import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d, ConvTranspose2d, AvgPool2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from common.utils import init_weights, get_padding, print_once LRELU_SLOPE = 0.1 class ResBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlock1, self).__init__() self.h = h self.convs1 = nn.ModuleList([ weight_norm(Conv2d(channels, channels, (kernel_size, 1), 1, dilation=(dilation[0], 1), padding=(get_padding(kernel_size, dilation[0]), 0))), weight_norm(Conv2d(channels, channels, (kernel_size, 1), 1, dilation=(dilation[1], 1), padding=(get_padding(kernel_size, dilation[1]), 0))), weight_norm(Conv2d(channels, channels, (kernel_size, 1), 1, dilation=(dilation[2], 1), padding=(get_padding(kernel_size, dilation[2]), 0))) ]) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList([ weight_norm(Conv2d(channels, channels, (kernel_size, 1), 1, dilation=1, padding=(get_padding(kernel_size, 1), 0))), weight_norm(Conv2d(channels, channels, (kernel_size, 1), 1, dilation=1, padding=(get_padding(kernel_size, 1), 0))), weight_norm(Conv2d(channels, channels, (kernel_size, 1), 1, dilation=1, padding=(get_padding(kernel_size, 1), 0))) ]) self.convs2.apply(init_weights) def forward(self, x): for c1, c2 in zip(self.convs1, self.convs2): xt = F.leaky_relu(x, LRELU_SLOPE) xt = c1(xt) xt = F.leaky_relu(xt, LRELU_SLOPE) xt = c2(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) class ResBlock2(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): super(ResBlock2, self).__init__() self.h = h self.convs = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))) ]) self.convs.apply(init_weights) def forward(self, x): for c in self.convs: xt = F.leaky_relu(x, LRELU_SLOPE) xt = c(xt) x = xt + x return x def remove_weight_norm(self): for l in self.convs: remove_weight_norm(l) class Generator(torch.nn.Module): def __init__(self, h): super(Generator, self).__init__() self.h = h self.num_kernels = len(h.resblock_kernel_sizes) self.num_upsamples = len(h.upsample_rates) self.conv_pre = weight_norm(Conv2d(80, h.upsample_initial_channel, (7,1), (1,1), padding=(3,0))) assert h.resblock == '1', 'Only ResBlock1 currently supported for NHWC' resblock = ResBlock1 if h.resblock == '1' else ResBlock2 self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): self.ups.append(weight_norm( # ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)), # k, u, padding=(k-u)//2))) ConvTranspose2d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)), (k, 1), (u, 1), padding=((k-u)//2, 0)))) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = h.upsample_initial_channel//(2**(i+1)) for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): self.resblocks.append(resblock(h, ch, k, d)) self.conv_post = weight_norm(Conv2d(ch, 1, (7,1), (1,1), padding=(3,0))) self.ups.apply(init_weights) self.conv_post.apply(init_weights) def forward(self, x): x = x.unsqueeze(-1).to(memory_format=torch.channels_last) x = self.conv_pre(x) for i in range(self.num_upsamples): x = F.leaky_relu(x, LRELU_SLOPE) # x = self.ups[i](x.unsqueeze(-1)).squeeze(-1) x = self.ups[i](x) xs = 0 for j in range(self.num_kernels): xs += self.resblocks[i*self.num_kernels+j](x) x = xs / self.num_kernels x = F.leaky_relu(x) x = self.conv_post(x) x = torch.tanh(x) x = x.squeeze(-1) return x def remove_weight_norm(self): print('Removing weight norm...') for l in self.ups: remove_weight_norm(l) for l in self.resblocks: l.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) class DiscriminatorP(torch.nn.Module): def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): super(DiscriminatorP, self).__init__() self.period = period norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), ]) self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) def forward(self, x): fmap = [] # 1d to 2d b, c, t, unit = x.shape assert unit == 1 if t % self.period != 0: # pad first n_pad = self.period - (t % self.period) x = F.pad(x, (0, 0, 0, n_pad), "reflect") t = t + n_pad # print_once('x pre channels last:', x.is_contiguous(memory_format=torch.channels_last)) x = x.view(b, c, t // self.period, self.period) # print_once('x post channels last:', x.is_contiguous(memory_format=torch.channels_last)) for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) # x = torch.flatten(x, 1, -1) return x, fmap def share_params_of(self, dp): assert len(self.convs) == len(dp.convs) for c1, c2 in zip(self.convs, dp.convs): c1.weight = c2.weight c1.bias = c2.bias class DiscriminatorPConv1d(torch.nn.Module): def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): super(DiscriminatorPConv1d, self).