engine.py

"""
Contains function for training and testing a Pytorch model.
"""

import torch

from tqdm.auto import tqdm
from typing import Dict, List, Tuple

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]:
    # 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 the 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 /=len(dataloader)
    train_acc /=len(dataloader)
    return train_loss, train_acc

def test_step(model: torch.nn.Module,
              dataloader: torch.utils.data.DataLoader,
              loss_fn: torch.nn.Module,
              device: torch.device) -> Tuple[float, float]:
    """
    Tests a Pytorch model for a single epoch.
    """
    # Put the 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 data loader 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_logits))
            
    # 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(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 test a Pytorch model.
    """
    # Create a 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 whats 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 results