model.py

import torch
import torchvision
from torch import nn

def create_effnetb2_model(num_classes: int=3,
                          seed:int=42):
  #1,2,3 create weights transforms and model
  #get effnets weight
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT

  #get effnets transforms
  transforms = weights.transforms()

  #Setup pretrained model
  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 to our desired num_classes
  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