model.py

import torch 
import torchvision

from torch import nn
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

def create_effnetb3_model(num_classes:int=30,
                          seed:int=42):
  weights = torchvision.models.EfficientNet_B3_Weights.DEFAULT
  transforms = weights.transforms()
  model = torchvision.models.efficientnet_b3(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=1536, out_features=128),
      nn.ReLU(),
      nn.Linear(in_features=128,
                out_features=num_classes),
  )
  return model, transforms