dataset.show_test()
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test_items = dataset.query
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if mapper is not None:
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transforms = mapper
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else:
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transforms = build_transforms(cfg, is_train=False)
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test_set = CommDataset(test_items, transforms, relabel=False)
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mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
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data_sampler = samplers.InferenceSampler(len(test_set))
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batch_sampler = torch.utils.data.BatchSampler(data_sampler, mini_batch_size, False)
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test_loader = DataLoader(
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test_set,
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batch_sampler=batch_sampler,
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num_workers=4, # save some memory
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collate_fn=fast_batch_collator,
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pin_memory=True,
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)
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return test_loader
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