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Add some extra info to diagnostics

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This commit is contained in:
Daniel Povey 2022-03-15 13:49:15 +08:00
parent 1e5455ba29
commit 21ebd356e7
1 changed files with 17 additions and 6 deletions
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23
egs/librispeech/ASR/transducer_stateless/diagnostics.py
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@ -79,7 +79,7 @@ def get_diagnostics_for_dim(dim: int, tensors: List[Tensor],
dim: the dimension to analyze, with 0 <= dim < tensors[0].ndim
options: options object
sizes_same: true if all the tensor sizes are the same on this dimension
stats_type: either "abs" or "positive" or "eigs" or "value,
stats_type: either "abs" or "positive" or "eigs" or "value",
imdictates the type of stats
we accumulate, abs is mean absolute value, "positive"
is proportion of positive to nonnegative values, "eigs"
@ -129,12 +129,23 @@ def get_diagnostics_for_dim(dim: int, tensors: List[Tensor],
percentiles.append(stats[index].item())
percentiles = [ '%.2g' % x for x in percentiles ]
percentiles = ' '.join(percentiles)
return f'percentiles: [{percentiles}]'
ans = f'percentiles: [{percentiles}]'
else:
stats = stats.tolist()
stats = [ '%.2g' % x for x in stats ]
stats = '[' + ' '.join(stats) + ']'
return stats
ans = stats.tolist()
ans = [ '%.2g' % x for x in ans ]
ans = '[' + ' '.join(ans) + ']'
if stats_type == "value":
norm = (stats ** 2).sum().sqrt().item()
mean_abs = stats.abs().mean().item()
# This norm is useful because it is strictly less than the largest
# sqrt(eigenvalue) of the variance, which we print out, and shows,
# speaking in an approximate way, how much of that largest eigenvalue
# can be attributed to the mean of the distribution.
ans += f', norm={norm:.2g}, mean_abs={mean_abs:.2g}'
else:
mean = stats.mean().item()
ans += f', mean={mean:.2g}'
return ans