Convert dataset to Parquet

Causal3D.py

@@ -1,165 +0,0 @@
-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"))
-        

README.md

@@ -19,10 +19,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2197142
+    num_bytes: 2137802.16
     num_examples: 14368
-  download_size: 0
-  dataset_size: 2197142
+  download_size: 1216402
+  dataset_size: 2137802.16
 - config_name: hypothetical_scenes_Hypothetic_v2_nonlinear
   features:
   - name: image
@@ -33,10 +33,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1809956
+    num_bytes: 1768656.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 1809956
+  download_size: 939321
+  dataset_size: 1768656.0
 - config_name: hypothetical_scenes_Hypothetic_v3_fully_connected_linear
   features:
   - name: image
@@ -47,10 +47,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1397093
+    num_bytes: 1355793.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 1397093
+  download_size: 617191
+  dataset_size: 1355793.0
 - config_name: hypothetical_scenes_Hypothetic_v4_linear_full_connected
   features:
   - name: image
@@ -61,10 +61,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1699598
+    num_bytes: 1658091.5
     num_examples: 10050
-  download_size: 0
-  dataset_size: 1699598
+  download_size: 915357
+  dataset_size: 1658091.5
 - config_name: hypothetical_scenes_Hypothetic_v4_linear_v
   features:
   - name: image
@@ -75,10 +75,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2053379
+    num_bytes: 2012079.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 2053379
+  download_size: 907646
+  dataset_size: 2012079.0
 - config_name: hypothetical_scenes_Hypothetic_v4_nonlinear_v
   features:
   - name: image
@@ -89,10 +89,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2828217
+    num_bytes: 2786917.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 2828217
+  download_size: 1262319
+  dataset_size: 2786917.0
 - config_name: hypothetical_scenes_Hypothetic_v5_linear
   features:
   - name: image
@@ -103,10 +103,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1956461
+    num_bytes: 1915161.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 1956461
+  download_size: 1048013
+  dataset_size: 1915161.0
 - config_name: hypothetical_scenes_Hypothetic_v5_linear_full_connected
   features:
   - name: image
@@ -117,10 +117,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1955921
+    num_bytes: 1914621.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 1955921
+  download_size: 1051232
+  dataset_size: 1914621.0
 - config_name: hypothetical_scenes_rendered_h3_linear_128P
   features:
   - name: image
@@ -131,10 +131,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 5425498
+    num_bytes: 5363548.0
     num_examples: 15000
-  download_size: 0
-  dataset_size: 5425498
+  download_size: 2476630
+  dataset_size: 5363548.0
 - config_name: hypothetical_scenes_rendered_h3_nonlinear_128P
   features:
   - name: image
@@ -145,10 +145,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3852500
+    num_bytes: 3810279.01
     num_examples: 10223
-  download_size: 0
-  dataset_size: 3852500
+  download_size: 1726102
+  dataset_size: 3810279.01
 - config_name: hypothetical_scenes_rendered_h5_nonlinear
   features:
   - name: image
@@ -159,10 +159,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 5459126
+    num_bytes: 5416339.2
     num_examples: 10360
-  download_size: 0
-  dataset_size: 5459126
+  download_size: 2056220
+  dataset_size: 5416339.2
 - config_name: real_scenes_Real_Parabola
   features:
   - name: image
@@ -173,10 +173,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1323548
+    num_bytes: 1282248.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 1323548
+  download_size: 768322
+  dataset_size: 1282248.0
 - config_name: real_scenes_Real_magnet_v3
   features:
   - name: image
@@ -187,10 +187,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 28397
+    num_bytes: 72702.0
     num_examples: 481
-  download_size: 0
-  dataset_size: 28397
+  download_size: 48333
+  dataset_size: 72702.0
 - config_name: real_scenes_Real_magnet_v3_5
   features:
   - name: image
@@ -201,10 +201,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 93977
+    num_bytes: 228301.613
     num_examples: 1503
-  download_size: 0
-  dataset_size: 93977
+  download_size: 152240
+  dataset_size: 228301.613
 - config_name: real_scenes_Real_parabola_multi_view
   features:
   - name: image
@@ -229,10 +229,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 136325
+    num_bytes: 134466.0
     num_examples: 450
-  download_size: 0
-  dataset_size: 136325
+  download_size: 24433
+  dataset_size: 134466.0
 - config_name: real_scenes_Water_flow_scene_render
   features:
   - name: image
@@ -243,10 +243,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2792618
+    num_bytes: 3533718.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 2792618
+  download_size: 1813070
+  dataset_size: 3533718.0
