oral-a: measure training-state apical alignment

1 files changed, 2 insertions, 1 deletions

diff --git a/sdil/conv.py b/sdil/conv.py
index 40def21..0436525 100644
--- a/sdil/conv.py
+++ b/sdil/conv.py
@@ -753,7 +753,7 @@ def conv_alignment_report(net, x, y, config):
     parameters = net.W + net.gamma + net.beta + [net.W_out, net.b_out]
     for parameter in parameters:
         parameter.requires_grad_(True)
-    forward = net.forward(x)
+    forward = net.forward(x, training=True, update_stats=False)
     gradients = torch.autograd.grad(
         F.cross_entropy(forward["logits"], y), forward["hiddens"])
     batch = x.shape[0]
@@ -771,6 +771,7 @@ def conv_alignment_report(net, x, y, config):
             return float(F.cosine_similarity(left, right, dim=1).mean())
 
         report = {
+            "normalization_state": "training_batch_stats_without_running_update",
             "teaching_negative_gradient_cosine": [
                 cosine(left, right) for left, right in zip(teaching, negative_gradients)],
             "raw_negative_gradient_cosine": [