mmp_wise2526_franksim/mmp/a2/main.py

from typing import Tuple
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import models, datasets, transforms
from torchvision.models import MobileNet_V2_Weights
import logging

logging.basicConfig(
    level=logging.INFO,
    format="[%(asctime)s] %(levelname)s: %(message)s",
    datefmt="%H:%M:%S",
)
logger = logging.getLogger(__name__)

# these are the labels from the Cifar10 dataset:
CLASSES = (
    "plane",
    "car",
    "bird",
    "cat",
    "deer",
    "dog",
    "frog",
    "horse",
    "ship",
    "truck",
)


class MmpNet(nn.Module):
    """Exercise 2.1"""

    def __init__(self, num_classes: int):
        super().__init__()
        self.mobilenet = models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT)
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.mobilenet.last_channel, num_classes),
        )

    def forward(self, x: torch.Tensor):
        x = self.mobilenet.features(x)
        x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


def get_dataloader(
    is_train: bool, data_root: str, batch_size: int, num_workers: int
) -> DataLoader:
    """Exercise 2.2

    @param is_train: Whether this is the training or validation split
    @param data_root: Where to download the dataset to
    @param batch_size: Batch size for the data loader
    @param num_workers: Number of workers for the data loader
    """
    transform = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]
            ),
        ]
    )
    dataset = datasets.CIFAR10(
        root=data_root, train=is_train, download=True, transform=transform
    )
    dataloader = DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=is_train,
        num_workers=num_workers,
        pin_memory=True,
    )
    return dataloader


def get_criterion_optimizer(model: nn.Module) -> Tuple[nn.Module, optim.Optimizer]:
    """Exercise 2.3a

    @param model: The model that is being trained.
    @return: Returns a tuple of the criterion and the optimizer.
    """
    error_function = nn.CrossEntropyLoss()
    epsilon = 0.004
    optimizer = torch.optim.SGD(model.parameters(), lr=epsilon)
    return (error_function, optimizer)


def log_epoch_progress(epoch: int, total_epochs: int, phase: str):
    if phase == "start":
        logger.info(f"Epoch {epoch + 1}/{total_epochs} started.")
    elif phase == "end":
        logger.info(f"Epoch {epoch + 1}/{total_epochs} completed.")


def train_epoch(
    model: nn.Module,
    loader: DataLoader,
    criterion: nn.Module,
    optimizer: optim.Optimizer,
    device: torch.device,
):
    """Exercise 2.3b

    @param model: The model that should be trained
    @param loader: The DataLoader that contains the training data
    @param criterion: The criterion that is used to calculate the loss for backpropagation
    @param optimizer: Executes the update step
    @param device: The device where the epoch should run on
    """
    model.train()
    running_loss = 0.0
    log_interval = max(len(loader) // 5, 1)

    for batch_idx, (inputs, labels) in enumerate(loader, 1):
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)

        if batch_idx % log_interval == 0 or batch_idx == len(loader):
            avg_batch_loss = running_loss / (batch_idx * loader.batch_size)
            logger.info(
                f"  [Batch {batch_idx}/{len(loader)}] Train Loss: {avg_batch_loss:.4f}"
            )

    epoch_loss = running_loss / len(loader.dataset)
    logger.info(f"  ---> Train Loss (Epoch): {epoch_loss:.4f}")
    return epoch_loss


def eval_epoch(model: nn.Module, loader: DataLoader, device: torch.device) -> float:
    """Exercise 2.3c

    @param model: The model that should be evaluated
    @param loader: The DataLoader that contains the evaluation data
    @param device: The device where the epoch should run on

    @return: Returns the accuracy over the full validation dataset as a float."""
    model.eval()
    correct = 0
    total = 0

    with torch.no_grad():
        for inputs, labels in loader:
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            _, preds = outputs.max(1)
            correct += (preds == labels).sum().item()
            total += labels.size(0)

    accuracy = correct / total if total > 0 else 0.0
    logger.info(f"  ---> Eval Accuracy: {accuracy * 100:.2f}%")
    return float(accuracy)


def main():
    """Exercise 2.3d"""
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    train_epochs = 10
    model = MmpNet(num_classes=10).to(device=device)
    dataloader_train = get_dataloader(True, "../../.data", 32, 6)
    dataloader_eval = get_dataloader(False, "../../.data", 32, 6)
    criterion, optimizer = get_criterion_optimizer(model=model)

    for epoche in range(train_epochs):
        log_epoch_progress(epoche, train_epochs, "start")
        train_epoch(
            model=model,
            loader=dataloader_train,
            optimizer=optimizer,
            device=device,
            criterion=criterion,
        )
        eval_epoch(model=model, loader=dataloader_eval, device=device)
        log_epoch_progress(epoche, train_epochs, "end")


if __name__ == "__main__":
    main()