model.py

import torch
import torchvision
from torch import nn

def effnet_feature_extractor(
                             num_classes: int=102,
                             seed: int=42):
  # 1, 2, 3,
  weights= torchvision.models.EfficientNet_B3_Weights.DEFAULT
  transforms = weights.transforms()
  model = torchvision.models.efficientnet_b3(weights=weights)

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

  # 5. CHange head
  torch.manual_seed(seed)
  model.classifier= nn.Sequential(
      nn.Dropout(p=0.2, inplace=True),
      nn.Linear(in_features=1536,
                out_features=num_classes)
  )
  return model, transforms