Causal3D_Dataset.py

import datasets
import pandas as pd
import os
from pathlib import Path
from tqdm import tqdm

_CITATION = """\
@article{liu2025causal3d,
  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},
  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},
  journal={arXiv preprint arXiv:2503.04852},
  year={2025}
}
"""

_DESCRIPTION = """\
Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. 
It includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.
"""

_HOMEPAGE = "https://huggingface.co/datasets/LLDDSS/Causal3D"
_LICENSE = "CC-BY-4.0"

class Causal3dDataset(datasets.GeneratorBasedBuilder):
    DEFAULT_CONFIG_NAME = "Real_Water_flow"
    BUILDER_CONFIGS = [
        # hypothetical_scenes
        datasets.BuilderConfig(name="Hypothetical_V2_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V2_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V2_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V2_nonlinear scene"),
        datasets.BuilderConfig(name="Hypothetical_V3_fully_connected_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_fully_connected_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V3_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_v_structure_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V3_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_v_structure_nonlinear scene"),
        datasets.BuilderConfig(name="Hypothetical_V4_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V4_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_v_structure_nonlinear scene"),
        datasets.BuilderConfig(name="Hypothetical_V4_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_v_structure_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V5_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V5_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_v_structure_linear scene"),
        datasets.BuilderConfig(name="Hypothetical_V5_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_v_structure_nonlinear scene"),

        # real_scenes
        datasets.BuilderConfig(name="Real_Parabola", version=datasets.Version("1.0.0"), description="Real_Parabola scene"),
        datasets.BuilderConfig(name="Real_Magnet", version=datasets.Version("1.0.0"), description="Real_Magnet scene"),
        datasets.BuilderConfig(name="Real_Spring", version=datasets.Version("1.0.0"), description="Real_Spring scene"),
        datasets.BuilderConfig(name="Real_Water_flow", version=datasets.Version("1.0.0"), description="Real_Water_flow scene"),
        datasets.BuilderConfig(name="Real_Seesaw", version=datasets.Version("1.0.0"), description="Real_Seesaw scene"),
        datasets.BuilderConfig(name="Real_Reflection", version=datasets.Version("1.0.0"), description="Real_Reflection scene"),
        datasets.BuilderConfig(name="Real_Pendulum", version=datasets.Version("1.0.0"), description="Real_Pendulum scene"),
        datasets.BuilderConfig(name="Real_Convex_len", version=datasets.Version("1.0.0"), description="Real_Convex_len scene"),

        # multi_view_scenes
        datasets.BuilderConfig(name="MV_Pendulum", version=datasets.Version("1.0.0"), description="Multi_View_Real_Pendulum scene"),



        datasets.BuilderConfig(name="MV_H3_v_structure_linear", version=datasets.Version("1.0.0"), description="MV_H3_v_structure_linear scene"),
        datasets.BuilderConfig(name="MV_H2_linear", version=datasets.Version("1.0.0"), description="MV_H2_linear scene"),
        datasets.BuilderConfig(name="MV_H2_nonlinear", version=datasets.Version("1.0.0"), description="MV_H2_nonlinear scene"),
        datasets.BuilderConfig(name="MV_H4_fully_connected_linear", version=datasets.Version("1.0.0"), description="MV_H4_fully_connected_linear scene"),
        datasets.BuilderConfig(name="MV_H4_v_structure_linear", version=datasets.Version("1.0.0"), description="MV_H4_v_structure_linear scene"),
        datasets.BuilderConfig(name="MV_H4_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="MV_H4_v_structure_nonlinear scene"),
        datasets.BuilderConfig(name="MV_H5_fully_connected_linear", version=datasets.Version("1.0.0"), description="MV_H5_fully_connected_linear scene"),
        datasets.BuilderConfig(name="MV_H5_v_structure_linear", version=datasets.Version("1.0.0"), description="MV_H5_v_structure_linear scene"),
        datasets.BuilderConfig(name="MV_H5_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="MV_H5_v_structure_nonlinear scene"),

    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "image": datasets.Image(),
                "file_name": datasets.Value("string"),
                "metadata": datasets.Value("string"),  # optionally replace with structured fields
            }),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )
    
    def _split_generators(self, dl_manager):
        print(">>>>>>>>>>>>>>>>>>>>>>> Starting to load dataset <<<<<<<<<<<<<<<<<<<<<<<")
        parts = self.config.name.split("_", 1)   # 🚩 Real_Parabola -> ["Real", "Parabola"]
        category = parts[0]
        scene = parts[1]

        local_scene_dir = os.path.join(category, scene)

        if os.path.exists(local_scene_dir):
            data_dir = local_scene_dir
            print(f"Using local folder: {data_dir}")
        else:
            zip_name = f"{self.config.name}.zip"
            archive_path = dl_manager.download_and_extract(zip_name)
            data_dir = archive_path  
            print(f"Downloaded and extracted: {zip_name}")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_dir": data_dir},
            )
        ]
    
    def _generate_examples(self, data_dir):
        image_files = {}
        for ext in ("*.png", "*.jpg", "*.jpeg"):
            for img_path in Path(data_dir).rglob(ext):
                relative = str(img_path.relative_to(data_dir))
                image_files[relative] = str(img_path)

        csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")]
        df = pd.read_csv(csv_files[0]) if csv_files else None

        if df is not None and "imgs" in df.columns:
            images = df["imgs"].tolist()
        else:
            images = []

        for idx, row in tqdm(df.iterrows(), total=len(df)) if df is not None else enumerate(image_files):
            if df is not None:
                fname = row["imgs"] if "imgs" in row else str(idx)
                
                # catch error if happen
                try:
                    image_name = images[idx].split("/")[-1].split(".")[0] if images else ""
                    record_img_path = next((key for key in image_files if image_name in key), None)
                except Exception as e:
                    print(f"Error: {e} in row {idx}, using index as file name")
                    print(images[idx])
                    record_img_path = None
                    break

                # raise error if the path does not exist
                # check the path existance
                if not os.path.exists(image_files[record_img_path]) if record_img_path else None:
                    raise FileNotFoundError(f"Image file not found: {image_files[record_img_path]}")

                yield idx, {
                    "image": image_files[record_img_path] if record_img_path else None,
                    "file_name": fname,
                    "metadata": row.to_json(),
                }
            else:
                fname = Path(image_files[idx]).stem
                yield idx, {
                    "image": image_files[idx],
                    "file_name": fname,
                    "metadata": None,
                }