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# 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,
)
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