model.py

import torch
import torchvision
from torch import nn
from torchvision import transforms
import torch.nn.functional as F



def create_effnetb2_model(num_classes: int = 3,
                          seed: int = 42,
                          ):
  # 1, 2, 3 above
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
  transforms = weights.transforms()
  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 the classifier head with randomseed
  torch.manual_seed(seed=seed)
  model.classifier = nn.Sequential(
      nn.Dropout(p=0.3),
      nn.Linear(in_features=1408, out_features=num_classes)
  )
  return model, transforms