update dataloader

Causal3D.py

@@ -23,9 +23,6 @@ _HOMEPAGE = "https://huggingface.co/datasets/LLDDSS/Causal3D"
 _LICENSE = "CC-BY-4.0"
 
 class Causal3D(datasets.GeneratorBasedBuilder):
-    # def __init__(self):
-    #     super().__init__()
-    #     print("Causal3D dataset builder initialized.")
     DEFAULT_CONFIG_NAME = "real_scenes_Real_magnet_v3"
     BUILDER_CONFIGS = [
         # hypothetical_scenes
@@ -45,7 +42,7 @@ class Causal3D(datasets.GeneratorBasedBuilder):
         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_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"),
@@ -87,34 +84,9 @@ class Causal3D(datasets.GeneratorBasedBuilder):
         ]
 
     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 = {}
@@ -131,38 +103,61 @@ class Causal3D(datasets.GeneratorBasedBuilder):
         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
-            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
+            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:

README.md

@@ -8,6 +8,329 @@ pretty_name: Causal3D
 tags:
 - Causality
 - Computer_Vision
+dataset_info:
+- config_name: hypothetical_scenes_Hypothetic_v2_linear
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2197142
+    num_examples: 14368
+  download_size: 0
+  dataset_size: 2197142
+- config_name: hypothetical_scenes_Hypothetic_v2_nonlinear
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1809956
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 1809956
+- config_name: hypothetical_scenes_Hypothetic_v3_fully_connected_linear
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1397093
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 1397093
+- config_name: hypothetical_scenes_Hypothetic_v4_linear_full_connected
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1699598
+    num_examples: 10050
+  download_size: 0
+  dataset_size: 1699598
+- config_name: hypothetical_scenes_Hypothetic_v4_linear_v
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2053379
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 2053379
+- config_name: hypothetical_scenes_Hypothetic_v4_nonlinear_v
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2828217
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 2828217
+- config_name: hypothetical_scenes_Hypothetic_v5_linear
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1956461
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 1956461
+- config_name: hypothetical_scenes_Hypothetic_v5_linear_full_connected
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1955921
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 1955921
+- config_name: hypothetical_scenes_rendered_h3_linear_128P
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5425498
+    num_examples: 15000
+  download_size: 0
+  dataset_size: 5425498
+- config_name: hypothetical_scenes_rendered_h3_nonlinear_128P
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3852500
+    num_examples: 10223
+  download_size: 0
+  dataset_size: 3852500
+- config_name: hypothetical_scenes_rendered_h5_nonlinear
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5459126
+    num_examples: 10360
+  download_size: 0
+  dataset_size: 5459126
+- config_name: real_scenes_Real_Parabola
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1323548
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 1323548
+- config_name: real_scenes_Real_magnet_v3
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 28397
+    num_examples: 481
+  download_size: 0
+  dataset_size: 28397
+- config_name: real_scenes_Real_magnet_v3_5
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 93977
+    num_examples: 1503
+  download_size: 0
+  dataset_size: 93977
+- config_name: real_scenes_Real_parabola_multi_view
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 0
+  download_size: 0
+  dataset_size: 0
+- config_name: real_scenes_Real_spring_v3_256P
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 136325
+    num_examples: 450
+  download_size: 0
+  dataset_size: 136325
+- config_name: real_scenes_Water_flow_scene_render
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2792618
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 2792618
+- config_name: real_scenes_convex_len_render_images
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 72448
+    num_examples: 1078
+  download_size: 0
+  dataset_size: 72448
+- config_name: real_scenes_real_pendulum
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2925963
+    num_examples: 9999
+  download_size: 0
+  dataset_size: 2925963
+- config_name: real_scenes_rendered_magnetic_128
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2324526
+    num_examples: 8350
+  download_size: 0
+  dataset_size: 2324526
+- config_name: real_scenes_rendered_reflection_128P
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2765222
+    num_examples: 9995
+  download_size: 0
+  dataset_size: 2765222
+- config_name: real_scenes_seesaw_scene_128P
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2275814
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 2275814
+- config_name: real_scenes_spring_scene_128P
+  features:
+  - name: image
+    dtype: image
+  - name: file_name
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2547386
+    num_examples: 10000
+  download_size: 0
+  dataset_size: 2547386
 ---
 # 🧠 Causal3D: A Benchmark for Visual Causal Reasoning