model.py

import torch
import torchvision
from torchvision.models import efficientnet_b2, EfficientNet_B2_Weights
from torchvision.models._api import WeightsEnum
from torch.hub import load_state_dict_from_url

from torch import nn

def get_state_dict(self, *args, **kwargs):
        kwargs.pop("check_hash")
        return load_state_dict_from_url(self.url, *args, **kwargs)
        
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.
    """
    # Create EffNetB2 pretrained weights, transforms and model
    

    
    WeightsEnum.get_state_dict = get_state_dict

    efficientnet_b2(weights=EfficientNet_B2_Weights.DEFAULT)

    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    transforms = weights.transforms()
    model = efficientnet_b2(weights=weights)

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

    # Change classifier head with random seed for reproducibility
    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