rpotter6298
280060db82
- Introduced multiple regression experiment configurations targeting vf_md, including: - cd_solo_reg_set.json: CD tower only regression setup. - img_solo_reg_set.json: Image tower only regression setup. - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100). - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations. - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis. - Added ensemble configurations for OrthoBridge with different inner bridges: - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge. - ortho_alts_tritower.json: Tritower tests with the same inner bridges. - Created V2-M specific configurations: - baseline_reg_nt50.json: Regression baseline with V2-M backbone. - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M. - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types. - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks. - Promoted existing experiments to higher repetitions for robustness.
117 lines
3.8 KiB
JSON
117 lines
3.8 KiB
JSON
[
|
|
{
|
|
"_note": "1) Baseline classification — sanity check that the runner refactor didn't break anything. Should match baseline_ensemble at 0.896.",
|
|
"run_name": "experiments/reg_head/baseline_class",
|
|
"reps": 3
|
|
},
|
|
|
|
{
|
|
"_note": "2) Same architecture as baseline, but all heads (img_aux, cd_aux, nt_head, hb_head) are regression heads targeting vf_md. label_filter expanded to include suspect patients (label=2) since regression handles continuous targets naturally.",
|
|
"run_name": "experiments/reg_head/baseline_reg",
|
|
"reps": 3,
|
|
"overrides": {
|
|
"label_filter": [0, 1, 2]
|
|
},
|
|
"stage_overrides": {
|
|
"img_aux": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"cd_aux": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"nt_head": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"hb_head": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
}
|
|
}
|
|
},
|
|
|
|
{
|
|
"_note": "3) OrthoBridge (w=0.1) wrapping Hadamard inner + regression heads",
|
|
"run_name": "experiments/reg_head/ortho_reg",
|
|
"reps": 3,
|
|
"overrides": {
|
|
"label_filter": [0, 1, 2]
|
|
},
|
|
"stage_overrides": {
|
|
"nt": {
|
|
"module": "v4.classes.bridges.ortho_bridge",
|
|
"class": "OrthoBridge",
|
|
"args": {
|
|
"fusion_dim": 256,
|
|
"ortho_weight": 0.1,
|
|
"inner_module": "v4.classes.bridges.fusion_bridge",
|
|
"inner_class": "FusionBridge",
|
|
"inner_args": { "fusion_dim": 256 }
|
|
}
|
|
},
|
|
"img_aux": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"cd_aux": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"nt_head": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"hb_head": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
}
|
|
}
|
|
},
|
|
|
|
{
|
|
"_note": "4) PairwiseAdditiveBridge + regression heads",
|
|
"run_name": "experiments/reg_head/pairwise_reg",
|
|
"reps": 3,
|
|
"overrides": {
|
|
"label_filter": [0, 1, 2]
|
|
},
|
|
"stage_overrides": {
|
|
"nt": {
|
|
"module": "v4.classes.bridges.pairwise_bridge",
|
|
"class": "PairwiseAdditiveBridge",
|
|
"args": { "fusion_dim": 256 }
|
|
},
|
|
"img_aux": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"cd_aux": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"nt_head": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
},
|
|
"hb_head": {
|
|
"module": "v4.classes.heads.regression",
|
|
"class": "RegressionHead",
|
|
"args": { "dropout": 0.3, "target_key": "vf_md", "loss": "mse" }
|
|
}
|
|
}
|
|
}
|
|
]
|