arcanus/koala2
0
1import copy
2import math
3import numpy as np
4import scipy
5import torch
6from torch import nn
7from torch.nn import functional as F
8
9from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
10from torch.nn.utils import weight_norm, remove_weight_norm
11
12from lib.infer_pack import commons
13from lib.infer_pack.commons import init_weights, get_padding
14from lib.infer_pack.transforms import piecewise_rational_quadratic_transform
15
16
17LRELU_SLOPE = 0.1
18
19
20class LayerNorm(nn.Module):
21 def __init__(self, channels, eps=1e-5):
22 super().__init__()
23 self.channels = channels
24 self.eps = eps
25
26 self.gamma = nn.Parameter(torch.ones(channels))
27 self.beta = nn.Parameter(torch.zeros(channels))
28
29 def forward(self, x):
30 x = x.transpose(1, -1)
31 x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
32 return x.transpose(1, -1)
33
34
35class ConvReluNorm(nn.Module):
36 def __init__(
37 self,
38 in_channels,
39 hidden_channels,
40 out_channels,
41 kernel_size,
42 n_layers,
43 p_dropout,
44 ):
45 super().__init__()
46 self.in_channels = in_channels
47 self.hidden_channels = hidden_channels
48 self.out_channels = out_channels
49 self.kernel_size = kernel_size
50 self.n_layers = n_layers
51 self.p_dropout = p_dropout
52 assert n_layers > 1, "Number of layers should be larger than 0."
53
54 self.conv_layers = nn.ModuleList()
55 self.norm_layers = nn.ModuleList()
56 self.conv_layers.append(
57 nn.Conv1d(
58 in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
59 )
60 )
61 self.norm_layers.append(LayerNorm(hidden_channels))
62 self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
63 for _ in range(n_layers - 1):
64 self.conv_layers.append(
65 nn.Conv1d(
66 hidden_channels,
67 hidden_channels,
68 kernel_size,
69 padding=kernel_size // 2,
70 )
71 )
72 self.norm_layers.append(LayerNorm(hidden_channels))
73 self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
74 self.proj.weight.data.zero_()
75 self.proj.bias.data.zero_()
76
77 def forward(self, x, x_mask):
78 x_org = x
79 for i in range(self.n_layers):
80 x = self.conv_layers[i](x * x_mask)
81 x = self.norm_layers[i](x)
82 x = self.relu_drop(x)
83 x = x_org + self.proj(x)
84 return x * x_mask
85
86
87class DDSConv(nn.Module):
88 """
89 Dialted and Depth-Separable Convolution
90 """
91
92 def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
93 super().__init__()
94 self.channels = channels
95 self.kernel_size = kernel_size
96 self.n_layers = n_layers
97 self.p_dropout = p_dropout
98
99 self.drop = nn.Dropout(p_dropout)
100 self.convs_sep = nn.ModuleList()
101 self.convs_1x1 = nn.ModuleList()
102 self.norms_1 = nn.ModuleList()
103 self.norms_2 = nn.ModuleList()
104 for i in range(n_layers):
105 dilation = kernel_size**i
106 padding = (kernel_size * dilation - dilation) // 2
107 self.convs_sep.append(
108 nn.Conv1d(
109 channels,
110 channels,
111 kernel_size,
112 groups=channels,
113 dilation=dilation,
114 padding=padding,
115 )
116 )
117 self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
118 self.norms_1.append(LayerNorm(channels))
119 self.norms_2.append(LayerNorm(channels))
120
121 def forward(self, x, x_mask, g=None):
122 if g is not None:
123 x = x + g
124 for i in range(self.n_layers):
125 y = self.convs_sep[i](x * x_mask)
126 y = self.norms_1[i](y)
127 y = F.gelu(y)
128 y = self.convs_1x1[i](y)
129 y = self.norms_2[i](y)
130 y = F.gelu(y)
131 y = self.drop(y)
132 x = x + y
133 return x * x_mask
134
135
136class WN(torch.nn.Module):
137 def __init__(
138 self,
139 hidden_channels,
140 kernel_size,
141 dilation_rate,
142 n_layers,
143 gin_channels=0,
144 p_dropout=0,
145 ):
146 super(WN, self).__init__()
147 assert kernel_size % 2 == 1
148 self.hidden_channels = hidden_channels
149 self.kernel_size = (kernel_size,)
150 self.dilation_rate = dilation_rate
151 self.n_layers = n_layers
152 self.gin_channels = gin_channels
153 self.p_dropout = p_dropout
154
155 self.in_layers = torch.nn.ModuleList()
156 self.res_skip_layers = torch.nn.ModuleList()
157 self.drop = nn.Dropout(p_dropout)
158
159 if gin_channels != 0:
160 cond_layer = torch.nn.Conv1d(
161 gin_channels, 2 * hidden_channels * n_layers, 1
162 )
163 self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
164
165 for i in range(n_layers):
166 dilation = dilation_rate**i
167 padding = int((kernel_size * dilation - dilation) / 2)
