Spaces:
Runtime error
Runtime error
File size: 11,191 Bytes
11120b4 |
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 |
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
import math
import torch
from einops import rearrange
from model.base import BaseModule
class Mish(BaseModule):
def forward(self, x):
return x * torch.tanh(torch.nn.functional.softplus(x))
class Upsample(BaseModule):
def __init__(self, dim):
super(Upsample, self).__init__()
self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
def forward(self, x):
return self.conv(x)
class Downsample(BaseModule):
def __init__(self, dim):
super(Downsample, self).__init__()
self.conv = torch.nn.Conv2d(dim, dim, 3, 2, 1)
def forward(self, x):
return self.conv(x)
class Rezero(BaseModule):
def __init__(self, fn):
super(Rezero, self).__init__()
self.fn = fn
self.g = torch.nn.Parameter(torch.zeros(1))
def forward(self, x):
return self.fn(x) * self.g
class Block(BaseModule):
def __init__(self, dim, dim_out, groups=8):
super(Block, self).__init__()
self.block = torch.nn.Sequential(torch.nn.Conv2d(dim, dim_out, 3,
padding=1), torch.nn.GroupNorm(
groups, dim_out), Mish())
def forward(self, x, mask):
output = self.block(x * mask)
return output * mask
class ResnetBlock(BaseModule):
def __init__(self, dim, dim_out, time_emb_dim, groups=8):
super(ResnetBlock, self).__init__()
self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim,
dim_out))
self.block1 = Block(dim, dim_out, groups=groups)
self.block2 = Block(dim_out, dim_out, groups=groups)
if dim != dim_out:
self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
else:
self.res_conv = torch.nn.Identity()
def forward(self, x, mask, time_emb):
h = self.block1(x, mask)
h += self.mlp(time_emb).unsqueeze(-1).unsqueeze(-1)
h = self.block2(h, mask)
output = h + self.res_conv(x * mask)
return output
class LinearAttention(BaseModule):
def __init__(self, dim, heads=4, dim_head=32):
super(LinearAttention, self).__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
self.to_out = torch.nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)',
heads = self.heads, qkv=3)
k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w',
heads=self.heads, h=h, w=w)
return self.to_out(out)
class Residual(BaseModule):
def __init__(self, fn):
super(Residual, self).__init__()
self.fn = fn
def forward(self, x, *args, **kwargs):
output = self.fn(x, *args, **kwargs) + x
return output
class SinusoidalPosEmb(BaseModule):
def __init__(self, dim):
super(SinusoidalPosEmb, self).__init__()
self.dim = dim
def forward(self, x, scale=1000):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class GradLogPEstimator2d(BaseModule):
def __init__(self, dim, dim_mults=(1, 2, 4), groups=8,
n_spks=None, spk_emb_dim=64, n_feats=80, pe_scale=1000):
super(GradLogPEstimator2d, self).__init__()
self.dim = dim
self.dim_mults = dim_mults
self.groups = groups
self.n_spks = n_spks if not isinstance(n_spks, type(None)) else 1
self.spk_emb_dim = spk_emb_dim
self.pe_scale = pe_scale
if n_spks > 1:
self.spk_mlp = torch.nn.Sequential(torch.nn.Linear(spk_emb_dim, spk_emb_dim * 4), Mish(),
torch.nn.Linear(spk_emb_dim * 4, n_feats))
self.time_pos_emb = SinusoidalPosEmb(dim)
self.mlp = torch.nn.Sequential(torch.nn.Linear(dim, dim * 4), Mish(),
torch.nn.Linear(dim * 4, dim))
dims = [2 + (1 if n_spks > 1 else 0), *map(lambda m: dim * m, dim_mults)]
in_out = list(zip(dims[:-1], dims[1:]))
self.downs = torch.nn.ModuleList([])
self.ups = torch.nn.ModuleList([])
num_resolutions = len(in_out)
for ind, (dim_in, dim_out) in enumerate(in_out):
is_last = ind >= (num_resolutions - 1)
self.downs.append(torch.nn.ModuleList([
ResnetBlock(dim_in, dim_out, time_emb_dim=dim),
ResnetBlock(dim_out, dim_out, time_emb_dim=dim),
Residual(Rezero(LinearAttention(dim_out))),
Downsample(dim_out) if not is_last else torch.nn.Identity()]))
mid_dim = dims[-1]
self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
self.mid_attn = Residual(Rezero(LinearAttention(mid_dim)))
