utils.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import transforms
import torch.optim as opt
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, models, datasets
from typing import Tuple, List, Dict
import pathlib
from PIL import Image


## for custom_dataset
# Make function to find classes in target directory
def find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:
    """Finds the class folder names in a target directory.
    
    Assumes target directory is in standard image classification format.

    Args:
        directory (str): target directory to load classnames from.

    Returns:
        Tuple[List[str], Dict[str, int]]: (list_of_class_names, dict(class_name: idx...))
    
    Example:
        find_classes("food_images/train")
        >>> (["class_1", "class_2"], {"class_1": 0, ...})
    """
    # 1. Get the class names by scanning the target directory
    classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
    
    # 2. Raise an error if class names not found
    if not classes:
        raise FileNotFoundError(f"Couldn't find any classes in {directory}.")
        
    # 3. Crearte a dictionary of index labels (computers prefer numerical rather than string labels)
    class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
    return classes, class_to_idx