removed loading script

data/{annotations/train.jsonl → test.zip}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c4027591fa99abdd8b7786dcae7999ac7b78be89f9f010234cfd68946f6a4e34
-size 15165087
+oid sha256:59407de4abc93a1c5a1d2f39dd298a21e46c05af627a14c6b0e905c223b301d3
+size 245399580

data/{images.tar.gz → train.zip}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e077862371637ebbc821466e6e3df3f77ea5ee3a75c0968eddd08f4a7adcfe8c
-size 2439435515
+oid sha256:172a3be2ecc747577ab76dc77986cf4f1c520d07ed984ce591be81224e496c57
+size 2200080694

hl.py

@@ -1,131 +0,0 @@
-# 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 = """\
-@inproceedings{Cafagna2023HLDG,
-  title={HL Dataset: Grounding High-Level Linguistic Concepts in Vision},
-  author={Michele Cafagna and Kees van Deemter and Albert Gatt},
-  year={2023}
-}
-"""
-
-_DESCRIPTION = """\
-High-level Dataset
-"""
-
-# github link
-_HOMEPAGE = "https://github.com/michelecafagna26/HL-dataset"
-
-_LICENSE = "Apache 2.0"
-
-_IMG = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/images.tar.gz"
-_TRAIN = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/train.jsonl"
-_TEST = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/test.jsonl"
-
-
-
-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")),
-                "confidence": {
-                        "scene": datasets.Sequence(datasets.Value("float32")),
-                        "action": datasets.Sequence(datasets.Value("float32")),
-                        "rationale": 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):
-        image_files = dl_manager.download(_IMG)
-        annotation_files = dl_manager.download_and_extract([_TRAIN, _TEST])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "annotation_file_path": annotation_files[0],
-                    "images": dl_manager.iter_archive(image_files),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "annotation_file_path": annotation_files[1],
-                    "images": dl_manager.iter_archive(image_files),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, annotation_file_path, images):
-        
-        idx = 0
-
-        #assert Path(annotation_file_path).suffix == ".jsonl"
-        
-        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
-            
-            if file_name in metadata:
-                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]['confidence'],
-                        "purity": metadata[file_name]['purity'],
-                        "diversity": metadata[file_name]['diversity'],
-                    }
-                idx += 1