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, 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])