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):
  """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.
  """
  # 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=0.3, inplace=True),
    nn.Linear(in_features=1408, out_features=num_classes)
  )

  return model, transforms