update dataset loading

Causal3D.py

@@ -4,6 +4,7 @@ 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},
@@ -65,37 +66,15 @@ class Causal3D(datasets.GeneratorBasedBuilder):
             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 _split_generators(self, dl_manager):
         parts = self.config.name.split("_", 2)
-        category = parts[0] + "_" + parts[1]
+        category = parts[0] + "_" + parts[1]  # real_scenes or hypothetical_scenes
+
         if category not in ["real_scenes", "hypothetical_scenes"]:
-            raise ValueError(f"Invalid category '{category}'.")
+            raise ValueError(f"Invalid category '{category}'. Must be one of ['real_scenes', 'hypothetical_scenes']")
 
         scene = parts[2]
-        scene_path = os.path.join(category, scene)
-
-        if os.path.exists(scene_path):  
-          data_dir = scene_path
-        else: 
-          archive_path = dl_manager.download_and_extract(f"{scene_path}.zip")
-          data_dir = os.path.join(archive_path, scene)
+        data_dir = os.path.join(category, scene)
 
         return [
             datasets.SplitGenerator(
@@ -103,10 +82,12 @@ class Causal3D(datasets.GeneratorBasedBuilder):
                 gen_kwargs={"data_dir": data_dir},
             )
         ]
+
     def _generate_examples(self, data_dir):
         def color(text, code):
             return f"\033[{code}m{text}\033[0m"
-        print("load data from {}".format(data_dir))  
+        
+        # Load image paths
         try:
             image_files = {}
             for ext in ("*.png", "*.jpg", "*.jpeg"):

README.md

@@ -336,48 +336,6 @@ dataset_info:
 
 **Causal3D** is a comprehensive benchmark designed to evaluate models’ abilities to uncover *latent causal relations* from structured and visual data. This dataset integrates **3D-rendered scenes** with **tabular causal annotations**, providing a unified testbed for advancing *causal discovery*, *causal representation learning*, and *causal reasoning* with **vision-language models (VLMs)** and **large language models (LLMs)**.
 
-## 🖼️ Visual Previews
-
-Below are example images from different Causal3D scenes:
-
-<table>
-  <tr>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/parabola.png" width="250"/><br/>parabola
-    </td>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/convex.png" width="250"/><br/>convex
-    </td>
-  </tr>
-  <tr>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/magnetic.png" width="200"/><br/>magnetic
-    </td>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/pendulum.png" width="200"/><br/>pendulum
-    </td>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/reflection.png" width="200"/><br/>reflection
-    </td>
-  </tr>
-  <tr>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/seesaw.png" width="200"/><br/>seesaw
-    </td>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/spring.png" width="200"/><br/>spring
-    </td>
-    <td align="center">
-      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/water_flow.png" width="200"/><br/>water_flow
-    </td>
-  </tr>
-</table>
-
-<!-- - `causal_graph.json`: Ground-truth causal structure (as adjacency matrix or graph).
-- `view_info.json`: Camera/viewpoint metadata.
-- `split.json`: Recommended train/val/test splits for benchmarking. -->
-
-
 ## 📚 Usage
 
 #### 🔹 Option 1: Load from Hugging Face
@@ -398,7 +356,7 @@ print(dataset)
 ```
 
 #### 🔹 Option 2: Download via [**Kaggle**](https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset) + Croissant
-```python
+```
 import mlcroissant as mlc
 import pandas as pd
 
@@ -407,9 +365,11 @@ croissant_dataset = mlc.Dataset(
     "https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset/croissant/download"
 )
 
+# List available record sets
 record_sets = croissant_dataset.metadata.record_sets
 print(record_sets)
 
+# Load records from the first record set
 df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid))
 print(df.head())
 ```
@@ -434,6 +394,48 @@ Each sub-dataset (scene) contains:
 - `images/`: Rendered images under different camera views and backgrounds.
 - `tabular.csv`: Instance-level annotations including object attributes in causal graph.
 
+
+## 🖼️ Visual Previews
+
+Below are example images from different Causal3D scenes:
+
+<table>
+  <tr>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/parabola.png" width="250"/><br/>parabola
+    </td>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/convex.png" width="250"/><br/>convex
+    </td>
+  </tr>
+  <tr>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/magnetic.png" width="200"/><br/>magnetic
+    </td>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/pendulum.png" width="200"/><br/>pendulum
+    </td>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/reflection.png" width="200"/><br/>reflection
+    </td>
+  </tr>
+  <tr>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/seesaw.png" width="200"/><br/>seesaw
+    </td>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/spring.png" width="200"/><br/>spring
+    </td>
+    <td align="center">
+      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/water_flow.png" width="200"/><br/>water_flow
+    </td>
+  </tr>
+</table>
+
+<!-- - `causal_graph.json`: Ground-truth causal structure (as adjacency matrix or graph).
+- `view_info.json`: Camera/viewpoint metadata.
+- `split.json`: Recommended train/val/test splits for benchmarking. -->
+
 ---
 
 ## 🎯 Evaluation Tasks

hypothetical_scenes/Hypothetic_v2_linear.zip

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-size 1110377090

hypothetical_scenes/Hypothetic_v2_nonlinear.zip

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-size 800467917

hypothetical_scenes/Hypothetic_v3_fully_connected_linear.zip

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-size 822752525

hypothetical_scenes/Hypothetic_v4_linear_full_connected.zip

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hypothetical_scenes/Hypothetic_v4_linear_v.zip

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hypothetical_scenes/Hypothetic_v4_nonlinear_v.zip

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hypothetical_scenes/Hypothetic_v5_linear.zip

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hypothetical_scenes/Hypothetic_v5_linear_full_connected.zip

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hypothetical_scenes/rendered_h3_linear_128P.zip

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hypothetical_scenes/rendered_h3_nonlinear_128P.zip

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hypothetical_scenes/rendered_h5_nonlinear.zip

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real_scenes/Real_Parabola.zip

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-size 1383346876

real_scenes/Real_magnet_v3.zip

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-size 61633001

real_scenes/Real_magnet_v3_5.zip

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real_scenes/Real_spring_v3_256P.zip

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real_scenes/Water_flow_scene_render.zip

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real_scenes/convex_len_render_images.zip

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real_scenes/real_pendulum.zip

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real_scenes/rendered_magnetic_128.zip

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real_scenes/rendered_reflection_128P.zip

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