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
from typing import Optional

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`.")
    raise e
from .class_map import load_class_map
from .reader import Reader


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


class ReaderHfds(Reader):

    def __init__(
            self,
            name: str,
            root: Optional[str] = None,
            split: str = 'train',
            class_map: dict = None,
            input_key: str = 'image',
            target_key: str = 'label',
            download: bool = False,
            trust_remote_code: bool = 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 if root set
            trust_remote_code=trust_remote_code
        )
        # leave decode for caller, plus we want easy access to original path names...
        self.dataset = self.dataset.cast_column(input_key, datasets.Image(decode=False))

        self.image_key = input_key
        self.label_key = target_key
        self.remap_class = False
        if class_map:
            self.class_to_idx = load_class_map(class_map)
            self.remap_class = True
        else:
            self.class_to_idx = get_class_labels(self.dataset.info, self.label_key)
        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[self.image_key]
        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')
        label = item[self.label_key]
        if self.remap_class:
            label = self.class_to_idx[label]
        return image, label

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

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