File size: 9,182 Bytes
6f98a26 03a45bc 6f98a26 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | #!/usr/bin/env python3
"""Main execution script for SAM3 metrics evaluation."""
import argparse
import json
import logging
import sys
from pathlib import Path
from metrics_evaluation.config.config_loader import load_config
from metrics_evaluation.extraction.cvat_extractor import CVATExtractor
from metrics_evaluation.inference.sam3_inference import SAM3Inferencer
from metrics_evaluation.metrics.metrics_calculator import MetricsCalculator
from metrics_evaluation.utils.logging_config import setup_logging
from metrics_evaluation.visualization.visual_comparison import VisualComparator
logger = logging.getLogger(__name__)
def write_metrics_summary(metrics: dict, output_path: Path) -> None:
"""Write human-readable metrics summary.
Args:
metrics: Metrics dictionary
output_path: Path to output file
"""
with open(output_path, "w") as f:
f.write("=" * 80 + "\n")
f.write("SAM3 EVALUATION METRICS SUMMARY\n")
f.write("=" * 80 + "\n\n")
aggregate = metrics["aggregate"]
f.write(f"Total Images Evaluated: {aggregate['total_images']}\n\n")
for threshold_str, threshold_data in aggregate["by_threshold"].items():
iou = threshold_data["iou_threshold"]
f.write(f"\n{'='*80}\n")
f.write(f"IoU Threshold: {iou:.0%}\n")
f.write(f"{'='*80}\n\n")
overall = threshold_data["overall"]
f.write("Overall Metrics:\n")
f.write(f" True Positives: {overall['true_positives']}\n")
f.write(f" False Positives: {overall['false_positives']}\n")
f.write(f" False Negatives: {overall['false_negatives']}\n")
f.write(f" Precision: {overall['precision']:.2%}\n")
f.write(f" Recall: {overall['recall']:.2%}\n")
f.write(f" F1-Score: {overall['f1']:.2%}\n")
f.write(f" mAP: {overall['map']:.2%}\n")
f.write(f" mAR: {overall['mar']:.2%}\n\n")
f.write("Per-Class Metrics:\n")
f.write("-" * 80 + "\n")
f.write(f"{'Class':<20} {'GT':>6} {'Pred':>6} {'TP':>6} {'FP':>6} {'FN':>6} {'Prec':>8} {'Rec':>8} {'F1':>8}\n")
f.write("-" * 80 + "\n")
for label, stats in sorted(threshold_data["by_label"].items()):
f.write(
f"{label:<20} "
f"{stats['gt_total']:>6} "
f"{stats['pred_total']:>6} "
f"{stats['tp']:>6} "
f"{stats['fp']:>6} "
f"{stats['fn']:>6} "
f"{stats['precision']:>8.2%} "
f"{stats['recall']:>8.2%} "
f"{stats['f1']:>8.2%}\n"
)
f.write("\n")
# Confusion Matrix
cm = threshold_data["confusion_matrix"]
labels = cm["labels"]
matrix = cm["matrix"]
if labels:
f.write("Confusion Matrix:\n")
f.write("-" * 80 + "\n")
# Header
header = "Actual \\ Pred |"
for label in labels:
header += f" {label[:10]:>10} |"
f.write(header + "\n")
f.write("-" * len(header) + "\n")
# Rows
for i, actual_label in enumerate(labels):
row = f"{actual_label[:13]:>13} |"
for j in range(len(labels)):
row += f" {matrix[i][j]:>10} |"
f.write(row + "\n")
f.write("\n")
f.write("=" * 80 + "\n")
f.write("END OF REPORT\n")
f.write("=" * 80 + "\n")
logger.info(f"Wrote metrics summary to {output_path}")
def main() -> int:
"""Main execution function.
