model.py

import torch
import torchvision

from torch import nn


def create_effnetb2_model(num_classes: int = 3,seed:int=42):

    """Creates an EfficientNetB2 feature extractor model and transforms.

    Args:
        num_classes (int, optional): number of classes in the classifier head.
            Defaults to 3.
        seed (int, optional): random seed value. Defaults to 42.

    Returns:
        model (torch.nn.Module): EffNetB2 feature extractor model.
        transforms (torchvision.transforms): EffNetB2 image transforms.
    """
    weights  = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    transformers = weights.transforms()
    model  = torchvision.models.efficientnet_b2(weights=weights)

    for param in model.parameters():
        param.requires_grad = False
    torch.manual_seed(seed)
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.2, inplace= True),
        nn.Linear(in_features = 1408,
        out_features = num_classes )
    )
    return model, transformers