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11b4f27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import datasets
import pytorch_lightning as pl
from s1.QuantizeDataset import QuantizeDataset
from s1.sampler import RandomBucketSampler
from torch.utils import data
class QuantizeDataModule(pl.LightningDataModule):
def __init__(self, hp, data_dir='dump/phone_semantic_dataset'):
super().__init__()
self.hp = hp
dataset_all = datasets.load_from_disk(data_dir, keep_in_memory=True)
self.train_data = QuantizeDataset(hp, dataset_all['train'])
self.val_data = QuantizeDataset(hp, dataset_all['test'])
def train_dataloader(self):
length = self.train_data.lengths
sampler = RandomBucketSampler(self.hp.train_bucket_size, length, self.hp.batch_size, drop_last=True, distributed=self.hp.distributed,
world_size=self.trainer.world_size, rank=self.trainer.local_rank)
dataset = data.DataLoader(self.train_data,
num_workers=self.hp.nworkers,
batch_sampler=sampler,
collate_fn=self.train_data.seqCollate)
return dataset
def val_dataloader(self):
dataset = data.DataLoader(self.val_data,
num_workers=self.hp.nworkers,
collate_fn=self.val_data.seqCollate,
shuffle=False)
return dataset |