glow_old.py: adding WN to glow_old to avoid res_skip_layers

glow_old.py

@@ -1,7 +1,88 @@
 import copy
 import torch
 from glow import Invertible1x1Conv, remove
-from glow import WN
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b,n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a+input_b
+    t_act = torch.nn.functional.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.nn.functional.sigmoid(in_act[:,n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class WN(torch.nn.Module):
+    """
+    This is the WaveNet like layer for the affine coupling.  The primary difference
+    from WaveNet is the convolutions need not be causal.  There is also no dilation
+    size reset.  The dilation only doubles on each layer
+    """
+    def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
+                 kernel_size):
+        super(WN, self).__init__()
+        assert(kernel_size % 2 == 1)
+        assert(n_channels % 2 == 0)
+        self.n_layers = n_layers
+        self.n_channels = n_channels
+        self.in_layers = torch.nn.ModuleList()
+        self.res_layers = torch.nn.ModuleList()
+        self.skip_layers = torch.nn.ModuleList()
+        self.cond_layers = torch.nn.ModuleList()
+
+        start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
+        start = torch.nn.utils.weight_norm(start, name='weight')
+        self.start = start
+
+        # Initializing last layer to 0 makes the affine coupling layers
+        # do nothing at first.  This helps with training stability
+        end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
+        end.weight.data.zero_()
+        end.bias.data.zero_()
+        self.end = end
+
+        for i in range(n_layers):
+            dilation = 2 ** i
+            padding = int((kernel_size*dilation - dilation)/2)
+            in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
+                                       dilation=dilation, padding=padding)
+            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+            self.in_layers.append(in_layer)
+
+            cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels, 1)
+            cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+            self.cond_layers.append(cond_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_layer = torch.nn.Conv1d(n_channels, n_channels, 1)
+                res_layer = torch.nn.utils.weight_norm(res_layer, name='weight')
+                self.res_layers.append(res_layer)
+
+            skip_layer = torch.nn.Conv1d(n_channels, n_channels, 1)
+            skip_layer = torch.nn.utils.weight_norm(skip_layer, name='weight')
+            self.skip_layers.append(skip_layer)
+
+    def forward(self, forward_input):
+        audio, spect = forward_input
+        audio = self.start(audio)
+
+        for i in range(self.n_layers):
+            acts = fused_add_tanh_sigmoid_multiply(
+                self.in_layers[i](audio),
+                self.cond_layers[i](spect),
+                torch.IntTensor([self.n_channels]))
+
+            if i < self.n_layers - 1:
+                res_acts = self.res_layers[i](acts)
+                audio = res_acts + audio
+
+            if i == 0:
+                output = self.skip_layers[i](acts)
+            else:
+                output = self.skip_layers[i](acts) + output
+
+        return self.end(output)
 
 
 class WaveGlow(torch.nn.Module):
@@ -140,12 +221,13 @@ def infer(self, spect, sigma=1.0):
 
         return audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
 
-    def remove_weightnorm(self):
-        waveglow = copy.deepcopy(self)
+    @staticmethod
+    def remove_weightnorm(model):
+        waveglow = copy.deepcopy(model)
         for WN in waveglow.WN:
             WN.start = torch.nn.utils.remove_weight_norm(WN.start)
             WN.in_layers = remove(WN.in_layers)
             WN.cond_layers = remove(WN.cond_layers)
             WN.res_layers = remove(WN.res_layers)
             WN.skip_layers = remove(WN.skip_layers)
-        self = waveglow
+        return waveglow