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Merge branch 'specaugmod_baseline' into randcombine1_expscale3_rework2c_maxabs1000_maxp0.95_noexp_convderiv3warmup_embed

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Daniel Povey 2022-03-15 14:33:08 +08:00
commit 2e6d170be8
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 dim: the dimension to analyze, with 0 <= dim < tensors[0].ndim
options: options object options: options object
sizes_same: true if all the tensor sizes are the same on this dimension 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 imdictates the type of stats
we accumulate, abs is mean absolute value, "positive" we accumulate, abs is mean absolute value, "positive"
is proportion of positive to nonnegative values, "eigs" 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.append(stats[index].item())
percentiles = [ '%.2g' % x for x in percentiles ] percentiles = [ '%.2g' % x for x in percentiles ]
percentiles = ' '.join(percentiles) percentiles = ' '.join(percentiles)
return f'percentiles: [{percentiles}]' ans = f'percentiles: [{percentiles}]'
else: else:
stats = stats.tolist() ans = stats.tolist()
stats = [ '%.2g' % x for x in stats ] ans = [ '%.2g' % x for x in ans ]
stats = '[' + ' '.join(stats) + ']' ans = '[' + ' '.join(ans) + ']'
return stats 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