Initial dataset file

TeTIm-Eval.py

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+import os
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+TODO
+"""
+
+_HOMEPAGE = ""
+
+_DESCRIPTION = """\
+Text To Image Evaluation (TeTIm-Eval)
+"""
+
+_URLS = {
+    "mini": "https://huggingface.co/datasets/galatolo/TeTIm-Eval/resolve/main/data/TeTIm-Eval-Mini.zip"
+}
+
+_CATEGORIES = [
+    "digital_art",
+    "sketch_art",
+    "traditional_art",
+    "baroque_painting",
+    "high_renaissance_painting",
+    "neoclassical_painting",
+    "animal_photo",
+    "food_photo",
+    "landscape_photo",
+    "person_photo"
+]
+
+
+_FOLDERS = {
+    "mini": {
+        _CATEGORIES[0]: "TeTIm-Eval-Mini/sampled_art_digital",
+        _CATEGORIES[1]: "TeTIm-Eval-Mini/sampled_art_sketch",
+        _CATEGORIES[2]: "TeTIm-Eval-Mini/sampled_art_traditional",
+        _CATEGORIES[3]: "TeTIm-Eval-Mini/sampled_painting_baroque",
+        _CATEGORIES[4]: "TeTIm-Eval-Mini/sampled_painting_high-renaissance",
+        _CATEGORIES[5]: "TeTIm-Eval-Mini/sampled_painting_neoclassicism",
+        _CATEGORIES[6]: "TeTIm-Eval-Mini/sampled_photo_animal",
+        _CATEGORIES[7]: "TeTIm-Eval-Mini/sampled_photo_food",
+        _CATEGORIES[8]: "TeTIm-Eval-Mini/sampled_photo_landscape",
+        _CATEGORIES[9]: "TeTIm-Eval-Mini/sampled_photo_person",
+    }
+}
+
+class TeTImConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(TeTImConfig, self).__init__(**kwargs)
+
+
+class TeTIm(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        TeTImConfig(
+            name="mini",
+            version=datasets.Version("1.0.0", ""),
+            description="A random sampling of 300 images (30 for category) from the TeTIm dataset, manually annotated by the same person",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "image": datasets.Value("string"),
+                    "caption": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        target = os.environ.get(f"TETIMEVAL_{self.config.name}", _URLS[self.config.name])
+        downloaded_files = dl_manager.download_and_extract(target)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"path": downloaded_files}),
+        ]
+
+    
+    def _generate_examples(self, path):
+        id = 0
+        for category, folder in _FOLDERS[self.config.name].items():
+            images_folder = os.path.join(path, folder, "images")
+            annotations_folder = os.path.join(path, folder, "annotations")
+
+            for image in os.listdir(images_folder):
+                image_id = int(image.split(".")[0])
+                annotation_file = os.path.join(annotations_folder, f"{image_id}.json")
+                with open(annotation_file) as f:
+                    annotation = json.load(f)
+                
+                yield id, {
+                    "id": id,
+                    "image": os.path.join(images_folder, image),
+                    "caption": annotation["caption"],
+                    "category": category
+                }
+                id += 1
+
+
+if __name__ == "__main__":
+    from datasets import load_dataset
+    dataset_config = {
+        "LOADING_SCRIPT_FILES": os.path.join(os.getcwd(), "TeTIm-Eval.py"),
+        "CONFIG_NAME": "mini",
+    }
+    ds = load_dataset(
+        dataset_config["LOADING_SCRIPT_FILES"],
+        dataset_config["CONFIG_NAME"],
+    )
+    print(ds)