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RVC-Boss
769
GPT_SoVITS/module/modules.py
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769
GPT_SoVITS/module/modules.py
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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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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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self.gamma = nn.Parameter(torch.ones(channels))
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self.beta = nn.Parameter(torch.zeros(channels))
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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__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
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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(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.relu_drop = nn.Sequential(
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nn.ReLU(),
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nn.Dropout(p_dropout))
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for _ in range(n_layers-1):
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self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
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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.):
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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(nn.Conv1d(channels, channels, kernel_size,
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groups=channels, dilation=dilation, padding=padding
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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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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__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
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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(gin_channels, 2*hidden_channels*n_layers, 1)
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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(hidden_channels, 2*hidden_channels, kernel_size,
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dilation=dilation, padding=padding)
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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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# last one is not necessary
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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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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(
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x_in,
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g_l,
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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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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2])))
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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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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1)))
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])
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self.convs2.apply(init_weights)
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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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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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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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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1])))
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])
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self.convs.apply(init_weights)
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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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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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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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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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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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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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class ResidualCouplingLayer(nn.Module):
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def __init__(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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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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self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
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self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
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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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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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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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class ConvFlow(nn.Module):
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def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
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super().__init__()
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.num_bins = num_bins
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self.tail_bound = tail_bound
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self.half_channels = in_channels // 2
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self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
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self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
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self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 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, 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)
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h = self.convs(h, x_mask, g=g)
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h = self.proj(h) * x_mask
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b, c, t = x0.shape
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h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
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unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
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unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
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unnormalized_derivatives = h[..., 2 * self.num_bins:]
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x1, logabsdet = piecewise_rational_quadratic_transform(x1,
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unnormalized_widths,
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unnormalized_heights,
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unnormalized_derivatives,
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inverse=reverse,
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tails='linear',
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tail_bound=self.tail_bound
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)
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x = torch.cat([x0, x1], 1) * x_mask
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logdet = torch.sum(logabsdet * x_mask, [1,2])
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if not reverse:
