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import os |
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import sys |
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import copy |
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import importlib |
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__dir__ = os.path.dirname(os.path.abspath(__file__)) |
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) |
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from torch.utils.data import DataLoader, DistributedSampler |
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DATASET_MODULES = { |
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'SimpleDataSet': 'tools.data.simple_dataset', |
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'LMDBDataSet': 'tools.data.lmdb_dataset', |
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'TextLMDBDataSet': 'tools.data.text_lmdb_dataset', |
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'MultiScaleDataSet': 'tools.data.simple_dataset', |
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'STRLMDBDataSet': 'tools.data.strlmdb_dataset', |
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'LMDBDataSetTest': 'tools.data.lmdb_dataset_test', |
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'RatioDataSet': 'tools.data.ratio_dataset', |
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'RatioDataSetTest': 'tools.data.ratio_dataset_test', |
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'RatioDataSetTVResize': 'tools.data.ratio_dataset_tvresize', |
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'RatioDataSetTVResizeTest': 'tools.data.ratio_dataset_tvresize_test' |
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} |
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SAMPLER_MODULES = { |
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'MultiScaleSampler': 'tools.data.multi_scale_sampler', |
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'RatioSampler': 'tools.data.ratio_sampler' |
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} |
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__all__ = [ |
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'build_dataloader', |
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] |
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def build_dataloader(config, mode, logger, seed=None, epoch=3, task='rec'): |
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config = copy.deepcopy(config) |
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mode = mode.capitalize() |
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dataset_config = config[mode]['dataset'] |
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module_name = dataset_config['name'] |
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if module_name not in DATASET_MODULES: |
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raise ValueError( |
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f'Unsupported dataset: {module_name}. Supported datasets: {list(DATASET_MODULES.keys())}' |
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) |
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dataset_module = importlib.import_module(DATASET_MODULES[module_name]) |
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dataset_class = getattr(dataset_module, module_name) |
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dataset = dataset_class(config, mode, logger, seed, epoch=epoch, task=task) |
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loader_config = config[mode]['loader'] |
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batch_size = loader_config['batch_size_per_card'] |
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drop_last = loader_config['drop_last'] |
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shuffle = loader_config['shuffle'] |
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num_workers = loader_config['num_workers'] |
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pin_memory = loader_config.get('pin_memory', False) |
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sampler = None |
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batch_sampler = None |
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if 'sampler' in config[mode]: |
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sampler_config = config[mode]['sampler'] |
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sampler_name = sampler_config.pop('name') |
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if sampler_name not in SAMPLER_MODULES: |
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raise ValueError( |
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f'Unsupported sampler: {sampler_name}. Supported samplers: {list(SAMPLER_MODULES.keys())}' |
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) |
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sampler_module = importlib.import_module(SAMPLER_MODULES[sampler_name]) |
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sampler_class = getattr(sampler_module, sampler_name) |
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batch_sampler = sampler_class(dataset, **sampler_config) |
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elif config['Global']['distributed'] and mode == 'Train': |
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sampler = DistributedSampler(dataset=dataset, shuffle=shuffle) |
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if 'collate_fn' in loader_config: |
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from . import collate_fn |
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collate_fn = getattr(collate_fn, loader_config['collate_fn'])() |
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else: |
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collate_fn = None |
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if batch_sampler is None: |
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data_loader = DataLoader( |
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dataset=dataset, |
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sampler=sampler, |
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num_workers=num_workers, |
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pin_memory=pin_memory, |
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collate_fn=collate_fn, |
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batch_size=batch_size, |
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drop_last=drop_last, |
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) |
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else: |
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data_loader = DataLoader( |
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dataset=dataset, |
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batch_sampler=batch_sampler, |
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num_workers=num_workers, |
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pin_memory=pin_memory, |
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collate_fn=collate_fn, |
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) |
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if len(data_loader) == 0: |
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logger.error( |
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f'No Images in {mode.lower()} dataloader. Please check:\n' |
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'\t1. The images num in the train label_file_list should be >= batch size.\n' |
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'\t2. The annotation file and path in the configuration are correct.\n' |
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'\t3. The BatchSize is not larger than the number of images.') |
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sys.exit() |
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return data_loader |
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