99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
'''
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@ Date: 2021-07-20 12:32:29
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@ Author: Qing Shuai
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@ LastEditors: Qing Shuai
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@ LastEditTime: 2021-09-05 20:19:11
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@ FilePath: /EasyMocap/easymocap/neuralbody/trainer/dataloader.py
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'''
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from easymocap.config.baseconfig import load_object
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import torch
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def make_data_sampler(cfg, dataset, shuffle, is_distributed, is_train):
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if not is_train and cfg.test.sampler == 'FrameSampler':
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from .samplers import FrameSampler
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sampler = FrameSampler(dataset)
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return sampler
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if is_distributed:
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from .samplers import DistributedSampler
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return DistributedSampler(dataset, shuffle=shuffle)
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if shuffle:
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sampler = torch.utils.data.sampler.RandomSampler(dataset)
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else:
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sampler = torch.utils.data.sampler.SequentialSampler(dataset)
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return sampler
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def make_batch_data_sampler(cfg, sampler, batch_size, drop_last, max_iter,
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is_train):
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if is_train:
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batch_sampler = cfg.train.batch_sampler
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else:
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batch_sampler = cfg.test.batch_sampler
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if batch_sampler == 'default':
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batch_sampler = torch.utils.data.sampler.BatchSampler(
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sampler, batch_size, drop_last)
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elif batch_sampler == 'image_size':
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raise NotImplementedError
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if max_iter != -1:
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from .samplers import IterationBasedBatchSampler
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batch_sampler = IterationBasedBatchSampler(
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batch_sampler, max_iter)
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return batch_sampler
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def worker_init_fn(worker_id):
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import numpy as np
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import time
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# np.random.seed(worker_id + (int(round(time.time() * 1000) % (2**16))))
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def make_collator(cfg, is_train):
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_collators = {
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}
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from torch.utils.data.dataloader import default_collate
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collator = cfg.train.collator if is_train else cfg.test.collator
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if collator in _collators:
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return _collators[collator]
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else:
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return default_collate
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def Dataloader(cfg, split='train', is_train=True, start=0):
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is_distributed = cfg.distributed
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if split == 'train' and is_train:
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batch_size = cfg.train.batch_size
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max_iter = cfg.train.ep_iter
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# shuffle = True
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shuffle = cfg.train.shuffle
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drop_last = False
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else:
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batch_size = cfg.test.batch_size
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shuffle = True if is_distributed else False
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drop_last = False
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max_iter = -1
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if split == 'train' and is_train:
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dataset = load_object(cfg.data_train_module, cfg.data_train_args)
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elif split == 'train' and not is_train:
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cfg.data_train_args.split = 'test'
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dataset = load_object(cfg.data_train_module, cfg.data_train_args)
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elif split in ['test', 'val']:
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dataset = load_object(cfg.data_val_module, cfg.data_val_args)
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elif split == 'demo':
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dataset = load_object(cfg.data_demo_module, cfg.data_demo_args)
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elif split == 'mesh':
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dataset = load_object(cfg.data_mesh_module, cfg.data_mesh_args)
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else:
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raise NotImplementedError
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is_train = (split == 'train') and is_train
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sampler = make_data_sampler(cfg, dataset, shuffle, is_distributed, is_train)
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batch_sampler = make_batch_data_sampler(cfg, sampler, batch_size,
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drop_last, max_iter, is_train)
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num_workers = cfg.train.num_workers if is_train else cfg.test.num_workers
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collator = make_collator(cfg, is_train)
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data_loader = torch.utils.data.DataLoader(dataset,
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batch_sampler=batch_sampler,
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num_workers=num_workers,
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collate_fn=collator,
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worker_init_fn=worker_init_fn)
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return data_loader |