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import math |
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import numpy as np |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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|
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from torch.nn import Conv1d |
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from torch.nn.utils import weight_norm, remove_weight_norm |
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from module import commons |
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from module.commons import init_weights, get_padding |
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from module.transforms import piecewise_rational_quadratic_transform |
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import torch.distributions as D |
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LRELU_SLOPE = 0.1 |
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class LayerNorm(nn.Module): |
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def __init__(self, channels, eps=1e-5): |
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super().__init__() |
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self.channels = channels |
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self.eps = eps |
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|
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self.gamma = nn.Parameter(torch.ones(channels)) |
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self.beta = nn.Parameter(torch.zeros(channels)) |
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|
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def forward(self, x): |
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x = x.transpose(1, -1) |
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x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) |
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return x.transpose(1, -1) |
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class ConvReluNorm(nn.Module): |
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def __init__( |
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self, |
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in_channels, |
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hidden_channels, |
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out_channels, |
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kernel_size, |
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n_layers, |
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p_dropout, |
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): |
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super().__init__() |
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self.in_channels = in_channels |
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self.hidden_channels = hidden_channels |
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self.out_channels = out_channels |
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self.kernel_size = kernel_size |
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self.n_layers = n_layers |
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self.p_dropout = p_dropout |
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assert n_layers > 1, "Number of layers should be larger than 0." |
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self.conv_layers = nn.ModuleList() |
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self.norm_layers = nn.ModuleList() |
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self.conv_layers.append( |
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nn.Conv1d( |
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in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 |
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) |
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) |
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self.norm_layers.append(LayerNorm(hidden_channels)) |
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self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) |
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for _ in range(n_layers - 1): |
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self.conv_layers.append( |
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nn.Conv1d( |
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hidden_channels, |
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hidden_channels, |
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kernel_size, |
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padding=kernel_size // 2, |
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) |
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) |
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self.norm_layers.append(LayerNorm(hidden_channels)) |
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1) |
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self.proj.weight.data.zero_() |
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self.proj.bias.data.zero_() |
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def forward(self, x, x_mask): |
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x_org = x |
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for i in range(self.n_layers): |
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x = self.conv_layers[i](x * x_mask) |
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x = self.norm_layers[i](x) |
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x = self.relu_drop(x) |
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x = x_org + self.proj(x) |
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return x * x_mask |
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class DDSConv(nn.Module): |
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""" |
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Dialted and Depth-Separable Convolution |
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""" |
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def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): |
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super().__init__() |
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self.channels = channels |
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self.kernel_size = kernel_size |
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self.n_layers = n_layers |
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self.p_dropout = p_dropout |
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self.drop = nn.Dropout(p_dropout) |
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self.convs_sep = nn.ModuleList() |
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self.convs_1x1 = nn.ModuleList() |
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self.norms_1 = nn.ModuleList() |
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self.norms_2 = nn.ModuleList() |
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for i in range(n_layers): |
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dilation = kernel_size**i |
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padding = (kernel_size * dilation - dilation) // 2 |
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self.convs_sep.append( |
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nn.Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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groups=channels, |
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dilation=dilation, |
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padding=padding, |
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) |
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) |
