Refactor geometry feature loaders and enhance distributed client status reporting

- Updated GTGeometryLoader to streamline geometry vector computation and caching. - Introduced UNetGeometryLoader for UNet-derived geometry vectors. - Added sample collection method in PapilaBundle for better data handling. - Refined GeometrySegEncoder and ImageEncoder to utilize new sample collection. - Enhanced distributed client with heartbeat mechanism for improved job tracking. - Added new configuration files for UNet-derived geometry integration.
2026-04-29 11:05:19 +02:00
parent 512ebd13b2
commit af813bbb62
11 changed files with 467 additions and 153 deletions
@@ -0,0 +1,142 @@
+{
+  "_notes": [
+    "Geometry vector injection via UNet (no GT, no seg-CNN tower).",
+    "img tower runs UNet per-fold to predict disc/cup masks, computes 5 CDR",
+    "features per eye, publishes to EPC; cd tower consumes them.",
+    "Same shape as ensemble_fused.json — just adds the UNet-derived geometry hook."
+  ],
+  "run_name": "v4/ensemble_fused_geom_unet",
+  "num_classes": 2,
+  "label_filter": [0, 1],
+  "split_identity_level": 1,
+  "eval_stage": "hb",
+  "save_predictions": true,
+  "seed": 1234,
+  "folds": 5,
+  "fold_seed": 100,
+  "output_root": "v4/results",
+  "out_dir_tags": ["binary"],
+
+  "data": {
+    "module": "v4.classes.profiles.v4papila",
+    "args": {
+      "image_dir": "Papila/FundusImages",
+      "clinical_dir": "Papila/ClinicalData",
+      "label_col": "Diagnosis",
+      "iop_corr_method": "ratio",
+      "iop_drop_raw": true,
+      "exclude_cols": ["Axial_Length"],
+      "in_memory_cache": true
+    }
+  },
+
+  "towers": [
+    {
+      "name": "img",
+      "module": "v4.classes.towers.image_tower",
+      "class": "ImageEncoder",
+      "data_source": "image",
+      "epc_supplies": ["geometry_vectors"],
+      "args": {
+        "backbone":            "refugelike",
+        "freeze_ratio":        0.0,
+        "augment":             true,
+        "geometry_source":     "unet",
+        "weights_path":        "models/v2/refuge/segmentation/per_image/best.pt",
+        "contour_dir":         "Papila/ExpertsSegmentations/Contours",
+        "unet_size":           512,
+        "normalize":           "per_image",
+        "threshold":           0.5,
+        "finetune_epochs":     10,
+        "finetune_lr":         1e-5,
+        "finetune_batch_size": 4
+      }
+    },
+    {
+      "name": "cd",
+      "module": "v4.classes.towers.clinical_tower",
+      "class": "ClinicalEncoder",
+      "data_source": "matrix",
+      "epc_requests": ["geometry_vectors"],
+      "args": {
+        "hidden_dim": 128,
+        "geom_dim":   5
+      }
+    }
+  ],
+
+  "stages": [
+    {
+      "name": "cd_warm",
+      "type": "warm",
+      "tower": "cd",
+      "head_name": "cd_aux",
+      "level": "eye",
+      "epochs": 40
+    },
+    {
+      "name": "img_aux",
+      "type": "head",
+      "input": "img",
+      "train_with": "nt",
+      "bcd": true
+    },
+    {
+      "name": "cd_aux",
+      "type": "head",
+      "input": "cd",
+      "train_with": "nt",
+      "bcd": true
+    },
+    {
+      "name": "nt",
+      "type": "fusion",
+      "module": "v4.classes.bridges.fusion_bridge",
+      "class": "FusionBridge",
+      "inputs": ["img", "cd"],
+      "level": "eye",
+      "epochs": 36,
+      "train_towers": true,
+      "warmup": {
+        "tower_epochs": 3,
+        "fused_epochs": 3
+      },
+      "args": {
+        "fusion_dim": 256
+      }
+    },
+    {
+      "name": "nt_head",
+      "type": "head",
+      "input": "nt",
+      "train_with": "nt"
+    },
+    {
+      "name": "hb",
+      "type": "fusion",
+      "module": "v4.classes.bridges.hyperbridge",
+      "class": "HyperBridge",
+      "inputs": { "a": "nt", "b": "nt" },
+      "level": "patient",
+      "epochs": 10,
+      "args": {
+        "hidden_dim": 256,
+        "mode": "embedding_mlp"
+      }
+    },
+    {
+      "name": "hb_head",
+      "type": "head",
+      "input": "hb",
+      "train_with": "hb",
+      "args": { "dropout": 0.3 }
+    }
+  ],
+
+  "training": {
+    "lr": 1e-4,
+    "batch_size": 8,
+    "bcd_prob": 0.5,
+    "tune_binary_threshold": true
+  }
+}