update

Causal3D_Dataset.py

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+import datasets
+import pandas as pd
+import os
+from pathlib import Path
+from tqdm import tqdm
+
+_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 Causal3dDataset(datasets.GeneratorBasedBuilder):
+    DEFAULT_CONFIG_NAME = "Real_Water_flow"
+    BUILDER_CONFIGS = [
+        # hypothetical_scenes
+        datasets.BuilderConfig(name="Hypothetical_V2_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V2_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V2_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V2_nonlinear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V3_fully_connected_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_fully_connected_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V3_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_v_structure_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V3_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_v_structure_nonlinear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V4_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V4_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_v_structure_nonlinear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V4_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_v_structure_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V5_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V5_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_v_structure_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V5_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_v_structure_nonlinear scene"),
+
+        # real_scenes
+        datasets.BuilderConfig(name="Real_Parabola", version=datasets.Version("1.0.0"), description="Real_Parabola scene"),
+        datasets.BuilderConfig(name="Real_Magnet", version=datasets.Version("1.0.0"), description="Real_Magnet scene"),
+        datasets.BuilderConfig(name="Real_Spring", version=datasets.Version("1.0.0"), description="Real_Spring scene"),
+        datasets.BuilderConfig(name="Real_Water_flow", version=datasets.Version("1.0.0"), description="Real_Water_flow scene"),
+        datasets.BuilderConfig(name="Real_Seesaw", version=datasets.Version("1.0.0"), description="Real_Seesaw scene"),
+        datasets.BuilderConfig(name="Real_Reflection", version=datasets.Version("1.0.0"), description="Real_Reflection scene"),
+        datasets.BuilderConfig(name="Real_Pendulum", version=datasets.Version("1.0.0"), description="Real_Pendulum scene"),
+        datasets.BuilderConfig(name="Real_Convex_len", version=datasets.Version("1.0.0"), description="Real_Convex_len 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):
+        print(">>>>>>>>>>>>>>>>>>>>>>> Starting to load dataset <<<<<<<<<<<<<<<<<<<<<<<")
+        parts = self.config.name.split("_", 1)   # 🚩 Real_Parabola -> ["Real", "Parabola"]
+        category = parts[0]
+        scene = parts[1]
+
+        local_scene_dir = os.path.join(category, scene)
+
+        if os.path.exists(local_scene_dir):
+            data_dir = local_scene_dir
+            print(f"Using local folder: {data_dir}")
+        else:
+            zip_name = f"{self.config.name}.zip"
+            archive_path = dl_manager.download_and_extract(zip_name)
+            data_dir = archive_path  
+            print(f"Downloaded and extracted: {zip_name}")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            )
+        ]
+    
+    def _generate_examples(self, data_dir):
+        print(f"Generating examples from: {data_dir}")
+        image_files = {}
+        for ext in ("*.png", "*.jpg", "*.jpeg"):
+            for img_path in Path(data_dir).rglob(ext):
+                relative = str(img_path.relative_to(data_dir))
+                image_files[relative] = str(img_path)
+
+        csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")]
+        df = pd.read_csv(csv_files[0]) if csv_files else None
+
+        if df is not None and "imgs" in df.columns:
+            images = df["imgs"].tolist()
+        else:
+            images = []
+
+        for idx, row in tqdm(df.iterrows(), total=len(df)) if df is not None else enumerate(image_files):
+            if df is not None:
+                fname = row["imgs"] if "imgs" in row else str(idx)
+                image_name = images[idx].split("/")[-1].split(".")[0] if images else ""
+                record_img_path = next((key for key in image_files if image_name in key), None)
+                yield idx, {
+                    "image": image_files[record_img_path] if record_img_path else None,
+                    "file_name": fname,
+                    "metadata": row.to_json(),
+                }
+            else:
+                fname = Path(image_files[idx]).stem
+                yield idx, {
+                    "image": image_files[idx],
+                    "file_name": fname,
+                    "metadata": None,
+                }
+
+
+    # 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))  
+    #     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 = [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 "imgs" not in df.columns:
+    #         image_col_exists = False
+
+    #     images = df["imgs"].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["imgs"]
+    #                 raw_record_img_path = row["imgs"] #images[idx] if images else "" #row["image"] 
+    #                 record_img_name = raw_record_img_path.split('/')[-1]
+    #                 render_img_path = record_img_name
+
+
+    #                 # 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"))
+