diff --git a/Causal3D.py b/Causal3D.py deleted file mode 100644 index 278d1292ff06ef1050aab34ff44202a70f321084..0000000000000000000000000000000000000000 --- a/Causal3D.py +++ /dev/null @@ -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")) - \ No newline at end of file diff --git a/README.md b/README.md index b6ab68dc75dc0a55b458cd7ab5b3675130dad575..ccaae64e2ed66a1ac4cd386a91dd4aaee40609f8 100644 --- a/README.md +++ b/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 diff --git a/__init__.py b/__init__.py deleted file mode 100644 index aab0a9edc247b59d79fb47deb303018b972c2856..0000000000000000000000000000000000000000 --- a/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .Causal3D import Causal3D \ No newline at end of file diff --git a/dataset.py b/dataset.py deleted file mode 100644 index 651ae8c0e6b44c77dfb3895fb3806113243157e6..0000000000000000000000000000000000000000 --- a/dataset.py +++ /dev/null @@ -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")) - - - \ No newline at end of file diff --git a/dataset_infos.json b/dataset_infos.json deleted file mode 100644 index 5d404e969cd7e41b5af2bc952bd4b98a62aaaabe..0000000000000000000000000000000000000000 --- a/dataset_infos.json +++ /dev/null @@ -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, 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