# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/data_module.py from pytorch_lightning import LightningDataModule from AR.data.bucket_sampler import DistributedBucketSampler from AR.data.dataset import Text2SemanticDataset from torch.utils.data import DataLoader class Text2SemanticDataModule(LightningDataModule): def __init__(self, config, train_semantic_path, train_phoneme_path,dev_semantic_path=None, dev_phoneme_path=None): super().__init__() self.config = config self.train_semantic_path = train_semantic_path self.train_phoneme_path = train_phoneme_path self.dev_semantic_path = dev_semantic_path self.dev_phoneme_path = dev_phoneme_path self.num_workers = self.config['data']['num_workers'] def prepare_data(self): pass def setup(self, stage=None, output_logs=False): self._train_dataset = Text2SemanticDataset( phoneme_path=self.train_phoneme_path, semantic_path=self.train_semantic_path, max_sec=self.config['data']['max_sec'], pad_val=self.config['data']['pad_val']) self._dev_dataset = self._train_dataset # self._dev_dataset = Text2SemanticDataset( # phoneme_path=self.dev_phoneme_path, # semantic_path=self.dev_semantic_path, # max_sample=self.config['data']['max_eval_sample'], # max_sec=self.config['data']['max_sec'], # pad_val=self.config['data']['pad_val']) def train_dataloader(self): batch_size = self.config['train']['batch_size'] sampler = DistributedBucketSampler( self._train_dataset, batch_size=batch_size) return DataLoader( self._train_dataset, batch_size=batch_size, sampler=sampler, collate_fn=self._train_dataset.collate, num_workers=self.num_workers, persistent_workers=True, prefetch_factor=16 ) def val_dataloader(self): return DataLoader( self._dev_dataset, batch_size=1, shuffle=False, collate_fn=self._train_dataset.collate, num_workers=max(self.num_workers,12), persistent_workers=True, prefetch_factor=16 ) # 这个会使用到嘛? def test_dataloader(self): return DataLoader( self._dev_dataset, batch_size=1, shuffle=False, collate_fn=self._train_dataset.collate)