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Revert ConvNorm1d to BasicNorm in Conv2dSubsampling and ZipformerLayer to BasicNorm

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This commit is contained in:
Daniel Povey 2022-12-21 20:13:42 +08:00
parent 0995970f29
commit 678be7a2eb
1 changed files with 3 additions and 7 deletions
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10
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -451,7 +451,7 @@ class ZipformerEncoderLayer(nn.Module):
self.attention_squeeze = AttentionSqueeze(embed_dim, embed_dim // 2) self.attention_squeeze = AttentionSqueeze(embed_dim, embed_dim // 2)
self.norm_final = ConvNorm1d(embed_dim) self.norm_final = BasicNorm(embed_dim)
self.bypass_scale = nn.Parameter(torch.full((embed_dim,), 0.5)) self.bypass_scale = nn.Parameter(torch.full((embed_dim,), 0.5))
@ -571,9 +571,7 @@ class ZipformerEncoderLayer(nn.Module):
src = src + self.feed_forward2(src) src = src + self.feed_forward2(src)
src = self.balancer(src) src = self.balancer(src)
src = src.permute(1, 2, 0) # (batch, channels, time) src = self.norm_final(src)
src = self.norm_final(src, src_key_padding_mask)
src = src.permute(2, 0, 1) # (time, batch, channels)
delta = src - src_orig delta = src - src_orig
@ -1847,7 +1845,7 @@ class Conv2dSubsampling(nn.Module):
self.out = nn.Linear(out_height * layer3_channels, out_channels) self.out = nn.Linear(out_height * layer3_channels, out_channels)
self.out_norm = ConvNorm1d(out_channels) self.out_norm = BasicNorm(out_channels)
self.dropout = Dropout2(dropout) self.dropout = Dropout2(dropout)
@ -1884,9 +1882,7 @@ class Conv2dSubsampling(nn.Module):
# Now x is of shape (N, ((T-1)//2 - 1))//2, odim) # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
x = self.out(x) x = self.out(x)
x = x.transpose(1, 2) # (batch, channels, time)
x = self.out_norm(x) x = self.out_norm(x)
x = x.transpose(1, 2) # (batch, time=((T-1)//2 - 1))//2, channels)
x = self.dropout(x) x = self.dropout(x)
return x return x