model.py

import torch
import torchvision 

from torch import nn 

def create_effnetb2_model(num_classes:int=101, 
                          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.
    """

  # Create EffNetB2 pretrained weights, transforms and model 
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT 
  transforms = weights.transforms() 
  model = torchvision.models.efficientnet_b2(weights=weights) 

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

  # 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