1 files changed, 4 insertions, 21 deletions
diff --git a/dnn/torch/osce/models/silk_feature_net_pl.py b/dnn/torch/osce/models/silk_feature_net_pl.py
index 89e5cc6b..c766d0ab 100644
--- a/dnn/torch/osce/models/silk_feature_net_pl.py
+++ b/dnn/torch/osce/models/silk_feature_net_pl.py
@@ -50,9 +50,7 @@ class SilkFeatureNetPL(nn.Module):
                  softquant=False,
                  sparsify=True,
                  sparsification_density=0.5,
-                 apply_weight_norm=False,
-                 repeat_upsamp=False,
-                 repeat_upsamp_dim=16):
+                 apply_weight_norm=False):
 
         super(SilkFeatureNetPL, self).__init__()
 
@@ -62,17 +60,12 @@ class SilkFeatureNetPL(nn.Module):
         self.feature_dim = feature_dim
         self.num_channels = num_channels
         self.hidden_feature_dim = hidden_feature_dim
-        self.repeat_upsamp = repeat_upsamp
-        self.repeat_upsamp_dim = 16
 
         norm = weight_norm if apply_weight_norm else lambda x, name=None: x
 
         self.conv1 = norm(nn.Conv1d(feature_dim, self.hidden_feature_dim, 1))
         self.conv2 = norm(nn.Conv1d(4 * self.hidden_feature_dim, num_channels, 2))
-        if self.repeat_upsamp:
-            self.upsamp_embedding = nn.Embedding(4, self.repeat_upsamp_dim)
-        else:
-            self.tconv = norm(nn.ConvTranspose1d(num_channels, num_channels, 4, 4))
+        self.tconv = norm(nn.ConvTranspose1d(num_channels, num_channels, 4, 4))
         gru_input_dim = num_channels + self.repeat_upsamp_dim if self.repeat_upsamp else num_channels
         self.gru   = norm(norm(nn.GRU(gru_input_dim, num_channels, batch_first=True), name='weight_hh_l0'), name='weight_ih_l0')
 
@@ -127,18 +120,8 @@ class SilkFeatureNetPL(nn.Module):
         c = torch.tanh(self.conv2(F.pad(c, [1, 0])))
 
         # upsampling
-        if self.repeat_upsamp:
-            a = torch.arange(num_frames, device=features.device) % 4
-            embeddings = torch.repeat_interleave(
-                torch.tanh(self.upsamp_embedding(a)).unsqueeze(0),
-                batch_size,
-                0
-            )
-            c = c.permute(0, 2, 1)
-            c = torch.cat((torch.repeat_interleave(c, 4, 1), embeddings), dim=2)
-        else:
-            c = torch.tanh(self.tconv(c))
-            c = c.permute(0, 2, 1)
+        c = torch.tanh(self.tconv(c))
+        c = c.permute(0, 2, 1)
 
         c, _ = self.gru(c, state)