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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchaudio.transforms import MelSpectrogram
def adversarial_g_loss(y_disc_gen):
loss = 0.0
for i in range(len(y_disc_gen)):
#print(y_disc_gen[i].shape)
# assert 1==2
stft_loss = F.relu(1-y_disc_gen[i]).mean().squeeze()
loss += stft_loss
return loss/len(y_disc_gen)
def feature_loss(fmap_r, fmap_gen):
loss = 0.0
for i in range(len(fmap_r)):
for j in range(len(fmap_r[i])):
stft_loss = ((fmap_r[i][j]-fmap_gen[i][j]).abs()/(fmap_r[i][j].abs().mean())).mean()
loss += stft_loss
return loss/(len(fmap_r)*len(fmap_r[0]))
def sim_loss(y_disc_r, y_disc_gen):
loss = 0.0
for i in range(len(y_disc_r)):
loss += F.mse_loss(y_disc_r[i], y_disc_gen[i])
return loss/len(y_disc_r)
def sisnr_loss(x, s, eps=1e-8):
"""
calculate training loss
input:
x: separated signal, N x S tensor, estimate value
s: reference signal, N x S tensor, True value
Return:
sisnr: N tensor
"""
if x.shape != s.shape:
if x.shape[-1] > s.shape[-1]:
x = x[:, :s.shape[-1]]
else:
s = s[:, :x.shape[-1]]
def l2norm(mat, keepdim=False):
return torch.norm(mat, dim=-1, keepdim=keepdim)
if x.shape != s.shape:
raise RuntimeError(
"Dimention mismatch when calculate si-snr, {} vs {}".format(
x.shape, s.shape))
x_zm = x - torch.mean(x, dim=-1, keepdim=True)
s_zm = s - torch.mean(s, dim=-1, keepdim=True)
t = torch.sum(
x_zm * s_zm, dim=-1,
keepdim=True) * s_zm / (l2norm(s_zm, keepdim=True)**2 + eps)
loss = -20. * torch.log10(eps + l2norm(t) / (l2norm(x_zm - t) + eps))
return torch.sum(loss) / x.shape[0]
def reconstruction_loss(x, G_x, args, eps=1e-7):
L = 100*F.mse_loss(x, G_x) # wav L1 loss
#loss_sisnr = sisnr_loss(G_x, x) #
#L += 0.01*loss_sisnr
# print('L0 ', L)
# print('loss_sisnr ', 0.01*loss_sisnr)
# print('L0 ', L)
for i in range(6,11):
s = 2**i
melspec = MelSpectrogram(sample_rate=args.sr, n_fft=s, hop_length=s//4, n_mels=64, wkwargs={"device": args.device}).to(args.device)
S_x = melspec(x)
S_G_x = melspec(G_x)
loss = ((S_x-S_G_x).abs().mean() + (((torch.log(S_x.abs()+eps)-torch.log(S_G_x.abs()+eps))**2).mean(dim=-2)**0.5).mean())/(i)
L += loss
#print('i ,loss ', i, loss)
#assert 1==2
return L
def criterion_d(y_disc_r, y_disc_gen, fmap_r_det, fmap_gen_det):
loss = 0.0
loss_f = feature_loss(fmap_r_det, fmap_gen_det)
for i in range(len(y_disc_r)):
loss += F.relu(1-y_disc_r[i]).mean() + F.relu(1+y_disc_gen[i]).mean()
return loss/len(y_disc_gen) + 0.0*loss_f
def criterion_g(commit_loss, x, G_x, fmap_r, fmap_gen, y_disc_r, y_disc_gen, args):
adv_g_loss = adversarial_g_loss(y_disc_gen)
feat_loss = feature_loss(fmap_r, fmap_gen) + sim_loss(y_disc_r, y_disc_gen) # 预测结果也应该尽可能相似
rec_loss = reconstruction_loss(x.contiguous(), G_x.contiguous(), args)
total_loss = args.LAMBDA_COM * commit_loss + args.LAMBDA_ADV*adv_g_loss + \
args.LAMBDA_FEAT*feat_loss + args.LAMBDA_REC*rec_loss
return total_loss, adv_g_loss, feat_loss, rec_loss
def adopt_weight(weight, global_step, threshold=0, value=0.):
if global_step < threshold:
weight = value
return weight
def adopt_dis_weight(weight, global_step, threshold=0, value=0.):
if global_step % 3 == 0: # 0,3,6,9,13....这些时间步，不更新dis
weight = value
return weight
def calculate_adaptive_weight(nll_loss, g_loss, last_layer, args):
if last_layer is not None:
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
else:
print('last_layer cannot be none')
assert 1==2
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 1.0, 1.0).detach()
d_weight = d_weight * args.LAMBDA_ADV
return d_weight
def loss_g(codebook_loss, inputs, reconstructions, fmap_r, fmap_gen,
y_disc_r, y_disc_gen, global_step, last_layer=None, is_training=True, args=None):
rec_loss = reconstruction_loss(inputs.contiguous(), reconstructions.contiguous(), args)
adv_g_loss = adversarial_g_loss(y_disc_gen)
feat_loss = feature_loss(fmap_r, fmap_gen) + sim_loss(y_disc_r, y_disc_gen) #
d_weight = torch.tensor(1.0)
# try:
# d_weight = calculate_adaptive_weight(rec_loss, adv_g_loss, last_layer, args) # 动态调整重构损失和对抗损失
# except RuntimeError:
# assert not is_training
# d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(args.LAMBDA_ADV, global_step, threshold=args.discriminator_iter_start)
#feat_factor = adopt_weight(args.LAMBDA_FEAT, global_step, threshold=args.discriminator_iter_start)
loss = rec_loss + d_weight * disc_factor * adv_g_loss + \
args.LAMBDA_FEAT*feat_loss + args.LAMBDA_COM * codebook_loss
return loss, rec_loss, adv_g_loss, feat_loss, d_weight
def loss_dis(y_disc_r_det, y_disc_gen_det, fmap_r_det, fmap_gen_det, global_step, args):
disc_factor = adopt_weight(args.LAMBDA_ADV, global_step, threshold=args.discriminator_iter_start)
d_loss = disc_factor * criterion_d(y_disc_r_det, y_disc_gen_det, fmap_r_det, fmap_gen_det)
return d_loss
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