Extension of pytorch WN2d layer to 1d convolution/deconvolution

[lifelongeek]

Thanks for sharing interesting, practical work. I am thinking about replacing BN to proposed WN layer for speech GAN.

Is it possible to apply 'WeightNormalizedConv2d' & 'WeightNormalizedConvTranspose2d' layer to 1d convolution/deconvolution by simply put kernel size as tuple (H, window_size)? H is height of data.

Or can we think about new module with F.conv1d?
For example, its forward computation looks like this

	def forward(self, input):
		return self.norm_scale_bias(F.conv1d(input, self.weight, None, self.stride,
						self.padding, self.dilation, 1))