model.py

import torch
import torchvision

from torch import nn

def create_effnetb2_model(num_classes:int=10,
                          seed:int=42,
                          is_TrivialAugmentWide = True,
                          freeze_layers=True):
    """Creates an EfficientNetB2 feature extractor model and transforms.

    Args:
        num_classes (int, optional): number of classes in the classifier head. Defaults to 10.
        seed (int, optional): random seed value. Defaults to 42.
        is_TrivialAugmentWide (boolean): Artificially increase the diversity of a training dataset
                                      with data augmentation, default = True

    Returns:
        effnetb2_model (torch.nn.Module): EffNetB2 feature extractor model.
        effnetb2_transforms (torchvision.transforms): EffNetB2 image transforms.
    """
    # 1, 2, 3. Create EffNetB2 pretrained weights, transforms and model
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    effnetb2_transforms = weights.transforms()

    if is_TrivialAugmentWide:
      effnetb2_transforms = torchvision.transforms.Compose([
          torchvision.transforms.TrivialAugmentWide(),
          effnetb2_transforms,
      ])


    effnetb2_model = torchvision.models.efficientnet_b2(weights=weights)

    # 4. Freeze all layers in base model
    if freeze_layers:
        for param in effnetb2_model.parameters():
            param.requires_grad = False

    # 5. Change classifier head with random seed for reproducibility
    torch.manual_seed(seed)
    effnetb2_model.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes),
    )

    return effnetb2_model, effnetb2_transforms