File size: 11,920 Bytes
a726cc5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm, remove_weight_norm
from torch.nn import Conv1d, ConvTranspose1d
LRELU_SLOPE = 0.1
alpha = 1.0
def get_padding(kernel_size, dilation=1):
return int((kernel_size*dilation - dilation)/2)
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
class ResBlock1(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.h = h
self.convs1 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2])))
])
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1)))
])
self.convs2.apply(init_weights)
self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
self.activations = nn.ModuleList([nn.LeakyReLU(LRELU_SLOPE) for _ in range(self.num_layers)])
def forward(self, x):
acts1, acts2 = self.activations[::2], self.activations[1::2]
for c1, c2,a1,a2 in zip(self.convs1, self.convs2,acts1,acts2):
xt = a1(x)
xt = c1(xt)
xt = a2(xt)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class Encoder(torch.nn.Module):
def __init__(self, h):
super(Encoder, self).__init__()
self.n_filters = h.en_filters
self.vq_dim = h.vq_dim
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h.upsample_rates)
self.upsample_initial_channel = self.n_filters * ( 2**self.num_upsamples )
self.conv_pre = weight_norm(Conv1d(h.channel, self.n_filters, 7, 1, padding=3))
self.normalize = nn.ModuleList()
resblock = ResBlock1
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(list(reversed(list(zip(h.upsample_rates, h.upsample_kernel_sizes))))):
self.ups.append(weight_norm(
Conv1d(self.n_filters*(2**i), self.n_filters*(2**(i+1)),
k, u,
padding=((k-u)//2)
)))
self.resblocks = nn.ModuleList()
ch = 1
for i in range(len(self.ups)):
ch = self.n_filters*(2**(i+1))
for j, (k, d) in enumerate(
zip(
list(reversed(h.resblock_kernel_sizes)),
list(reversed(h.resblock_dilation_sizes))
)
):
self.resblocks.append(resblock(h, ch, k, d))
self.normalize.append(torch.nn.LayerNorm([ch],eps=1e-6,elementwise_affine=True))
self.activation_post = nn.LeakyReLU(LRELU_SLOPE)
self.conv_post = Conv1d(ch, self.vq_dim, 3, 1, padding=1)
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x):
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i*self.num_kernels+j](x)
xs = self.normalize[i*self.num_kernels+j](xs.transpose(1,2)).transpose(1,2)
else:
xs += self.resblocks[i*self.num_kernels+j](x)
xs = self.normalize[i*self.num_kernels+j](xs.transpose(1,2)).transpose(1,2)
x = xs / self.num_kernels
x = self.activation_post(x)
x = self.conv_post(x)
return x
def remove_weight_norm(self):
print('Removing weight norm...')
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
class Quantizer_module(torch.nn.Module):
def __init__(self, n_e, e_dim):
super(Quantizer_module, self).__init__()
self.embedding = nn.Embedding(n_e, e_dim)
self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e)
self.target = torch.arange(0,n_e)
def forward(self, x, idx=0):
loss=torch.Tensor([0.0])
d = torch.sum(x ** 2, 1, keepdim=True) + torch.sum(self.embedding.weight ** 2, 1) \
- 2 * torch.matmul(x, self.embedding.weight.T)
min_indicies = torch.argmin(d, 1)
z_q = self.embedding(min_indicies)
embed_vec = self.embedding.weight
embed_dis = torch.mm(embed_vec , embed_vec.T)*3
self.target = torch.arange(0,embed_vec.shape[0]).to(x.device)
loss = F.cross_entropy(embed_dis,self.target)*(idx==0)
return z_q, min_indicies,loss
class Quantizer(torch.nn.Module):
def __init__(self, h):
super(Quantizer, self).__init__()
assert h.vq_dim % h.n_code_groups == 0
self.lm_offset = 0
self.lm_states = None
self.vq_dim = h.vq_dim
self.residul_layer = h.n_q
self.n_code_groups = h.n_code_groups
self.quantizer_modules = nn.ModuleList()
for i in range(self.residul_layer):
self.quantizer_modules.append(nn.ModuleList([
