dataloader_error.py

dataloader_error.py

import os

import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms

import pytorch_lightning as pl

class CoolSystem(pl.LightningModule):

    def __init__(self, classes=10):
        super().__init__()
        self.save_hyperparameters()

        # not the best model...
        self.l1 = torch.nn.Linear(28 * 28, self.hparams.classes)

    def forward(self, x):
        return torch.relu(self.l1(x.view(x.size(0), -1)))

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        tensorboard_logs = {'train_loss': loss}
        return {'loss': loss, 'log': tensorboard_logs}

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        return {'val_loss': loss}

    def validation_epoch_end(self, outputs):
        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
        return {'val_loss': avg_loss}

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.001)

from pytorch_lightning import Trainer, seed_everything
seed_everything(0)

# data
mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
mnist_train = DataLoader(mnist_train, batch_size=32, num_workers=4)
mnist_val = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
mnist_val = DataLoader(mnist_val, batch_size=32, num_workers=4)

# model
model = CoolSystem()

# most basic trainer, uses good defaults
trainer = Trainer(progress_bar_refresh_rate=20, max_epochs=10)
trainer.fit(model, mnist_train, mnist_val)