fix: report gradient snr in db

astrai/trainer/metric_util.py

@@ -1,3 +1,4 @@
+import math
 from typing import Dict
 
 import torch
@@ -27,6 +28,8 @@ class GradSNRTracker:
 
     SNR = E[g]^2 / Var(g) = E[g]^2 / (E[g^2] - E[g]^2)
 
+    The reported value is the power ratio in decibels: ``10 * log10(SNR)``.
+
     The tracker accumulates per-parameter EMA moments across optimizer steps.
     Call ``update`` after backward (before ``optimizer.step``) and read
     ``snr`` to get the aggregate SNR across all parameters.
@@ -64,7 +67,8 @@ class GradSNRTracker:
             noise = (v - m.pow(2)).clamp(min=0).sum().item()
             total_signal += signal
             total_noise += noise
-        return total_signal / (total_noise + self.eps)
+        snr = total_signal / (total_noise + self.eps)
+        return 10.0 * math.log10(max(snr, self.eps))
 
 
 def ctx_get_loss(ctx):

tests/trainer/test_metric_util.py

@@ -0,0 +1,17 @@
+import pytest
+import torch
+
+from astrai.trainer.metric_util import GradSNRTracker
+
+
+def test_grad_snr_is_reported_in_decibels():
+    model = torch.nn.Linear(1, 1, bias=False)
+    tracker = GradSNRTracker(beta=0.5, eps=1e-8)
+
+    model.weight.grad = torch.tensor([[1.0]])
+    tracker.update(model)
+    model.weight.grad = torch.tensor([[3.0]])
+    tracker.update(model)
+
+    # E[g]^2 / Var(g) = 4 / 1 = 4, which is 6.0206 dB.
+    assert tracker.snr == pytest.approx(10.0 * torch.log10(torch.tensor(4.0)).item())