began work on v3

2026-03-19 16:58:29 +01:00
parent 786457b30d
commit eb9eafe715
42 changed files with 11214 additions and 1 deletions
@@ -0,0 +1,115 @@
+from torch.utils.data import Dataset
+from PIL import Image
+import numpy as np
+import torch
+
+
+class ClinicalDataset(Dataset):
+    """Generic dataset wrapping a DataBundle-like instance.
+    Returns (img_tensor, meta_tensor, label)."""
+
+    def __init__(
+        self,
+        clinical_data,
+        img_transform,
+        meta_transform=None,
+        image_preprocessor=None,
+        geometry_provider=None,
+        geometry_dim: int = 0,
+        image_cache: "dict | None" = None,
+    ):
+        self.clinical = clinical_data
+        self.transform_image = img_transform
+        self.meta_transform = meta_transform or (lambda x: x)
+        self.image_preprocessor = image_preprocessor
+        self.geometry_provider = geometry_provider
+        self.geometry_dim = geometry_dim if geometry_provider is not None else 0
+        self.image_cache = image_cache
+
+    def __len__(self):
+        return len(self.clinical.df)
+
+    def __getitem__(self, idx: int):
+        row = self.clinical.df.iloc[idx]
+        # load & transform image
+        img_path = self.clinical.get_image_path(row)
+        cache_key = str(img_path)
+        if self.image_cache is not None and cache_key in self.image_cache:
+            orig_img = Image.fromarray(self.image_cache[cache_key])
+        else:
+            orig_img = Image.open(img_path).convert("RGB")
+            if self.image_cache is not None:
+                self.image_cache[cache_key] = np.asarray(orig_img, dtype=np.uint8)
+        img = orig_img
+        if self.image_preprocessor is not None:
+            img = self.image_preprocessor(img, img_path)
+        img_t = self.transform_image(img)
+        # encode & transform metadata
+        meta = self.clinical.encode_metadata(row)
+        meta_t = self.meta_transform(meta)
+        # label
+        label = self.clinical.get_label(row)
+        if self.geometry_dim > 0:
+            features = None
+            if self.geometry_provider is not None and hasattr(self.geometry_provider, "geometry_features"):
+                features = self.geometry_provider.geometry_features(orig_img, img_path)
+            if features is None:
+                geom_vec = torch.zeros(self.geometry_dim, dtype=torch.float32)
+            else:
+                features = np.asarray(features, dtype=np.float32)
+                if features.shape[0] != self.geometry_dim:
+                    geom_vec = torch.zeros(self.geometry_dim, dtype=torch.float32)
+                else:
+                    geom_vec = torch.from_numpy(features)
+            return img_t, meta_t, geom_vec, label
+        return img_t, meta_t, label
+
+
+# ---------------------------------------------------------------------------
+# _ClinicalView — shim used by V2HyperTower._run_fold
+# ---------------------------------------------------------------------------
+
+from .data_bundle import DataBundle  # noqa: E402
+
+
+class _ClinicalView:
+    """Minimal shim so ClinicalDataset can iterate an epoch-specific DataFrame
+    while still delegating encoding/paths/labels to the DataBundle object."""
+
+    def __init__(self, base: DataBundle, df):
+        self.base = base
+        self.df = df
+
+    @property
+    def image_dir(self):
+        return self.base.image_dir
+
+    @property
+    def clinical_dir(self):
+        return self.base.clinical_dir
+
+    @property
+    def id_cols(self):
+        return ("Patient ID", "eyeID")
+
+    @property
+    def label_col(self):
+        return self.base.label_col
+
+    @property
+    def filename_template(self):
+        return getattr(self.base, "filename_template", "RET{pid:03d}{eye}.jpg")
+
+    @property
+    def dim(self):
+        return self.base.feature_dim
+
+    def encode_metadata(self, row):
+        vec = self.base.vectorize_row(row)
+        return torch.as_tensor(vec, dtype=torch.float32)
+
+    def get_image_path(self, row):
+        return self.base.get_image_path(row)
+
+    def get_label(self, row):
+        return int(row[self.base.label_col])