model.py

from torchvision.models._api import WeightsEnum
from torch.hub import load_state_dict_from_url

def get_state_dict(self, *args, **kwargs):
    kwargs.pop("check_hash")
    return load_state_dict_from_url(self.url, *args, **kwargs)
WeightsEnum.get_state_dict = get_state_dict

import torch
import torchvision

from torch import nn

def create_effnetb2_model(num_classes: int = 3,
                          seed: int = 42):
    # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    transforms = weights.transforms()
    model = torchvision.models.efficientnet_b2(weights=weights)

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

    # 5. Change classifier head with random seed for reproducibility
    torch.manual_seed(seed)
    model.classifier = nn.Sequential(
        nn.Dropout(p= .3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes, bias=True)
    )
    return model, transforms