model.py

import torch, torchvision
from torch import nn

def create_effnet_b2_model(num_classes:int=3, # Default to suit our dataset
                           seed:int=42):
  # Import pretraind model
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
  transforms = weights.transforms()
  model = torchvision.models.efficientnet_b2(weights=weights)
  # Freeze the base layers
  for param in model.parameters():
    param.requires_grad = False

  # Change classifier head with random seed for reproducability
  torch.manual_seed(seed)
  model.classifier = nn.Sequential(
      nn.Dropout(p=0.3, inplace = True),
      nn.Linear(in_features = 1408, out_features = num_classes, bias = True)
  )

  return model, transforms