model.py

import torch
import torchvision
from torch import nn


def create_effnetb2_model(num_classes: int = 101, seed: int = 42):
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    effnetb2_transforms = weights.transforms()
    effnetb2 = torchvision.models.efficientnet_b2(weights=weights)

    for param in effnetb2.parameters():
        param.requires_grad = False

    torch.manual_seed(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_transforms