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Remove unused ResBlock check as we only use ResBlock 1 #2405

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Remove unused ResBlock check as we only use ResBlock 1 #2405

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4 changes: 2 additions & 2 deletions infer/lib/infer_pack/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -219,7 +219,7 @@ def __init__(
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
resblock = modules.ResBlock

self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
Expand Down Expand Up @@ -471,7 +471,7 @@ def __init__(
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
resblock = modules.ResBlock

self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
Expand Down
4 changes: 2 additions & 2 deletions infer/lib/infer_pack/models_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -230,7 +230,7 @@ def __init__(
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
resblock = modules.ResBlock

self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
Expand Down Expand Up @@ -442,7 +442,7 @@ def __init__(
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
resblock = modules.ResBlock

self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
Expand Down
60 changes: 2 additions & 58 deletions infer/lib/infer_pack/modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,9 +249,9 @@ def __prepare_scriptable__(self):
return self


class ResBlock1(torch.nn.Module):
class ResBlock(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
super(ResBlock, self).__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Expand Down Expand Up @@ -364,62 +364,6 @@ def __prepare_scriptable__(self):
return self


class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
]
)
self.convs.apply(init_weights)
self.lrelu_slope = LRELU_SLOPE

def forward(self, x, x_mask: Optional[torch.Tensor] = None):
for c in self.convs:
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x

def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)

def __prepare_scriptable__(self):
for l in self.convs:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self


class Log(nn.Module):
def forward(
self,
Expand Down