How to create a custom data loader in Pytorch?

Question

I have a file containing paths to images I would like to load into Pytorch, while utilizing the built-in dataloader features (multiprocess loading pipeline, data augmentations, and so on).

def create_links():
    data_dir = "/myfolder"

    full_path_list = []
    assert os.path.isdir(data_dir)
    for _, _, filenames in os.walk(data_dir):
        for filename in filenames:
            full_path_list.append(os.path.join(data_dir, filename))

    with open(config.data.links_file, 'w+') as links_file:
        for full_path in full_path_list:
            links_file.write(f"{full_path}\n")


def read_links_file_to_list():
    config = ConfigProvider.config()
    links_file_path = config.data.links_file
    if not os.path.isfile(links_file_path):
        raise RuntimeError("did you forget to create a file with links to images? Try using 'create_links()'")
    with open(links_file_path, 'r') as links_file:
        return links_file.readlines()

So I have a list of files (or a generator, or whatever works), file_list = read_links_file_to_list().

How can I build a Pytorch dataloader around it, and how would I use it?

You can't get much more beginner than the docs at the pytorch.org official website with the suffix beginner/data_loading_tutorial.html. If you want to break into the DL field you are going to have to do some reading and work - there is no way around it. — Nothing here
– Nothing here, Commented Jul 18, 2020 at 18:36
Here is another way it was explained as answer to a similar post: stackoverflow.com/a/78862393/26775041 — Sarah Belouchi
– Sarah Belouchi, Commented Aug 12, 2024 at 15:51

Hossein · Accepted Answer · 2020-07-19 12:29:30Z

What you want is a Custom Dataset. The __getitem__ method is where you would apply transforms such as data-augmentation etc. To give you an idea of what it looks like in practice you can take a look at this Custom Dataset I wrote the other day:

class GTSR43Dataset(Dataset):
    """German Traffic Sign Recognition dataset."""
    def __init__(self, root_dir, train_file, transform=None):
        self.root_dir = root_dir
        self.train_file_path = train_file
        self.label_df = pd.read_csv(os.path.join(self.root_dir, self.train_file_path))
        self.transform = transform
        self.classes = list(self.label_df['ClassId'].unique())

    def __getitem__(self, idx):
        """Return (image, target) after resize and preprocessing."""
        img = os.path.join(self.root_dir, self.label_df.iloc[idx, 7])
        
        X = Image.open(img)
        y = self.class_to_index(self.label_df.iloc[idx, 6])

        if self.transform:
            X = self.transform(X)

        return X, y
    
    def class_to_index(self, class_name):
        """Returns the index of a given class."""
        return self.classes.index(class_name)
    
    def index_to_class(self, class_index):
        """Returns the class of a given index."""
        return self.classes[class_index] 
    
    def get_class_count(self):
        """Return a list of label occurences"""
        cls_count = dict(self.label_df.ClassId.value_counts())
#         cls_percent = list(map(lambda x: (1 - x / sum(cls_count)), cls_count))
        return cls_count
    
    def __len__(self):
        """Returns the length of the dataset."""
        return len(self.label_df)

Good job. As a side note for the viewers, The full name for Dataset is torch.utils.data.Dataset

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