import scipy from torch.nn import functional as F import torch from torch import nn import numpy as np from modules.commons.wavenet import WN from modules.tts.glow import utils class ActNorm(nn.Module): def __init__(self, channels, ddi=False, **kwargs): super().__init__() self.channels = channels self.initialized = not ddi self.logs = nn.Parameter(torch.zeros(1, channels, 1)) self.bias = nn.Parameter(torch.zeros(1, channels, 1)) def forward(self, x, x_mask=None, reverse=False, **kwargs): if x_mask is None: x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) x_len = torch.sum(x_mask, [1, 2]) if not self.initialized: self.initialize(x, x_mask) self.initialized = True if reverse: z = (x - self.bias) * torch.exp(-self.logs) * x_mask logdet = torch.sum(-self.logs) * x_len else: z = (self.bias + torch.exp(self.logs) * x) * x_mask logdet = torch.sum(self.logs) * x_len # [b] return z, logdet def store_inverse(self): pass def set_ddi(self, ddi): self.initialized = not ddi def initialize(self, x, x_mask): with torch.no_grad(): denom = torch.sum(x_mask, [0, 2]) m = torch.sum(x * x_mask, [0, 2]) / denom m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom v = m_sq - (m ** 2) logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) self.bias.data.copy_(bias_init) self.logs.data.copy_(logs_init) class InvConvNear(nn.Module): def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs): super().__init__() assert (n_split % 2 == 0) self.channels = channels self.n_split = n_split self.n_sqz = n_sqz self.no_jacobian = no_jacobian w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] if torch.det(w_init) < 0: w_init[:, 0] = -1 * w_init[:, 0] self.lu = lu if lu: # LU decomposition can slightly speed up the inverse np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1) eye = np.eye(*w_init.shape, dtype=float) self.register_buffer('p', torch.Tensor(np_p.astype(float))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True) self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True) self.register_buffer('l_mask', torch.Tensor(l_mask)) self.register_buffer('eye', torch.Tensor(eye)) else: self.weight = nn.Parameter(w_init) def forward(self, x, x_mask=None, reverse=False, **kwargs): b, c, t = x.size() assert (c % self.n_split == 0) if x_mask is None: x_mask = 1 x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t) x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) if self.lu: self.weight, log_s = self._get_weight() logdet = log_s.sum() logdet = logdet * (c / self.n_split) * x_len else: logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] if reverse: if hasattr(self, "weight_inv"): weight = self.weight_inv else: weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) logdet = -logdet else: weight = self.weight if self.no_jacobian: logdet = 0 weight = weight.view(self.n_split, self.n_split, 1, 1) z = F.conv2d(x, weight) z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t) z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask return z, logdet def _get_weight(self): l, log_s, u = self.l, self.log_s, self.u l = l * self.l_mask + self.eye u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s)) weight = torch.matmul(self.p, torch.matmul(l, u)) return weight, log_s def store_inverse(self): weight, _ = self._get_weight() self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device) class InvConv(nn.Module): def __init__(self, channels, no_jacobian=False, lu=True, **kwargs): super().__init__() w_shape = [channels, channels] w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float) LU_decomposed = lu if not LU_decomposed: # Sample a random orthogonal matrix: self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init))) else: np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_shape, dtype=float), -1) eye = np.eye(*w_shape, dtype=float) self.register_buffer('p', torch.Tensor(np_p.astype(float))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) self.l = nn.Parameter(torch.Tensor(np_l.astype(float))) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float))) self.u = nn.Parameter(torch.Tensor(np_u.astype(float))) self.l_mask = torch.Tensor(l_mask) self.eye = torch.Tensor(eye) self.w_shape = w_shape self.LU = LU_decomposed self.weight = None def get_weight(self, device, reverse): w_shape = self.w_shape self.p = self.p.to(device) self.sign_s = self.sign_s.to(device) self.l_mask = self.l_mask.to(device) self.eye = self.eye.to(device) l = self.l * self.l_mask + self.eye u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s)) dlogdet = self.log_s.sum() if not reverse: w = torch.matmul(self.p, torch.matmul(l, u)) else: l = torch.inverse(l.double()).float() u = torch.inverse(u.double()).float() w = torch.matmul(u, torch.matmul(l, self.p.inverse())) return w.view(w_shape[0], w_shape[1], 1), dlogdet def forward(self, x, x_mask=None, reverse=False, **kwargs): """ log-det = log|abs(|W|)| * pixels """ b, c, t = x.size() if x_mask is None: x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) logdet = 0 if not reverse: weight, dlogdet = self.get_weight(x.device, reverse) z = F.conv1d(x, weight) if logdet is not None: logdet = logdet + dlogdet * x_len return z, logdet else: if self.weight is None: weight, dlogdet = self.get_weight(x.device, reverse) else: weight, dlogdet = self.weight, self.dlogdet z = F.conv1d(x, weight) if logdet is not None: logdet = logdet - dlogdet * x_len return z, logdet def store_inverse(self): self.weight, self.dlogdet = self.get_weight('cuda', reverse=True) class CouplingBlock(nn.Module): def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False, wn=None): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.sigmoid_scale = sigmoid_scale start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) start = torch.nn.utils.weight_norm(start) self.start = start # Initializing last layer to 0 makes the affine coupling layers # do nothing at first. This helps with training stability end = torch.nn.Conv1d(hidden_channels, in_channels, 1) end.weight.data.zero_() end.bias.data.zero_() self.end = end self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout) if wn is not None: self.wn.in_layers = wn.in_layers self.wn.res_skip_layers = wn.res_skip_layers def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): if x_mask is None: x_mask = 1 x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] x = self.start(x_0) * x_mask x = self.wn(x, x_mask, g) out = self.end(x) z_0 = x_0 m = out[:, :self.in_channels // 2, :] logs = out[:, self.in_channels // 2:, :] if self.sigmoid_scale: logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) if reverse: z_1 = (x_1 - m) * torch.exp(-logs) * x_mask logdet = torch.sum(-logs * x_mask, [1, 2]) else: z_1 = (m + torch.exp(logs) * x_1) * x_mask logdet = torch.sum(logs * x_mask, [1, 2]) z = torch.cat([z_0, z_1], 1) return z, logdet def store_inverse(self): self.wn.remove_weight_norm() class Glow(nn.Module): def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_blocks, n_layers, p_dropout=0., n_split=4, n_sqz=2, sigmoid_scale=False, gin_channels=0, inv_conv_type='near', share_cond_layers=False, share_wn_layers=0, ): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_blocks = n_blocks self.n_layers = n_layers self.p_dropout = p_dropout self.n_split = n_split self.n_sqz = n_sqz self.sigmoid_scale = sigmoid_scale self.gin_channels = gin_channels self.share_cond_layers = share_cond_layers if gin_channels != 0 and share_cond_layers: cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') wn = None self.flows = nn.ModuleList() for b in range(n_blocks): self.flows.append(ActNorm(channels=in_channels * n_sqz)) if inv_conv_type == 'near': self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz)) if inv_conv_type == 'invconv': self.flows.append(InvConv(channels=in_channels * n_sqz)) if share_wn_layers > 0: if b % share_wn_layers == 0: wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz, p_dropout, share_cond_layers) self.flows.append( CouplingBlock( in_channels * n_sqz, hidden_channels, kernel_size=kernel_size, dilation_rate=dilation_rate, n_layers=n_layers, gin_channels=gin_channels * n_sqz, p_dropout=p_dropout, sigmoid_scale=sigmoid_scale, wn=wn )) def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False): logdet_tot = 0 if not reverse: flows = self.flows else: flows = reversed(self.flows) if return_hiddens: hs = [] if self.n_sqz > 1: x, x_mask_ = utils.squeeze(x, x_mask, self.n_sqz) if g is not None: g, _ = utils.squeeze(g, x_mask, self.n_sqz) x_mask = x_mask_ if self.share_cond_layers and g is not None: g = self.cond_layer(g) for f in flows: x, logdet = f(x, x_mask, g=g, reverse=reverse) if return_hiddens: hs.append(x) logdet_tot += logdet if self.n_sqz > 1: x, x_mask = utils.unsqueeze(x, x_mask, self.n_sqz) if return_hiddens: return x, logdet_tot, hs return x, logdet_tot def store_inverse(self): def remove_weight_norm(m): try: nn.utils.remove_weight_norm(m) except ValueError: # this module didn't have weight norm return self.apply(remove_weight_norm) for f in self.flows: f.store_inverse()