| | |
|---|
| | | self.act = nn.GLU() |
|---|
| | | |
|---|
| | | # lightconv related |
|---|
| | | self.weight = nn.Parameter( |
|---|
| | | torch.Tensor(self.wshare, 1, kernel_size).uniform_(0, 1) |
|---|
| | | ) |
|---|
| | | self.weight = nn.Parameter(torch.Tensor(self.wshare, 1, kernel_size).uniform_(0, 1)) |
|---|
| | | self.weight_f = nn.Parameter(torch.Tensor(1, 1, kernel_size).uniform_(0, 1)) |
|---|
| | | self.use_bias = use_bias |
|---|
| | | if self.use_bias: |
|---|
| | |
|---|
| | | # convolution along frequency axis |
|---|
| | | weight_f = F.softmax(self.weight_f, dim=-1) |
|---|
| | | weight_f = F.dropout(weight_f, self.dropout_rate, training=self.training) |
|---|
| | | weight_new = torch.zeros( |
|---|
| | | B * T, 1, self.kernel_size, device=x.device, dtype=x.dtype |
|---|
| | | ).copy_(weight_f) |
|---|
| | | weight_new = torch.zeros(B * T, 1, self.kernel_size, device=x.device, dtype=x.dtype).copy_( |
|---|
| | | weight_f |
|---|
| | | ) |
|---|
| | | xf = F.conv1d( |
|---|
| | | x.view(1, B * T, C), weight_new, padding=self.padding_size, groups=B * T |
|---|
| | | ).view(B, T, C) |
|---|
| | |
|---|
| | | self.kernel_mask = self.kernel_mask.to(x.device) |
|---|
| | | weight = weight.masked_fill(self.kernel_mask == 0.0, float("-inf")) |
|---|
| | | weight = F.softmax(weight, dim=-1) |
|---|
| | | x = F.conv1d(x, weight, padding=self.padding_size, groups=self.wshare).view( |
|---|
| | | B, C, T |
|---|
| | | ) |
|---|
| | | x = F.conv1d(x, weight, padding=self.padding_size, groups=self.wshare).view(B, C, T) |
|---|
| | | if self.use_bias: |
|---|
| | | x = x + self.bias.view(1, -1, 1) |
|---|
| | | x = x.transpose(1, 2) # B x T x C |
|---|
