model.py

import torch, torchvision
from torch import nn

def create_effnetb2_model(
    num_classes: int = 3,
    seed: int = 42
):
    """
    Creates an EfficientNetB2 feature extractor model and transforms.

    Args:
        num_classes (int, optional): number of classes in the 
            classifier head. Defaults to 3.
        seed (int, optional): random seed value. Defaults to 42.
    
    Returns:
        model (torch.nn.Module): EffNetB2 feature extractor model.
        transforms (torchvision.transforms): EffNetB2 image transforms.
    """
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    transforms = weights.transforms()
    model = torchvision.models.efficientnet_b2(weights=weights)

    for param in model.parameters():
        param.requires_grad = False
    
    torch.manual_seed(seed)
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes)
    )
    return model, transforms