82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
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import platform
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import random
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from functools import partial
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import numpy as np
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from mmcv.parallel import collate
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from mmcv.runner import get_dist_info
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from torch.utils.data import DataLoader
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#from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler
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from .sampler import DistributedSampler, DistributedGivenIterationSampler
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from torch.utils.data import RandomSampler
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if platform.system() != 'Windows':
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# https://github.com/pytorch/pytorch/issues/973
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import resource
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rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
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resource.setrlimit(resource.RLIMIT_NOFILE, (4096, rlimit[1]))
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def build_dataloader(dataset,
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imgs_per_gpu,
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workers_per_gpu,
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num_gpus=1,
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dist=True,
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shuffle=True,
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replace=False,
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seed=None,
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**kwargs):
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"""Build PyTorch DataLoader.
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In distributed training, each GPU/process has a dataloader.
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In non-distributed training, there is only one dataloader for all GPUs.
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Args:
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dataset (Dataset): A PyTorch dataset.
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imgs_per_gpu (int): Number of images on each GPU, i.e., batch size of
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each GPU.
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workers_per_gpu (int): How many subprocesses to use for data loading
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for each GPU.
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num_gpus (int): Number of GPUs. Only used in non-distributed training.
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dist (bool): Distributed training/test or not. Default: True.
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shuffle (bool): Whether to shuffle the data at every epoch.
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Default: True.
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replace (bool): Replace or not in random shuffle.
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It works on when shuffle is True.
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kwargs: any keyword argument to be used to initialize DataLoader
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Returns:
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DataLoader: A PyTorch dataloader.
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"""
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if dist:
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rank, world_size = get_dist_info()
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sampler = DistributedSampler(
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dataset, world_size, rank, shuffle=shuffle, replace=replace)
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batch_size = imgs_per_gpu
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num_workers = workers_per_gpu
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else:
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if replace:
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raise NotImplemented
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sampler = RandomSampler(
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dataset) if shuffle else None # TODO: set replace
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batch_size = num_gpus * imgs_per_gpu
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num_workers = num_gpus * workers_per_gpu
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data_loader = DataLoader(
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dataset,
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batch_size=batch_size,
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sampler=sampler,
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num_workers=num_workers,
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collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu),
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pin_memory=False,
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worker_init_fn=worker_init_fn if seed is not None else None,
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**kwargs)
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return data_loader
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def worker_init_fn(seed):
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np.random.seed(seed)
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random.seed(seed)
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