Feat/tensorrt int8 light patchcore

anomavision/__init__.py

@@ -12,6 +12,7 @@
 from .datasets.mvtec_dataset import MVTecDataset
 from .feature_extraction import ResnetEmbeddingsExtractor
 from .padim import Padim
+from .patchcore import PatchCore
 from .sampling_methods.kcenter_greedy import kCenterGreedy
 from .test import optimal_threshold, visualize_eval_data, visualize_eval_pair
 from .utils import get_logger  # Export for users

anomavision/cli.py

@@ -68,6 +68,7 @@ def create_parser() -> argparse.ArgumentParser:
     _add_export_parser(subparsers)
     _add_detect_parser(subparsers)
     _add_eval_parser(subparsers)
+    _add_autopilot_parser(subparsers)
 
     return parser
 
@@ -133,6 +134,17 @@ def _add_eval_parser(subparsers) -> None:
     ).set_defaults(func=_dispatch_eval)
 
 
+def _add_autopilot_parser(subparsers) -> None:
+    from anomavision.autopilot import create_parser as _cp
+
+    subparsers.add_parser(
+        "autopilot",
+        help="Calibrate, profile, and package a production model",
+        parents=[_cp(add_help=False)],
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    ).set_defaults(func=_dispatch_autopilot)
+
+
 # ============================================================
 # Dispatch functions — one line each, Namespace passed directly.
 # No sys.argv manipulation. No double-parsing.
@@ -163,6 +175,12 @@ def _dispatch_eval(args: argparse.Namespace) -> None:
     eval_module.main(args)
 
 
+def _dispatch_autopilot(args: argparse.Namespace) -> None:
+    from anomavision import autopilot
+
+    autopilot.main(args)
+
+
 # ============================================================
 # Entry point
 # ============================================================

anomavision/detect.py

@@ -30,7 +30,9 @@
 from anomavision.utils import (
     adaptive_gaussian_blur,
     get_logger,
+    make_localization_mask,
     merge_config,
+    resolve_threshold,
     setup_logging,
 )
 
@@ -199,6 +201,8 @@ def run_inference(args):
 
     # Merge config with CLI args
     config = edict(merge_config(args, cfg))
+    config.thresh = resolve_threshold(config)
+    algorithm_name = str(config.get("algorithm", "")).lower()
 
     # Setup logging
     setup_logging(enabled=True, log_level=config.log_level, log_to_file=True)
@@ -412,6 +416,17 @@ def run_inference(args):
                     else:
                         is_anomaly = np.zeros_like(image_scores)
 
+                    if algorithm_name == "patchcore":
+                        localization_masks = make_localization_mask(
+                            score_maps, is_anomaly, quantile=0.90
+                        )
+                    else:
+                        localization_masks = (
+                            anomavision.classification(score_maps, config.thresh)
+                            if config.thresh is not None
+                            else np.zeros_like(score_maps)
+                        )
+
                     # Accumulate Results (Offline only)
                     if not stream_mode:
                         results_accumulator["scores"].extend(image_scores.tolist())
@@ -432,13 +447,7 @@ def run_inference(args):
                         boundary_images = (
                             anomavision.visualization.framed_boundary_images(
                                 images,
-                                (
-                                    anomavision.classification(
-                                        score_maps, config.thresh
-                                    )
-                                    if config.thresh
-                                    else np.zeros_like(score_maps)
-                                ),
+                                localization_masks,
                                 is_anomaly,
                                 padding=config.get("viz_padding", 40),
                             )
@@ -447,17 +456,15 @@ def run_inference(args):
                         heatmap_images = anomavision.visualization.heatmap_images(
                             images,
                             score_maps,
+                            masks=localization_masks,
                             alpha=config.get("viz_alpha", 0.5),
                         )
-                        highlighted_images = anomavision.visualization.highlighted_images(
-                            [images[i] for i in range(len(images))],
-                            # Dummy mask if threshold not set
-                            (
-                                anomavision.classification(score_maps, config.thresh)
-                                if config.thresh
-                                else np.zeros_like(score_maps)
-                            ),
-                            color=viz_color,
+                        highlighted_images = (
+                            anomavision.visualization.highlighted_images(
+                                [images[i] for i in range(len(images))],
+                                localization_masks,
+                                color=viz_color,
+                            )
                         )
 
                         # Save/Show

anomavision/eval.py

@@ -23,6 +23,7 @@
     find_optimal_threshold,
     get_logger,
     merge_config,
+    resolve_threshold,
     setup_logging,
 )
 
@@ -218,6 +219,7 @@ def run_evaluation(args):
         cfg = load_config(str(config_path)) if config_path.exists() else {}
 
     config = edict(merge_config(args, cfg))
+    config.thresh = resolve_threshold(config)
 
     setup_logging(enabled=True, log_level=config.log_level, log_to_file=True)
     logger = get_logger("anomavision.eval")
@@ -316,9 +318,11 @@ def run_evaluation(args):
 
     # Compute Metrics
     if config.thresh is None:
-        best_thresh, _ = find_optimal_threshold(labels, scores)
+        best_thresh, best_f1 = find_optimal_threshold(labels, scores)
+        logger.info("threshold: auto-selected %.6f (F1=%.4f)", best_thresh, best_f1)
     else:
         best_thresh = config.thresh
+        logger.info("threshold: configured %.6f", best_thresh)
 
     metrics = compute_metrics(labels, scores, thresh=best_thresh)
 
@@ -412,9 +416,9 @@ def run_evaluation(args):
 
     for k, v in metrics.items():
         if isinstance(v, float):
-            logger.info(f"{k.replace('_',' ').title():<28} {v:.6f}")
+            logger.info(f"{k.replace('_', ' ').title():<28} {v:.6f}")
         else:
-            logger.info(f"{k.replace('_',' ').title():<28} {v}")
+            logger.info(f"{k.replace('_', ' ').title():<28} {v}")
 
     logger.info("=" * 60)