import os import glob from pathlib import Path from typing import List import pandas as pd import numpy as np from tqdm import tqdm import datasets print("✅ Custom Causal3D loaded - outside code") _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_Real_magnet_v3" 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): print(">>> Loaded config:", self.config.name) # 🟡 加这个调试输出 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): # 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") return # ✅ 跳过该 config，不报错 csv_path = csv_files[0] df = pd.read_csv(csv_path) if "image" not in df.columns: print(f"\033[31m[SKIP] 'image' column not found in {csv_path}, skipping this config.\033[0m") return # sub_folders = [os.path.join(data_dir, i) for i in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, i))] def color(text, code): return f"\033[{code}m{text}\033[0m" # print() # print(color(f"data_dir: {data_dir}", "36")) # Cyan # print(color(f"csv_path: {csv_path}", "33")) # Yellow # print(color(f"csv_path.name: {csv_path.name}", "35")) # Magenta # print(color(f"CSV columns: {list(df.columns)}", "32")) # Green images = df["image"].tolist() # images only contain image names images = [i.split('/')[-1].split('.')[0] for i in images if i.endswith(('.png', '.jpg', '.jpeg'))] # 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 try: # Match CSV rows with image paths for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows", unit="row"): fname = row["ID"] raw_record_img_path = 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"]) # print(f"raw_record_img_path: {raw_record_img_path}") # print(f"record_img_name: {record_img_name}") # print("part: ", part) # print(f"part: {part}, record_img_name: {record_img_name}, record_img_path: {record_img_path}") # print(f"record_img_path in image_files: {record_img_path in image_files}") # print(image_files.keys()) # print(f"part: {part}, record_img_name: {record_img_name}, record_img_path: {record_img_path}, " # f"record_image_path in image_files: {record_img_path in image_files}, image_files,key[0]: {list(image_files.keys())[0]}") # print(image_files) # exit(0) 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 except Exception as e: print(color(f"Error processing CSV rows: {e}", "31"))