import functools import torch.nn as nn class ActNorm(nn.Module): def __init__(self, num_features, logdet=False, affine=True, allow_reverse_init=False): assert affine super().__init__() self.logdet = logdet self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) self.allow_reverse_init = allow_reverse_init self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) def initialize(self, input): with torch.no_grad(): flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) mean = ( flatten.mean(1) .unsqueeze(1) .unsqueeze(2) .unsqueeze(3) .permute(1, 0, 2, 3) ) std = ( flatten.std(1) .unsqueeze(1) .unsqueeze(2) .unsqueeze(3) .permute(1, 0, 2, 3) ) self.loc.data.copy_(-mean) self.scale.data.copy_(1 / (std + 1e-6)) def forward(self, input, reverse=False): if reverse: return self.reverse(input) if len(input.shape) == 2: input = input[:, :, None, None] squeeze = True else: squeeze = False _, _, height, width = input.shape if self.training and self.initialized.item() == 0: self.initialize(input) self.initialized.fill_(1) h = self.scale * (input + self.loc) if squeeze: h = h.squeeze(-1).squeeze(-1) if self.logdet: log_abs = torch.log(torch.abs(self.scale)) logdet = height * width * torch.sum(log_abs) logdet = logdet * torch.ones(input.shape[0]).to(input) return h, logdet return h def reverse(self, output): if self.training and self.initialized.item() == 0: if not self.allow_reverse_init: raise RuntimeError( "Initializing ActNorm in reverse direction is " "disabled by default. Use allow_reverse_init=True to enable." ) else: self.initialize(output) self.initialized.fill_(1) if len(output.shape) == 2: output = output[:, :, None, None] squeeze = True else: squeeze = False h = output / self.scale - self.loc if squeeze: h = h.squeeze(-1).squeeze(-1) return h def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm') != -1: nn.init.normal_(m.weight.data, 1.0, 0.02) nn.init.constant_(m.bias.data, 0) class NLayerDiscriminator(nn.Module): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super(NLayerDiscriminator, self).__init__() if not use_actnorm: norm_layer = nn.BatchNorm2d else: norm_layer = ActNorm if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters use_bias = norm_layer.func != nn.BatchNorm2d else: use_bias = norm_layer != nn.BatchNorm2d kw = 4 padw = 1 sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] # w/2 nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): # gradually increase the number of filters # w/(2**nlayers) nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),# w - 1 norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] # output 1 channel prediction map sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # w - 1 self.main = nn.Sequential(*sequence) def forward(self, input): """Standard forward.""" return self.main(input) class NLayerDiscriminator1dFeats(NLayerDiscriminator): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input feats ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) if not use_actnorm: norm_layer = nn.BatchNorm1d else: norm_layer = ActNorm if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters use_bias = norm_layer.func != nn.BatchNorm1d else: use_bias = norm_layer != nn.BatchNorm1d kw = 4 padw = 1 sequence = [nn.Conv1d(input_nc, input_nc//2, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] nf_mult = input_nc//2 nf_mult_prev = 1 for n in range(1, n_layers): # gradually decrease the number of filters nf_mult_prev = nf_mult nf_mult = max(nf_mult_prev // (2 ** n), 8) sequence += [ nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = max(nf_mult_prev // (2 ** n), 8) sequence += [ nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = max(nf_mult_prev // (2 ** n), 8) sequence += [ nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(nf_mult), nn.LeakyReLU(0.2, True) ] # output 1 channel prediction map sequence += [nn.Conv1d(nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] self.main = nn.Sequential(*sequence) class NLayerDiscriminator1dSpecs(NLayerDiscriminator): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=80, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input specs ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) if not use_actnorm: norm_layer = nn.BatchNorm1d else: norm_layer = ActNorm if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters use_bias = norm_layer.func != nn.BatchNorm1d else: use_bias = norm_layer != nn.BatchNorm1d kw = 4 padw = 1 sequence = [nn.Conv1d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): # gradually decrease the number of filters nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) sequence += [ nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] # output 1 channel prediction map sequence += [nn.Conv1d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] self.main = nn.Sequential(*sequence) def forward(self, input): """Standard forward.""" # (B, C, L) input = input.squeeze(1) input = self.main(input) return input if __name__ == '__main__': import torch ## FEATURES disc_in_channels = 2048 disc_num_layers = 2 use_actnorm = False disc_ndf = 64 discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) inputs = torch.rand((6, 2048, 212)) outputs = discriminator(inputs) print(outputs.shape) ## AUDIO disc_in_channels = 1 disc_num_layers = 3 use_actnorm = False disc_ndf = 64 discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) inputs = torch.rand((6, 1, 80, 848)) outputs = discriminator(inputs) print(outputs.shape) ## IMAGE disc_in_channels = 3 disc_num_layers = 3 use_actnorm = False disc_ndf = 64 discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) inputs = torch.rand((6, 3, 256, 256)) outputs = discriminator(inputs) print(outputs.shape)