Update nova.py

nova.py

@@ -1,11 +1,11 @@
 import os
 import pandas as pd
-from datasets import DatasetBuilder, SplitGenerator, Split, Value, Features, Image
+from datasets import GeneratorBasedBuilder, SplitGenerator, Split, Value, Features, Image, DatasetInfo, Sequence
 
 
-class NovaDataset(DatasetBuilder):
+class NovaDataset(GeneratorBasedBuilder):
     def _info(self):
-        return datasets.DatasetInfo(
+        return DatasetInfo(
             description="NOVA benchmark: anomaly localization and clinical reasoning in brain MRI.",
             features=Features({
                 "image": Image(),
@@ -19,29 +19,40 @@ class NovaDataset(DatasetBuilder):
                 "differential_diagnosis": Value("string"),
                 "final_diagnosis": Value("string"),
                 "link": Value("string"),
-                "bbox_gold": [["int32"]],  # [[x, y, width, height], ...]
-                "bbox_raters": [["string"]],  # [[rater, x, y, width, height], ...]
-            }),
+                "bbox_gold": Sequence({
+                    "x": Value("float32"),
+                    "y": Value("float32"),
+                    "width": Value("float32"),
+                    "height": Value("float32"),
+                }),                
+                "bbox_raters": Sequence({
+                    "rater": Value("string"),
+                    "x": Value("float32"),
+                    "y": Value("float32"),
+                    "width": Value("float32"),
+                    "height": Value("float32"),
+                }),            }),
             supervised_keys=None,
         )
 
     def _split_generators(self, dl_manager):
-        archive = dl_manager.download_and_extract("https://huggingface.co/datasets/Ano-2090/Nova/resolve/main/test/Images.zip")
-        csvs = dl_manager.download_and_extract({
-            "captions": "https://huggingface.co/datasets/Ano-2090/Nova/resolve/main/captions.csv",
-            "bboxes_gold": "https://huggingface.co/datasets/Ano-2090/Nova/resolve/main/bboxes_gold.csv",
-            "bboxes_raters": "https://huggingface.co/datasets/Ano-2090/Nova/resolve/main/bboxes_raters.csv",
-            "case_metadata": "https://huggingface.co/datasets/Ano-2090/Nova/resolve/main/case_metadata.csv",
-        })
+        # Download CSVs
+        csvs = {
+            "captions": dl_manager.download("captions.csv"),
+            "bboxes_gold": dl_manager.download("bboxes_gold.csv"),
+            "bboxes_raters": dl_manager.download("bboxes_raters.csv"),
+            "case_metadata": dl_manager.download("case_metadata.csv"),
+        }
+        image_files = list(dl_manager.iter_files("test"))
 
         return [
             SplitGenerator(
                 name=Split.TEST,
-                gen_kwargs={"image_dir": archive, "csvs": csvs},
+                gen_kwargs={"image_files": image_files, "csvs": csvs},
             )
         ]
 
-    def _generate_examples(self, image_dir, csvs):
+    def _generate_examples(self, image_files, csvs):
         captions = pd.read_csv(csvs["captions"])
         gold = pd.read_csv(csvs["bboxes_gold"])
         raters = pd.read_csv(csvs["bboxes_raters"])
@@ -50,18 +61,21 @@ class NovaDataset(DatasetBuilder):
         captions["case_id"] = captions["filename"].str.extract(r"^(case\d+)_")
 
         gold_grouped = gold.groupby("filename").apply(
-            lambda df: df[["x", "y", "width", "height"]].values.tolist()
+            lambda df: df[["x", "y", "width", "height"]].astype(float).to_dict(orient="records")
         ).to_dict()
+
         raters_grouped = raters.groupby("filename").apply(
-            lambda df: df[["rater", "x", "y", "width", "height"]].values.tolist()
+            lambda df: df[["rater", "x", "y", "width", "height"]].astype({"x": float, "y": float, "width": float, "height": float}).to_dict(orient="records")
         ).to_dict()
         meta = meta.set_index("case_id").to_dict("index")
 
+        images_map = {os.path.basename(f): f for f in image_files}
+
         for idx, row in captions.iterrows():
             filename = row["filename"]
             case_id = row["case_id"]
             record = {
-                "image": os.path.join(image_dir, filename),
+                "image": images_map.get(filename, None),
                 "filename": filename,
                 "caption": row["caption"],
                 "case_id": case_id,