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optimize(rvc.utils): more type defs & rename

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源文雨
2024-06-07 19:33:45 +09:00
parent c10c527264
commit 49488dcae9
6 changed files with 41 additions and 75 deletions
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44
infer/lib/infer_pack/attentions.py
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@@ -18,7 +18,6 @@ class Encoder(nn.Module):
kernel_size=1,
p_dropout=0.0,
window_size=10,
**kwargs
):
super(Encoder, self).__init__()
self.hidden_channels = hidden_channels
@@ -55,8 +54,11 @@ class Encoder(nn.Module):
)
)
self.norm_layers_2.append(LayerNorm(hidden_channels))
def __call__(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
return super().__call__(x, x_mask)
def forward(self, x, x_mask):
def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
x = x * x_mask
zippep = zip(
@@ -86,7 +88,6 @@ class Decoder(nn.Module):
p_dropout=0.0,
proximal_bias=False,
proximal_init=True,
**kwargs
):
super(Decoder, self).__init__()
self.hidden_channels = hidden_channels
@@ -311,7 +312,6 @@ class MultiHeadAttention(nn.Module):
if pad_length > 0:
padded_relative_embeddings = F.pad(
relative_embeddings,
# commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
[0, 0, pad_length, pad_length, 0, 0],
)
else:
@@ -328,19 +328,11 @@ class MultiHeadAttention(nn.Module):
"""
batch, heads, length, _ = x.size()
# Concat columns of pad to shift from relative to absolute indexing.
x = F.pad(
x,
# commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])
[0, 1, 0, 0, 0, 0, 0, 0],
)
x = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0], )
# Concat extra elements so to add up to shape (len+1, 2*len-1).
x_flat = x.view([batch, heads, length * 2 * length])
x_flat = F.pad(
x_flat,
# commons.convert_pad_shape([[0, 0], [0, 0], [0, int(length) - 1]])
[0, length - 1, 0, 0, 0, 0],
)
x_flat = F.pad(x_flat, [0, length - 1, 0, 0, 0, 0])
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
@@ -355,18 +347,10 @@ class MultiHeadAttention(nn.Module):
"""
batch, heads, length, _ = x.size()
# padd along column
x = F.pad(
x,
# commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, int(length) - 1]])
[0, length - 1, 0, 0, 0, 0, 0, 0],
)
x = F.pad(x, [0, length - 1, 0, 0, 0, 0, 0, 0])
x_flat = x.view([batch, heads, (length**2) + (length * (length - 1))])
# add 0's in the beginning that will skew the elements after reshape
x_flat = F.pad(
x_flat,
# commons.convert_pad_shape([[0, 0], [0, 0], [int(length), 0]])
[length, 0, 0, 0, 0, 0],
)
x_flat = F.pad(x_flat, [length, 0, 0, 0, 0, 0])
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
return x_final
@@ -435,11 +419,7 @@ class FFN(nn.Module):
pad_l: int = self.kernel_size - 1
pad_r: int = 0
# padding = [[0, 0], [0, 0], [pad_l, pad_r]]
x = F.pad(
x,
# commons.convert_pad_shape(padding)
[pad_l, pad_r, 0, 0, 0, 0],
)
x = F.pad(x, [pad_l, pad_r, 0, 0, 0, 0])
return x
def _same_padding(self, x):
@@ -448,9 +428,5 @@ class FFN(nn.Module):
pad_l: int = (self.kernel_size - 1) // 2
pad_r: int = self.kernel_size // 2
# padding = [[0, 0], [0, 0], [pad_l, pad_r]]
x = F.pad(
x,
# commons.convert_pad_shape(padding)
[pad_l, pad_r, 0, 0, 0, 0],
)
x = F.pad(x, [pad_l, pad_r, 0, 0, 0, 0])
return x