model.py

import torch
import torchvision

from torch import nn

def create_effnetb2_model(num_classes:int=3,
                          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.
  """
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
  transforms = weights.transforms()
  model = torchvision.models.efficientnet_b2(weights=weights)

  for param in model.parameters():
    param.requires_grad = False

  torch.manual_seed(seed)
  model.classifier = nn.Sequential(
      nn.Dropout(p=0.2, inplace=True),
      nn.Linear(in_features=1408, out_features=num_classes),
  )
  return model, transforms