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import os |
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import glob |
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from pathlib import Path |
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from typing import List |
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import pandas as pd |
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import numpy as np |
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from tqdm import tqdm |
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import datasets |
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print("✅ Custom Causal3D loaded - outside code") |
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_CITATION = """\ |
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@article{liu2025causal3d, |
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title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data}, |
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author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing}, |
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journal={arXiv preprint arXiv:2503.04852}, |
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year={2025} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. |
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It includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/LLDDSS/Causal3D" |
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_LICENSE = "CC-BY-4.0" |
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class Causal3D(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG_NAME = "real_scenes_Real_magnet_v3" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v2_linear", |
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version=datasets.Version("1.0.0"), |
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description="Hypothetic_v2_linear scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v2_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetic_v2_nonlinear scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v3_fully_connected_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v3_fully_connected_linear scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_linear_full_connected", version=datasets.Version("1.0.0"), description="Hypothetic_v4_linear_full_connected scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_linear_v", version=datasets.Version("1.0.0"), description="Hypothetic_v4_linear_v scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_nonlinear_v", version=datasets.Version("1.0.0"), description="Hypothetic_v4_nonlinear_v scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v5_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v5_linear scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v5_linear_full_connected", version=datasets.Version("1.0.0"), description="Hypothetic_v5_linear_full_connected scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_rendered_h3_linear_128P", version=datasets.Version("1.0.0"), description="rendered_h3_linear_128P scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_rendered_h3_nonlinear_128P", version=datasets.Version("1.0.0"), description="rendered_h3_nonlinear_128P scene"), |
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datasets.BuilderConfig(name="hypothetical_scenes_rendered_h5_nonlinear", version=datasets.Version("1.0.0"), description="rendered_h5_nonlinear scene"), |
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datasets.BuilderConfig(name="real_scenes_Real_Parabola", version=datasets.Version("1.0.0"), description="Real_Parabola scene"), |
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datasets.BuilderConfig(name="real_scenes_Real_magnet_v3", version=datasets.Version("1.0.0"), description="Real_magnet_v3 scene"), |
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datasets.BuilderConfig(name="real_scenes_Real_magnet_v3_5", version=datasets.Version("1.0.0"), description="Real_magnet_v3_5 scene"), |
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datasets.BuilderConfig(name="real_scenes_Real_parabola_multi_view", version=datasets.Version("1.0.0"), description="Real_parabola_multi_view scene"), |
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datasets.BuilderConfig(name="real_scenes_Real_spring_v3_256P", version=datasets.Version("1.0.0"), description="Real_spring_v3_256P scene"), |
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datasets.BuilderConfig(name="real_scenes_Water_flow_scene_render", version=datasets.Version("1.0.0"), description="Water_flow_scene_render scene"), |
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datasets.BuilderConfig(name="real_scenes_convex_len_render_images", version=datasets.Version("1.0.0"), description="convex_len_render_images scene"), |
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datasets.BuilderConfig(name="real_scenes_real_pendulum", version=datasets.Version("1.0.0"), description="real_pendulum scene"), |
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datasets.BuilderConfig(name="real_scenes_rendered_magnetic_128", version=datasets.Version("1.0.0"), description="rendered_magnetic_128 scene"), |
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datasets.BuilderConfig(name="real_scenes_rendered_reflection_128P", version=datasets.Version("1.0.0"), description="rendered_reflection_128P scene"), |
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datasets.BuilderConfig(name="real_scenes_seesaw_scene_128P", version=datasets.Version("1.0.0"), description="seesaw_scene_128P scene"), |
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datasets.BuilderConfig(name="real_scenes_spring_scene_128P", version=datasets.Version("1.0.0"), description="spring_scene_128P scene"), |
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] |
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def _info(self): |
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print(">>> Loaded config:", self.config.name) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"image": datasets.Image(), |
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"file_name": datasets.Value("string"), |
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"metadata": datasets.Value("string"), |
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}), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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parts = self.config.name.split("_", 2) |
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category = parts[0] + "_" + parts[1] |
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if category not in ["real_scenes", "hypothetical_scenes"]: |
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raise ValueError(f"Invalid category '{category}'. Must be one of ['real_scenes', 'hypothetical_scenes']") |
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scene = parts[2] |
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data_dir = os.path.join(category, scene) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"data_dir": data_dir}, |
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) |
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] |
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def _generate_examples(self, data_dir): |
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csv_files = list(Path(data_dir).rglob("*.csv")) |
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csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")] |
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if not csv_files: |
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print(f"\033[33m[SKIP] No CSV found in {data_dir}, skipping this config.\033[0m") |
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return |
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csv_path = csv_files[0] |
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df = pd.read_csv(csv_path) |
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if "image" not in df.columns: |
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print(f"\033[31m[SKIP] 'image' column not found in {csv_path}, skipping this config.\033[0m") |
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return |
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def color(text, code): |
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return f"\033[{code}m{text}\033[0m" |
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images = df["image"].tolist() |
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images = [i.split('/')[-1].split('.')[0] for i in images if i.endswith(('.png', '.jpg', '.jpeg'))] |
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try: |
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image_files = {} |
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for ext in ("*.png", "*.jpg", "*.jpeg"): |
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for img_path in Path(data_dir).rglob(ext): |
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relative_path = str(img_path.relative_to(data_dir)) |
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image_files[relative_path] = str(img_path) |
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parts = [i.split('/')[0] for i in list(image_files.keys())] |
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parts = set(parts) |
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if "part_000" not in parts: |
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parts= [''] |
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except Exception as e: |
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print(color(f"Error loading images: {e}", "31")) |
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return |
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try: |
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for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows", unit="row"): |
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fname = row["ID"] |
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raw_record_img_path = row["image"] |
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record_img_name = raw_record_img_path.split('/')[-1] |
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for part in parts: |
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if part == '': |
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record_img_path = record_img_name |
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else: |
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record_img_path = "/".join([part, record_img_name.strip()]) |
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if "Water_flow_scene_render" in data_dir: |
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record_img_path = "/".join([part, str(int(record_img_name.strip().split('.')[0]))+".png"]) |
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if record_img_path in image_files: |
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yield idx, { |
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"image": image_files[record_img_path], |
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"file_name": fname, |
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"metadata": row.to_json(), |
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} |
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break |
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except Exception as e: |
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print(color(f"Error processing CSV rows: {e}", "31")) |
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