Create laion2b-en-vit_embeddings.py

laion2b-en-vit_embeddings.py

@@ -0,0 +1,56 @@
+import numpy as np
+import datasets
+
+from huggingface_hub import HfApi
+api = HfApi()
+repo_files = list(api.dataset_info(repo_id="laion/laion2b-en-vit-h-14-embeddings").siblings)
+filenames = [x.rfilename for x in repo_files]
+img_embs = [x for x in filenames if x.startswith("img_emb/")]
+
+
+class LAIONEmbeddingsConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(LAIONEmbeddingsConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+
+
+class LAIONEmbeddings(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        LAIONEmbeddingsConfig()
+    ]
+
+    def _get_features(self) -> datasets.Features:
+        return datasets.Features({
+            "embedding": datasets.Sequence(datasets.Value("float32")),
+        })
+
+    def _info(self):
+        features = self._get_features()
+
+        return datasets.DatasetInfo(
+            features=features,
+        )
+
+    def _split_generators(self, dl_manager):
+        main_url = "https://huggingface.co/datasets/laion/laion2b-en-vit-h-14-embeddings/resolve/main/"
+        archive_paths = dl_manager.download([main_url + x for x in img_embs])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "chunks": archive_paths,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, chunks, split):
+        for chunk in chunks:
+            file = np.DataSource().open(chunk)
+            data = np.load(file.name)
+            for example in data:
+                yield "", {
+                    "embedding": example
+                }