engine.py

import torch
from torch.utils.data import DataLoader
from typing import Dict, List, Tuple
from tqdm.auto import tqdm


def train(model: torch.nn.Module,
          train_dataloader: torch.utils.data.DataLoader,
          test_dataloader: torch.utils.data.DataLoader,
          optimizer: torch.optim.Optimizer,
          loss_fn: torch.nn.Module,
          epochs: int,
          device: torch.device) -> Dict[str, List]:
    """Trains and tests a PyTorch model.

  Passes a target PyTorch models through train_step() and test_step()
  functions for a number of epochs, training and testing the model
  in the same epoch loop.

  Calculates, prints and stores evaluation metrics throughout.

  Args:
    model: A PyTorch model to be trained and tested.
    train_dataloader: A DataLoader instance for the model to be trained on.
    test_dataloader: A DataLoader instance for the model to be tested on.
    optimizer: A PyTorch optimizer to help minimize the loss function.
    loss_fn: A PyTorch loss function to calculate loss on both datasets.
    epochs: An integer indicating how many epochs to train for.
    device: A target device to compute on (e.g. "cuda" or "cpu").

  Returns:
    A dictionary of training and testing loss as well as training and
    testing accuracy metrics. Each metric has a value in a list for
    each epoch.
    In the form: {train_loss: [...],
                  train_acc: [...],
                  test_loss: [...],
                  test_acc: [...]}
    For example if training for epochs=2:
                 {train_loss: [2.0616, 1.0537],
                  train_acc: [0.3945, 0.3945],
                  test_loss: [1.2641, 1.5706],
                  test_acc: [0.3400, 0.2973]}
  """
    # Create empty results dictionary
    results = {"train_loss": [],
               "train_acc": [],
               "test_loss": [],
               "test_acc": []
               }

    # Loop through training and testing steps for a number of epochs
    for epoch in tqdm(range(epochs)):
        train_loss, train_acc = train_step(model=model,
                                           dataloader=train_dataloader,
                                           loss_fn=loss_fn,
                                           optimizer=optimizer,
                                           device=device)
        test_loss, test_acc = test_step(model=model,
                                        dataloader=test_dataloader,
                                        loss_fn=loss_fn,
                                        device=device)

        # Print out what's happening
        print(
            f"Epoch: {epoch + 1} | "
            f"train_loss: {train_loss:.4f} | "
            f"train_acc: {train_acc:.4f} | "
            f"test_loss: {test_loss:.4f} | "
            f"test_acc: {test_acc:.4f}"
        )

        # Update results dictionary
        results["train_loss"].append(train_loss)
        results["train_acc"].append(train_acc)
        results["test_loss"].append(test_loss)
        results["test_acc"].append(test_acc)

    # Return the filled results at the end of the epochs
    return results


def test_step(model: torch.nn.Module,
              dataloader: DataLoader,
              loss_fn: torch.nn.Module,
              device: torch.device):
    """Tests a PyTorch model for a single epoch.

        Turns a target PyTorch model to "eval" mode and then performs
        a forward pass on a testing dataset.

        Args:
        model: A PyTorch model to be tested.
        dataloader: A DataLoader instance for the model to be tested on.
        loss_fn: A PyTorch loss function to calculate loss on the test data.
        device: A target device to compute on (e.g. "cuda" or "cpu").

        Returns:
        A tuple of testing loss and testing accuracy metrics.
        In the form (test_loss, test_accuracy). For example:

        (0.0223, 0.8985)
    """
    # Put model in eval mode
    model.eval()

    # Setup test loss and test accuracy values
    test_loss, test_acc = 0, 0

    # Turn on inference context manager
    with torch.inference_mode():
        # Loop through DataLoader batches
        for batch, (X, y) in enumerate(dataloader):
            # Send data to target device
            X, y = X.to(device), y.to(device)

            # 1. Forward pass
            test_pred_logits = model(X)

            # 2. Calculate and accumulate loss
            loss = loss_fn(test_pred_logits, y)
            test_loss += loss.item()

            # Calculate and accumulate accuracy
            test_pred_labels = test_pred_logits.argmax(dim=1)
            test_acc += ((test_pred_labels == y).sum().item() / len(test_pred_labels))

    # Adjust metrics to get average loss and accuracy per batch
    test_loss = test_loss / len(dataloader)
    test_acc = test_acc / len(dataloader)
    return test_loss, test_acc


def train_step(model: torch.nn.Module,
               dataloader: torch.utils.data.DataLoader,
               loss_fn: torch.nn.Module,
               optimizer: torch.optim.Optimizer,
               device: torch.device) -> Tuple[float, float]:
    """Trains a PyTorch model for a single epoch.

    Turns a target PyTorch model to training mode and then
    runs through all of the required training steps (forward
    pass, loss calculation, optimizer step).

    Args:
    model: A PyTorch model to be trained.
    dataloader: A DataLoader instance for the model to be trained on.
    loss_fn: A PyTorch loss function to minimize.
    optimizer: A PyTorch optimizer to help minimize the loss function.
    device: A target device to compute on (e.g. "cuda" or "cpu").

    Returns:
    A tuple of training loss and training accuracy metrics.
    In the form (train_loss, train_accuracy). For example:

    (0.1112, 0.8743)
    """
    # Put model in train mode
    model.train()

    # Setup train loss and train accuracy values
    train_loss, train_acc = 0, 0

    # Loop through data loader data batches
    for batch, (X, y) in enumerate(dataloader):
        # Send data to target device
        X, y = X.to(device), y.to(device)

        # 1. Forward pass
        y_pred = model(X)

        # 2. Calculate  and accumulate loss
        loss = loss_fn(y_pred, y)
        train_loss += loss.item()

        # 3. Optimizer zero grad
        optimizer.zero_grad()

        # 4. Loss backward
        loss.backward()

        # 5. Optimizer step
        optimizer.step()

        # Calculate and accumulate accuracy metric across all batches
        y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)
        train_acc += (y_pred_class == y).sum().item() / len(y_pred)

    # Adjust metrics to get average loss and accuracy per batch
    train_loss = train_loss / len(dataloader)
    train_acc = train_acc / len(dataloader)
    return train_loss, train_acc