import scipy from scipy import linalg from torch.nn import functional as F import torch from torch import nn import numpy as np import modules.audio2motion.utils as utils from modules.audio2motion.transformer_models import FFTBlocks from utils.commons.hparams import hparams def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): n_channels_int = n_channels[0] in_act = input_a + input_b t_act = torch.tanh(in_act[:, :n_channels_int, :]) s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) acts = t_act * s_act return acts class WN(torch.nn.Module): def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0, share_cond_layers=False): super(WN, self).__init__() assert (kernel_size % 2 == 1) assert (hidden_channels % 2 == 0) 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.share_cond_layers = share_cond_layers self.in_layers = torch.nn.ModuleList() self.res_skip_layers = torch.nn.ModuleList() self.drop = nn.Dropout(p_dropout) self.use_adapters = hparams.get("use_adapters", False) if self.use_adapters: self.adapter_layers = torch.nn.ModuleList() if gin_channels != 0 and not share_cond_layers: cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') for i in range(n_layers): dilation = dilation_rate ** i padding = int((kernel_size * dilation - dilation) / 2) in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding) in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') self.in_layers.append(in_layer) # last one is not necessary if i < n_layers - 1: res_skip_channels = 2 * hidden_channels else: res_skip_channels = hidden_channels res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') self.res_skip_layers.append(res_skip_layer) if self.use_adapters: adapter_layer = MlpAdapter(in_out_dim=res_skip_channels, hid_dim=res_skip_channels//4) self.adapter_layers.append(adapter_layer) def forward(self, x, x_mask=None, g=None, **kwargs): output = torch.zeros_like(x) n_channels_tensor = torch.IntTensor([self.hidden_channels]) if g is not None and not self.share_cond_layers: g = self.cond_layer(g) for i in range(self.n_layers): x_in = self.in_layers[i](x) x_in = self.drop(x_in) if g is not None: cond_offset = i * 2 * self.hidden_channels g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :] else: g_l = torch.zeros_like(x_in) acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) res_skip_acts = self.res_skip_layers[i](acts) if self.use_adapters: res_skip_acts = self.adapter_layers[i](res_skip_acts.transpose(1,2)).transpose(1,2) if i < self.n_layers - 1: x = (x + res_skip_acts[:, :self.hidden_channels, :]) * x_mask output = output + res_skip_acts[:, self.hidden_channels:, :] else: output = output + res_skip_acts return output * x_mask def remove_weight_norm(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) def enable_adapters(self): if not self.use_adapters: return for adapter_layer in self.adapter_layers: adapter_layer.enable() def disable_adapters(self): if not self.use_adapters: return for adapter_layer in self.adapter_layers: adapter_layer.disable() class Permute(nn.Module): def __init__(self, *args): super(Permute, self).__init__() self.args = args def forward(self, x): return x.permute(self.args) class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-4): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): n_dims = len(x.shape) mean = torch.mean(x, 1, keepdim=True) variance = torch.mean((x - mean) ** 2, 1, keepdim=True) x = (x - mean) * torch.rsqrt(variance + self.eps) shape = [1, -1] + [1] * (n_dims - 2) x = x * self.gamma.view(*shape) + self.beta.view(*shape) return x class ConvReluNorm(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.out_channels = out_channels self.kernel_size = kernel_size self.n_layers = n_layers self.p_dropout = p_dropout assert n_layers > 1, "Number of layers should be larger than 0." self.conv_layers = nn.ModuleList() self.norm_layers = nn.ModuleList() self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.relu_drop = nn.Sequential( nn.ReLU(), nn.Dropout(p_dropout)) for _ in range(n_layers - 1): self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj.weight.data.zero_() self.proj.bias.data.zero_() def forward(self, x, x_mask): x_org = x for i in range(self.n_layers): x = self.conv_layers[i](x * x_mask) x = self.norm_layers[i](x) x = self.relu_drop(x) x = x_org + self.proj(x) return x * x_mask 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 = 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 = 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 Flip(nn.Module): def forward(self, x, *args, reverse=False, **kwargs): x = torch.flip(x, [1]) logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) return x, logdet def store_inverse(self): pass 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, share_cond_layers=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, share_cond_layers) 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 GlowFFTBlocks(FFTBlocks): def __init__(self, hidden_size=128, gin_channels=256, num_layers=2, ffn_kernel_size=5, dropout=None, num_heads=4, use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True): super().