File size: 5,612 Bytes
590304f | 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 | #!/usr/bin/env python3
"""Evaluate one SAM3 LoRA checkpoint across equivalent lesion prompts."""
from __future__ import annotations
import argparse
import itertools
import json
import os
import re
import subprocess
import sys
from pathlib import Path
import numpy as np
import pandas as pd
from PIL import Image
PROMPTS = ["breast lesion", "breast tumor", "breast mass", "breast nodule", "ultrasound breast lesion", "breast ultrasound mass", "hypoechoic breast lesion"]
SCRIPT_DIR = Path(__file__).resolve().parent
BUNDLE_ROOT = SCRIPT_DIR.parent
def slug(text):
return re.sub(r"[^A-Za-z0-9]+", "_", text).strip("_").lower()
def args_parser():
p = argparse.ArgumentParser()
p.add_argument("--index_csv", default="data/sam3_multiprompt_lora_qc/index/test_index.csv")
p.add_argument("--sam3_base_model_path", default=str(Path(os.environ.get("SAM3_CHECKPOINT", BUNDLE_ROOT / "model" / "sam3_base.pt"))))
p.add_argument("--sam3_lora_path", default=str(BUNDLE_ROOT / "model" / "best_model.pt"))
p.add_argument("--output_dir", default="outputs/sam3_multiprompt_lora_qc/single_prompt_lora_multiprompt_eval")
p.add_argument("--prompts", nargs="+", default=PROMPTS)
p.add_argument("--max_samples", type=int)
p.add_argument("--overwrite", action="store_true")
p.add_argument("--resume", action="store_true")
return p.parse_args()
def mask(path):
return np.asarray(Image.open(path).convert("L")) > 0
def dice(a, b):
if a.shape != b.shape:
b = np.asarray(Image.fromarray(b.astype(np.uint8) * 255).resize((a.shape[1], a.shape[0]), Image.Resampling.NEAREST)) > 0
den = int(a.sum() + b.sum())
return 2 * int(np.logical_and(a, b).sum()) / den if den else 1.0
def main():
args = args_parser()
out = Path(args.output_dir)
out.mkdir(parents=True, exist_ok=True)
lora = Path(args.sam3_lora_path)
checkpoint = lora / "best_model.pt" if lora.is_dir() else lora
if not checkpoint.exists():
raise FileNotFoundError(checkpoint)
command = [
sys.executable, "-u", str(SCRIPT_DIR / "evaluate_sam3_test_text_prompt_segmentation.py"),
"--index_csv", args.index_csv, "--sam3_model_path", args.sam3_base_model_path,
"--checkpoint_path", str(checkpoint), "--output_dir", str(out),
"--encoder_trainable", "lora", "--lora_rank", "8", "--lora_alpha", "16",
"--prompts", *args.prompts,
]
if args.max_samples:
command += ["--max_samples", str(args.max_samples)]
if args.overwrite:
command.append("--overwrite")
if args.resume:
command.append("--resume")
subprocess.run(command, check=True)
frames = []
for prompt in args.prompts:
frame = pd.read_csv(out / f"predictions_{slug(prompt)}.csv")
frame["prompt"] = prompt
frames.append(frame)
predictions = pd.concat(frames, ignore_index=True)
predictions.to_csv(out / "all_prompt_predictions.csv", index=False)
per_sample = []
for sid, group in predictions.groupby("sample_id"):
valid = group[group["valid_mask"].astype(bool)]
masks = [(row["prompt"], mask(row["pred_mask_path"])) for _, row in valid.iterrows()]
pairs = [dice(a, b) for (_, a), (_, b) in itertools.combinations(masks, 2)]
areas = valid["pred_area"].astype(float).to_numpy()
per_sample.append({
"sample_id": sid,
"prompt_dice_std": float(valid["dice"].std(ddof=0)),
"mask_area_cv": float(areas.std() / areas.mean()) if len(areas) and areas.mean() else np.nan,
"pairwise_mask_dice_mean": float(np.mean(pairs)) if pairs else np.nan,
"pairwise_mask_dice_min": float(np.min(pairs)) if pairs else np.nan,
"valid_prompt_count": len(valid),
})
sample_summary = pd.DataFrame(per_sample)
sample_summary.to_csv(out / "per_sample_prompt_sensitivity.csv", index=False)
metrics = pd.read_csv(out / "all_prompt_metrics.csv")
best = metrics.loc[metrics["mean Dice"].idxmax()]
worst = metrics.loc[metrics["mean Dice"].idxmin()]
training = metrics[metrics["prompt"] == "breast lesion"].iloc[0]
robustness = {
"checkpoint_path": str(checkpoint.resolve()),
"best_prompt_by_mean_dice": best["prompt"],
"best_mean_dice": best["mean Dice"],
"worst_prompt_by_mean_dice": worst["prompt"],
"worst_mean_dice": worst["mean Dice"],
"mean_dice_range": best["mean Dice"] - worst["mean Dice"],
"std_mean_dice_across_prompts": metrics["mean Dice"].std(ddof=0),
"mean_per_sample_prompt_dice_std": sample_summary["prompt_dice_std"].mean(),
"mean_per_sample_area_cv": sample_summary["mask_area_cv"].mean(),
"mean_pairwise_mask_dice": sample_summary["pairwise_mask_dice_mean"].mean(),
"fraction_pairwise_mean_below_0p8": (sample_summary["pairwise_mask_dice_mean"] < 0.8).mean(),
"max_nontraining_prompt_drop_vs_breast_lesion": training["mean Dice"] - metrics[metrics["prompt"] != "breast lesion"]["mean Dice"].min(),
}
robustness["large_prompt_sensitivity"] = bool(
robustness["mean_dice_range"] >= 0.05
or robustness["fraction_pairwise_mean_below_0p8"] >= 0.1
or robustness["max_nontraining_prompt_drop_vs_breast_lesion"] > 0.05
)
pd.DataFrame([robustness]).to_csv(out / "prompt_sensitivity_summary.csv", index=False)
with open(out / "prompt_sensitivity_summary.json", "w") as handle:
json.dump(robustness, handle, indent=2)
print(pd.DataFrame([robustness]).to_string(index=False))
if __name__ == "__main__":
main()
|