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Daniel Povey 2023-05-18 15:12:57 +08:00
parent e4a774cb98
commit 299482d02d
1 changed files with 4 additions and 3 deletions
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7
egs/libriheavy/LM/zipformer1/subformer.py
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@ -862,9 +862,6 @@ class LearnedDownsamplingModule(nn.Module):
d = self.downsampling_factor d = self.downsampling_factor
seq_len_reduced = (seq_len + d - 1) // d seq_len_reduced = (seq_len + d - 1) // d
if random.random() < 0.01 or __name__ == '__main__':
logging.info(f"mean weight={weights.mean()}, mean-abs-scores={scores.abs().mean()} positive-scores={(scores>0).to(torch.float32).mean()}, seq_len={seq_len}, seq_len_reduced={seq_len_reduced}")
weights_discarded = weights[:, seq_len_reduced:2*seq_len_reduced] weights_discarded = weights[:, seq_len_reduced:2*seq_len_reduced]
missing = weights_discarded.shape[1] - seq_len_reduced missing = weights_discarded.shape[1] - seq_len_reduced
@ -875,6 +872,10 @@ class LearnedDownsamplingModule(nn.Module):
dtype=weights.dtype)), dtype=weights.dtype)),
dim=1) dim=1)
if random.random() < 0.01 or __name__ == '__main__':
logging.info(f"mean weight={weights.mean()}, mean-abs-scores={scores.abs().mean()} positive-scores={(scores>0).to(torch.float32).mean()}, discarded-weights={weights_discarded.mean()}, seq_len={seq_len}, seq_len_reduced={seq_len_reduced}")
# randomly rotate `weights_discarded` on the sequence axis; this is # randomly rotate `weights_discarded` on the sequence axis; this is
# intended to ensure that it doesn't assign the highest scores to # intended to ensure that it doesn't assign the highest scores to