model.py

import torch
import torchvision
from torch import nn


def create_vit_model(num_classes: int = 3, seed: int = 42):
    weights = torchvision.models.ViT_B_16_Weights.DEFAULT
    transforms = weights.transforms()
    model = torchvision.models.vit_b_16(weights=weights)

    # Freeze all layers in base model
    for param in model.parameters():
        param.requires_grad = False

    # Change classifier head with random seed for reproducibility
    torch.manual_seed(seed)
    model.heads = nn.Sequential(
        nn.Linear(in_features=768, out_features=num_classes),
    )

    return model, transforms