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 = [ # hypothetical_scenes 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"), # real_scenes 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_Parabola_multi_view", version=datasets.Version("1.0.0"), description="Real_parabola_multi_view 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"), # optionally replace with structured fields }), homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): parts = self.config.name.split("_", 2) category = parts[0] + "_" + parts[1] # real_scenes or hypothetical_scenes 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" # Load image paths 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")) # Red return # Find the .csv file 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: # print(f"\033[33m[SKIP] No CSV found in {data_dir}, skipping this config.\033[0m") pass # print(f"\033[33m[INFO] Found CSV: {csv_files}\033[0m") 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: # Match CSV rows with image paths 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 "" #row["image"] 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: # print(color(f"record_img_path: { image_files[record_img_path]}", "34")) # Blue yield idx, { "image": image_files[record_img_path], "file_name": fname, "metadata": row.to_json(), } break else: yield idx, { # "image": "", "file_name": fname, "metadata": row.to_json(), } break except Exception as e: print(color(f"Error processing CSV rows: {e}", "31"))