Datasets:

LLDDSS
/

Causal3D

@@ -1,175 +0,0 @@
-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"))