PyTorch Image File Paths With Dataset Dataloader

pytorch_image_folder_with_file_paths.py

import torch
from torchvision import datasets

class ImageFolderWithPaths(datasets.ImageFolder):
    """Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    """

    # 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 = "your/data_dir/here"
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)

Thanks; hard to imagine that ImageFolder doesn't have this function / flag

May I ask under what license this snippet is released?

import torch
import torchvision
from torchvision import datasets, transforms

transforms = transforms.Compose([
transforms.Resize((64,64)),
transforms.ToTensor()
])

class ImageFolderWithPaths(torchvision.datasets.ImageFolder):
"""Custom dataset that includes image file paths. Extends
torchvision.datasets.ImageFolder
"""
#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 = './dog_vs_cat/train/'
dataset = ImageFolderWithPaths(data_dir, transform=transforms) # our custom dataset
dataloader = torch.utils.data.DataLoader(dataset)

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

This code worked for me.