| |
| """Plot per-class multimodal isolated AUPRO comparison across models.""" |
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import pandas as pd |
| import seaborn as sns |
|
|
|
|
| def find_data_file(explicit_path=None): |
| if explicit_path: |
| p = Path(explicit_path) |
| if p.is_file(): |
| return p |
| raise FileNotFoundError(f"Specified data file not found: {explicit_path}") |
|
|
| candidates = [ |
| Path("checkpoint_data/multimodal_isolated_metrics.json"), |
| Path("../checkpoint_data/multimodal_isolated_metrics.json"), |
| Path(__file__).resolve().parent.parent / "checkpoint_data" / "multimodal_isolated_metrics.json", |
| Path(__file__).resolve().parent / "multimodal_isolated_metrics.json", |
| Path("multimodal_isolated_metrics.json"), |
| ] |
| for c in candidates: |
| if c.is_file(): |
| return c |
| raise FileNotFoundError( |
| "Could not find 'multimodal_isolated_metrics.json'. Looked in: " |
| + ", ".join(str(c) for c in candidates) |
| ) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description="Plot per-class multimodal isolated AUPRO") |
| parser.add_argument( |
| "--data", |
| type=Path, |
| default=None, |
| help="Path to multimodal_isolated_metrics.json (auto-detected if omitted)", |
| ) |
| parser.add_argument( |
| "--out-dir", |
| type=Path, |
| default=None, |
| help="Output folder for figures (default: checkpoint_data/ or current dir)", |
| ) |
| parser.add_argument( |
| "--stem", |
| default="performance_aupro_multimodal", |
| help="Output filename stem without extension", |
| ) |
| parser.add_argument( |
| "--show", |
| action="store_true", |
| help="Show interactive plot window", |
| ) |
| args = parser.parse_args() |
|
|
| data_file = find_data_file(args.data) |
| print(f"Loading data from: {data_file}") |
|
|
| with open(data_file, "r", encoding="utf-8") as f: |
| data = json.load(f) |
|
|
| |
| rows = [] |
| for model, cameras in data.items(): |
| if model == "CMDR": |
| model = "CMDR-IAD" |
| for camera, scenes in cameras.items(): |
| for scene, classes in scenes.items(): |
| for cls, metrics in classes.items(): |
| rows.append({ |
| "Model": model, |
| "Class": cls, |
| "AUPRO": metrics.get("Pixel_AUPRO", metrics.get("aupro_30", 0.0)), |
| }) |
|
|
| if not rows: |
| raise SystemExit("No metric records found in input file.") |
|
|
| df = pd.DataFrame(rows) |
|
|
| |
| df_agg = df.groupby(["Model", "Class"])["AUPRO"].mean().reset_index() |
|
|
| |
| class_means = df_agg.groupby("Class")["AUPRO"].mean() |
|
|
| |
| sns.set_theme(style="whitegrid", font_scale=1.1) |
| fig = plt.figure(figsize=(14, 5)) |
|
|
| |
| chart = sns.barplot( |
| data=df_agg, |
| x="Class", |
| y="AUPRO", |
| hue="Model", |
| hue_order=["M3DM", "CMDR-IAD", "CFM", "WinCLIP", "UniVAD"], |
| palette="viridis", |
| ) |
|
|
| |
| classes = df_agg["Class"].unique() |
| for i, cls in enumerate(classes): |
| mean_val = class_means[cls] |
| plt.hlines( |
| y=mean_val, |
| xmin=i - 0.4, |
| xmax=i + 0.4, |
| colors="red", |
| linestyles=":", |
| linewidth=3, |
| label="Class Mean" if i == 0 else "", |
| ) |
| plt.text( |
| i + 0.3, |
| mean_val + 0.018, |
| f"{mean_val:.2f}", |
| color="red", |
| ha="left", |
| va="center", |
| fontsize=9, |
| fontweight="bold", |
| ) |
|
|
| plt.xticks(rotation=15, ha="right") |
| plt.xlabel("") |
| plt.ylabel("Mean Pixel AUPRO Score") |
|
|
| plt.legend( |
| loc="upper left", |
| ncol=3, |
| frameon=True, |
| framealpha=0.2, |
| facecolor="white", |
| edgecolor="black", |
| ) |
|
|
| plt.tight_layout() |
|
|
| if args.out_dir is not None: |
| out_dir = args.out_dir |
| elif Path("checkpoint_data").is_dir(): |
| out_dir = Path("checkpoint_data") |
| elif (Path(__file__).resolve().parent.parent / "checkpoint_data").is_dir(): |
| out_dir = Path(__file__).resolve().parent.parent / "checkpoint_data" |
| else: |
| out_dir = Path(".") |
|
|
| out_dir.mkdir(parents=True, exist_ok=True) |
| pdf_path = out_dir / f"{args.stem}.pdf" |
| png_path = out_dir / f"{args.stem}.png" |
|
|
| plt.savefig(pdf_path, bbox_inches="tight") |
| plt.savefig(png_path, dpi=300, bbox_inches="tight") |
| print(f"Saved: {pdf_path}") |
| print(f"Saved: {png_path}") |
|
|
| if args.show: |
| plt.show() |
| plt.close(fig) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
