model.py

import torch, 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 output neurons in the output layer. Defaults to 3
        seed (int, optional): Random seed value. Defaults to 42.

    Returns:
        torchvision.models.efficientnet_b2: EffNetB2 feature extractor model

    """
    # 1. Setup pretrained EffNMetB2 weights
    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    effnetb2_transform = effnetb2_weights.transforms()
    # 2. Setup pretrained model
    effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)
    # 3. Freeze the base layers
    for param in effnetb2.parameters():
        param.requires_grad = False

    # 4. Change the classsifier to 3 classes
    torch.manual_seed(seed)
    effnetb2.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes))
    
    return effnetb2, effnetb2_transform