Add initial Hugging Face Datasets parser impl.

timm/data/parsers/parser_hfds.py

@@ -0,0 +1,69 @@
+""" Dataset parser interface 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 .parser import Parser
+
+
+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 ParserHfds(Parser):
+
+    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_examples = 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']