delete dataset.py

dataset.py

@@ -1,203 +0,0 @@
-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"))
-