template to transform with resizing, keeping the aspect ratio preserved and loading my image dataset.

loader_cum_transformer.py

"""
The inference transforms are available at GoogLeNet_Weights.IMAGENET1K_V1.transforms 
and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W)
and single (C, H, W) image torch.Tensor objects. 
The images are resized to resize_size=[256] using interpolation=InterpolationMode.
BILINEAR, followed by a central crop of crop_size=[224]. Finally the values are first
rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
"""

import os
from PIL import Image
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
from torch.utils.data import DataLoader, Dataset

class ResizeWithPad:
    def __init__(self, target_size):
        self.target_size = target_size

    def __call__(self, img):
        img.thumbnail((self.target_size[0], self.target_size[1]), Image.ANTIALIAS)
        new_img = Image.new('RGB', self.target_size, (0, 0, 0))
        paste_position = ((self.target_size[0] - img.size[0]) // 2, (self.target_size[1] - img.size[1]) // 2)
        new_img.paste(img, paste_position)
        return new_img

transform = transforms.Compose([
    ResizeWithPad((224, 224)),  # Replace with your target dimensions
    transforms.ToTensor()
])

class CustomImageDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.image_files = [os.path.join(root_dir, file) for file in os.listdir(root_dir) if file.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif', '.tiff'))]

    def __len__(self):
        return len(self.image_files)

    def __getitem__(self, idx):
        img_path = self.image_files[idx]
        image = Image.open(img_path).convert("RGB")
        if self.transform:
            image = self.transform(image)
        return image

dataset = CustomImageDataset(root_dir='path_to_your_root_folder', transform=transform)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

for batch in dataloader:
    # Your training code here
    pass