pre-restructure

classes/refuge_classification.py

@@ -401,6 +401,7 @@ class RefugeClassification:
             f"[classifier] Building datasets from {len(candidates)} labelled samples (train/val)"
         )
 
+        skipped: List[str] = []
         for sample in tqdm(
             candidates,
             desc="Preparing records",
@@ -410,6 +411,7 @@ class RefugeClassification:
             try:
                 geom, disc_mask, cup_mask = self._resolve_geometry(sample, crop_scale)
             except RuntimeError:
+                skipped.append(sample.sample_id)
                 continue
             record = RefugeClassificationRecord(
                 sample=sample,
@@ -424,6 +426,12 @@ class RefugeClassification:
             else:
                 val_records.append(record)
 
+        if skipped:
+            print(
+                f"[classifier] WARNING: {len(skipped)}/{len(candidates)} samples skipped "
+                f"due to empty segmentation mask: {skipped}"
+            )
+
         if (not val_records or self.use_all_labeled) and train_records and self.auto_val_ratio > 0.0:
             rng = random.Random(42)
             label_groups: Dict[int, List[RefugeClassificationRecord]] = {}
@@ -578,17 +586,20 @@ class RefugeClassification:
     ) -> List[RefugeClassificationRecord]:
         scale = crop_scale if crop_scale is not None else self.crop_scale
         records: List[RefugeClassificationRecord] = []
+        skipped: List[str] = []
         iterator: Iterable[RefugeSample]
         if progress_prefix is not None:
             iterator = tqdm(samples, desc=progress_prefix, unit="sample", leave=False)
         else:
             iterator = samples
+        labeled = [s for s in samples if s.label is not None]
         for sample in iterator:
             if sample.label is None:
                 continue
             try:
                 geom, disc_mask, cup_mask = self._resolve_geometry(sample, scale)
             except RuntimeError:
+                skipped.append(sample.sample_id)
                 continue
             records.append(
                 RefugeClassificationRecord(
@@ -598,8 +609,27 @@ class RefugeClassification:
                     cup_mask=cup_mask,
                 )
             )
+        prefix = f"[{progress_prefix}]" if progress_prefix else "[classifier]"
+        if skipped:
+            print(
+                f"{prefix} WARNING: {len(skipped)}/{len(labeled)} samples skipped "
+                f"due to empty segmentation mask: {skipped}"
+            )
+        else:
+            print(f"{prefix} All {len(labeled)} samples processed successfully.")
         return records
 
+    def clear_disk_cache(self) -> None:
+        """Delete all cached geometry/mask .npz files in cache_dir."""
+        if self.cache_dir is None or not self.cache_dir.exists():
+            return
+        removed = 0
+        for f in self.cache_dir.glob("*.npz"):
+            f.unlink()
+            removed += 1
+        self.geometry_cache.clear()
+        print(f"[classifier] Cleared {removed} cached geometry files from {self.cache_dir}")
+
     def _cache_key(self, sample_id: str, scale: float) -> str:
         scale_tag = int(round(scale * 100))
         return f"{sample_id}_s{scale_tag}"