pytorch_lightning.py

import os

import torch
from pytorch_lightning import Trainer
from pytorch_lightning.core.lightning import LightningModule
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
from torchvision.models import resnet18

transform = transforms.Compose(
    [transforms.Resize(224), transforms.Grayscale(3), transforms.ToTensor()]
)
dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)
train_loader = DataLoader(dataset, batch_size=32)


class ResNetModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.model = resnet18(pretrained=False, num_classes=10)

    def forward(self, x):
        return self.model(x)

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


model = ResNetModel()
trainer = Trainer(num_nodes=1, max_epochs=50)
trainer.fit(model, train_loader)