Increase size, init convolutions with zerosexp_densenet3x

3 files changed, 31 insertions, 31 deletions

diff --git a/dnn/dred_rdovae_dec.c b/dnn/dred_rdovae_dec.c
index 6cfd6577..96c933bc 100644
--- a/dnn/dred_rdovae_dec.c
+++ b/dnn/dred_rdovae_dec.c
@@ -37,7 +37,7 @@ static void conv1_cond_init(float *mem, const float *input, int len, int dilatio
 {
     if (!*init) {
         int i;
-        for (i=0;i<dilation;i++) OPUS_COPY(&mem[i*len], input, len);
+        for (i=0;i<dilation;i++) OPUS_CLEAR(&mem[i*len], len);
     }
     *init = 1;
 }
diff --git a/dnn/dred_rdovae_enc.c b/dnn/dred_rdovae_enc.c
index b34ac40e..ee55e7cc 100644
--- a/dnn/dred_rdovae_enc.c
+++ b/dnn/dred_rdovae_enc.c
@@ -39,7 +39,7 @@ static void conv1_cond_init(float *mem, const float *input, int len, int dilatio
 {
     if (!*init) {
         int i;
-        for (i=0;i<dilation;i++) OPUS_COPY(&mem[i*len], input, len);
+        for (i=0;i<dilation;i++) OPUS_CLEAR(&mem[i*len], len);
     }
     *init = 1;
 }
diff --git a/dnn/torch/rdovae/rdovae/rdovae.py b/dnn/torch/rdovae/rdovae/rdovae.py
index ad118b99..1a16f3e4 100644
--- a/dnn/torch/rdovae/rdovae/rdovae.py
+++ b/dnn/torch/rdovae/rdovae/rdovae.py
@@ -233,7 +233,7 @@ class MyConv(nn.Module):
         self.conv = nn.Conv1d(input_dim, output_dim, kernel_size=2, padding='valid', dilation=dilation)
     def forward(self, x, state=None):
         device = x.device
-        conv_in = torch.cat([x[:,0:1,:].repeat(1,self.dilation,1), x], -2).permute(0, 2, 1)
+        conv_in = torch.cat([torch.zeros_like(x[:,0:self.dilation,:], device=device), x], -2).permute(0, 2, 1)
         return torch.tanh(self.conv(conv_in)).permute(0, 2, 1)
 
 class CoreEncoder(nn.Module):
@@ -263,20 +263,20 @@ class CoreEncoder(nn.Module):
         # layers
         self.dense_1 = nn.Linear(self.input_dim, 64)
         self.gru1 = nn.GRU(64, 64, batch_first=True)
-        self.conv1 = MyConv(128, 32)
-        self.gru2 = nn.GRU(160, 64, batch_first=True)
-        self.conv2 = MyConv(224, 32, dilation=2)
-        self.gru3 = nn.GRU(256, 64, batch_first=True)
-        self.conv3 = MyConv(320, 32, dilation=2)
-        self.gru4 = nn.GRU(352, 64, batch_first=True)
-        self.conv4 = MyConv(416, 32, dilation=2)
-        self.gru5 = nn.GRU(448, 64, batch_first=True)
-        self.conv5 = MyConv(512, 32, dilation=2)
+        self.conv1 = MyConv(128, 96)
+        self.gru2 = nn.GRU(224, 64, batch_first=True)
+        self.conv2 = MyConv(288, 96, dilation=2)
+        self.gru3 = nn.GRU(384, 64, batch_first=True)
+        self.conv3 = MyConv(448, 96, dilation=2)
+        self.gru4 = nn.GRU(544, 64, batch_first=True)
+        self.conv4 = MyConv(608, 96, dilation=2)
+        self.gru5 = nn.GRU(704, 64, batch_first=True)
+        self.conv5 = MyConv(768, 96, dilation=2)
 
-        self.z_dense = nn.Linear(544, self.output_dim)
+        self.z_dense = nn.Linear(864, self.output_dim)
 
 
-        self.state_dense_1 = nn.Linear(544, self.STATE_HIDDEN)
+        self.state_dense_1 = nn.Linear(864, self.STATE_HIDDEN)
 
         self.state_dense_2 = nn.Linear(self.STATE_HIDDEN, self.state_size)
         nb_params = sum(p.numel() for p in self.parameters())
@@ -341,20 +341,20 @@ class CoreDecoder(nn.Module):
 
         # layers
         self.dense_1    = nn.Linear(self.input_size, 96)
-        self.gru1 = nn.GRU(96, 64, batch_first=True)
-        self.conv1 = MyConv(160, 32)
-        self.gru2 = nn.GRU(192, 64, batch_first=True)
-        self.conv2 = MyConv(256, 32)
-        self.gru3 = nn.GRU(288, 64, batch_first=True)
-        self.conv3 = MyConv(352, 32)
-        self.gru4 = nn.GRU(384, 64, batch_first=True)
-        self.conv4 = MyConv(448, 32)
-        self.gru5 = nn.GRU(480, 64, batch_first=True)
-        self.conv5 = MyConv(544, 32)
-        self.output  = nn.Linear(576, self.FRAMES_PER_STEP * self.output_dim)
+        self.gru1 = nn.GRU(96, 96, batch_first=True)
+        self.conv1 = MyConv(192, 32)
+        self.gru2 = nn.GRU(224, 96, batch_first=True)
+        self.conv2 = MyConv(320, 32)
+        self.gru3 = nn.GRU(352, 96, batch_first=True)
+        self.conv3 = MyConv(448, 32)
+        self.gru4 = nn.GRU(480, 96, batch_first=True)
+        self.conv4 = MyConv(576, 32)
+        self.gru5 = nn.GRU(608, 96, batch_first=True)
+        self.conv5 = MyConv(704, 32)
+        self.output  = nn.Linear(736, self.FRAMES_PER_STEP * self.output_dim)
 
         self.hidden_init = nn.Linear(self.state_size, 128)
-        self.gru_init = nn.Linear(128, 320)
+        self.gru_init = nn.Linear(128, 480)
 
         nb_params = sum(p.numel() for p in self.parameters())
         print(f"decoder: {nb_params} weights")
@@ -365,11 +365,11 @@ class CoreDecoder(nn.Module):
 
         hidden = torch.tanh(self.hidden_init(initial_state))
         gru_state = torch.tanh(self.gru_init(hidden).permute(1, 0, 2))
-        h1_state = gru_state[:,:,:64].contiguous()
-        h2_state = gru_state[:,:,64:128].contiguous()
-        h3_state = gru_state[:,:,128:192].contiguous()
-        h4_state = gru_state[:,:,192:256].contiguous()
-        h5_state = gru_state[:,:,256:].contiguous()
+        h1_state = gru_state[:,:,:96].contiguous()
+        h2_state = gru_state[:,:,96:192].contiguous()
+        h3_state = gru_state[:,:,192:288].contiguous()
+        h4_state = gru_state[:,:,288:384].contiguous()
+        h5_state = gru_state[:,:,384:].contiguous()
 
         # run decoding layer stack
         x = torch.tanh(self.dense_1(z))