Create miap.py

miap.py

@@ -0,0 +1,77 @@
+# coding=utf-8
+# Copyright 2022 the HuggingFace Datasets Authors.
+#
+# 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.
+
+import os
+import pandas as pd 
+import datasets
+import json
+from huggingface_hub import hf_hub_url
+
+_INPUT_CSV = "open_images_extended_miap_boxes_test_labeled.csv"
+_INPUT_IMAGES = "images_miap"
+_REPO_ID = "nlphuji/open_images_dataset_v7"
+_IMAGE_EXTENSION = 'jpg'
+
+class Dataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="TEST", version=VERSION, description="test"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                 {
+                "ImageID": datasets.Value('string'),
+                "LabelName": datasets.Value('string'),
+                "Confidence": datasets.Value('float32'),
+                "XMin": datasets.Value('float32'),
+                "XMax": datasets.Value('float32'),
+                "YMin": datasets.Value('float32'),
+                "YMax": datasets.Value('float32'),
+                "IsOccluded": datasets.Value('int64'),
+                "IsTruncated": datasets.Value('int64'),
+                "IsGroupOf": datasets.Value('int64'),
+                "IsDepictionOf": datasets.Value('int64'),
+                "IsInsideOf": datasets.Value('int64'),
+                "GenderPresentation": datasets.Value('string'),
+                "AgePresentation": datasets.Value('string'),
+                "label": datasets.Value('string')
+                }
+            ),
+            task_templates=[],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        repo_id = _REPO_ID
+        data_dir = dl_manager.download_and_extract({
+            "examples_csv": hf_hub_url(repo_id=repo_id, repo_type='dataset', filename=_INPUT_CSV),
+            "images_dir": hf_hub_url(repo_id=repo_id, repo_type='dataset', filename=f"{_INPUT_IMAGES}.zip")
+        })
+
+        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
+
+
+    def _generate_examples(self, examples_csv, images_dir):
+        """Yields examples."""
+        df = pd.read_csv(examples_csv)
+
+        for r_idx, r in df.iterrows():
+            r_dict = r.to_dict()
+            image_path = os.path.join(images_dir, _INPUT_IMAGES, f"{r_dict['ImageID']}.{_IMAGE_EXTENSION}")
+            r_dict['image'] = image_path
+            yield r_idx, r_dict