mirror of
https://github.com/AIGC-Audio/AudioGPT.git
synced 2026-09-02 20:23:38 +02:00
363 lines
14 KiB
Python
363 lines
14 KiB
Python
import scipy
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from torch.nn import functional as F
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import torch
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from torch import nn
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import numpy as np
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from modules.commons.wavenet import WN
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from modules.glow import utils
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class ActNorm(nn.Module):
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def __init__(self, channels, ddi=False, **kwargs):
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super().__init__()
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self.channels = channels
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self.initialized = not ddi
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self.logs = nn.Parameter(torch.zeros(1, channels, 1))
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self.bias = nn.Parameter(torch.zeros(1, channels, 1))
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def forward(self, x, x_mask=None, reverse=False, **kwargs):
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if x_mask is None:
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x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype)
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x_len = torch.sum(x_mask, [1, 2])
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if not self.initialized:
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self.initialize(x, x_mask)
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self.initialized = True
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if reverse:
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z = (x - self.bias) * torch.exp(-self.logs) * x_mask
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logdet = torch.sum(-self.logs) * x_len
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else:
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z = (self.bias + torch.exp(self.logs) * x) * x_mask
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logdet = torch.sum(self.logs) * x_len # [b]
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return z, logdet
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def store_inverse(self):
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pass
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def set_ddi(self, ddi):
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self.initialized = not ddi
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def initialize(self, x, x_mask):
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with torch.no_grad():
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denom = torch.sum(x_mask, [0, 2])
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m = torch.sum(x * x_mask, [0, 2]) / denom
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m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
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v = m_sq - (m ** 2)
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logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
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bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
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logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
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self.bias.data.copy_(bias_init)
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self.logs.data.copy_(logs_init)
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class InvConvNear(nn.Module):
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def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs):
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super().__init__()
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assert (n_split % 2 == 0)
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self.channels = channels
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self.n_split = n_split
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self.n_sqz = n_sqz
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self.no_jacobian = no_jacobian
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w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0]
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if torch.det(w_init) < 0:
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w_init[:, 0] = -1 * w_init[:, 0]
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self.lu = lu
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if lu:
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# LU decomposition can slightly speed up the inverse
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np_p, np_l, np_u = scipy.linalg.lu(w_init)
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np_s = np.diag(np_u)
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np_sign_s = np.sign(np_s)
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np_log_s = np.log(np.abs(np_s))
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np_u = np.triu(np_u, k=1)
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l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1)
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eye = np.eye(*w_init.shape, dtype=float)
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self.register_buffer('p', torch.Tensor(np_p.astype(float)))
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self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float)))
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self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True)
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self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True)
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self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True)
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self.register_buffer('l_mask', torch.Tensor(l_mask))
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self.register_buffer('eye', torch.Tensor(eye))
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else:
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self.weight = nn.Parameter(w_init)
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def forward(self, x, x_mask=None, reverse=False, **kwargs):
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b, c, t = x.size()
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assert (c % self.n_split == 0)
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if x_mask is None:
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x_mask = 1
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x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
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else:
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x_len = torch.sum(x_mask, [1, 2])
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x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t)
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x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t)
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if self.lu:
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self.weight, log_s = self._get_weight()
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logdet = log_s.sum()
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logdet = logdet * (c / self.n_split) * x_len
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else:
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logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b]
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if reverse:
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if hasattr(self, "weight_inv"):
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weight = self.weight_inv
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else:
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weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
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logdet = -logdet
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else:
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weight = self.weight
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if self.no_jacobian:
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logdet = 0
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weight = weight.view(self.n_split, self.n_split, 1, 1)
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z = F.conv2d(x, weight)
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z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t)
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z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
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return z, logdet
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def _get_weight(self):
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l, log_s, u = self.l, self.log_s, self.u
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l = l * self.l_mask + self.eye
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u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s))
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weight = torch.matmul(self.p, torch.matmul(l, u))
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return weight, log_s
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def store_inverse(self):
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weight, _ = self._get_weight()
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self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device)
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class InvConv(nn.Module):
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def __init__(self, channels, no_jacobian=False, lu=True, **kwargs):
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super().__init__()
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w_shape = [channels, channels]
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w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float)
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LU_decomposed = lu
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if not LU_decomposed:
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# Sample a random orthogonal matrix:
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self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init)))
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else:
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np_p, np_l, np_u = scipy.linalg.lu(w_init)
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np_s = np.diag(np_u)
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np_sign_s = np.sign(np_s)
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np_log_s = np.log(np.abs(np_s))
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np_u = np.triu(np_u, k=1)
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l_mask = np.tril(np.ones(w_shape, dtype=float), -1)
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eye = np.eye(*w_shape, dtype=float)
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self.register_buffer('p', torch.Tensor(np_p.astype(float)))
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self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float)))
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self.l = nn.Parameter(torch.Tensor(np_l.astype(float)))
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self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)))
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self.u = nn.Parameter(torch.Tensor(np_u.astype(float)))
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self.l_mask = torch.Tensor(l_mask)
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self.eye = torch.Tensor(eye)
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self.w_shape = w_shape
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self.LU = LU_decomposed
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self.weight = None
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def get_weight(self, device, reverse):
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w_shape = self.w_shape
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self.p = self.p.to(device)
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self.sign_s = self.sign_s.to(device)
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self.l_mask = self.l_mask.to(device)
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self.eye = self.eye.to(device)
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l = self.l * self.l_mask + self.eye
