diff options
Diffstat (limited to 'dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py')
| -rw-r--r-- | dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py | 15 |
1 files changed, 2 insertions, 13 deletions
diff --git a/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py b/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py index 073ea1b1..a17b0e9b 100644 --- a/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py +++ b/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py @@ -46,12 +46,12 @@ class LimitedAdaptiveConv1d(nn.Module): feature_dim, frame_size=160, overlap_size=40, - use_bias=True, padding=None, name=None, gain_limits_db=[-6, 6], shape_gain_db=0, - norm_p=2): + norm_p=2, + **kwargs): """ Parameters: @@ -90,7 +90,6 @@ class LimitedAdaptiveConv1d(nn.Module): self.kernel_size = kernel_size self.frame_size = frame_size self.overlap_size = overlap_size - self.use_bias = use_bias self.gain_limits_db = gain_limits_db self.shape_gain_db = shape_gain_db self.norm_p = norm_p @@ -104,9 +103,6 @@ class LimitedAdaptiveConv1d(nn.Module): # network for generating convolution weights self.conv_kernel = nn.Linear(feature_dim, in_channels * out_channels * kernel_size) - if self.use_bias: - self.conv_bias = nn.Linear(feature_dim, out_channels) - self.shape_gain = min(1, 10**(shape_gain_db / 20)) self.filter_gain = nn.Linear(feature_dim, out_channels) @@ -133,10 +129,6 @@ class LimitedAdaptiveConv1d(nn.Module): count += 2 * (frame_rate * self.feature_dim * self.kernel_size) count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate) - # bias computation - if self.use_bias: - count += 2 * (frame_rate * self.feature_dim) + rate * (1 + overhead) - # gain computation count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels @@ -183,9 +175,6 @@ class LimitedAdaptiveConv1d(nn.Module): conv_kernels = self.shape_gain * conv_kernels + (1 - self.shape_gain) * id_kernels - if self.use_bias: - conv_biases = self.conv_bias(features).permute(0, 2, 1) - # calculate gains conv_gains = torch.exp(self.filter_gain_a * torch.tanh(self.filter_gain(features)) + self.filter_gain_b) if debug and batch_size == 1: |