Returns:
Exit code (0 for success, non-zero for failure)
"""
parser = argparse.ArgumentParser(
description="Run SAM3 metrics evaluation against CVAT ground truth"
)
parser.add_argument(
"--config",
type=str,
default="config/config.json",
help="Path to configuration file"
)
parser.add_argument(
"--force-download",
action="store_true",
help="Force re-download images from CVAT"
)
parser.add_argument(
"--force-inference",
action="store_true",
help="Force re-run SAM3 inference"
)
parser.add_argument(
"--skip-inference",
action="store_true",
help="Skip inference, use cached results"
)
parser.add_argument(
"--visualize",
action="store_true",
help="Generate visual comparisons"
)
parser.add_argument(
"--log-level",
type=str,
default="INFO",
choices=["DEBUG", "INFO", "WARNING", "ERROR"],
help="Logging level"
)
args = parser.parse_args()
# Load configuration
try:
config = load_config(args.config)
except Exception as e:
print(f"ERROR: Failed to load configuration: {e}", file=sys.stderr)
return 1
# Setup logging
cache_dir = config.get_cache_path()
log_file = cache_dir / "evaluation_log.txt"
setup_logging(log_file, getattr(logging, args.log_level))
logger.info("=" * 80)
logger.info("SAM3 METRICS EVALUATION")
logger.info("=" * 80)
try:
# Phase 1: Extract from CVAT
logger.info("\n" + "=" * 80)
logger.info("PHASE 1: CVAT Data Extraction")
logger.info("=" * 80)
extractor = CVATExtractor(config)
if args.force_download:
logger.info("Force download enabled - will re-download all images")
image_paths = extractor.run_extraction()
total_extracted = sum(len(paths) for paths in image_paths.values())
logger.info(f"Extraction complete: {total_extracted} images extracted")
if total_extracted == 0:
logger.error("No images extracted. Aborting.")
return 1
# Phase 2: Run SAM3 Inference
if not args.skip_inference:
logger.info("\n" + "=" * 80)
logger.info("PHASE 2: SAM3 Inference")
logger.info("=" * 80)
inferencer = SAM3Inferencer(config)
stats = inferencer.run_inference_batch(image_paths, args.force_inference)
logger.info(
f"Inference complete: {stats['successful']} successful, "
f"{stats['failed']} failed, {stats['skipped']} skipped"
)
if stats['successful'] == 0 and stats['skipped'] == 0:
logger.error("No successful inferences. Aborting.")
return 1
else:
logger.info("Skipping inference (--skip-inference)")
# Phase 3: Calculate Metrics
logger.info("\n" + "=" * 80)
logger.info("PHASE 3: Metrics Calculation")
logger.info("=" * 80)
calculator = MetricsCalculator(config)
metrics = calculator.run_evaluation(cache_dir)
# Save detailed metrics
metrics_json_path = cache_dir / "metrics_detailed.json"
with open(metrics_json_path, "w") as f:
json.dump(metrics, f, indent=2)
logger.info(f"Saved detailed metrics to {metrics_json_path}")
# Write summary
metrics_summary_path = cache_dir / "metrics_summary.txt"
write_metrics_summary(metrics, metrics_summary_path)
# Phase 4: Visualization (optional)
if args.visualize or config.output.generate_visualizations:
logger.info("\n" + "=" * 80)
logger.info("PHASE 4: Visual Comparisons")
logger.info("=" * 80)
comparator = VisualComparator()
comparison_paths = comparator.generate_all_comparisons(cache_dir)
logger.info(f"Generated {len(comparison_paths)} visual comparisons")
# Summary
logger.info("\n" + "=" * 80)
logger.info("EVALUATION COMPLETE")
logger.info("=" * 80)
aggregate = metrics["aggregate"]
logger.info(f"Images evaluated: {aggregate['total_images']}")
# Show metrics at 50% IoU
threshold_50 = aggregate["by_threshold"]["0.5"]
overall = threshold_50["overall"]
logger.info(f"\nMetrics at 50% IoU:")
logger.info(f" Precision: {overall['precision']:.2%}")
logger.info(f" Recall: {overall['recall']:.2%}")
logger.info(f" F1-Score: {overall['f1']:.2%}")
logger.info(f" mAP: {overall['map']:.2%}")
logger.info(f" mAR: {overall['mar']:.2%}")
logger.info(f"\nResults saved to:")
logger.info(f" Metrics Summary: {metrics_summary_path}")
logger.info(f" Detailed JSON: {metrics_json_path}")
logger.info(f" Execution Log: {log_file}")
return 0
except KeyboardInterrupt:
logger.warning("\nEvaluation interrupted by user")
return 130
except Exception as e:
logger.error(f"\nEvaluation failed with error: {e}", exc_info=True)
return 1
if __name__ == "__main__":
sys.exit(main())
|