pytorch-image-models/timm/data/readers/reader_hfds.py

""" Dataset reader that wraps Hugging Face datasets

Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
import torch
import torch.distributed as dist
from PIL import Image

try:
    import datasets
except ImportError as e:
    print("Please install Hugging Face datasets package `pip install datasets`.")
    exit(1)
from .reader import Reader


def get_class_labels(info):
    if 'label' not in info.features:
        return {}
    class_label = info.features['label']
    class_to_idx = {n: class_label.str2int(n) for n in class_label.names}
    return class_to_idx


class ReaderHfds(Reader):

    def __init__(
            self,
            root,
            name,
            split='train',
            class_map=None,
            download=False,
    ):
        """
        """
        super().__init__()
        self.root = root
        self.split = split
        self.dataset = datasets.load_dataset(
            name,  # 'name' maps to path arg in hf datasets
            split=split,
            cache_dir=self.root,  # timm doesn't expect hidden cache dir for datasets, specify a path
            #use_auth_token=True,
        )
        # leave decode for caller, plus we want easy access to original path names...
        self.dataset = self.dataset.cast_column('image', datasets.Image(decode=False))

        self.class_to_idx = get_class_labels(self.dataset.info)
        self.split_info = self.dataset.info.splits[split]
        self.num_samples = self.split_info.num_examples

    def __getitem__(self, index):
        item = self.dataset[index]
        image = item['image']
        if 'bytes' in image and image['bytes']:
            image = io.BytesIO(image['bytes'])
        else:
            assert 'path' in image and image['path']
            image = open(image['path'], 'rb')
        return image, item['label']

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

    def _filename(self, index, basename=False, absolute=False):
        item = self.dataset[index]
        return item['image']['path']