Add script to load dataset

unsplash25k_image_embeddings.py

@@ -0,0 +1,92 @@
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+from safetensors import safe_open
+import pandas as pd
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2022}
+}
+"""
+
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "image_ids": "https://huggingface.co/datasets/JLD/unsplash25k-image-embeddings/blob/main/data/image_ids.feather.zstd",
+    "embeddings": "https://huggingface.co/datasets/JLD/unsplash25k-image-embeddings/blob/main/data/unsplash_embeddings.safetensors"
+}
+
+class Unsplash25kImageEmbeddingsDataset(datasets.GeneratorBasedBuilder):
+    """_summary_
+
+    Args:
+        datasets (_type_): _description_
+    """
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("string"),
+                "image_embedding": datasets.Features({'x': datasets.Array2D(shape=(1, 512), dtype='float16')})
+            }
+        )   
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        embeddings_path = dl_manager.download(_URLS["embeddings"])
+        image_ids_path = dl_manager.download(_URLS["image_ids"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.ALL,
+                gen_kwargs={
+                    "embeddings_path": embeddings_path,
+                    "image_ids": image_ids_path
+                }
+            )
+        ]
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, embeddings_path, image_ids_path):
+        tensors = {}
+        image_ids = pd.read_feather(image_ids_path, compression="zstd")
+        with safe_open(embeddings_path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                tensors[key] = f.get_tensor(key)
+        for num_id, image_id in enumerate(image_ids):
+            yield {"image_id": image_id, "image_embedding": tensors["embeddings"][num_id]}
+