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6d5f1ce 1262684 6d5f1ce 1262684 6d5f1ce | 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 | #!/usr/bin/env python3
"""Summarize EffB2 predictions for paired diffusion augmentation QC."""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Create QC summary from paired manifest and EffB2 debug predictions.")
parser.add_argument(
"--manifest",
type=Path,
default=Path("Stable_diffusion_augmentation/out_minority_pairs/paired_augmentation_manifest.csv"),
)
parser.add_argument(
"--predictions",
type=Path,
default=Path("Stable_diffusion_augmentation/out_minority_pairs/effb2_qc_predictions.csv"),
)
parser.add_argument(
"--output",
type=Path,
default=Path("Stable_diffusion_augmentation/out_minority_pairs/effb2_qc_summary.csv"),
)
return parser.parse_args()
def read_by_key(path: Path, key: str) -> dict[str, dict[str, str]]:
with path.open(newline="") as f:
return {row[key]: row for row in csv.DictReader(f)}
def probability_for(row: dict[str, str], class_name: str) -> float:
for key in (class_name, f"prob_{class_name}"):
value = row.get(key)
if value not in (None, ""):
return float(value)
return 0.0
def main() -> None:
args = parse_args()
manifest = read_by_key(args.manifest.expanduser().resolve(), "synthetic_lesion_id")
predictions = read_by_key(args.predictions.expanduser().resolve(), "lesion_id")
output = args.output.expanduser().resolve()
output.parent.mkdir(parents=True, exist_ok=True)
fields = [
"synthetic_lesion_id",
"source_lesion_id",
"target_class",
"label_pred",
"confidence",
"target_class_probability",
"is_target_predicted",
"clinical_generated_path",
"dermoscopic_generated_path",
]
with output.open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=fields)
writer.writeheader()
for lesion_id, manifest_row in sorted(manifest.items()):
pred_row = predictions.get(lesion_id, {})
target_class = manifest_row["class_name"]
label_pred = pred_row.get("label_pred", "")
target_prob = probability_for(pred_row, target_class) if pred_row else 0.0
writer.writerow(
{
"synthetic_lesion_id": lesion_id,
"source_lesion_id": manifest_row.get("source_lesion_id", ""),
"target_class": target_class,
"label_pred": label_pred,
"confidence": pred_row.get("confidence", ""),
"target_class_probability": target_prob,
"is_target_predicted": str(label_pred == target_class),
"clinical_generated_path": manifest_row["clinical_generated_path"],
"dermoscopic_generated_path": manifest_row["dermoscopic_generated_path"],
}
)
print(f"Saved QC summary: {output}")
if __name__ == "__main__":
main()
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