Upload topviewrs.py

topviewrs.py

@@ -0,0 +1,152 @@
+"""
+This is the huggingface data loader for TOPVIEWRS Benchmark.
+"""
+
+import json
+import os
+import shutil
+
+import datasets
+
+
+_CITATION = """
+@misc{li2024topviewrs,
+        title={TopViewRS: Vision-Language Models as Top-View Spatial Reasoners},
+        author={Chengzu Li and Caiqi Zhang and Han Zhou and Nigel Collier and Anna Korhonen and Ivan Vulić},
+        year={2024},
+        eprint={2406.02537},
+        archivePrefix={arXiv},
+        primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """
+TopViewRS dataset, comprising 11,384 multiple-choice questions with either photo-realistic 
+or semantic top-view maps of real-world scenarios through a pipeline of automatic collection followed by human alignment.
+"""
+
+_HOMEPAGE = "https://topviewrs.github.io/"
+
+_LICENSE = "MIT"
+
+TASK_SPLIT = ['top_view_recognition', 'top_view_localization', 'static_spatial_reasoning', 'dynamic_spatial_reasoning']
+
+_URLS = {
+    "rgb_json": f"released_realistic_datasets.json",
+    "semantic_json": f"released_semantic_datasets.json",
+    "images": f"release_data.zip"
+}
+
+
+class TOPVIEWRSConfig(datasets.BuilderConfig):
+    """BuilderConfig for TOPVIEWRS."""
+
+    def __init__(self, task_split, map_type,  **kwargs):
+        """BuilderConfig for TOPVIEWRS.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TOPVIEWRSConfig, self).__init__(**kwargs)
+        self.task_split = task_split
+        self.map_type = map_type
+
+
+class TOPVIEWRS(datasets.GeneratorBasedBuilder):
+    """TOPVIEWRS Dataset"""
+
+    BUILDER_CONFIG_CLASS = TOPVIEWRSConfig
+    BUILDER_CONFIGS = [
+        TOPVIEWRSConfig(
+            name="topviewrs",
+            version=datasets.Version("0.0.0"),
+            description=_DESCRIPTION,
+            task_split=None,
+            map_type=None,
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "topviewrs"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "index": datasets.Value("int32"),
+                "scene_id": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "choices": datasets.Sequence(datasets.Value("string")),
+                "labels": datasets.Sequence(datasets.Value("string")),
+                "choice_type": datasets.Value("string"),
+                "map_path": datasets.Value("string"),
+                "question_ability": datasets.Value("string"),
+            }
+        )
+        if self.config.task_split == "dynamic_spatial_reasoning":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("int32"),
+                    "scene_id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "choices": datasets.Sequence(datasets.Value("string")),
+                    "labels": datasets.Sequence(datasets.Value("string")),
+                    "choice_type": datasets.Value("string"),
+                    "map_path": datasets.Value("string"),
+                    "question_ability": datasets.Value("string"),
+                    "reference_path": datasets.Sequence(datasets.Sequence(datasets.Value("int32")))
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = _URLS
+
+        for k, v in downloaded_files.items():
+            if v.endswith("zip"):
+                try:
+                    shutil.unpack_archive(v)
+                except:
+                    raise FileNotFoundError(f"Unpacking the image data.zip failed. Make sure that you have enough space at {os.path.dirname(v)}. ")
+        
+        base_file_dir = os.path.dirname(v)    
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split('val'),
+                gen_kwargs={
+                    "file_path": base_file_dir
+                },
+            )
+        ]
+
+    def _generate_examples(self, file_path: str):
+        task = self.config.task_split
+        map_type = self.config.map_type
+
+        file_name = "RGB_datasets.json" if map_type.lower() == "realistic" else "semantic_datasets.json"
+        map_key = "rgb" if map_type.lower() == "realistic" else map_type
+        with open(os.path.join(file_path, file_name)) as f:
+            data_list = json.load(f)[task]
+
+        for idx, data_item in enumerate(data_list):
+            return_item = {
+                "index": idx,
+                "scene_id": data_item['scene_id'],
+                "question": data_item['question'],
+                "choices": data_item['choices'],
+                "labels": data_item['labels'],
+                "choice_type": str(data_item["question_meta_data"]["choices"]),
+                "map_path": os.path.join(file_path, data_item[f"{map_key}_map"]),
+                "question_ability": data_item['ability'],
+            }
+            if "reference_path" in data_item.keys():
+                return_item["reference_path"] = data_item["reference_path"]
+
+            yield idx, return_item
+            idx += 1