rpotter6298
af813bbb62
- 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.
143 lines
3.3 KiB
JSON
143 lines
3.3 KiB
JSON
{
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"_notes": [
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"Geometry vector injection via UNet (no GT, no seg-CNN tower).",
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"img tower runs UNet per-fold to predict disc/cup masks, computes 5 CDR",
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"features per eye, publishes to EPC; cd tower consumes them.",
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"Same shape as ensemble_fused.json — just adds the UNet-derived geometry hook."
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],
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"run_name": "v4/ensemble_fused_geom_unet",
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"num_classes": 2,
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"label_filter": [0, 1],
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"split_identity_level": 1,
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"eval_stage": "hb",
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"save_predictions": true,
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"seed": 1234,
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"folds": 5,
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"fold_seed": 100,
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"output_root": "v4/results",
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"out_dir_tags": ["binary"],
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"data": {
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"module": "v4.classes.profiles.v4papila",
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"args": {
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"image_dir": "Papila/FundusImages",
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"clinical_dir": "Papila/ClinicalData",
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"label_col": "Diagnosis",
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"iop_corr_method": "ratio",
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"iop_drop_raw": true,
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"exclude_cols": ["Axial_Length"],
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"in_memory_cache": true
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}
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},
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"towers": [
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{
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"name": "img",
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"module": "v4.classes.towers.image_tower",
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"class": "ImageEncoder",
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"data_source": "image",
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"epc_supplies": ["geometry_vectors"],
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"args": {
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"backbone": "refugelike",
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"freeze_ratio": 0.0,
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"augment": true,
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"geometry_source": "unet",
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"weights_path": "models/v2/refuge/segmentation/per_image/best.pt",
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"contour_dir": "Papila/ExpertsSegmentations/Contours",
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"unet_size": 512,
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"normalize": "per_image",
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"threshold": 0.5,
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"finetune_epochs": 10,
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"finetune_lr": 1e-5,
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"finetune_batch_size": 4
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}
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},
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{
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"name": "cd",
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"module": "v4.classes.towers.clinical_tower",
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"class": "ClinicalEncoder",
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"data_source": "matrix",
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"epc_requests": ["geometry_vectors"],
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"args": {
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"hidden_dim": 128,
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"geom_dim": 5
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}
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}
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],
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"stages": [
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{
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"name": "cd_warm",
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"type": "warm",
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"tower": "cd",
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"head_name": "cd_aux",
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"level": "eye",
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"epochs": 40
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},
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{
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"name": "img_aux",
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"type": "head",
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"input": "img",
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"train_with": "nt",
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"bcd": true
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},
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{
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"name": "cd_aux",
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"type": "head",
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"input": "cd",
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"train_with": "nt",
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"bcd": true
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},
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{
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"name": "nt",
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"type": "fusion",
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"module": "v4.classes.bridges.fusion_bridge",
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"class": "FusionBridge",
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"inputs": ["img", "cd"],
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"level": "eye",
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"epochs": 36,
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"train_towers": true,
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"warmup": {
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"tower_epochs": 3,
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"fused_epochs": 3
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},
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"args": {
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"fusion_dim": 256
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}
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},
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{
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"name": "nt_head",
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"type": "head",
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"input": "nt",
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"train_with": "nt"
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},
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{
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"name": "hb",
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"type": "fusion",
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"module": "v4.classes.bridges.hyperbridge",
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"class": "HyperBridge",
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"inputs": { "a": "nt", "b": "nt" },
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"level": "patient",
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"epochs": 10,
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"args": {
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"hidden_dim": 256,
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"mode": "embedding_mlp"
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}
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},
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{
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"name": "hb_head",
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"type": "head",
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"input": "hb",
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"train_with": "hb",
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"args": { "dropout": 0.3 }
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}
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],
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"training": {
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"lr": 1e-4,
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"batch_size": 8,
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"bcd_prob": 0.5,
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"tune_binary_threshold": true
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}
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}
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