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ball_roll/train.parquet

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lens_p1/eval.parquet

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lens_p1/test.parquet

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lens_p2/test.parquet

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lens_p2/train.parquet

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physicsgen.py

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+import os
+import csv
+from PIL import Image
+import datasets
+
+# Define configurations for each flavor.
+BUILDER_CONFIGS = [
+    datasets.BuilderConfig(
+        name="sound_baseline",
+        description="Physical dataset: baseline variant",
+        data_dir="./physicsgen/urban_sound_25k_baseline"
+    ),
+    datasets.BuilderConfig(
+        name="sound_reflection",
+        description="Physical dataset: reflection variant",
+        data_dir="./physicsgen/urban_sound_25k_reflection"
+    ),
+    datasets.BuilderConfig(
+        name="sound_diffraction",
+        description="Physical dataset: reflection variant",
+        data_dir="./physicsgen/urban_sound_25k_diffraction"
+    ),
+    datasets.BuilderConfig(
+        name="sound_combined",
+        description="Physical dataset: reflection variant",
+        data_dir="./physicsgen/urban_sound_25k_combined"
+    ),
+    datasets.BuilderConfig(
+        name="lens_p1",
+        description="Distortion dataset variant",
+        data_dir="./physicsgen/lens_distortion_p1"
+    ),
+    datasets.BuilderConfig(
+        name="lens_p2",
+        description="Distortion dataset variant",
+        data_dir="./physicsgen/lens_distortion_p2"
+    ),
+    datasets.BuilderConfig(
+        name="ball_roll",
+        description="Double image dataset variant",
+        data_dir="./physicsgen/ball_roll"
+    ),
+    datasets.BuilderConfig(
+        name="ball_bounce",
+        description="Double image dataset variant",
+        data_dir="./physicsgen/ball_bounce"
+    ),
+]
+
+class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = BUILDER_CONFIGS
+    VERSION = datasets.Version("1.0.2")
+
+    def _info(self):
+        if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
+            features = datasets.Features({
+                "lat": datasets.Value("float"),
+                "long": datasets.Value("float"),
+                "db": datasets.Value("string"),
+                "soundmap": datasets.Image(),
+                "osm": datasets.Image(),
+                "temperature": datasets.Value("int32"),
+                "humidity": datasets.Value("int32"),
+                "yaw": datasets.Value("float"),
+                "sample_id": datasets.Value("int32"),
+                "soundmap_512": datasets.Image(),
+            })
+        elif self.config.name in ["lens_p1", "lens_p2"]:
+            features = datasets.Features({
+                "label_path": datasets.Value("string"),
+                "fx": datasets.Value("float"),
+                "k1": datasets.Value("float"),
+                "k2": datasets.Value("float"),
+                "k3": datasets.Value("float"),
+                "p1": datasets.Value("float"),
+                "p2": datasets.Value("float"),
+                "cx": datasets.Value("float"),
+                "distortion_path": datasets.Value("string"),
+            })
+        elif self.config.name in ["ball_roll", "ball_bounce"]:
+            features = datasets.Features({
+                "ImgName": datasets.Value("string"),
+                "StartHeight": datasets.Value("int32"),
+                "GroundIncli": datasets.Value("float"),
+                "InputTime": datasets.Value("int32"),
+                "TargetTime": datasets.Value("int32"),
+                "input_image": datasets.Image(),
+                "target_image": datasets.Image(),
+            })
+        else:
+            raise ValueError(f"Unknown config name: {self.config.name}")
+        return datasets.DatasetInfo(
+            description="Multiple variant physical tasks dataset.",
+            features=features,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = self.config.data_dir
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split_dir": os.path.join(data_dir, "train")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split_dir": os.path.join(data_dir, "test")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"split_dir": os.path.join(data_dir, "eval")},
+            ),
+        ]
+
+    def _generate_examples(self, split_dir):
+        if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
+            csv_path = os.path.join(split_dir, "meta_data.csv")
+            with open(csv_path, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for idx, row in enumerate(reader):
+                    row["soundmap"] = os.path.join(split_dir, row["soundmap"])
+                    row["osm"] = os.path.join(split_dir, row["osm"])
+                    row["soundmap_512"] = os.path.join(split_dir, row["soundmap_512"])
+                    row["lat"] = float(row["lat"])
+                    row["long"] = float(row["long"])
+                    row["temperature"] = int(row["temperature"])
+                    row["humidity"] = int(row["humidity"])
+                    row["sample_id"] = int(row["sample_id"])
+                    row["yaw"] = float(row["yaw"]) if row["yaw"] else 0.0
+                    yield idx, row
+
+        elif self.config.name in ["lens_p1", "lens_p2"]:
+            csv_path = os.path.join(split_dir, "meta_data.csv")
+            with open(csv_path, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for idx, row in enumerate(reader):
+                    row["label_path"] = str(row["label_path"])
+                    row["distortion_path"] = str(row["distortion_path"])
+                    row["fx"] = float(row["fx"])
+                    row["k1"] = float(row["k1"])
+                    row["k2"] = float(row["k2"])
+                    row["k3"] = float(row["k3"])
+                    row["p1"] = float(row["p1"])
+                    row["p2"] = float(row["p2"])
+                    row["cx"] = float(row["cx"])
+                    yield idx, row
+
+        elif self.config.name in ["ball_roll", "ball_bounce"]:
+            csv_path = os.path.join(split_dir, "meta_data.csv")
+            with open(csv_path, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for idx, row in enumerate(reader):
+                    # Construct image path from ImgName, e.g., "DoubleImg_0.jpg"
+                    image_filename = "DoubleImg_" + row["ImgName"] + ".jpg"
+                    input_image_path = os.path.join(split_dir, "x", image_filename)
+                    target_image_path = os.path.join(split_dir, "y", image_filename)
+                    row["input_image"] = input_image_path
+                    row["target_image"] = target_image_path
+                    row["ImgName"] = row["ImgName"]
+                    row["StartHeight"] = int(row["StartHeight"])
+                    row["GroundIncli"] = float(row["GroundIncli"])
+                    row["InputTime"] = int(row["InputTime"])
+                    row["TargetTime"] = int(row["TargetTime"])
+                    yield idx, row

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