model.py

import torch
import torchvision

from torch import nn

def create_effnetb2_model(num_classes:int=3, # default output classes =3 (pizza, steak, sushi)
                          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 params in model.parameters():
    params.requires_grad=False
  
  # 5. change classifier head with random seed for reproducibility
  torch.manual_seed(seed)
  torch.cuda.manual_seed(seed)
  model.classifier=nn.Sequential(
      nn.Dropout(p=0.3, inplace=True),
      nn.Linear(in_features=1408, out_features=num_classes, bias=True)
  )
  return model, transforms