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self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) |
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self.norms_1.append(LayerNorm(channels)) |
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self.norms_2.append(LayerNorm(channels)) |
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|
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def forward(self, x, x_mask, g=None): |
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if g is not None: |
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x = x + g |
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for i in range(self.n_layers): |
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y = self.convs_sep[i](x * x_mask) |
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y = self.norms_1[i](y) |
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y = F.gelu(y) |
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y = self.convs_1x1[i](y) |
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y = self.norms_2[i](y) |
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y = F.gelu(y) |
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y = self.drop(y) |
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x = x + y |
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return x * x_mask |
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class WN(torch.nn.Module): |
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def __init__( |
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self, |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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gin_channels=0, |
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p_dropout=0, |
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): |
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super(WN, self).__init__() |
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assert kernel_size % 2 == 1 |
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self.hidden_channels = hidden_channels |
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self.kernel_size = (kernel_size,) |
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self.dilation_rate = dilation_rate |
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self.n_layers = n_layers |
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self.gin_channels = gin_channels |
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self.p_dropout = p_dropout |
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self.in_layers = torch.nn.ModuleList() |
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self.res_skip_layers = torch.nn.ModuleList() |
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self.drop = nn.Dropout(p_dropout) |
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if gin_channels != 0: |
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cond_layer = torch.nn.Conv1d( |
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gin_channels, 2 * hidden_channels * n_layers, 1 |
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) |
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self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") |
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for i in range(n_layers): |
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dilation = dilation_rate**i |
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padding = int((kernel_size * dilation - dilation) / 2) |
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in_layer = torch.nn.Conv1d( |
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hidden_channels, |
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2 * hidden_channels, |
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kernel_size, |
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dilation=dilation, |
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padding=padding, |
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) |
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in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") |
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self.in_layers.append(in_layer) |
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if i < n_layers - 1: |
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res_skip_channels = 2 * hidden_channels |
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else: |
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res_skip_channels = hidden_channels |
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res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) |
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res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") |
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self.res_skip_layers.append(res_skip_layer) |
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|
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def forward(self, x, x_mask, g=None, **kwargs): |
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output = torch.zeros_like(x) |
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n_channels_tensor = torch.IntTensor([self.hidden_channels]) |
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if g is not None: |
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g = self.cond_layer(g) |
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for i in range(self.n_layers): |
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x_in = self.in_layers[i](x) |
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if g is not None: |
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cond_offset = i * 2 * self.hidden_channels |
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g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] |
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else: |
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g_l = torch.zeros_like(x_in) |
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acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) |
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acts = self.drop(acts) |
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res_skip_acts = self.res_skip_layers[i](acts) |
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if i < self.n_layers - 1: |
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res_acts = res_skip_acts[:, : self.hidden_channels, :] |
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x = (x + res_acts) * x_mask |
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output = output + res_skip_acts[:, self.hidden_channels :, :] |
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else: |
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output = output + res_skip_acts |
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return output * x_mask |
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def remove_weight_norm(self): |
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if self.gin_channels != 0: |
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torch.nn.utils.remove_weight_norm(self.cond_layer) |
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for l in self.in_layers: |
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torch.nn.utils.remove_weight_norm(l) |
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for l in self.res_skip_layers: |
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torch.nn.utils.remove_weight_norm(l) |
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class ResBlock1(torch.nn.Module): |
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): |
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super(ResBlock1, self).__init__() |
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self.convs1 = nn.ModuleList( |
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[ |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=dilation[0], |