168 in_layer = torch.nn.Conv1d(
169 hidden_channels,
170 2 * hidden_channels,
171 kernel_size,
172 dilation=dilation,
173 padding=padding,
174 )
175 in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
176 self.in_layers.append(in_layer)
177
178 # last one is not necessary
179 if i < n_layers - 1:
180 res_skip_channels = 2 * hidden_channels
181 else:
182 res_skip_channels = hidden_channels
183
184 res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
185 res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
186 self.res_skip_layers.append(res_skip_layer)
187
188 def forward(self, x, x_mask, g=None, **kwargs):
189 output = torch.zeros_like(x)
190 n_channels_tensor = torch.IntTensor([self.hidden_channels])
191
192 if g is not None:
193 g = self.cond_layer(g)
194
195 for i in range(self.n_layers):
196 x_in = self.in_layers[i](x)
197 if g is not None:
198 cond_offset = i * 2 * self.hidden_channels
199 g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
200 else:
201 g_l = torch.zeros_like(x_in)
202
203 acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
204 acts = self.drop(acts)
205
206 res_skip_acts = self.res_skip_layers[i](acts)
207 if i < self.n_layers - 1:
208 res_acts = res_skip_acts[:, : self.hidden_channels, :]
209 x = (x + res_acts) * x_mask
210 output = output + res_skip_acts[:, self.hidden_channels :, :]
211 else:
212 output = output + res_skip_acts
213 return output * x_mask
214
215 def remove_weight_norm(self):
216 if self.gin_channels != 0:
217 torch.nn.utils.remove_weight_norm(self.cond_layer)
218 for l in self.in_layers:
219 torch.nn.utils.remove_weight_norm(l)
220 for l in self.res_skip_layers:
221 torch.nn.utils.remove_weight_norm(l)
222
223
224class ResBlock1(torch.nn.Module):
225 def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
226 super(ResBlock1, self).__init__()
227 self.convs1 = nn.ModuleList(
228 [
229 weight_norm(
230 Conv1d(
231 channels,
232 channels,
233 kernel_size,
234 1,
235 dilation=dilation[0],
236 padding=get_padding(kernel_size, dilation[0]),
237 )
238 ),
239 weight_norm(
240 Conv1d(
241 channels,
242 channels,
243 kernel_size,
244 1,
245 dilation=dilation[1],
246 padding=get_padding(kernel_size, dilation[1]),
247 )
248 ),
249 weight_norm(
250 Conv1d(
251 channels,
252 channels,
253 kernel_size,
254 1,
255 dilation=dilation[2],
256 padding=get_padding(kernel_size, dilation[2]),
257 )
258 ),
259 ]
260 )
261 self.convs1.apply(init_weights)
262
263 self.convs2 = nn.ModuleList(
264 [
265 weight_norm(
266 Conv1d(
267 channels,
268 channels,
269 kernel_size,
270 1,
271 dilation=1,
272 padding=get_padding(kernel_size, 1),
273 )
274 ),
275 weight_norm(
276 Conv1d(
277 channels,
278 channels,
279 kernel_size,
280 1,
281 dilation=1,
282 padding=get_padding(kernel_size, 1),
283 )
284 ),
285 weight_norm(
286 Conv1d(
287 channels,
288 channels,
289 kernel_size,
290 1,
291 dilation=1,
292 padding=get_padding(kernel_size, 1),
293 )
294 ),
295 ]
296 )
297 self.convs2.apply(init_weights)
298
299 def forward(self, x, x_mask=None):
300 for c1, c2 in zip(self.convs1, self.convs2):
301 xt = F.leaky_relu(x, LRELU_SLOPE)
302 if x_mask is not None:
303 xt = xt * x_mask
304 xt = c1(xt)
305 xt = F.leaky_relu(xt, LRELU_SLOPE)
306 if x_mask is not None:
307 xt = xt * x_mask
308 xt = c2(xt)
309 x = xt + x
310 if x_mask is not None:
311 x = x * x_mask
312 return x
313
314 def remove_weight_norm(self):
315 for l in self.convs1:
316 remove_weight_norm(l)
317 for l in self.convs2:
318 remove_weight_norm(l)
319
320
321class ResBlock2(torch.nn.Module):
322 def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
323 super(ResBlock2, self).__init__()
324 self.convs = nn.ModuleList(
325 [
326 weight_norm(
327 Conv1d(
328 channels,
329 channels,
330 kernel_size,
331 1,
332 dilation=dilation[0],
333 padding=get_padding(kernel_size, dilation[0]),
334 )
335 ),
336 weight_norm(
337 Conv1d(
338 channels,
339 channels,
340 kernel_size,
341 1,
342 dilation=dilation[1],
343 padding=get_padding(kernel_size, dilation[1]),
344 )
345 ),
346 ]
347 )
348 self.convs.apply(init_weights)
349
350 def forward(self, x, x_mask=None):
351 for c in self.convs:
352 xt = F.leaky_relu(x, LRELU_SLOPE)
353 if x_mask is not None:
354 xt = xt * x_mask
355 xt = c(xt)
356 x = xt + x
357 if x_mask is not None:
358 x = x * x_mask
359 return x
360
361 def remove_weight_norm(self):
