def forward(self, input): x = torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding[0]) #print(x.size()) #print(x) #return F.glu(x, dim=0), x # TODO: our target return convtbcglu.forward(input.contiguous(), self.weight, self.bias, self.padding[0]), x
def forward(self, vec): ret = torch.conv_tbc(vec.contiguous(), self.weight, self.bias, pad = self.padding) return ret
def conv_tbc(self, input: Tensor): return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding[0])
def forward(self, input): return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding[0])
def forward(self, x: torch.Tensor) -> torch.Tensor: x, mean = BinActive()(x) return torch.conv_tbc(x.contiguous(), self.weight, self.bias, self.padding[0])