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padding=get_padding(kernel_size, dilation[0]), |
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) |
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), |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=dilation[1], |
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padding=get_padding(kernel_size, dilation[1]), |
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) |
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), |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=dilation[2], |
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padding=get_padding(kernel_size, dilation[2]), |
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) |
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), |
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] |
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) |
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self.convs1.apply(init_weights) |
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self.convs2 = nn.ModuleList( |
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[ |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=1, |
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padding=get_padding(kernel_size, 1), |
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) |
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), |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=1, |
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padding=get_padding(kernel_size, 1), |
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) |
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), |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=1, |
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padding=get_padding(kernel_size, 1), |
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) |
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), |
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] |
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) |
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self.convs2.apply(init_weights) |
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|
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def forward(self, x, x_mask=None): |
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for c1, c2 in zip(self.convs1, self.convs2): |
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xt = F.leaky_relu(x, LRELU_SLOPE) |
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if x_mask is not None: |
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xt = xt * x_mask |
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xt = c1(xt) |
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xt = F.leaky_relu(xt, LRELU_SLOPE) |
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if x_mask is not None: |
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xt = xt * x_mask |
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xt = c2(xt) |
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x = xt + x |
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if x_mask is not None: |
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x = x * x_mask |
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return x |
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|
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def remove_weight_norm(self): |
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for l in self.convs1: |
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remove_weight_norm(l) |
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for l in self.convs2: |
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remove_weight_norm(l) |
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|
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class ResBlock2(torch.nn.Module): |
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def __init__(self, channels, kernel_size=3, dilation=(1, 3)): |
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super(ResBlock2, self).__init__() |
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self.convs = nn.ModuleList( |
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[ |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=dilation[0], |
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padding=get_padding(kernel_size, dilation[0]), |
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) |
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), |
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weight_norm( |
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Conv1d( |
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channels, |
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channels, |
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kernel_size, |
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1, |
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dilation=dilation[1], |
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padding=get_padding(kernel_size, dilation[1]), |
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) |
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), |
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] |
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) |
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self.convs.apply(init_weights) |
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|
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def forward(self, x, x_mask=None): |
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for c in self.convs: |
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xt = F.leaky_relu(x, LRELU_SLOPE) |
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if x_mask is not None: |
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xt = xt * x_mask |
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xt = c(xt) |
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x = xt + x |
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if x_mask is not None: |
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x = x * x_mask |
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return x |
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|
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def remove_weight_norm(self): |
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for l in self.convs: |
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remove_weight_norm(l) |
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|
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class Log(nn.Module): |
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def forward(self, x, x_mask, reverse=False, **kwargs): |
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if not reverse: |
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y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask |
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logdet = torch.sum(-y, [1, 2]) |
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return y, logdet |
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else: |
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x = torch.exp(x) * x_mask |
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return x |
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|
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class Flip(nn.Module): |
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def forward(self, x, *args, reverse=False, **kwargs): |
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x = torch.flip(x, [1]) |
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if not reverse: |
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logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) |
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return x, logdet |
