55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
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from clrnet.utils import Registry, build_from_cfg
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import torch
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from functools import partial
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import numpy as np
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import random
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from mmcv.parallel import collate
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DATASETS = Registry('datasets')
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PROCESS = Registry('process')
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def build(cfg, registry, default_args=None):
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if isinstance(cfg, list):
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modules = [
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build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
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]
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return nn.Sequential(*modules)
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else:
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return build_from_cfg(cfg, registry, default_args)
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def build_dataset(split_cfg, cfg):
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return build(split_cfg, DATASETS, default_args=dict(cfg=cfg))
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def worker_init_fn(worker_id, seed):
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worker_seed = worker_id + seed
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np.random.seed(worker_seed)
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random.seed(worker_seed)
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def build_dataloader(split_cfg, cfg, is_train=True):
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if is_train:
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shuffle = True
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else:
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shuffle = False
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dataset = build_dataset(split_cfg, cfg)
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init_fn = partial(worker_init_fn, seed=cfg.seed)
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samples_per_gpu = cfg.batch_size // cfg.gpus
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data_loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=cfg.batch_size,
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shuffle=shuffle,
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num_workers=cfg.workers,
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pin_memory=False,
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drop_last=False,
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collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
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worker_init_fn=init_fn)
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return data_loader
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