| import datasets |
| import pandas as pd |
| import os |
| from pathlib import Path |
| from tqdm import tqdm |
|
|
| print("✅ Custom Causal3D loaded: outside Causal3D.py") |
| _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 Causal3D(datasets.GeneratorBasedBuilder): |
| DEFAULT_CONFIG_NAME = "real_scenes_Water_flow_scene_render" |
| BUILDER_CONFIGS = [ |
| |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v2_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v2_linear scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v2_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetic_v2_nonlinear scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v3_fully_connected_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v3_fully_connected_linear scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_linear_full_connected", version=datasets.Version("1.0.0"), description="Hypothetic_v4_linear_full_connected scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_linear_v", version=datasets.Version("1.0.0"), description="Hypothetic_v4_linear_v scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_nonlinear_v", version=datasets.Version("1.0.0"), description="Hypothetic_v4_nonlinear_v scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v5_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v5_linear scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v5_linear_full_connected", version=datasets.Version("1.0.0"), description="Hypothetic_v5_linear_full_connected scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_rendered_h3_linear_128P", version=datasets.Version("1.0.0"), description="rendered_h3_linear_128P scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_rendered_h3_nonlinear_128P", version=datasets.Version("1.0.0"), description="rendered_h3_nonlinear_128P scene"), |
| datasets.BuilderConfig(name="hypothetical_scenes_rendered_h5_nonlinear", version=datasets.Version("1.0.0"), description="rendered_h5_nonlinear scene"), |
|
|
| |
| datasets.BuilderConfig(name="real_scenes_Real_Parabola", version=datasets.Version("1.0.0"), description="Real_Parabola scene"), |
| datasets.BuilderConfig(name="real_scenes_Real_magnet_v3", version=datasets.Version("1.0.0"), description="Real_magnet_v3 scene"), |
| datasets.BuilderConfig(name="real_scenes_Real_magnet_v3_5", version=datasets.Version("1.0.0"), description="Real_magnet_v3_5 scene"), |
| |
| datasets.BuilderConfig(name="real_scenes_Real_spring_v3_256P", version=datasets.Version("1.0.0"), description="Real_spring_v3_256P scene"), |
| datasets.BuilderConfig(name="real_scenes_Water_flow_scene_render", version=datasets.Version("1.0.0"), description="Water_flow_scene_render scene"), |
| datasets.BuilderConfig(name="real_scenes_convex_len_render_images", version=datasets.Version("1.0.0"), description="convex_len_render_images scene"), |
| datasets.BuilderConfig(name="real_scenes_real_pendulum", version=datasets.Version("1.0.0"), description="real_pendulum scene"), |
| datasets.BuilderConfig(name="real_scenes_rendered_magnetic_128", version=datasets.Version("1.0.0"), description="rendered_magnetic_128 scene"), |
| datasets.BuilderConfig(name="real_scenes_rendered_reflection_128P", version=datasets.Version("1.0.0"), description="rendered_reflection_128P scene"), |
| datasets.BuilderConfig(name="real_scenes_seesaw_scene_128P", version=datasets.Version("1.0.0"), description="seesaw_scene_128P scene"), |
| datasets.BuilderConfig(name="real_scenes_spring_scene_128P", version=datasets.Version("1.0.0"), description="spring_scene_128P scene"), |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| "image": datasets.Image(), |
| "file_name": datasets.Value("string"), |
| "metadata": datasets.Value("string"), |
| }), |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
| |
| def _split_generators(self, dl_manager): |
| parts = self.config.name.split("_", 2) |
| category = parts[0] + "_" + parts[1] |
|
|
| if category not in ["real_scenes", "hypothetical_scenes"]: |
| raise ValueError(f"Invalid category '{category}'. Must be one of ['real_scenes', 'hypothetical_scenes']") |
|
|
| scene = parts[2] |
| data_dir = os.path.join(category, scene) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"data_dir": data_dir}, |
| ) |
| ] |
|
|
| def _generate_examples(self, data_dir): |
| def color(text, code): |
| return f"\033[{code}m{text}\033[0m" |
| |
| |
| try: |
| image_files = {} |
| for ext in ("*.png", "*.jpg", "*.jpeg"): |
| for img_path in Path(data_dir).rglob(ext): |
| relative_path = str(img_path.relative_to(data_dir)) |
| image_files[relative_path] = str(img_path) |
| parts = [i.split('/')[0] for i in list(image_files.keys())] |
| parts = set(parts) |
| if "part_000" not in parts: |
| parts= [''] |
|
|
|
|
| except Exception as e: |
| print(color(f"Error loading images: {e}", "31")) |
| return |
| |
| |
| csv_files = list(Path(data_dir).rglob("*.csv")) |
| csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")] |
| if not csv_files: |
| |
| pass |
| |
| csv_path = csv_files[0] if csv_files else None |
| df = pd.read_csv(csv_path) if csv_path else None |
| image_col_exists = True |
| if df is not None and "image" not in df.columns: |
| image_col_exists = False |
| |
| images = df["image"].tolist() if image_col_exists and df is not None else [] |
| images = [i.split('/')[-1].split('.')[0] for i in images if i.endswith(('.png', '.jpg', '.jpeg'))] |
|
|
| try: |
| |
| if df is None: |
| for i, j in tqdm(image_files.items(), desc="Processing images", unit="image"): |
| yield i, { |
| "image": j, |
| "file_name": i, |
| "metadata": None, |
| } |
|
|
| else: |
| for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows", unit="row"): |
| fname = row["ID"] |
| raw_record_img_path = images[idx] if images else "" |
| record_img_name = raw_record_img_path.split('/')[-1] |
| for part in parts: |
| if part == '': |
| record_img_path = record_img_name |
| else: |
| record_img_path = "/".join([part, record_img_name.strip()]) |
| if "Water_flow_scene_render" in data_dir: |
| record_img_path = "/".join([part, str(int(record_img_name.strip().split('.')[0]))+".png"]) |
| if record_img_path in image_files: |
| |
| yield idx, { |
| "image": image_files[record_img_path], |
| "file_name": fname, |
| "metadata": row.to_json(), |
| } |
| break |
| |
| else: |
| yield idx, { |
| |
| "file_name": fname, |
| "metadata": row.to_json(), |
| } |
| break |
|
|
|
|
| except Exception as e: |
| print(color(f"Error processing CSV rows: {e}", "31")) |
| |
