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More debug print
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@ -862,9 +862,6 @@ class LearnedDownsamplingModule(nn.Module):
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d = self.downsampling_factor
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seq_len_reduced = (seq_len + d - 1) // d
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if random.random() < 0.01 or __name__ == '__main__':
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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}")
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weights_discarded = weights[:, seq_len_reduced:2*seq_len_reduced]
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missing = weights_discarded.shape[1] - seq_len_reduced
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@ -875,6 +872,10 @@ class LearnedDownsamplingModule(nn.Module):
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dtype=weights.dtype)),
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dim=1)
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if random.random() < 0.01 or __name__ == '__main__':
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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}")
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# randomly rotate `weights_discarded` on the sequence axis; this is
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# intended to ensure that it doesn't assign the highest scores to
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