experiments: audit residual block usage with lesions

1 files changed, 13 insertions, 1 deletions

diff --git a/experiments/protocol_smoke.py b/experiments/protocol_smoke.py
index 339ac47..18de4c9 100644
--- a/experiments/protocol_smoke.py
+++ b/experiments/protocol_smoke.py
@@ -7,7 +7,8 @@ import torch
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from sdil.data import get_dataset_splits
 from sdil.core import SDILConfig, SDILNet
-from experiments.run import calibration_work_per_event, fixed_training_probe
+from experiments.run import (calibration_work_per_event, fixed_training_probe,
+                             residual_lesion_report)
 
 
 def loader_labels(loader):
@@ -65,11 +66,22 @@ def main():
     }
     assert layerwise["batch_loss_evaluations"] == 9
     assert abs(layerwise["forward_equivalent_batches"] - 11.0 / 3.0) < 1e-12
+
+    lesion_net = SDILNet([1, 4, 4, 4, 4, 2], act="relu", residual=True, device="cpu")
+    lesion_x = torch.linspace(0, 1, 16).view(-1, 1)
+    lesion_y = torch.zeros(16, dtype=torch.long)
+    lesion = residual_lesion_report(
+        lesion_net, [(lesion_x, lesion_y)], lesion_x[:8], fraction=1.0 / 3.0)
+    assert lesion["interior_layers"] == [1, 2, 3]
+    assert lesion["lesioned_layers"] == [3]
+    assert len(lesion["branch_to_skip_rms"]) == 3
+    assert 0.0 <= lesion["lesion_eval_acc"] <= 1.0
     print("validation split hash:", metadata["validation_index_sha256"])
     print("train/validation/test: 59000/1000/10000; stratification exact")
     print("FashionMNIST recovery split: 55000/5000/10000; stratification exact")
     print("training-prefix diagnostic probe: deterministic; shuffle state unchanged")
     print("simultaneous/layerwise calibration cost accounting: exact")
+    print("final-third residual-block lesion: exact block selection and finite report")
     print("ALL PROTOCOL SMOKE CHECKS PASSED")