model.py

import torch
from torchvision import models

from torch import nn

def create_effnetb2(num_classes:int = 101, seed:int = 42):
    torch.manual_seed(seed)

    weights = models.EfficientNet_B2_Weights.DEFAULT
    model = models.efficientnet_b2(weights=weights)
    transform = weights.transforms()

    for param in model.parameters():
        param.requires_grad = False
    
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes)
    )

    return model, transform