model.py

import torch
import torchvision
from torch import nn

device = "cuda" if torch.cuda.is_available() else "cpu"
def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)
                          seed:int=42):
    # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
    weights = torchvision.models.efficientnet.EfficientNet_B2_Weights.DEFAULT
    transforms = weights.transforms()
    model = torchvision.models.efficientnet.efficientnet_b2(weights=weights).to(device)
    
    # 4. Freeze all layers in the base model
    for param in model.parameters():
        param.requires_grad = False
    
    # 5. Change classifier head with random seed for reproducibility
    torch.manual_seed(seed)
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1408, out_features=num_classes, bias=True)
    ).to(device)
    
    return model, transforms