From a2ff0b17dc7d7ca6c7e2dd608dfa222cb5032287 Mon Sep 17 00:00:00 2001
From: Blaise <blaise@applio.org>
Date: Wed, 11 Dec 2024 23:22:05 +0100
Subject: [PATCH] Remove unused ResBlock check as we only use ResBlock 1

---
 infer/lib/infer_pack/models.py      |  4 +-
 infer/lib/infer_pack/models_onnx.py |  4 +-
 infer/lib/infer_pack/modules.py     | 60 +----------------------------
 3 files changed, 6 insertions(+), 62 deletions(-)

diff --git a/infer/lib/infer_pack/models.py b/infer/lib/infer_pack/models.py
index a900048..afc246a 100644
--- a/infer/lib/infer_pack/models.py
+++ b/infer/lib/infer_pack/models.py
@@ -219,7 +219,7 @@ class Generator(torch.nn.Module):
         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)):
@@ -471,7 +471,7 @@ class GeneratorNSF(torch.nn.Module):
         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)):
diff --git a/infer/lib/infer_pack/models_onnx.py b/infer/lib/infer_pack/models_onnx.py
index e327019..e2b5987 100644
--- a/infer/lib/infer_pack/models_onnx.py
+++ b/infer/lib/infer_pack/models_onnx.py
@@ -230,7 +230,7 @@ class Generator(torch.nn.Module):
         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)):
@@ -442,7 +442,7 @@ class GeneratorNSF(torch.nn.Module):
         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)):
diff --git a/infer/lib/infer_pack/modules.py b/infer/lib/infer_pack/modules.py
index 51aeaf0..1189c03 100644
--- a/infer/lib/infer_pack/modules.py
+++ b/infer/lib/infer_pack/modules.py
@@ -249,9 +249,9 @@ class WN(torch.nn.Module):
         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(
@@ -364,62 +364,6 @@ class ResBlock1(torch.nn.Module):
         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,