model.py

import torch


import torchvision




from torch import nn

def create_effnetb2_model(num_classes: int=3,
                          seed: int=42):
    
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT

    transforms = 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=0.3, inplace = True),
        nn.Linear(in_features = 1408, out_features = num_classes))

    return model, transforms