Upload hl.py

hl.py

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+# coding=utf-8
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""High-Level dataset."""
+
+
+import json
+from pathlib import Path
+
+import datasets
+
+
+_CITATION = """\
+@misc{}
+"""
+
+_DESCRIPTION = """\
+High-level Dataset
+"""
+
+# github link
+_HOMEPAGE = ""
+
+_LICENSE = "Apache 2.0"
+
+_URL = "https://huggingface.co/datasets/michelecafagna26/hl/main/data/images.zip"
+
+
+class HL(datasets.GeneratorBasedBuilder):
+    """High Level Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "file_name": datasets.Value("string"),
+                "image": datasets.Image(),
+                "scene": datasets.Sequence(datasets.Value("string")),
+                "action": datasets.Sequence(datasets.Value("string")),
+                "rationale": datasets.Sequence(datasets.Value("string")),
+                "object": datasets.Sequence(datasets.Value("string")),
+                # "captions": {
+                #         "scene": datasets.Sequence(datasets.Value("string")),
+                #         "action": datasets.Sequence(datasets.Value("string")),
+                #         "rationale": datasets.Sequence(datasets.Value("string")),
+                #         "object": datasets.Sequence(datasets.Value("string")),
+                #     },
+                "confidence": {
+                        "scene": datasets.Sequence(datasets.Value("float32")),
+                        "action": datasets.Sequence(datasets.Value("float32")),
+                        "rationale": datasets.Sequence(datasets.Value("float32")),
+                        "object": datasets.Sequence(datasets.Value("float32")),
+                    }
+                # "purity": {
+                #     "scene": datasets.Sequence(datasets.Value("float32")),
+                #     "action": datasets.Sequence(datasets.Value("float32")),
+                #     "rationale": datasets.Sequence(datasets.Value("float32")),
+                # },
+                # "diversity": {
+                #     "scene": datasets.Value("float32"),
+                #     "action": datasets.Value("float32"),
+                #     "rationale": datasets.Value("float32"),
+                # },
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotation_file_path": "data/annotations/train.jsonl",
+                    "images": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "annotation_file_path": "data/annotations/test.jsonl",
+                    "images": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, annotation_file_path, images):
+        idx = 0
+
+        with open(annotation_file_path, "r") as fp:
+            metadata = {json.loads(item)['file_name']: json.loads(item) for item in fp}
+
+        # This loop relies on the ordering of the files in the archive:
+        # Annotation files come first, then the images.
+        for img_file_path, img_obj in images:
+
+            file_name = Path(img_file_path).name
+
+            yield idx, {
+                    "file_name": file_name,
+                    "image": {"path": img_file_path, "bytes": img_obj.read()},
+                    "scene": metadata[file_name]['captions']['scene'],
+                    "action": metadata[file_name]['captions']['action'],
+                    "rationale": metadata[file_name]['captions']['rationale'],
+                    "object": metadata[file_name]['captions']['object'],
+                    "confidence": metadata[file_name]['captions']['confidence'],
+                }
+            idx += 1