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u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s))
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dlogdet = self.log_s.sum()
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if not reverse:
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w = torch.matmul(self.p, torch.matmul(l, u))
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else:
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l = torch.inverse(l.double()).float()
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u = torch.inverse(u.double()).float()
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w = torch.matmul(u, torch.matmul(l, self.p.inverse()))
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return w.view(w_shape[0], w_shape[1], 1), dlogdet
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def forward(self, x, x_mask=None, reverse=False, **kwargs):
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"""
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log-det = log|abs(|W|)| * pixels
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"""
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b, c, t = x.size()
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if x_mask is None:
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x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
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else:
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x_len = torch.sum(x_mask, [1, 2])
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logdet = 0
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if not reverse:
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weight, dlogdet = self.get_weight(x.device, reverse)
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z = F.conv1d(x, weight)
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if logdet is not None:
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logdet = logdet + dlogdet * x_len
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return z, logdet
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else:
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if self.weight is None:
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weight, dlogdet = self.get_weight(x.device, reverse)
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else:
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weight, dlogdet = self.weight, self.dlogdet
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z = F.conv1d(x, weight)
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if logdet is not None:
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logdet = logdet - dlogdet * x_len
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return z, logdet
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def store_inverse(self):
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self.weight, self.dlogdet = self.get_weight('cuda', reverse=True)
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class CouplingBlock(nn.Module):
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def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers,
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gin_channels=0, p_dropout=0, sigmoid_scale=False, wn=None):
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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.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.sigmoid_scale = sigmoid_scale
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start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
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start = torch.nn.utils.weight_norm(start)
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self.start = start
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# Initializing last layer to 0 makes the affine coupling layers
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# do nothing at first. This helps with training stability
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end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
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end.weight.data.zero_()
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end.bias.data.zero_()
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self.end = end
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self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout)
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if wn is not None:
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self.wn.in_layers = wn.in_layers
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self.wn.res_skip_layers = wn.res_skip_layers
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def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
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if x_mask is None:
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x_mask = 1
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x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:]
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x = self.start(x_0) * x_mask
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x = self.wn(x, x_mask, g)
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out = self.end(x)
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z_0 = x_0
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m = out[:, :self.in_channels // 2, :]
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logs = out[:, self.in_channels // 2:, :]
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if self.sigmoid_scale:
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logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
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if reverse:
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z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
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logdet = torch.sum(-logs * x_mask, [1, 2])
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else:
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z_1 = (m + torch.exp(logs) * x_1) * x_mask
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logdet = torch.sum(logs * x_mask, [1, 2])
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z = torch.cat([z_0, z_1], 1)
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return z, logdet
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def store_inverse(self):
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self.wn.remove_weight_norm()
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class Glow(nn.Module):
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def __init__(self,
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in_channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_blocks,
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n_layers,
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p_dropout=0.,
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n_split=4,
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n_sqz=2,
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sigmoid_scale=False,
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gin_channels=0,
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inv_conv_type='near',
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share_cond_layers=False,
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share_wn_layers=0,
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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.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_blocks = n_blocks
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self.n_layers = n_layers
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self.p_dropout = p_dropout
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self.n_split = n_split
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self.n_sqz = n_sqz
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self.sigmoid_scale = sigmoid_scale
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self.gin_channels = gin_channels
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self.share_cond_layers = share_cond_layers
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if gin_channels != 0 and share_cond_layers:
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cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 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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wn = None
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self.flows = nn.ModuleList()
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for b in range(n_blocks):
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self.flows.append(ActNorm(channels=in_channels * n_sqz))
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if inv_conv_type == 'near':
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self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz))
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if inv_conv_type == 'invconv':
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self.flows.append(InvConv(channels=in_channels * n_sqz))
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if share_wn_layers > 0:
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if b % share_wn_layers == 0:
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wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz,
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p_dropout, share_cond_layers)
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self.flows.append(
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CouplingBlock(
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in_channels * n_sqz,
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hidden_channels,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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n_layers=n_layers,
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gin_channels=gin_channels * n_sqz,
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p_dropout=p_dropout,
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sigmoid_scale=sigmoid_scale,
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wn=wn
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))
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def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False):
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logdet_tot = 0
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if not reverse:
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flows = self.flows
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else:
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flows = reversed(self.flows)
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if return_hiddens:
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hs = []
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if self.n_sqz > 1:
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x, x_mask_ = utils.squeeze(x, x_mask, self.n_sqz)
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if g is not None:
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g, _ = utils.squeeze(g, x_mask, self.n_sqz)
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x_mask = x_mask_
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if self.share_cond_layers and g is not None:
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g = self.cond_layer(g)
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for f in flows:
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x, logdet = f(x, x_mask, g=g, reverse=reverse)
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if return_hiddens:
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hs.append(x)
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logdet_tot += logdet
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if self.n_sqz > 1:
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x, x_mask = utils.unsqueeze(x, x_mask, self.n_sqz)
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if return_hiddens:
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return x, logdet_tot, hs
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return x, logdet_tot
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def store_inverse(self):
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def remove_weight_norm(m):
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try:
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nn.utils.remove_weight_norm(m)
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except ValueError: # this module didn't have weight norm
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return
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self.apply(remove_weight_norm)
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for f in self.flows:
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f.store_inverse()
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