alnn_rewrite/utils/training.py

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import xarray as xr
from data.dataset import ADNIDataset
from typing import Tuple
from tqdm import tqdm
import numpy as np
import pathlib as pl
import pandas as pd

type TrainMetrics = Tuple[
    float, float, float, float
]  # (train_loss, val_loss, train_acc, val_acc)

type TestMetrics = Tuple[float, float]  # (test_loss, test_acc)


def test_model(
    model: nn.Module,
    test_loader: DataLoader[ADNIDataset],
    criterion: nn.Module,
) -> TestMetrics:
    """
    Tests the model on the test dataset.

    Args:
        model (nn.Module): The model to test.
        test_loader (DataLoader[ADNIDataset]): DataLoader for the test dataset.
        criterion (nn.Module): Loss function to compute the loss.

    Returns:
        TrainMetrics: A tuple containing the test loss and test accuracy.
    """
    model.eval()
    test_loss = 0.0
    correct = 0
    total = 0

    with torch.no_grad():
        for _, (mri, xls, targets) in tqdm(
            enumerate(test_loader), desc="Testing", total=len(test_loader), unit="batch"
        ):
            outputs = model((mri, xls))
            loss = criterion(outputs, targets)
            test_loss += loss.item() * (mri.size(0) + xls.size(0))

            # Calculate accuracy
            predicted = (outputs > 0.5).float()
            correct += (predicted == targets).sum().item()
            total += targets.numel()

    test_loss /= len(test_loader)
    test_acc = correct / total if total > 0 else 0.0
    return test_loss, test_acc


def train_epoch(
    model: nn.Module,
    train_loader: DataLoader[ADNIDataset],
    val_loader: DataLoader[ADNIDataset],
    optimizer: torch.optim.Optimizer,
    criterion: nn.Module,
) -> Tuple[float, float, float, float]:
    """
    Trains the model for one epoch and evaluates it on the validation set.

    Args:
        model (nn.Module): The model to train.
        train_loader (DataLoader[ADNIDataset]): DataLoader for the training dataset.
        val_loader (DataLoader[ADNIDataset]): DataLoader for the validation dataset.
        optimizer (torch.optim.Optimizer): Optimizer for updating model parameters.
        criterion (nn.Module): Loss function to compute the loss.

    Returns:
        Tuple[float, float, float, float]: A tuple containing the training loss, validation loss, training accuracy, and validation accuracy.
    """
    model.train()
    train_loss = 0.0

    # Training loop
    for _, (mri, xls, targets) in tqdm(
        enumerate(train_loader), desc="Training", total=len(train_loader), unit="batch"
    ):
        optimizer.zero_grad()
        outputs = model((mri, xls))
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * (mri.size(0) + xls.size(0))
    train_loss /= len(train_loader)

    model.eval()
    val_loss = 0.0
    correct = 0
    total = 0

    with torch.no_grad():
        for _, (mri, xls, targets) in tqdm(
            enumerate(val_loader),
            desc="Validation",
            total=len(val_loader),
            unit="batch",
        ):
            outputs = model((mri, xls))
            loss = criterion(outputs, targets)
            val_loss += loss.item() * (mri.size(0) + xls.size(0))

            # Calculate accuracy
            predicted = (outputs > 0.5).float()
            correct += (predicted == targets).sum().item()
            total += targets.numel()

    val_loss /= len(val_loader)
    val_acc = correct / total if total > 0 else 0.0
    train_acc = correct / total if total > 0 else 0.0
    return train_loss, val_loss, train_acc, val_acc


def train_model(
    model: nn.Module,
    train_loader: DataLoader[ADNIDataset],
    val_loader: DataLoader[ADNIDataset],
    optimizer: torch.optim.Optimizer,
    criterion: nn.Module,
    num_epochs: int,
    output_path: pl.Path,
) -> Tuple[nn.Module, pd.DataFrame]:
    """
    Trains the model using the provided training and validation data loaders.

    Args:
        model (nn.Module): The model to train.
        train_loader (DataLoader[ADNIDataset]): DataLoader for the training dataset.
        val_loader (DataLoader[ADNIDataset]): DataLoader for the validation dataset.
        num_epochs (int): Number of epochs to train the model.
        learning_rate (float): Learning rate for the optimizer.

    Returns:
        Result[nn.Module, str]: A Result object containing the trained model or an error message.
    """

    # Record the training history
    # We record the Epoch, Training Loss, Validation Loss, Training Accuracy, and Validation Accuracy
    # use a (num_epochs, 4) shape ndarray to store the history before creating the DataArray

    nhist = np.zeros((num_epochs, 4), dtype=np.float32)

    for epoch in range(num_epochs):
        train_loss, val_loss, train_acc, val_acc = train_epoch(
            model,
            train_loader,
            val_loader,
            optimizer,
            criterion,
        )

        # Update the history
        nhist[epoch, 0] = train_loss
        nhist[epoch, 1] = val_loss
        nhist[epoch, 2] = train_acc
        nhist[epoch, 3] = val_acc

        print(
            f"Epoch [{epoch + 1}/{num_epochs}], "
            f"Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, "
            f"Train Acc: {train_acc:.4f}, Val Acc: {val_acc:.4f}"
        )

        # If we are at 25, 50, or 75% of the epochs, save the model
        if num_epochs > 4:
            if (epoch + 1) % (num_epochs // 4) == 0:
                model_save_path = (
                    output_path / "intermediate_models" / f"model_epoch_{epoch + 1}.pt"
                )
                torch.save(model.state_dict(), model_save_path)
                print(f"Model saved at epoch {epoch + 1}")

    # return the trained model and the traning history

    history = pd.DataFrame(
        data=nhist.astype(np.float32),
        columns=["train_loss", "val_loss", "train_acc", "val_acc"],
        index=np.arange(1, num_epochs + 1),
    )

    return model, history