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else: |
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return x |
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|
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class ElementwiseAffine(nn.Module): |
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def __init__(self, channels): |
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super().__init__() |
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self.channels = channels |
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self.m = nn.Parameter(torch.zeros(channels, 1)) |
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self.logs = nn.Parameter(torch.zeros(channels, 1)) |
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|
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def forward(self, x, x_mask, reverse=False, **kwargs): |
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if not reverse: |
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y = self.m + torch.exp(self.logs) * x |
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y = y * x_mask |
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logdet = torch.sum(self.logs * x_mask, [1, 2]) |
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return y, logdet |
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else: |
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x = (x - self.m) * torch.exp(-self.logs) * x_mask |
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return x |
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|
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class ResidualCouplingLayer(nn.Module): |
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def __init__( |
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self, |
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channels, |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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p_dropout=0, |
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gin_channels=0, |
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mean_only=False, |
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): |
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assert channels % 2 == 0, "channels should be divisible by 2" |
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super().__init__() |
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self.channels = channels |
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self.hidden_channels = hidden_channels |
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self.kernel_size = kernel_size |
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self.dilation_rate = dilation_rate |
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self.n_layers = n_layers |
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self.half_channels = channels // 2 |
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self.mean_only = mean_only |
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|
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self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) |
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self.enc = WN( |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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p_dropout=p_dropout, |
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gin_channels=gin_channels, |
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) |
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self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) |
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self.post.weight.data.zero_() |
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self.post.bias.data.zero_() |
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|
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def forward(self, x, x_mask, g=None, reverse=False): |
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x0, x1 = torch.split(x, [self.half_channels] * 2, 1) |
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h = self.pre(x0) * x_mask |
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h = self.enc(h, x_mask, g=g) |
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stats = self.post(h) * x_mask |
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if not self.mean_only: |
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m, logs = torch.split(stats, [self.half_channels] * 2, 1) |
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else: |
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m = stats |
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logs = torch.zeros_like(m) |
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|
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if not reverse: |
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x1 = m + x1 * torch.exp(logs) * x_mask |
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x = torch.cat([x0, x1], 1) |
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logdet = torch.sum(logs, [1, 2]) |
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return x, logdet |
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else: |
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x1 = (x1 - m) * torch.exp(-logs) * x_mask |
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x = torch.cat([x0, x1], 1) |
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return x |
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|
|
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class ConvFlow(nn.Module): |
|
def __init__( |
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self, |
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in_channels, |
|
filter_channels, |
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kernel_size, |
|
n_layers, |
|
num_bins=10, |
|
tail_bound=5.0, |
|
): |
|
super().__init__() |
|
self.in_channels = in_channels |
|
self.filter_channels = filter_channels |
|
self.kernel_size = kernel_size |
|
self.n_layers = n_layers |
|
self.num_bins = num_bins |
|
self.tail_bound = tail_bound |
|
self.half_channels = in_channels // 2 |
|
|
|
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) |
|
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) |
|
self.proj = nn.Conv1d( |
|
filter_channels, self.half_channels * (num_bins * 3 - 1), 1 |
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) |
|
self.proj.weight.data.zero_() |
|
self.proj.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) |
|
h = self.convs(h, x_mask, g=g) |
|
h = self.proj(h) * x_mask |
|
|
|
b, c, t = x0.shape |
|
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) |
|
|
|
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) |
|
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( |
|
self.filter_channels |
|
) |
|
unnormalized_derivatives = h[..., 2 * self.num_bins :] |
|
|
|
x1, logabsdet = piecewise_rational_quadratic_transform( |
|
x1, |
|
unnormalized_widths, |
|
unnormalized_heights, |
|
unnormalized_derivatives, |
|
inverse=reverse, |
|
tails="linear", |
|
tail_bound=self.tail_bound, |
|
) |
|
|
|
x = torch.cat([x0, x1], 1) * x_mask |
|
logdet = torch.sum(logabsdet * x_mask, [1, 2]) |
|
if not reverse: |
|
return x, logdet |
|
else: |
|
return x |
|
|
|
|
|
class LinearNorm(nn.Module): |
|
def __init__( |
|
self, |
|
in_channels, |
|
out_channels, |
|
bias=True, |
|
spectral_norm=False, |
|
): |
|
super(LinearNorm, self).__init__() |
|
self.fc = nn.Linear(in_channels, out_channels, bias) |
|
|
|
if spectral_norm: |
|
self.fc = nn.utils.spectral_norm(self.fc) |
|
|
|
def forward(self, input): |
|
out = self.fc(input) |
|
return out |
|
|
|
|
|
class Mish(nn.Module): |
|
def __init__(self): |
|
super(Mish, self).__init__() |
|
|
|
def forward(self, x): |
|
