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 = 'DEFAULT')

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

  #5. Change the 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