Upload crepe_vlms.py

crepe_vlms.py

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+import os
+from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Version, Features, Value, Sequence, Image, Split
+
+_CITATION = """\
+@inproceedings{ma2023crepe,
+  title={Crepe: Can vision-language foundation models reason compositionally?},
+  author={Ma, Zixian and Hong, Jerry and Gul, Mustafa Omer and Gandhi, Mona and Gao, Irena and Krishna, Ranjay},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={10910--10921},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+Code and datasets for "CREPE: Can Vision-Language Foundation Models Reason Compositionally?".
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Mayfull/crepe_vlms"
+_LICENSE = "MIT License"
+
+class CREPEVLMsDataset(GeneratorBasedBuilder):
+    VERSION = Version("1.0.0")
+
+    def _info(self):
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            features=Features(
+                {
+                    "images": Sequence(Image()),  # Sequence of images
+                    "positive_caption": Sequence(Value("string")),
+                    "negative_caption": Sequence(Value("string")),
+                    "x": Value("float"),
+                    "y": Value("float"),
+                    "width": Value("float"),
+                    "height": Value("float"),
+                    "original_file_name": Value("string"),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        # URLs for images.zip and examples.jsonl
+        urls_to_download = {
+            "images": "https://huggingface.co/datasets/Mayfull/crepe_vlms/resolve/main/images.zip",
+            "examples": "https://huggingface.co/datasets/Mayfull/crepe_vlms/resolve/main/examples.jsonl",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "examples_file": downloaded_files["examples"],
+                    "images_dir": downloaded_files["images"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, examples_file, images_dir):
+        # Read the examples.jsonl file
+        with open(examples_file, "r", encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                data = eval(line.strip())
+
+                # Get image file path
+                image_file_name = data.get("image")
+                image_path = os.path.join(images_dir, image_file_name)
+
+                # Ensure the image file exists
+                images = [image_path] if os.path.exists(image_path) else []
+
+                # Convert bounding box values to float
+                x = float(data.get("x", 0.0))
+                y = float(data.get("y", 0.0))
+                width = float(data.get("width", 0.0))
+                height = float(data.get("height", 0.0))
+
+                # Prepare the example
+                yield idx, {
+                    "images": images,
+                    "positive_caption": data.get("positive_caption", []),
+                    "negative_caption": data.get("negative_caption", []),
+                    "x": x,
+                    "y": y,
+                    "width": width,
+                    "height": height,
+                    "original_file_name": data.get("original_file_name", ""),
+                }