__init__() self.period = period norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.convs = nn.ModuleList([ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0), dilation=(period, 1))), norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0), dilation=(period, 1))), norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0), dilation=(period, 1))), norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0), dilation=(period, 1))), norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0), dilation=(period, 1))), ]) # self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1, dilation=period)) self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0), dilation=(period, 1))) def forward(self, x): fmap = [] # 1d to 2d b, c, t, unit = x.shape assert unit == 1 # if t % self.period != 0: # pad first # n_pad = self.period - (t % self.period) # x = F.pad(x, (0, n_pad), "reflect") # t = t + n_pad # x = x.view(b, c, t // self.period, self.period) for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap def share_params_of(self, dp): assert len(self.convs) == len(dp.convs) for c1, c2 in zip(self.convs, dp.convs): c1.weight = c2.weight c1.bias = c2.bias class MultiPeriodDiscriminator(torch.nn.Module): def __init__(self, periods, use_conv1d=False, shared=False): super(MultiPeriodDiscriminator, self).__init__() print('MPD PERIODS:', periods) if use_conv1d: print('Constructing dilated MPD') layers = [DiscriminatorPConv1d(p) for p in periods] else: layers = [DiscriminatorP(p) for p in periods] if shared: print('MPD HAS SHARED PARAMS') for l in layers[1:]: l.share_params_of(layers[0]) self.discriminators = nn.ModuleList(layers) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): y_d_r, fmap_r = d(y) y_d_g, fmap_g = d(y_hat) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs class DiscriminatorS(torch.nn.Module): def __init__(self, use_spectral_norm=False, amp_groups=False): super(DiscriminatorS, self).__init__() norm_f = weight_norm if use_spectral_norm == False else spectral_norm # self.convs = nn.ModuleList([ # norm_f(Conv1d(1, 128, 15, 1, padding=7)), # norm_f(Conv1d(128, 128, 41, 2, groups=1 if amp_groups else 4, padding=20)), # was: groups=4 # norm_f(Conv1d(128, 256, 41, 2, groups=1 if amp_groups else 16, padding=20)), # was: groups=16 # norm_f(Conv1d(256, 512, 41, 4, groups=1 if amp_groups else 16, padding=20)), # was: groups=16 # norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), # norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), # norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), # ]) self.convs = nn.ModuleList([ norm_f(Conv2d(1, 128, (15,1), (1,1), padding=(7 , 0))), norm_f(Conv2d(128, 128, (41,1), (2,1), groups=1 if amp_groups else 4, padding=(20, 0))), # was: groups=4 norm_f(Conv2d(128, 256, (41,1), (2,1), groups=1 if amp_groups else 16, padding=(20, 0))), # was: groups=16 norm_f(Conv2d(256, 512, (41,1), (4,1), groups=1 if amp_groups else 16, padding=(20, 0))), # was: groups=16 norm_f(Conv2d(512, 1024, (41,1), (4,1), groups=16 , padding=(20, 0))), norm_f(Conv2d(1024, 1024, (41,1), (1,1), groups=16 , padding=(20, 0))), norm_f(Conv2d(1024, 1024, ( 5,1), (1,1), padding=(2 , 0))), ]) self.conv_post = norm_f(Conv2d(1024, 1, (3,1), (1,1), padding=(1,0))) def forward(self, x): fmap = [] for l in self.convs: x = l(x) x = F.leaky_relu(x, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) # x = x.squeeze(-1) # x = torch.flatten(x, 1, -1) return x, fmap class MultiScaleDiscriminator(torch.nn.Module): def __init__(self, amp_groups=False): super(MultiScaleDiscriminator, self).__init__() if amp_groups: print('MSD: AMP groups') self.discriminators = nn.ModuleList([ DiscriminatorS(use_spectral_norm=True, amp_groups=amp_groups), DiscriminatorS(amp_groups=amp_groups), DiscriminatorS(amp_groups=amp_groups), ]) self.meanpools = nn.ModuleList([ AvgPool2d((4, 1), (2, 1), padding=(1, 0)), AvgPool2d((4, 1), (2, 1), padding=(1, 0)) ]) def forward(self, y, y_hat): y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(self.discriminators): if i != 0: y = self.meanpools[i-1](y) y_hat = self.meanpools[i-1](y_hat) y_d_r, fmap_r = d(y) y_d_g, fmap_g = d(y_hat) y_d_rs.append(y_d_r) fmap_rs.append(fmap_r) y_d_gs.append(y_d_g) fmap_gs.append(fmap_g) return y_d_rs, y_d_gs, fmap_rs, fmap_gs def feature_loss(fmap_r, fmap_g, keys=[]): loss = 0 meta = {} assert len(keys) == len(fmap_r) for key, dr, dg in zip(keys, fmap_r, fmap_g): k = 'loss_gen_feat_' + key meta[k] = 0 for rl, gl in zip(dr, dg): # loss += torch.mean(torch.abs(rl - gl)) diff = torch.mean(torch.abs(rl - gl)) loss += diff meta[k] += diff.item() return loss*2, meta def discriminator_loss(disc_real_outputs, disc_generated_outputs, keys=[]): loss = 0 r_losses = [] g_losses = [] meta = {} assert len(keys) == len(disc_real_outputs) for key, dr, dg in zip(keys, disc_real_outputs, disc_generated_outputs): r_loss = torch.mean((1-dr)**2) g_loss = torch.mean(dg**2) loss += (r_loss + g_loss) r_losses.append(r_loss.item()) g_losses.append(g_loss.item()) meta['loss_disc_real_' + key] = r_loss.item() meta['loss_disc_gen_' + key] = g_loss.item() return loss, r_losses, g_losses, meta def generator_loss(disc_outputs, keys=[]): loss = 0 gen_losses = [] meta = {} assert len(keys) == len(disc_outputs) for key, dg in zip(keys, disc_outputs): l = torch.mean((1-dg)**2) gen_losses.append(l) loss += l meta['loss_gen_' + key] = l.item() return loss, gen_losses, meta