Quantizer_module(h.n_codes, self.vq_dim // h.n_code_groups) for _ in range(h.n_code_groups)
]))
self.h = h
self.codebook_loss_lambda = self.h.codebook_loss_lambda # e.g., 1
self.commitment_loss_lambda = self.h.commitment_loss_lambda # e.g., 0.25
def for_one_step(self, xin, idx):
xin = xin.transpose(1, 2)
x = xin.reshape(-1, self.vq_dim)
x = torch.split(x, self.vq_dim // self.h.n_code_groups, dim=-1)
min_indicies = []
z_q = []
all_losses = []
for _x, m in zip(x, self.quantizer_modules[idx]):
_z_q, _min_indicies,_loss = m(_x,idx)
all_losses.append(_loss)
z_q.append(_z_q)
min_indicies.append(_min_indicies)
z_q = torch.cat(z_q, -1).reshape(xin.shape)
z_q = z_q.transpose(1, 2)
all_losses = torch.stack(all_losses)
loss = torch.mean(all_losses)
return z_q, min_indicies, loss
def forward(self, xin,bw=-1,mask_id=None):
quantized_out = 0.0
residual = xin
all_losses = []
all_indices = []
if bw<=0:
bw = self.residul_layer
for i in range(bw):
quantized, indices, e_loss = self.for_one_step(residual, i) #
if mask_id is not None:
mask = (
torch.full([xin.shape[0],xin.shape[2],1], fill_value=i, device=xin.device) < mask_id.unsqueeze(2) + 1
)
mask = mask.repeat(1,1,xin.shape[1]).transpose(1,2)
if mask_id is not None:
loss = 0.1 * e_loss + self.codebook_loss_lambda * torch.mean((quantized - residual.detach()) ** 2 * mask) \
+ self.commitment_loss_lambda * torch.mean((quantized.detach() - residual) ** 2 * mask )
else:
loss = 0.1 * e_loss \
+ self.codebook_loss_lambda * torch.mean((quantized - residual.detach()) ** 2 ) \
+ self.commitment_loss_lambda * torch.mean((quantized.detach() - residual) ** 2 )
quantized = residual + (quantized - residual).detach()
residual = residual - quantized
if mask_id is not None:
quantized_out = quantized_out + quantized * mask
else:
quantized_out = quantized_out + quantized
all_indices.extend(indices) #
all_losses.append(loss)
all_losses = torch.stack(all_losses)
loss = torch.mean(all_losses)
return quantized_out, loss, all_indices
def embed(self, x , bw=-1):
quantized_out = torch.tensor(0.0, device=x.device)
x = torch.split(x, 1, 2)
if bw <= 0 or bw > self.residul_layer:
bw = self.residul_layer
for i in range(bw):
ret = []
for j in range(self.n_code_groups):
q = x[j+self.n_code_groups*i]
embed = self.quantizer_modules[i][j]
q = embed.embedding(q.squeeze(-1))
ret.append(q)
ret = torch.cat(ret, -1)
quantized_out = quantized_out + ret
return quantized_out.transpose(1, 2)
class Generator(torch.nn.Module):
def __init__(self, h):
super(Generator, self).__init__()
self.h = h
self.n_filters = h.de_filters
self.vq_dim = h.vq_dim
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h.upsample_rates)
self.upsample_initial_channel = self.n_filters * ( 2**self.num_upsamples )
self.conv_pre = weight_norm(Conv1d(self.vq_dim, self.upsample_initial_channel, 7, 1, padding=3))
resblock = ResBlock1
self.norm = nn.Identity()
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
self.ups.append(weight_norm(
ConvTranspose1d(
self.upsample_initial_channel//(2**i), self.upsample_initial_channel//(2**(i+1)),
k, u,
padding=(k - u )//2,
)
))
ch = 1
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = self.upsample_initial_channel//(2**(i+1))
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
self.resblocks.append(resblock(h, ch, k, d))
self.activation_post = nn.LeakyReLU(LRELU_SLOPE)
self.conv_post = weight_norm(Conv1d(ch, h.channel, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x):
x = self.norm(x)
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i*self.num_kernels+j](x)
else:
xs += self.resblocks[i*self.num_kernels+j](x)
x = xs / self.num_kernels
x = self.activation_post(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print('Removing weight norm...')
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post) |