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4f3323a b6f22ac 4f3323a b6f22ac 4f3323a b6f22ac 4f3323a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | #!/usr/bin/env python3
"""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)
# 2. Flatten the data
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)
# 3. Aggregate: Mean performance per Model and Class
df_agg = df.groupby(["Model", "Class"])["AUPRO"].mean().reset_index()
# 4. Calculate Mean per Class (across all models)
class_means = df_agg.groupby("Class")["AUPRO"].mean()
# 5. Visualization
sns.set_theme(style="whitegrid", font_scale=1.1)
fig = plt.figure(figsize=(14, 5))
# Create barplot
chart = sns.barplot(
data=df_agg,
x="Class",
y="AUPRO",
hue="Model",
hue_order=["M3DM", "CMDR-IAD", "CFM", "WinCLIP", "UniVAD"],
palette="viridis",
)
# 6. Add dotted line segments and text labels
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()
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