Getting image path through a torchvision dataloader using local images
I want to use a dataloader in my script.
normaly the default function call would be like this.
dataset = ImageFolderWithPaths(
    data_dir,
    transforms.Compose([
            transforms.ColorJitter(0.1, 0.1, 0.1, 0.1),
            transforms.Resize((img_size_XY, img_size_XY)),
            transforms.ToTensor(),
            transforms.Normalize(_mean , _std)
        ])
)

dataloader = torch.utils.data.DataLoader(
    dataset,
    batch_size=batch_size,
    shuffle=False,
    num_workers=2
)

and to iterate through this dataloader i use
for inputs, labels , paths in _dataloader:
    break

now i need to collect the path for each image.
i found in github this code: (https://gist.github.com/andrewjong/6b02ff237533b3b2c554701fb53d5c4d)
class ImageFolderWithPaths(datasets.ImageFolder):
    &quot;&quot;&quot;Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    &quot;&quot;&quot;

    # override the __getitem__ method. this is the method that dataloader calls
    def __getitem__(self, index):
        # this is what ImageFolder normally returns 
        original_tuple = super(ImageFolderWithPaths, self).__getitem__(index)
        # the image file path
        path = self.imgs[index][0]
        # make a new tuple that includes original and the path
        tuple_with_path = (original_tuple + (path,))
        return tuple_with_path

# EXAMPLE USAGE:
# instantiate the dataset and dataloader
data_dir = &quot;your/data_dir/here&quot;
dataset = ImageFolderWithPaths(data_dir) # our custom dataset
dataloader = torch.utils.DataLoader(dataset)

# iterate over data
for inputs, labels, paths in dataloader:
    # use the above variables freely
    print(inputs, labels, paths)

But this code does not take transforms into account, like in my original code.
Can anybody help in how I should go about making it work with that?

Since ImageFolderWithPaths inherits from datasets.ImageFolder as shown in the code from GitHub and datasets.ImageFolder has the following arguments including transform: (see here for more info)

torchvision.datasets.ImageFolder(root: str, transform:
Optional[Callable] = None, target_transform: Optional[Callable] =
None, loader: Callable[[str], Any] = ,
is_valid_file: Optional[Callable[[str], bool]] = None)

Solution: you can use your transformations directly when you instantiate ImageFolderWithPaths.
import torch
from torchvision import datasets
from torch.utils.data import DataLoader 

class ImageFolderWithPaths(datasets.ImageFolder):

    def __getitem__(self, index):
  
        img, label = super(ImageFolderWithPaths, self).__getitem__(index)
        
        path = self.imgs[index][0]
        
        return (img, label ,path)


# put here your root directory not subfolders directory
# subfolders should be names of classes or encodings
root_dir = &quot;training&quot; 

transform = transforms.Compose([transforms.Resize((32, 32)), 
                                transforms.ToTensor()]) # my transformations.

dataset = ImageFolderWithPaths(root_dir,transform=transform) # add transformation directly

dataloader = DataLoader(dataset)


for inputs, labels, paths in dataloader:
    print(inputs.shape, labels, paths)

# output
torch.Size([1, 3, 32, 32]) tensor([0]) ('training\\0\\1.jpg',)
torch.Size([1, 3, 32, 32]) tensor([0]) ('training\\0\\1000.jpg',)
torch.Size([1, 3, 32, 32]) tensor([0]) ('training\\0\\10005.jpg',)
torch.Size([1, 3, 32, 32]) tensor([0]) ('training\\0\\10010.jpg',)

I also edited the code from github because there is no torch.utils.DataLoader but torch.utils.data.DataLoader.