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return x, logdet
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else:
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return x
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class LinearNorm(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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bias=True,
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spectral_norm=False,
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):
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super(LinearNorm, self).__init__()
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self.fc = nn.Linear(in_channels, out_channels, bias)
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if spectral_norm:
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self.fc = nn.utils.spectral_norm(self.fc)
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def forward(self, input):
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out = self.fc(input)
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return out
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class Mish(nn.Module):
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def __init__(self):
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super(Mish, self).__init__()
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def forward(self, x):
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return x * torch.tanh(F.softplus(x))
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class Conv1dGLU(nn.Module):
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'''
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Conv1d + GLU(Gated Linear Unit) with residual connection.
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For GLU refer to https://arxiv.org/abs/1612.08083 paper.
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'''
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def __init__(self, in_channels, out_channels, kernel_size, dropout):
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super(Conv1dGLU, self).__init__()
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self.out_channels = out_channels
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self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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residual = x
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x = self.conv1(x)
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x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1)
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x = x1 * torch.sigmoid(x2)
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x = residual + self.dropout(x)
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return x
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|
||||
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., spectral_norm=False):
|
||||
super().__init__()
|
||||
|
||||
self.n_head = n_head
|
||||
self.d_k = d_k
|
||||
self.d_v = d_v
|
||||
|
||||
self.w_qs = nn.Linear(d_model, n_head * d_k)
|
||||
self.w_ks = nn.Linear(d_model, n_head * d_k)
|
||||
self.w_vs = nn.Linear(d_model, n_head * d_v)
|
||||
|
||||
self.attention = ScaledDotProductAttention(temperature=np.power(d_model, 0.5), dropout=dropout)
|
||||
|
||||
self.fc = nn.Linear(n_head * d_v, d_model)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
if spectral_norm:
|
||||
self.w_qs = nn.utils.spectral_norm(self.w_qs)
|
||||
self.w_ks = nn.utils.spectral_norm(self.w_ks)
|
||||
self.w_vs = nn.utils.spectral_norm(self.w_vs)
|
||||
self.fc = nn.utils.spectral_norm(self.fc)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
|
||||
sz_b, len_x, _ = x.size()
|
||||
|
||||
residual = x
|
||||
|
||||
q = self.w_qs(x).view(sz_b, len_x, n_head, d_k)
|
||||
k = self.w_ks(x).view(sz_b, len_x, n_head, d_k)
|
||||
v = self.w_vs(x).view(sz_b, len_x, n_head, d_v)
|
||||
q = q.permute(2, 0, 1, 3).contiguous().view(-1,
|
||||
len_x, d_k) # (n*b) x lq x dk
|
||||
k = k.permute(2, 0, 1, 3).contiguous().view(-1,
|
||||
len_x, d_k) # (n*b) x lk x dk
|
||||
v = v.permute(2, 0, 1, 3).contiguous().view(-1,
|
||||
len_x, d_v) # (n*b) x lv x dv
|
||||
|
||||
if mask is not None:
|
||||
slf_mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
|
||||
else:
|
||||
slf_mask = None
|
||||
output, attn = self.attention(q, k, v, mask=slf_mask)
|
||||
|
||||
output = output.view(n_head, sz_b, len_x, d_v)
|
||||
output = output.permute(1, 2, 0, 3).contiguous().view(
|
||||
sz_b, len_x, -1) # b x lq x (n*dv)
|
||||
|
||||
output = self.fc(output)
|
||||
|
||||
output = self.dropout(output) + residual
|
||||
return output, attn
|
||||
|
||||
|
||||
class ScaledDotProductAttention(nn.Module):
|
||||
''' Scaled Dot-Product Attention '''
|
||||
|
||||
def __init__(self, temperature, dropout):
|
||||
super().__init__()
|
||||
self.temperature = temperature
|
||||
self.softmax = nn.Softmax(dim=2)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, q, k, v, mask=None):
|
||||
attn = torch.bmm(q, k.transpose(1, 2))
|
||||
attn = attn / self.temperature
|
||||
|
||||
if mask is not None:
|
||||
attn = attn.masked_fill(mask, -np.inf)
|
||||
|
||||
attn = self.softmax(attn)
|
||||
p_attn = self.dropout(attn)
|
||||
|
||||
output = torch.bmm(p_attn, v)
|
||||
return output, attn
|
||||
|
||||
|
||||
class MelStyleEncoder(nn.Module):
|
||||
''' MelStyleEncoder '''
|
||||
|
||||
def __init__(self, n_mel_channels=80,
|
||||
style_hidden=128,
|
||||
style_vector_dim=256,
|
||||
style_kernel_size=5,
|
||||
style_head=2,
|
||||
dropout=0.1):
|
||||
super(MelStyleEncoder, self).__init__()
|
||||
self.in_dim = n_mel_channels
|
||||
self.hidden_dim = style_hidden
|
||||
self.out_dim = style_vector_dim
|
||||
self.kernel_size = style_kernel_size
|
||||
self.n_head = style_head
|
||||
self.dropout = dropout
|
||||
|
||||
self.spectral = nn.Sequential(
|
||||
LinearNorm(self.in_dim, self.hidden_dim),
|
||||
Mish(),
|
||||
nn.Dropout(self.dropout),
|
||||
LinearNorm(self.hidden_dim, self.hidden_dim),
|
||||
Mish(),
|
||||
nn.Dropout(self.dropout)
|
||||
)
|
||||
|
||||
self.temporal = nn.Sequential(
|
||||
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
|
||||
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
|
||||
)
|
||||
|
||||
self.slf_attn = MultiHeadAttention(self.n_head, self.hidden_dim,
|
||||
self.hidden_dim // self.n_head, self.hidden_dim // self.n_head,
|
||||
self.dropout)
|
||||
|
||||
self.fc = LinearNorm(self.hidden_dim, self.out_dim)
|
||||
|
||||
def temporal_avg_pool(self, x, mask=None):
|
||||
if mask is None:
|
||||
out = torch.mean(x, dim=1)
|
||||
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_)
|
||||
return out
|
||||
|
||||
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
|
||||
|
||||
# spectral
|
||||
x = self.spectral(x)
|
||||
# temporal
|
||||
x = x.transpose(1, 2)
|
||||
x = self.temporal(x)
|
||||
x = x.transpose(1, 2)
|
||||
# self-attention
|
||||
if mask is not None:
|
||||
x = x.masked_fill(mask.unsqueeze(-1), 0)
|
||||
x, _ = self.slf_attn(x, mask=slf_attn_mask)
|
||||
# fc
|
||||
x = self.fc(x)
|
||||
# temoral average pooling
|
||||
w = self.temporal_avg_pool(x, mask=mask)
|
||||
|
||||
return w.unsqueeze(-1)
|
||||
|
||||
|
||||
class MelStyleEncoderVAE(nn.Module):
|
||||
def __init__(self, spec_channels, z_latent_dim, emb_dim):
|
||||
super().__init__()
|
||||
self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim)
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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)
|
||||
|
||||
return style_embed.unsqueeze(-1), loss_kl
|
||||
|
||||
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))
|
||||
|
||||
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 # [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, **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 # [b]
|
||||
|
||||
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)
|
||||
Reference in New Issue
Block a user