return x * torch.tanh(F.softplus(x)) |
|
|
|
|
|
class Conv1dGLU(nn.Module): |
|
""" |
|
Conv1d + GLU(Gated Linear Unit) with residual connection. |
|
For GLU refer to https://arxiv.org/abs/1612.08083 paper. |
|
""" |
|
|
|
def __init__(self, in_channels, out_channels, kernel_size, dropout): |
|
super(Conv1dGLU, self).__init__() |
|
self.out_channels = out_channels |
|
self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size) |
|
self.dropout = nn.Dropout(dropout) |
|
|
|
def forward(self, x): |
|
residual = x |
|
x = self.conv1(x) |
|
x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1) |
|
x = x1 * torch.sigmoid(x2) |
|
x = residual + self.dropout(x) |
|
return x |
|
|
|
|
|
class ConvNorm(nn.Module): |
|
def __init__( |
|
self, |
|
in_channels, |
|
out_channels, |
|
kernel_size=1, |
|
stride=1, |
|
padding=None, |
|
dilation=1, |
|
bias=True, |
|
spectral_norm=False, |
|
): |
|
super(ConvNorm, self).__init__() |
|
|
|
if padding is None: |
|
assert kernel_size % 2 == 1 |
|
padding = int(dilation * (kernel_size - 1) / 2) |
|
|
|
self.conv = torch.nn.Conv1d( |
|
in_channels, |
|
out_channels, |
|
kernel_size=kernel_size, |
|
stride=stride, |
|
padding=padding, |
|
dilation=dilation, |
|
bias=bias, |
|
) |
|
|
|
if spectral_norm: |
|
self.conv = nn.utils.spectral_norm(self.conv) |
|
|
|
def forward(self, input): |
|
out = self.conv(input) |
|
return out |
|
|
|
|
|
class MultiHeadAttention(nn.Module): |
|
"""Multi-Head Attention module""" |
|
|
|
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.0, spectral_norm=False): |
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super().__init__() |
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|
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self.n_head = n_head |
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self.d_k = d_k |
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self.d_v = d_v |
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|
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self.w_qs = nn.Linear(d_model, n_head * d_k) |
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self.w_ks = nn.Linear(d_model, n_head * d_k) |
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self.w_vs = nn.Linear(d_model, n_head * d_v) |
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|
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self.attention = ScaledDotProductAttention( |
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temperature=np.power(d_model, 0.5), dropout=dropout |
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) |
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|
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self.fc = nn.Linear(n_head * d_v, d_model) |
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self.dropout = nn.Dropout(dropout) |
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|
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if spectral_norm: |
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self.w_qs = nn.utils.spectral_norm(self.w_qs) |
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self.w_ks = nn.utils.spectral_norm(self.w_ks) |
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self.w_vs = nn.utils.spectral_norm(self.w_vs) |
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self.fc = nn.utils.spectral_norm(self.fc) |
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|
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def forward(self, x, mask=None): |
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d_k, d_v, n_head = self.d_k, self.d_v, self.n_head |
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sz_b, len_x, _ = x.size() |
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|
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residual = x |
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|
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q = self.w_qs(x).view(sz_b, len_x, n_head, d_k) |
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k = self.w_ks(x).view(sz_b, len_x, n_head, d_k) |
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v = self.w_vs(x).view(sz_b, len_x, n_head, d_v) |
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q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) |
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k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) |
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v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v) |
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|
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if mask is not None: |
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slf_mask = mask.repeat(n_head, 1, 1) |
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else: |
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slf_mask = None |
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output, attn = self.attention(q, k, v, mask=slf_mask) |
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|
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output = output.view(n_head, sz_b, len_x, d_v) |
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output = ( |
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output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1) |
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) |
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|
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output = self.fc(output) |
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|
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output = self.dropout(output) + residual |
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return output, attn |
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|
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class ScaledDotProductAttention(nn.Module): |
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"""Scaled Dot-Product Attention""" |
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|
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def __init__(self, temperature, dropout): |
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super().__init__() |
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self.temperature = temperature |
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self.softmax = nn.Softmax(dim=2) |
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self.dropout = nn.Dropout(dropout) |
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|
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def forward(self, q, k, v, mask=None): |
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attn = torch.bmm(q, k.transpose(1, 2)) |
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attn = attn / self.temperature |
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|
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if mask is not None: |
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attn = attn.masked_fill(mask, -np.inf) |
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|
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attn = self.softmax(attn) |
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p_attn = self.dropout(attn) |
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|
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output = torch.bmm(p_attn, v) |
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return output, attn |
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|
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class MelStyleEncoder(nn.Module): |
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"""MelStyleEncoder""" |
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|
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def __init__( |
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self, |
