model.py

from torch import manual_seed, nn
from torchvision import transforms, models

def create_model_alexnet(num_classes:int=2, seed:int=42):
    """Creates model and transforms.

    Args:
        num_classes (int, optional): number of classes in the classifier head, defaults to 2.
        seed (int, optional): random seed value. Defaults to 42.

    Returns:
        model (torch.nn.Module): Alexnet model.
        transforms (torchvision.transforms): Alexnet image transforms.
    """
    # Create Alexnet pretrained weights, transforms and model
    weights = models.AlexNet_Weights.IMAGENET1K_V1.DEFAULT
    auto_transform = weights.transforms()
    model_alexnet = models.alexnet(weights=weights)

    # Freeze all layers in base model
    for param in model_alexnet.parameters():
        param.requires_grad = False

    # Change classifier head with random seed for reproducibility
    manual_seed(seed)
    model_alexnet.classifier[6] = nn.Linear(4096, out_features=num_classes)
    return model_alexnet, auto_transform