| | 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")) |
| | |