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n_mel_channels=80, |
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style_hidden=128, |
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style_vector_dim=256, |
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style_kernel_size=5, |
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style_head=2, |
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dropout=0.1, |
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): |
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super(MelStyleEncoder, self).__init__() |
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self.in_dim = n_mel_channels |
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self.hidden_dim = style_hidden |
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self.out_dim = style_vector_dim |
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self.kernel_size = style_kernel_size |
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self.n_head = style_head |
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self.dropout = dropout |
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|
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self.spectral = nn.Sequential( |
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LinearNorm(self.in_dim, self.hidden_dim), |
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Mish(), |
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nn.Dropout(self.dropout), |
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LinearNorm(self.hidden_dim, self.hidden_dim), |
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Mish(), |
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nn.Dropout(self.dropout), |
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) |
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|
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self.temporal = nn.Sequential( |
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Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout), |
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Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout), |
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) |
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|
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self.slf_attn = MultiHeadAttention( |
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self.n_head, |
|
self.hidden_dim, |
|
self.hidden_dim // self.n_head, |
|
self.hidden_dim // self.n_head, |
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self.dropout, |
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) |
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|
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self.fc = LinearNorm(self.hidden_dim, self.out_dim) |
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|
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def temporal_avg_pool(self, x, mask=None): |
|
if mask is None: |
|
out = torch.mean(x, dim=1) |
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else: |
|
len_ = (~mask).sum(dim=1).unsqueeze(1) |
|
x = x.masked_fill(mask.unsqueeze(-1), 0) |
|
x = x.sum(dim=1) |
|
out = torch.div(x, len_) |
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return out |
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|
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def forward(self, x, mask=None): |
|
x = x.transpose(1, 2) |
|
if mask is not None: |
|
mask = (mask.int() == 0).squeeze(1) |
|
max_len = x.shape[1] |
|
slf_attn_mask = ( |
|
mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None |
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) |
|
|
|
|
|
x = self.spectral(x) |
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|
|
x = x.transpose(1, 2) |
|
x = self.temporal(x) |
|
x = x.transpose(1, 2) |
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|
|
if mask is not None: |
|
x = x.masked_fill(mask.unsqueeze(-1), 0) |
|
x, _ = self.slf_attn(x, mask=slf_attn_mask) |
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|
|
x = self.fc(x) |
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|
|
w = self.temporal_avg_pool(x, mask=mask) |
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|
|
return w.unsqueeze(-1) |
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|
|
|
|
class MelStyleEncoderVAE(nn.Module): |
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def __init__(self, spec_channels, z_latent_dim, emb_dim): |
|
super().__init__() |
|
self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim) |
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self.fc1 = nn.Linear(emb_dim, z_latent_dim) |
|
self.fc2 = nn.Linear(emb_dim, z_latent_dim) |
|
self.fc3 = nn.Linear(z_latent_dim, emb_dim) |
|
self.z_latent_dim = z_latent_dim |
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|
|
def reparameterize(self, mu, logvar): |
|
if self.training: |
|
std = torch.exp(0.5 * logvar) |
|
eps = torch.randn_like(std) |
|
return eps.mul(std).add_(mu) |
|
else: |
|
return mu |
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|
|
def forward(self, inputs, mask=None): |
|
enc_out = self.ref_encoder(inputs.squeeze(-1), mask).squeeze(-1) |
|
mu = self.fc1(enc_out) |
|
logvar = self.fc2(enc_out) |
|
posterior = D.Normal(mu, torch.exp(logvar)) |
|
kl_divergence = D.kl_divergence( |
|
posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar)) |
|
) |
|
loss_kl = kl_divergence.mean() |
|
|
|
z = posterior.rsample() |
|
style_embed = self.fc3(z) |
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|
|
return style_embed.unsqueeze(-1), loss_kl |
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|
|
def infer(self, inputs=None, random_sample=False, manual_latent=None): |
|
if manual_latent is None: |
|
if random_sample: |
|
dev = next(self.parameters()).device |
|
posterior = D.Normal( |
|
torch.zeros(1, self.z_latent_dim, device=dev), |
|
torch.ones(1, self.z_latent_dim, device=dev), |
|
) |
|
z = posterior.rsample() |
|
else: |
|
enc_out = self.ref_encoder(inputs.transpose(1, 2)) |
|
mu = self.fc1(enc_out) |
|
z = mu |
|
else: |
|
z = manual_latent |
|
style_embed = self.fc3(z) |
|
return style_embed.unsqueeze(-1), z |
|
|
|
|
|
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)) |
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|
|
def forward(self, x, x_mask=None, g=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 = None |
|
return z |
|
else: |
|
z = (self.bias + torch.exp(self.logs) * x) * x_mask |
|
logdet = torch.sum(self.logs) * x_len |
|
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, **kwargs): |
|
super().__init__() |
|
assert n_split % 2 == 0 |
|
self.channels = channels |
|
self.n_split = n_split |
|
self.no_jacobian = no_jacobian |
|
|
|
w_init = torch.linalg.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.weight = nn.Parameter(w_init) |
|
|
|
def forward(self, x, x_mask=None, g=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, 2, c // self.n_split, self.n_split // 2, t) |
|
x = ( |
|
x.permute(0, 1, 3, 2, 4) |
|
.contiguous() |
|
.view(b, self.n_split, c // self.n_split, t) |
|
) |
|
|
|
if reverse: |
|
if hasattr(self, "weight_inv"): |
|
weight = self.weight_inv |
|
else: |
|
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) |
|
logdet = None |
|
else: |
|
weight = self.weight |
|
if self.no_jacobian: |
|
logdet = 0 |
|
else: |
|
logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len |
|
|
|
weight = weight.view(self.n_split, self.n_split, 1, 1) |
|
z = F.conv2d(x, weight) |
|
|
|
z = z.view(b, 2, self.n_split // 2, c // self.n_split, t) |
|
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask |
|
if reverse: |
|
return z |
|
else: |
|
return z, logdet |
|
|
|
def store_inverse(self): |
|
self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) |
|
|
