Fix/encode check

src/stamp/encoding/__init__.py

@@ -54,7 +54,7 @@ def init_slide_encoder_(
 
             selected_encoder: Encoder = Gigapath()
 
-        case EncoderName.CHIEF:
+        case EncoderName.CHIEF_CTRANSPATH:
             from stamp.encoding.encoder.chief import CHIEF
 
             selected_encoder: Encoder = CHIEF()
@@ -140,7 +140,7 @@ def init_patient_encoder_(
 
             selected_encoder: Encoder = Gigapath()
 
-        case EncoderName.CHIEF:
+        case EncoderName.CHIEF_CTRANSPATH:
             from stamp.encoding.encoder.chief import CHIEF
 
             selected_encoder: Encoder = CHIEF()

src/stamp/encoding/config.py

@@ -9,7 +9,7 @@
 class EncoderName(StrEnum):
     COBRA = "cobra"
     EAGLE = "eagle"
-    CHIEF = "chief"
+    CHIEF_CTRANSPATH = "chief"
     TITAN = "titan"
     GIGAPATH = "gigapath"
     MADELEINE = "madeleine"

src/stamp/encoding/encoder/__init__.py

@@ -189,7 +189,8 @@ def _read_h5(
                 raise ValueError(
                     f"Feature file does not have extractor's name in the metadata: {os.path.basename(h5_path)}"
                 )
-            return feats, coords, extractor
+
+            return feats, coords, _resolve_extractor_name(extractor)
 
     def _save_features_(
         self, output_path: Path, feats: np.ndarray, feat_type: str
@@ -215,3 +216,35 @@ def _save_features_(
 
             Path(tmp_h5_file.name).rename(output_path)
             _logger.debug(f"saved features to {output_path}")
+
+
+def _resolve_extractor_name(raw: str) -> ExtractorName:
+    """
+    Resolve an extractor string to a valid ExtractorName.
+
+    Handles:
+      - exact matches ('gigapath', 'virchow-full')
+      - versioned strings like 'gigapath-ae23d', 'virchow-full-2025abc'
+    Raises ValueError if the base name is not recognized.
+    """
+    if not raw:
+        raise ValueError("Empty extractor string")
+
+    name = str(raw).strip().lower()
+
+    # Exact match
+    for e in ExtractorName:
+        if name == e.value.lower():
+            return e
+
+    # Versioned form: '<enum-value>-something'
+    for e in ExtractorName:
+        if name.startswith(e.value.lower() + "-"):
+            return e
+
+    # Otherwise fail
+    raise ValueError(
+        f"Unknown extractor '{raw}'. "
+        f"Expected one of {[e.value for e in ExtractorName]} "
+        f"or a versioned variant like '<name>-<hash>'."
+    )

src/stamp/encoding/encoder/chief.py

@@ -113,7 +113,7 @@ def __init__(self) -> None:
         model.load_state_dict(chief, strict=True)
         super().__init__(
             model=model,
-            identifier=EncoderName.CHIEF,
+            identifier=EncoderName.CHIEF_CTRANSPATH,
             precision=torch.float32,
             required_extractors=[
                 ExtractorName.CHIEF_CTRANSPATH,
@@ -178,7 +178,16 @@ def encode_patients_(
             for _, row in group.iterrows():
                 slide_filename = row[filename_label]
                 h5_path = os.path.join(feat_dir, slide_filename)
-                feats, _ = self._validate_and_read_features(h5_path=h5_path)
+                # Skip if not an .h5 file
+                if not h5_path.endswith(".h5"):
+                    tqdm.write(f"Skipping {slide_filename} (not an .h5 file)")
+                    continue
+
+                try:
+                    feats, coords = self._validate_and_read_features(h5_path=h5_path)
+                except (FileNotFoundError, ValueError, OSError) as e:
+                    tqdm.write(f"Skipping {slide_filename}: {e}")
+                    continue
                 feats_list.append(feats)
 
             if not feats_list:

src/stamp/encoding/encoder/gigapath.py

@@ -129,7 +129,16 @@ def encode_patients_(
                 slide_filename = row[filename_label]
                 h5_path = os.path.join(feat_dir, slide_filename)
 