self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
self.ups.append(torch.nn.ModuleList([
ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim),
ResnetBlock(dim_in, dim_in, time_emb_dim=dim),
Residual(Rezero(LinearAttention(dim_in))),
Upsample(dim_in)]))
self.final_block = Block(dim, dim)
self.final_conv = torch.nn.Conv2d(dim, 1, 1)
def forward(self, x, mask, mu, t, spk=None):
if not isinstance(spk, type(None)):
s = self.spk_mlp(spk)
t = self.time_pos_emb(t, scale=self.pe_scale)
t = self.mlp(t)
if self.n_spks < 2:
x = torch.stack([mu, x], 1)
else:
s = s.unsqueeze(-1).repeat(1, 1, x.shape[-1])
x = torch.stack([mu, x, s], 1)
mask = mask.unsqueeze(1)
hiddens = []
masks = [mask]
for resnet1, resnet2, attn, downsample in self.downs:
mask_down = masks[-1]
x = resnet1(x, mask_down, t)
x = resnet2(x, mask_down, t)
x = attn(x)
hiddens.append(x)
x = downsample(x * mask_down)
masks.append(mask_down[:, :, :, ::2])
masks = masks[:-1]
mask_mid = masks[-1]
x = self.mid_block1(x, mask_mid, t)
x = self.mid_attn(x)
x = self.mid_block2(x, mask_mid, t)
for resnet1, resnet2, attn, upsample in self.ups:
mask_up = masks.pop()
x = torch.cat((x, hiddens.pop()), dim=1)
x = resnet1(x, mask_up, t)
x = resnet2(x, mask_up, t)
x = attn(x)
x = upsample(x * mask_up)
x = self.final_block(x, mask)
output = self.final_conv(x * mask)
return (output * mask).squeeze(1)
def get_noise(t, beta_init, beta_term, cumulative=False):
if cumulative:
noise = beta_init*t + 0.5*(beta_term - beta_init)*(t**2)
else:
noise = beta_init + (beta_term - beta_init)*t
return noise
class Diffusion(BaseModule):
def __init__(self, n_feats, dim,
n_spks=1, spk_emb_dim=64,
beta_min=0.05, beta_max=20, pe_scale=1000):
super(Diffusion, self).__init__()
self.n_feats = n_feats
self.dim = dim
self.n_spks = n_spks
self.spk_emb_dim = spk_emb_dim
self.beta_min = beta_min
self.beta_max = beta_max
self.pe_scale = pe_scale
self.estimator = GradLogPEstimator2d(dim, n_spks=n_spks,
spk_emb_dim=spk_emb_dim,
pe_scale=pe_scale)
def forward_diffusion(self, x0, mask, mu, t):
time = t.unsqueeze(-1).unsqueeze(-1)
cum_noise = get_noise(time, self.beta_min, self.beta_max, cumulative=True)
mean = x0*torch.exp(-0.5*cum_noise) + mu*(1.0 - torch.exp(-0.5*cum_noise))
variance = 1.0 - torch.exp(-cum_noise)
z = torch.randn(x0.shape, dtype=x0.dtype, device=x0.device,
requires_grad=False)
xt = mean + z * torch.sqrt(variance)
return xt * mask, z * mask
@torch.no_grad()
def reverse_diffusion(self, z, mask, mu, n_timesteps, stoc=False, spk=None):
h = 1.0 / n_timesteps
xt = z * mask
for i in range(n_timesteps):
t = (1.0 - (i + 0.5)*h) * torch.ones(z.shape[0], dtype=z.dtype,
device=z.device)
time = t.unsqueeze(-1).unsqueeze(-1)
noise_t = get_noise(time, self.beta_min, self.beta_max,
cumulative=False)
if stoc: # adds stochastic term
dxt_det = 0.5 * (mu - xt) - self.estimator(xt, mask, mu, t, spk)
dxt_det = dxt_det * noise_t * h
dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device,
requires_grad=False)
dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h)
dxt = dxt_det + dxt_stoc
else:
dxt = 0.5 * (mu - xt - self.estimator(xt, mask, mu, t, spk))
dxt = dxt * noise_t * h
xt = (xt - dxt) * mask
return xt
@torch.no_grad()
def forward(self, z, mask, mu, n_timesteps, stoc=False, spk=None):
return self.reverse_diffusion(z, mask, mu, n_timesteps, stoc, spk)
def loss_t(self, x0, mask, mu, t, spk=None):
xt, z = self.forward_diffusion(x0, mask, mu, t)
time = t.unsqueeze(-1).unsqueeze(-1)
cum_noise = get_noise(time, self.beta_min, self.beta_max, cumulative=True)
noise_estimation = self.estimator(xt, mask, mu, t, spk)
noise_estimation *= torch.sqrt(1.0 - torch.exp(-cum_noise))
loss = torch.sum((noise_estimation + z)**2) / (torch.sum(mask)*self.n_feats)
return loss, xt
def compute_loss(self, x0, mask, mu, spk=None, offset=1e-5):
t = torch.rand(x0.shape[0], dtype=x0.dtype, device=x0.device,
requires_grad=False)
t = torch.clamp(t, offset, 1.0 - offset)
return self.loss_t(x0, mask, mu, t, spk)
|