__init__(hidden_size, num_layers, ffn_kernel_size, dropout, num_heads, use_pos_embed, use_last_norm, norm, use_pos_embed_alpha) self.inp_proj = nn.Conv1d(hidden_size + gin_channels, hidden_size, 1) def forward(self, x, x_mask=None, g=None): """ :param x: [B, C_x, T] :param x_mask: [B, 1, T] :param g: [B, C_g, T] :return: [B, C_x, T] """ if g is not None: x = self.inp_proj(torch.cat([x, g], 1)) x = x.transpose(1, 2) x = super(GlowFFTBlocks, self).forward(x, x_mask[:, 0] == 0) x = x.transpose(1, 2) return x class TransformerCouplingBlock(nn.Module): def __init__(self, in_channels, hidden_channels, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels 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) 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.fft_blocks = GlowFFTBlocks( hidden_size=hidden_channels, ffn_kernel_size=3, gin_channels=gin_channels, num_layers=n_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.fft_blocks(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): pass class FreqFFTCouplingBlock(nn.Module): def __init__(self, in_channels, hidden_channels, n_layers, gin_channels=0, p_dropout=0, sigmoid_scale=False): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.n_layers = n_layers self.gin_channels = gin_channels self.p_dropout = p_dropout self.sigmoid_scale = sigmoid_scale hs = hidden_channels stride = 8 self.start = torch.nn.Conv2d(3, hs, kernel_size=stride * 2, stride=stride, padding=stride // 2) end = nn.ConvTranspose2d(hs, 2, kernel_size=stride, stride=stride) end.weight.data.zero_() end.bias.data.zero_() self.end = nn.Sequential( nn.Conv2d(hs * 3, hs, 3, 1, 1), nn.ReLU(), nn.GroupNorm(4, hs), nn.Conv2d(hs, hs, 3, 1, 1), end ) self.fft_v = FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers) self.fft_h = nn.Sequential( nn.Conv1d(hs, hs, 3, 1, 1), nn.ReLU(), nn.Conv1d(hs, hs, 3, 1, 1), ) self.fft_g = nn.Sequential( nn.Conv1d( gin_channels - 160, hs, kernel_size=stride * 2, stride=stride, padding=stride // 2), Permute(0, 2, 1), FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers), Permute(0, 2, 1), ) def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): g_, _ = utils.unsqueeze(g) g_mel = g_[:, :80] g_txt = g_[:, 80:] g_mel, _ = utils.squeeze(g_mel) g_txt, _ = utils.squeeze(g_txt) # [B, C, T] if x_mask is None: x_mask = 1 x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] x = torch.stack([x_0, g_mel[:, :80], g_mel[:, 80:]], 1) x = self.start(x) # [B, C, N_bins, T] B, C, N_bins, T = x.shape x_v = self.fft_v(x.permute(0, 3, 2, 1).reshape(B * T, N_bins, C)) x_v = x_v.reshape(B, T, N_bins, -1).permute(0, 3, 2, 1) # x_v = x x_h = self.fft_h(x.permute(0, 2, 1, 3).reshape(B * N_bins, C, T)) x_h = x_h.reshape(B, N_bins, -1, T).permute(0, 2, 1, 3) # x_h = x x_g = self.fft_g(g_txt)[:, :, None, :].repeat(1, 1, 10, 1) x = torch.cat([x_v, x_h, x_g], 1) out = self.end(x) z_0 = x_0 m = out[:, 0] logs = out[:, 1] 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): pass class ResidualCouplingLayer(nn.Module): def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=0, mean_only=False, nn_type='wn'): assert channels % 2 == 0, "channels should be divisible by 2" super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.half_channels = channels // 2 self.mean_only = mean_only self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) if nn_type == 'wn': self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) # elif nn_type == 'conv': # self.enc = ConditionalConvBlocks( # hidden_channels, gin_channels, hidden_channels, [1] * n_layers, kernel_size, # layers_in_block=1, is_BTC=False) self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) self.post.weight.data.zero_() self.post.bias.data.zero_() def forward(self, x, x_mask, g=None, reverse=False): x0, x1 = torch.split(x, [self.half_channels] * 2, 1) h = self.pre(x0) * x_mask h = self.enc(h, x_mask=x_mask, g=g) stats = self.post(h) * x_mask if not self.mean_only: m, logs = torch.split(stats, [self.half_channels] * 2, 1) else: m = stats logs = torch.zeros_like(m) if not reverse: x1 = m + x1 * torch.exp(logs) * x_mask x = torch.cat([x0, x1], 1) logdet = torch.sum(logs, [1, 2]) return x, logdet else: x1 = (x1 - m) * torch.exp(-logs) * x_mask x = torch.cat([x0, x1], 1) logdet = -torch.sum(logs, [1, 2]) return x, logdet class ResidualCouplingBlock(nn.Module): def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0, nn_type='wn'): super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.n_flows = n_flows self.gin_channels = gin_channels self.flows = nn.ModuleList() for i in range(n_flows): self.flows.append( ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True, nn_type=nn_type)) self.flows.append(Flip()) def forward(self, x, x_mask, g=None, reverse=False): if not reverse: for flow in self.flows: x, _ = flow(x, x_mask, g=g, reverse=reverse) else: for flow in reversed(self.flows): x, _ = flow(x, x_mask, g=g, reverse=reverse) return x 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__() """ Note that regularization likes weight decay can leads to Nan error! """ 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, share_cond_layers=share_cond_layers, wn=wn )) def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False): """ x: [B,T,C] x_mask: [B,T] g: [B,T,C] """ x = x.transpose(1,2) x_mask = x_mask.unsqueeze(1) if g is not None: g = g.transpose(1,2) 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) x = x.transpose(1,2) 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() if __name__ == '__main__': model = Glow(in_channels=64, hidden_channels=128, kernel_size=5, dilation_rate=1, n_blocks=12, n_layers=4, p_dropout=0.0, n_split=4, n_sqz=2, sigmoid_scale=False, gin_channels=80 ) exp = torch.rand([1,1440,64]) mel = torch.rand([1,1440,80]) x_mask = torch.ones([1,1440],dtype=torch.float32) y, logdet = model(exp, x_mask,g=mel, reverse=False) pred_exp, logdet = model(y, x_mask,g=mel, reverse=False) # y: [b, t,c=64] print(" ")