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70 lines
2.0 KiB
70 lines
2.0 KiB
""" Dataset parser interface that wraps Hugging Face datasets
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Hacked together by / Copyright 2022 Ross Wightman
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"""
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import io
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import math
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import torch
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import torch.distributed as dist
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from PIL import Image
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try:
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import datasets
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except ImportError as e:
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print("Please install Hugging Face datasets package `pip install datasets`.")
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exit(1)
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from .parser import Parser
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def get_class_labels(info):
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if 'label' not in info.features:
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return {}
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class_label = info.features['label']
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class_to_idx = {n: class_label.str2int(n) for n in class_label.names}
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return class_to_idx
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class ParserHfds(Parser):
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def __init__(
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self,
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root,
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name,
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split='train',
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class_map=None,
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download=False,
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):
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"""
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"""
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super().__init__()
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self.root = root
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self.split = split
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self.dataset = datasets.load_dataset(
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name, # 'name' maps to path arg in hf datasets
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split=split,
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cache_dir=self.root, # timm doesn't expect hidden cache dir for datasets, specify a path
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#use_auth_token=True,
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)
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# leave decode for caller, plus we want easy access to original path names...
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self.dataset = self.dataset.cast_column('image', datasets.Image(decode=False))
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self.class_to_idx = get_class_labels(self.dataset.info)
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self.split_info = self.dataset.info.splits[split]
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self.num_examples = self.split_info.num_examples
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def __getitem__(self, index):
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item = self.dataset[index]
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image = item['image']
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if 'bytes' in image and image['bytes']:
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image = io.BytesIO(image['bytes'])
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else:
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assert 'path' in image and image['path']
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image = open(image['path'], 'rb')
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return image, item['label']
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def __len__(self):
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return len(self.dataset)
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def _filename(self, index, basename=False, absolute=False):
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item = self.dataset[index]
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return item['image']['path']
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