|
|
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 = [ |
|
|
|
|
|
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"), |
|
|
|
|
|
|
|
|
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_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"), |
|
|
}), |
|
|
homepage=_HOMEPAGE, |
|
|
license=_LICENSE, |
|
|
citation=_CITATION, |
|
|
) |
|
|
|
|
|
def _split_generators(self, dl_manager): |
|
|
parts = self.config.name.split("_", 2) |
|
|
category = parts[0] + "_" + parts[1] |
|
|
|
|
|
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" |
|
|
|
|
|
|
|
|
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")) |
|
|
return |
|
|
|
|
|
|
|
|
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: |
|
|
|
|
|
pass |
|
|
|
|
|
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: |
|
|
|
|
|
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 "" |
|
|
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: |
|
|
|
|
|
yield idx, { |
|
|
"image": image_files[record_img_path], |
|
|
"file_name": fname, |
|
|
"metadata": row.to_json(), |
|
|
} |
|
|
break |
|
|
|
|
|
else: |
|
|
yield idx, { |
|
|
|
|
|
"file_name": fname, |
|
|
"metadata": row.to_json(), |
|
|
} |
|
|
break |
|
|
|
|
|
|
|
|
except Exception as e: |
|
|
print(color(f"Error processing CSV rows: {e}", "31")) |
|
|
|