 - config_name: real_scenes_convex_len_render_images
   features:
   - name: image
@@ -257,10 +257,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 72448
+    num_bytes: 161948.95
     num_examples: 1078
-  download_size: 0
-  dataset_size: 72448
+  download_size: 106436
+  dataset_size: 161948.95
 - config_name: real_scenes_real_pendulum
   features:
   - name: image
@@ -271,10 +271,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2925963
+    num_bytes: 2884667.13
     num_examples: 9999
-  download_size: 0
-  dataset_size: 2925963
+  download_size: 1558722
+  dataset_size: 2884667.13
 - config_name: real_scenes_rendered_magnetic_128
   features:
   - name: image
@@ -285,10 +285,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2324526
+    num_bytes: 2290040.5
     num_examples: 8350
-  download_size: 0
-  dataset_size: 2324526
+  download_size: 933644
+  dataset_size: 2290040.5
 - config_name: real_scenes_rendered_reflection_128P
   features:
   - name: image
@@ -299,10 +299,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2765222
+    num_bytes: 2723942.65
     num_examples: 9995
-  download_size: 0
-  dataset_size: 2765222
+  download_size: 1665779
+  dataset_size: 2723942.65
 - config_name: real_scenes_seesaw_scene_128P
   features:
   - name: image
@@ -313,10 +313,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2275814
+    num_bytes: 2234514.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 2275814
+  download_size: 1257167
+  dataset_size: 2234514.0
 - config_name: real_scenes_spring_scene_128P
   features:
   - name: image
@@ -327,10 +327,100 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2547386
+    num_bytes: 2506086.0
     num_examples: 10000
-  download_size: 0
-  dataset_size: 2547386
+  download_size: 951360
+  dataset_size: 2506086.0
+configs:
+- config_name: hypothetical_scenes_Hypothetic_v2_linear
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v2_linear/train-*
+- config_name: hypothetical_scenes_Hypothetic_v2_nonlinear
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v2_nonlinear/train-*
+- config_name: hypothetical_scenes_Hypothetic_v3_fully_connected_linear
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v3_fully_connected_linear/train-*
+- config_name: hypothetical_scenes_Hypothetic_v4_linear_full_connected
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v4_linear_full_connected/train-*
+- config_name: hypothetical_scenes_Hypothetic_v4_linear_v
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v4_linear_v/train-*
+- config_name: hypothetical_scenes_Hypothetic_v4_nonlinear_v
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v4_nonlinear_v/train-*
+- config_name: hypothetical_scenes_Hypothetic_v5_linear
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v5_linear/train-*
+- config_name: hypothetical_scenes_Hypothetic_v5_linear_full_connected
+  data_files:
+  - split: train
+    path: hypothetical_scenes_Hypothetic_v5_linear_full_connected/train-*
+- config_name: hypothetical_scenes_rendered_h3_linear_128P
+  data_files:
+  - split: train
+    path: hypothetical_scenes_rendered_h3_linear_128P/train-*
+- config_name: hypothetical_scenes_rendered_h3_nonlinear_128P
+  data_files:
+  - split: train
+    path: hypothetical_scenes_rendered_h3_nonlinear_128P/train-*
+- config_name: hypothetical_scenes_rendered_h5_nonlinear
+  data_files:
+  - split: train
+    path: hypothetical_scenes_rendered_h5_nonlinear/train-*
+- config_name: real_scenes_Real_Parabola
+  data_files:
+  - split: train
+    path: real_scenes_Real_Parabola/train-*
+- config_name: real_scenes_Real_magnet_v3
+  data_files:
+  - split: train
+    path: real_scenes_Real_magnet_v3/train-*
+  default: true
+- config_name: real_scenes_Real_magnet_v3_5
+  data_files:
+  - split: train
+    path: real_scenes_Real_magnet_v3_5/train-*
+- config_name: real_scenes_Real_spring_v3_256P
+  data_files:
+  - split: train
+    path: real_scenes_Real_spring_v3_256P/train-*
+- config_name: real_scenes_Water_flow_scene_render
+  data_files:
+  - split: train
+    path: real_scenes_Water_flow_scene_render/train-*
+- config_name: real_scenes_convex_len_render_images
+  data_files:
+  - split: train
+    path: real_scenes_convex_len_render_images/train-*
+- config_name: real_scenes_real_pendulum
+  data_files:
+  - split: train
+    path: real_scenes_real_pendulum/train-*
+- config_name: real_scenes_rendered_magnetic_128
+  data_files:
+  - split: train
+    path: real_scenes_rendered_magnetic_128/train-*
+- config_name: real_scenes_rendered_reflection_128P
+  data_files:
+  - split: train
+    path: real_scenes_rendered_reflection_128P/train-*
+- config_name: real_scenes_seesaw_scene_128P
+  data_files:
+  - split: train
+    path: real_scenes_seesaw_scene_128P/train-*
+- config_name: real_scenes_spring_scene_128P
+  data_files:
+  - split: train
+    path: real_scenes_spring_scene_128P/train-*
 ---
 # 🧠 Causal3D: A Benchmark for Visual Causal Reasoning
 

