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import torch
import torch.nn.functional as F
from torchaudio.transforms import MelSpectrogram
def adversarial_g_loss(y_disc_gen):
"""Hinge loss"""
loss = 0.0
for i in range(len(y_disc_gen)):
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):
# NOTE (lsx): hard-coded now
L = args.LAMBDA_WAV * F.mse_loss(x, G_x) # wav L1 loss
# loss_sisnr = sisnr_loss(G_x, x) #
# L += 0.01*loss_sisnr
# 2^6=64 -> 2^10=1024
# NOTE (lsx): add 2^11
for i in range(6, 12):
# for i in range(5, 12): # Encodec setting
s = 2**i
melspec = MelSpectrogram(
sample_rate=args.sr,
n_fft=max(s, 512),
win_length=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)
l1_loss = (S_x - S_G_x).abs().mean()
l2_loss = (((torch.log(S_x.abs() + eps) - torch.log(S_G_x.abs() + eps))**2).mean(dim=-2)**0.5).mean()
alpha = (s / 2) ** 0.5
L += (l1_loss + alpha * l2_loss)
return L
def criterion_d(y_disc_r, y_disc_gen, fmap_r_det, fmap_gen_det, y_df_hat_r,
y_df_hat_g, fmap_f_r, fmap_f_g, y_ds_hat_r, y_ds_hat_g,
fmap_s_r, fmap_s_g):
"""Hinge Loss"""
loss = 0.0
loss1 = 0.0
loss2 = 0.0
loss3 = 0.0
for i in range(len(y_disc_r)):
loss1 += F.relu(1 - y_disc_r[i]).mean() + F.relu(1 + y_disc_gen[
i]).mean()
for i in range(len(y_df_hat_r)):
loss2 += F.relu(1 - y_df_hat_r[i]).mean() + F.relu(1 + y_df_hat_g[
i]).mean()
for i in range(len(y_ds_hat_r)):
loss3 += F.relu(1 - y_ds_hat_r[i]).mean() + F.relu(1 + y_ds_hat_g[
i]).mean()
loss = (loss1 / len(y_disc_gen) + loss2 / len(y_df_hat_r) + loss3 /
len(y_ds_hat_r)) / 3.0
return loss
def criterion_g(commit_loss, x, G_x, fmap_r, fmap_gen, y_disc_r, y_disc_gen,
y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g, y_ds_hat_r,
y_ds_hat_g, fmap_s_r, fmap_s_g, 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) + feature_loss(fmap_f_r, fmap_f_g) + sim_loss(
y_df_hat_r, y_df_hat_g) + feature_loss(fmap_s_r, fmap_s_g) +
sim_loss(y_ds_hat_r, y_ds_hat_g)) / 3.0
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.):
# 0,3,6,9,13....这些时间步，不更新dis
if global_step % 3 == 0:
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,
y_df_hat_r,
y_df_hat_g,
y_ds_hat_r,
y_ds_hat_g,
fmap_f_r,
fmap_f_g,
fmap_s_r,
fmap_s_g,
last_layer=None,
is_training=True,
args=None):
"""
args:
codebook_loss: commit loss.
inputs: ground-truth wav.
reconstructions: reconstructed wav.
fmap_r: real stft-D feature map.
fmap_gen: fake stft-D feature map.
y_disc_r: real stft-D logits.
y_disc_gen: fake stft-D logits.
global_step: global training step.
y_df_hat_r: real MPD logits.
y_df_hat_g: fake MPD logits.
y_ds_hat_r: real MSD logits.
y_ds_hat_g: fake MSD logits.
fmap_f_r: real MPD feature map.
fmap_f_g: fake MPD feature map.
fmap_s_r: real MSD feature map.
fmap_s_g: fake MSD feature map.
"""
rec_loss = reconstruction_loss(inputs.contiguous(),
reconstructions.contiguous(), args)
adv_g_loss = adversarial_g_loss(y_disc_gen)
adv_mpd_loss = adversarial_g_loss(y_df_hat_g)
adv_msd_loss = adversarial_g_loss(y_ds_hat_g)
adv_loss = (adv_g_loss + adv_mpd_loss + adv_msd_loss
) / 3.0 # NOTE(lsx): need to divide by 3?
feat_loss = feature_loss(
fmap_r,
fmap_gen) #+ sim_loss(y_disc_r, y_disc_gen) # NOTE(lsx): need logits?
feat_loss_mpd = feature_loss(fmap_f_r,
fmap_f_g) #+ sim_loss(y_df_hat_r, y_df_hat_g)
feat_loss_msd = feature_loss(fmap_s_r,
fmap_s_g) #+ sim_loss(y_ds_hat_r, y_ds_hat_g)
feat_loss_tot = (feat_loss + feat_loss_mpd + feat_loss_msd) / 3.0
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)
if disc_factor == 0.:
fm_loss_wt = 0
else:
fm_loss_wt = args.LAMBDA_FEAT
#feat_factor = adopt_weight(args.LAMBDA_FEAT, global_step, threshold=args.discriminator_iter_start)
loss = rec_loss + d_weight * disc_factor * adv_loss + \
fm_loss_wt * feat_loss_tot + args.LAMBDA_COM * codebook_loss
return loss, rec_loss, adv_loss, feat_loss_tot, d_weight
def loss_dis(y_disc_r_det, y_disc_gen_det, fmap_r_det, fmap_gen_det, y_df_hat_r,
y_df_hat_g, fmap_f_r, fmap_f_g, y_ds_hat_r, y_ds_hat_g, fmap_s_r,
fmap_s_g, 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, y_df_hat_r, y_df_hat_g,
fmap_f_r, fmap_f_g, y_ds_hat_r,
y_ds_hat_g, fmap_s_r, fmap_s_g)
return d_loss
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