| 68 | |
| 69 | |
| 70 | class Conv1dWN_v2(torch.nn.Conv1d): |
| 71 | def __init__( |
| 72 | self, |
| 73 | in_channels, |
| 74 | out_channels, |
| 75 | kernel_size, |
| 76 | stride=1, |
| 77 | padding=0, |
| 78 | padding_mode="zeros", |
| 79 | dilation=1, |
| 80 | groups=1, |
| 81 | ): |
| 82 | super(Conv1dWN_v2, self).__init__( |
| 83 | in_channels, |
| 84 | out_channels, |
| 85 | kernel_size, |
| 86 | stride, |
| 87 | padding, |
| 88 | dilation, |
| 89 | groups, |
| 90 | True, |
| 91 | ) |
| 92 | self.g = torch.nn.Parameter(torch.ones(out_channels)) |
| 93 | |
| 94 | self.padding_amount=padding |
| 95 | self.padding_mode=padding_mode |
| 96 | |
| 97 | def forward(self, x): |
| 98 | w= torch._weight_norm(self.weight, self.g, 0) |
| 99 | |
| 100 | |
| 101 | |
| 102 | if self.padding_mode != 'zeros': |
| 103 | x= F.pad(x, (self.padding_amount,self.padding_amount), mode=self.padding_mode) |
| 104 | |
| 105 | x= F.conv1d( |
| 106 | x, |
| 107 | w, |
| 108 | bias=self.bias, |
| 109 | stride=self.stride, |
| 110 | padding=0, |
| 111 | dilation=self.dilation, |
| 112 | groups=self.groups, |
| 113 | ) |
| 114 | # print("x after conv", x.shape) |
| 115 | return x |
| 116 | else: |
| 117 | |
| 118 | return F.conv1d( |
| 119 | x, |
| 120 | w, |
| 121 | bias=self.bias, |
| 122 | stride=self.stride, |
| 123 | padding=self.padding, |
| 124 | dilation=self.dilation, |
| 125 | groups=self.groups, |
| 126 | ) |
| 127 | |