Update modules.py

modules.py

@@ -12,7 +12,7 @@ from torch.nn.utils import weight_norm, remove_weight_norm
 import commons
 from commons import init_weights, get_padding
 from transforms import piecewise_rational_quadratic_transform
-
+from attentions import Encoder
 
 LRELU_SLOPE = 0.1
 
@@ -372,6 +372,55 @@ class ConvFlow(nn.Module):
     unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
     unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
     unnormalized_derivatives = h[..., 2 * self.num_bins:]
+    
+class TransformerCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      n_layers,
+      n_heads,
+      p_dropout=0,
+      filter_channels=0,
+      mean_only=False,
+      wn_sharing_parameter=None,
+      gin_channels = 0
+      ):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x
 
     x1, logabsdet = piecewise_rational_quadratic_transform(x1,
         unnormalized_widths,