82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import lightning as L
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import torch
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from torchmetrics.functional.classification.accuracy import accuracy
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from trainer import MyCustomTrainer
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class MNISTModule(L.LightningModule):
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def __init__(self) -> None:
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super().__init__()
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self.model = torch.nn.Sequential(
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torch.nn.Conv2d(
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in_channels=1,
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out_channels=16,
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kernel_size=5,
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stride=1,
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padding=2,
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),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d(kernel_size=2),
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torch.nn.Conv2d(16, 32, 5, 1, 2),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d(2),
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torch.nn.Flatten(),
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# fully connected layer, output 10 classes
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torch.nn.Linear(32 * 7 * 7, 10),
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)
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self.loss_fn = torch.nn.CrossEntropyLoss()
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def forward(self, x: torch.Tensor):
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return self.model(x)
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def training_step(self, batch, batch_idx: int):
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x, y = batch
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logits = self(x)
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loss = self.loss_fn(logits, y)
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accuracy_train = accuracy(logits.argmax(-1), y, num_classes=10, task="multiclass", top_k=1)
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return {"loss": loss, "accuracy": accuracy_train}
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def configure_optimizers(self):
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optim = torch.optim.Adam(self.parameters(), lr=1e-4)
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return {
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"optimizer": optim,
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"scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(optim, mode="max", verbose=True),
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"monitor": "val_accuracy",
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"interval": "epoch",
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"frequency": 1,
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}
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def validation_step(self, *args, **kwargs):
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return self.training_step(*args, **kwargs)
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def train(model):
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from torchvision.datasets import MNIST
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from torchvision.transforms import ToTensor
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train_set = MNIST(root="/tmp/data/MNIST", train=True, transform=ToTensor(), download=True)
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val_set = MNIST(root="/tmp/data/MNIST", train=False, transform=ToTensor(), download=False)
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train_loader = torch.utils.data.DataLoader(
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train_set, batch_size=64, shuffle=True, pin_memory=torch.cuda.is_available(), num_workers=4
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)
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val_loader = torch.utils.data.DataLoader(
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val_set, batch_size=64, shuffle=False, pin_memory=torch.cuda.is_available(), num_workers=4
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)
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# MPS backend currently does not support all operations used in this example.
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# If you want to use MPS, set accelerator='auto' and also set PYTORCH_ENABLE_MPS_FALLBACK=1
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accelerator = "cpu" if torch.backends.mps.is_available() else "auto"
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trainer = MyCustomTrainer(
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accelerator=accelerator, devices="auto", limit_train_batches=10, limit_val_batches=20, max_epochs=3
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)
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trainer.fit(model, train_loader, val_loader)
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if __name__ == "__main__":
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train(MNISTModule())
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