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Cosmetic improvements
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@ -487,7 +487,7 @@ class ConformerEncoder(nn.Module):
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if len(ans) == num_to_drop:
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break
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if shared_rng.random() < 0.005 or __name__ == "__main__":
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logging.info(f"warmup_begin={warmup_begin}, warmup_end={warmup_end}, warmup_count={warmup_count:.1f}, num_to_drop={num_to_drop}, layers_to_drop={ans}")
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logging.info(f"warmup_begin={warmup_begin:.1f}, warmup_end={warmup_end:.1f}, warmup_count={warmup_count:.1f}, num_to_drop={num_to_drop}, layers_to_drop={ans}")
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return ans
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@ -444,8 +444,8 @@ class MaxEig(torch.nn.Module):
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with torch.cuda.amp.autocast(enabled=False):
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eps = 1.0e-20
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assert x.dtype != torch.float16
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orig_x = x
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x = x.to(torch.float32)
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with torch.no_grad():
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x = x.transpose(self.channel_dim, -1).reshape(-1, self.num_channels)
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x = x - x.mean(dim=0)
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@ -461,7 +461,7 @@ class MaxEig(torch.nn.Module):
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# ensure new direction is nonzero even if x == 0, by including `direction`.
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self._set_direction(0.1 * self.max_eig_direction + new_direction)
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if random.random() < 0.0005 or __name__ == "__main__":
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if random.random() < 0.01 or __name__ == "__main__":
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logging.info(f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}")
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if variance_proportion >= self.max_var_per_eig:
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