__init__.py

@@ -1 +0,0 @@
-from .Causal3D import Causal3D

dataset.py

@@ -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"))
-            
-
-        

dataset_infos.json

@@ -1,899 +0,0 @@
-{
-  "hypothetical_scenes_Hypothetic_v2_linear": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    },
-    "splits": {
-      "train": {
-        "name": "train",
-        "num_bytes": 0,
-        "num_examples": 0,
-        "dataset_name": "dataset"
-      }
-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v2_linear"
-  },
-  "hypothetical_scenes_Hypothetic_v2_nonlinear": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    },
-    "splits": {
-      "train": {
-        "name": "train",
-        "num_bytes": 0,
-        "num_examples": 0,
-        "dataset_name": "dataset"
-      }
-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v2_nonlinear"
-  },
-  "hypothetical_scenes_Hypothetic_v3_fully_connected_linear": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    },
-    "splits": {
-      "train": {
-        "name": "train",
-        "num_bytes": 0,
-        "num_examples": 0,
-        "dataset_name": "dataset"
-      }
-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v3_fully_connected_linear"
-  },
-  "hypothetical_scenes_Hypothetic_v4_linear_full_connected": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    },
-    "splits": {
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-        "name": "train",
-        "num_bytes": 0,
-        "num_examples": 0,
-        "dataset_name": "dataset"
-      }
-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v4_linear_full_connected"
-  },
-  "hypothetical_scenes_Hypothetic_v4_linear_v": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    },
-    "splits": {
-      "train": {
-        "name": "train",
-        "num_bytes": 0,
-        "num_examples": 0,
-        "dataset_name": "dataset"
-      }
-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v4_linear_v"
-  },
-  "hypothetical_scenes_Hypothetic_v4_nonlinear_v": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
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-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v4_nonlinear_v"
-  },
-  "hypothetical_scenes_Hypothetic_v5_linear": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
-      },
-      "file_name": {
-        "dtype": "string",
-        "_type": "Value"
-      },
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-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
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-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v5_linear"
-  },
-  "hypothetical_scenes_Hypothetic_v5_linear_full_connected": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
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-      "metadata": {
-        "dtype": "string",
-        "_type": "Value"
-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
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-    },
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-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_Hypothetic_v5_linear_full_connected"
-  },
-  "hypothetical_scenes_rendered_h3_linear_128P": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
-        "_type": "Image"
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-        "dtype": "string",
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-      "metadata": {
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-      }
-    },
-    "builder_name": "dataset",
-    "version": {
-      "version_str": "1.0.0",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    },
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-      "train": {
-        "name": "train",
-        "num_bytes": 0,
-        "num_examples": 0,
-        "dataset_name": "dataset"
-      }
-    },
-    "download_checksums": {},
-    "download_size": 0,
-    "dataset_size": 0,
-    "size_in_bytes": 0,
-    "config_name": "hypothetical_scenes_rendered_h3_linear_128P"
-  },
-  "hypothetical_scenes_rendered_h3_nonlinear_128P": {
-    "description": "Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. \nIt includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.\n",
-    "citation": "@article{liu2025causal3d,\n  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},\n  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},\n  journal={arXiv preprint arXiv:2503.04852},\n  year={2025}\n}",
-    "homepage": "https://huggingface.co/datasets/LLDDSS/Causal3D",
-    "license": "CC-BY-4.0",
-    "features": {
-      "image": {
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