model.py

import torch as T
from torch import nn, optim
import torch.nn.functional as F
import torchvision.models as models

from typing import Union, List

def get_model(num_classes:int, unfreeze_layers:Union[None, List[int]] = None, drop_rate: Union[None, float] = None):

    model = models.efficientnet_b7(weights=models.EfficientNet_B7_Weights.DEFAULT)

 

    for param in model.parameters():

        param.requires_grad = False

 

    if unfreeze_layers is not None and len(unfreeze_layers) > 0:

        # Now unfreeze the layers in the unfreeze layer/ list

        for layer_num in unfreeze_layers:

            for name, child in model.features[layer_num].named_modules():

                if not isinstance(child, nn.BatchNorm2d) and \
                    not isinstance(child, nn.Sequential) and \
                    not hasattr(child, 'block'):

                    for param in child.parameters():

                        param.requires_grad = True

 

    if drop_rate is not None:

        model.classifier[0] = nn.Dropout(drop_rate)

 

    # Chagne the classifier head as per our need

    model.classifier[1] = nn.Linear(2560, num_classes)

    return model

if __name__ == "__main__":
    model = get_model(
        6,
        [-1],
        0.1
    )