more simplifications

dnn/torch/fargan/fargan.py

@@ -185,20 +185,18 @@ def __init__(self, subframe_size=40, nb_subframes=4, cond_size=256):
 
         #self.sig_dense1 = nn.Linear(4*self.subframe_size+self.passthrough_size+self.cond_size, self.cond_size, bias=False)
         self.fwc0 = FWConv(2*self.subframe_size+80+4, self.cond_size)
-        self.sig_dense2 = nn.Linear(self.cond_size, self.cond_size, bias=False)
         self.gru1 = nn.GRUCell(self.cond_size+2*self.subframe_size, self.cond_size, bias=False)
-        self.gru2 = nn.GRUCell(self.cond_size+2*self.subframe_size, self.cond_size, bias=False)
-        self.gru3 = nn.GRUCell(self.cond_size+2*self.subframe_size, self.cond_size, bias=False)
+        self.gru2 = nn.GRUCell(self.cond_size+2*self.subframe_size, 128, bias=False)
+        self.gru3 = nn.GRUCell(128+2*self.subframe_size, 128, bias=False)
 
         self.dense1_glu = GLU(self.cond_size)
-        self.dense2_glu = GLU(self.cond_size)
         self.gru1_glu = GLU(self.cond_size)
-        self.gru2_glu = GLU(self.cond_size)
-        self.gru3_glu = GLU(self.cond_size)
+        self.gru2_glu = GLU(128)
+        self.gru3_glu = GLU(128)
         self.skip_glu = GLU(self.cond_size)
         #self.ptaps_dense = nn.Linear(4*self.cond_size, 5)
 
-        self.skip_dense = nn.Linear(4*self.cond_size+2*self.subframe_size, self.cond_size, bias=False)
+        self.skip_dense = nn.Linear(2*128+2*self.cond_size+2*self.subframe_size, self.cond_size, bias=False)
         self.sig_dense_out = nn.Linear(self.cond_size, self.subframe_size, bias=False)
         self.gain_dense_out = nn.Linear(self.cond_size, 4)
 
@@ -228,16 +226,15 @@ def forward(self, cond, prev_pred, exc_mem, period, states, gain=None):
 
         #tmp = self.dense1_glu(torch.tanh(self.sig_dense1(tmp)))
         fwc0_out, fwc0_state = self.fwc0(tmp, states[3])
-        dense2_out = self.dense2_glu(torch.tanh(self.sig_dense2(fwc0_out)))
-        pitch_gain = torch.sigmoid(self.gain_dense_out(dense2_out))
+        pitch_gain = torch.sigmoid(self.gain_dense_out(fwc0_out))
 
-        gru1_state = self.gru1(torch.cat([dense2_out, pitch_gain[:,0:1]*fpitch, prev], 1), states[0])
+        gru1_state = self.gru1(torch.cat([fwc0_out, pitch_gain[:,0:1]*fpitch, prev], 1), states[0])
         gru1_out = self.gru1_glu(gru1_state)
         gru2_state = self.gru2(torch.cat([gru1_out, pitch_gain[:,1:2]*fpitch, prev], 1), states[1])
         gru2_out = self.gru2_glu(gru2_state)
         gru3_state = self.gru3(torch.cat([gru2_out, pitch_gain[:,2:3]*fpitch, prev], 1), states[2])
         gru3_out = self.gru3_glu(gru3_state)
-        gru3_out = torch.cat([gru1_out, gru2_out, gru3_out, dense2_out], 1)
+        gru3_out = torch.cat([gru1_out, gru2_out, gru3_out, fwc0_out], 1)
         skip_out = torch.tanh(self.skip_dense(torch.cat([gru3_out, pitch_gain[:,3:4]*fpitch, prev], 1)))
         skip_out = self.skip_glu(skip_out)
         sig_out = torch.tanh(self.sig_dense_out(skip_out))
@@ -278,8 +275,8 @@ def forward(self, features, period, nb_frames, pre=None, states=None):
 
         states = (
             torch.zeros(batch_size, self.cond_size, device=device),
-            torch.zeros(batch_size, self.cond_size, device=device),
-            torch.zeros(batch_size, self.cond_size, device=device),
+            torch.zeros(batch_size, 128, device=device),
+            torch.zeros(batch_size, 128, device=device),
             torch.zeros(batch_size, (2*self.subframe_size+80+4)*2, device=device)
         )