362 for l in self.convs:
363 remove_weight_norm(l)
364
365
366class Log(nn.Module):
367 def forward(self, x, x_mask, reverse=False, **kwargs):
368 if not reverse:
369 y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
370 logdet = torch.sum(-y, [1, 2])
371 return y, logdet
372 else:
373 x = torch.exp(x) * x_mask
374 return x
375
376
377class Flip(nn.Module):
378 def forward(self, x, *args, reverse=False, **kwargs):
379 x = torch.flip(x, [1])
380 if not reverse:
381 logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
382 return x, logdet
383 else:
384 return x
385
386
387class ElementwiseAffine(nn.Module):
388 def __init__(self, channels):
389 super().__init__()
390 self.channels = channels
391 self.m = nn.Parameter(torch.zeros(channels, 1))
392 self.logs = nn.Parameter(torch.zeros(channels, 1))
393
394 def forward(self, x, x_mask, reverse=False, **kwargs):
395 if not reverse:
396 y = self.m + torch.exp(self.logs) * x
397 y = y * x_mask
398 logdet = torch.sum(self.logs * x_mask, [1, 2])
399 return y, logdet
400 else:
401 x = (x - self.m) * torch.exp(-self.logs) * x_mask
402 return x
403
404
405class ResidualCouplingLayer(nn.Module):
406 def __init__(
407 self,
408 channels,
409 hidden_channels,
410 kernel_size,
411 dilation_rate,
412 n_layers,
413 p_dropout=0,
414 gin_channels=0,
415 mean_only=False,
416 ):
417 assert channels % 2 == 0, "channels should be divisible by 2"
418 super().__init__()
419 self.channels = channels
420 self.hidden_channels = hidden_channels
421 self.kernel_size = kernel_size
422 self.dilation_rate = dilation_rate
423 self.n_layers = n_layers
424 self.half_channels = channels // 2
425 self.mean_only = mean_only
426
427 self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
428 self.enc = WN(
429 hidden_channels,
430 kernel_size,
431 dilation_rate,
432 n_layers,
433 p_dropout=p_dropout,
434 gin_channels=gin_channels,
435 )
436 self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
437 self.post.weight.data.zero_()
438 self.post.bias.data.zero_()
439
440 def forward(self, x, x_mask, g=None, reverse=False):
441 x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
442 h = self.pre(x0) * x_mask
443 h = self.enc(h, x_mask, g=g)
444 stats = self.post(h) * x_mask
445 if not self.mean_only:
446 m, logs = torch.split(stats, [self.half_channels] * 2, 1)
447 else:
448 m = stats
449 logs = torch.zeros_like(m)
450
451 if not reverse:
452 x1 = m + x1 * torch.exp(logs) * x_mask
453 x = torch.cat([x0, x1], 1)
454 logdet = torch.sum(logs, [1, 2])
455 return x, logdet
456 else:
457 x1 = (x1 - m) * torch.exp(-logs) * x_mask
458 x = torch.cat([x0, x1], 1)
459 return x
460
461 def remove_weight_norm(self):
462 self.enc.remove_weight_norm()
463
464
465class ConvFlow(nn.Module):
466 def __init__(
467 self,
468 in_channels,
469 filter_channels,
470 kernel_size,
471 n_layers,
472 num_bins=10,
473 tail_bound=5.0,
474 ):
475 super().__init__()
476 self.in_channels = in_channels
477 self.filter_channels = filter_channels
478 self.kernel_size = kernel_size
479 self.n_layers = n_layers
480 self.num_bins = num_bins
481 self.tail_bound = tail_bound
482 self.half_channels = in_channels // 2
483
484 self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
485 self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
486 self.proj = nn.Conv1d(
487 filter_channels, self.half_channels * (num_bins * 3 - 1), 1
488 )
489 self.proj.weight.data.zero_()
490 self.proj.bias.data.zero_()
491
492 def forward(self, x, x_mask, g=None, reverse=False):
493 x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
494 h = self.pre(x0)
495 h = self.convs(h, x_mask, g=g)
496 h = self.proj(h) * x_mask
497
498 b, c, t = x0.shape
499 h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
500
501 unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
502 unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
503 self.filter_channels
504 )
505 unnormalized_derivatives = h[..., 2 * self.num_bins :]
506
507 x1, logabsdet = piecewise_rational_quadratic_transform(
508 x1,
509 unnormalized_widths,
510 unnormalized_heights,
511 unnormalized_derivatives,
512 inverse=reverse,
513 tails="linear",
514 tail_bound=self.tail_bound,
515 )
516
517 x = torch.cat([x0, x1], 1) * x_mask
518 logdet = torch.sum(logabsdet * x_mask, [1, 2])
519 if not reverse:
520 return x, logdet
521 else:
522 return x
523