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from utils.datasets_eval import AudioFileDataset
from torch.utils.data import DataLoader
import pytorch_lightning as pl
def test():
ds = AudioFileDataset()
dl = DataLoader(
ds, batch_size=None, collate_fn=lambda k: k
) # empty collate_fn is required to use mixed types.
for x, y in dl:
break
class MyModel(pl.LightningModule):
def __init__(self, **kwargs):
super().__init__()
def forward(self, x):
return x
def training_step(self, batch, batch_idx):
return 0
def validation_step(self, batch, batch_idx):
print(batch)
return 0
def train_dataloader(self):
return dl
def val_dataloader(self):
return dl
def configure_optimizers(self):
return None
model = MyModel()
trainer = pl.Trainer()
trainer.validate(model) |