-                feats, coords = self._validate_and_read_features(h5_path=h5_path)
+                # Skip if not an .h5 file
+                if not h5_path.endswith(".h5"):
+                    tqdm.write(f"Skipping {slide_filename} (not an .h5 file)")
+                    continue
+
+                try:
+                    feats, coords = self._validate_and_read_features(h5_path=h5_path)
+                except (FileNotFoundError, ValueError, OSError) as e:
+                    tqdm.write(f"Skipping {slide_filename}: {e}")
+                    continue
 
                 # Get the mpp of one slide and check that the rest have the same
                 if slides_mpp < 0:

src/stamp/encoding/encoder/titan.py

@@ -134,7 +134,16 @@ def encode_patients_(
                 slide_filename = row[filename_label]
                 h5_path = os.path.join(feat_dir, slide_filename)
 
-                feats, coords = self._validate_and_read_features(h5_path=h5_path)
+                # Skip if not an .h5 file
+                if not h5_path.endswith(".h5"):
+                    tqdm.write(f"Skipping {slide_filename} (not an .h5 file)")
+                    continue
+
+                try:
+                    feats, coords = self._validate_and_read_features(h5_path=h5_path)
+                except (FileNotFoundError, ValueError, OSError) as e:
+                    tqdm.write(f"Skipping {slide_filename}: {e}")
+                    continue
 
                 # Get the mpp of one slide and check that the rest have the same
                 if slides_mpp < 0:

src/stamp/preprocessing/__init__.py

@@ -240,15 +240,16 @@ def extract_(
 
     extractor_id = extractor.identifier
 
-    if generate_hash:
-        extractor_id += f"-{code_hash}"
-
     _logger.info(f"Using extractor {extractor.identifier}")
 
     if cache_dir:
         cache_dir.mkdir(parents=True, exist_ok=True)
 
-    feat_output_dir = output_dir / extractor_id
+    feat_output_dir = (
+        output_dir / f"{extractor_id}-{code_hash}"
+        if generate_hash
+        else output_dir / extractor_id
+    )
 
     # Collect slides for preprocessing
     if wsi_list is not None:

tests/test_encoders.py

@@ -33,7 +33,7 @@
 
 # They are not all, just one case that is accepted for each encoder
 used_extractor = {
-    EncoderName.CHIEF: ExtractorName.CHIEF_CTRANSPATH,
+    EncoderName.CHIEF_CTRANSPATH: ExtractorName.CHIEF_CTRANSPATH,
     EncoderName.COBRA: ExtractorName.CONCH,
     EncoderName.EAGLE: ExtractorName.CTRANSPATH,
     EncoderName.GIGAPATH: ExtractorName.GIGAPATH,
@@ -73,7 +73,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName):
     )
 
     cuda_required = [
-        EncoderName.CHIEF,
+        EncoderName.CHIEF_CTRANSPATH,
         EncoderName.COBRA,
         EncoderName.GIGAPATH,
         EncoderName.MADELEINE,
@@ -106,6 +106,28 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName):
                 feat_filename=feat_filename,
                 coords=coords,
             )
+    elif encoder == EncoderName.PRISM:
+        # Eagle requires the aggregated features, so we generate new ones
+        # with same name and coordinates as the other ctranspath feats.
+        agg_feat_dir = tmp_path / "agg_output"
+        agg_feat_dir.mkdir()
+        slide_df = pd.read_csv(slide_path)
+        feature_filenames = [Path(path).stem for path in slide_df["slide_path"]]
+
+        for feat_filename in feature_filenames:
+            # Read the coordinates from the ctranspath feature file
+            ctranspath_file = feature_dir / f"{feat_filename}.h5"
+            with h5py.File(ctranspath_file, "r") as h5_file:
+                coords: np.ndarray = h5_file["coords"][:]  # type: ignore
+            create_random_feature_file(
+                tmp_path=agg_feat_dir,
+                min_tiles=32,
+                max_tiles=32,
+                feat_dim=input_dims[ExtractorName.VIRCHOW_FULL],
+                extractor_name="virchow-full",
+                feat_filename=feat_filename,
+                coords=coords,
+            )
     elif encoder == EncoderName.TITAN:
         # A random conch1_5 feature does not work with titan so we just download
         # a real one