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multiscale subband stft loss and missing tb_logger for eval stats

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This commit is contained in:
erogol
2020-06-15 19:04:05 +02:00
parent d425daad5b
commit 3dff81e7e2
3 changed files with 51 additions and 31 deletions
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vocoder/train.py
+24 -29
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@@ -122,30 +122,27 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
# generator pass
optimizer_G.zero_grad()
y_hat = model_G(c_G)
in_real_D = y_hat
in_fake_D = y_G
y_hat_sub = None
y_G_sub = None
# PQMF formatting
if y_hat.shape[1] > 1:
in_real_D = y_G
in_fake_D = model_G.pqmf_synthesis(y_hat)
y_G = model_G.pqmf_analysis(y_G)
y_hat = y_hat.view(-1, 1, y_hat.shape[2])
y_G = y_G.view(-1, 1, y_G.shape[2])
y_hat_sub = y_hat
y_hat = model_G.pqmf_synthesis(y_hat)
y_G_sub = model_G.pqmf_analysis(y_G)
if global_step > c.steps_to_start_discriminator:
# run D with or without cond. features
if len(signature(model_D.forward).parameters) == 2:
D_out_fake = model_D(in_fake_D, c_G)
D_out_fake = model_D(y_hat, c_G)
else:
D_out_fake = model_D(in_fake_D)
D_out_fake = model_D(y_hat)
D_out_real = None
if c.use_feat_match_loss:
with torch.no_grad():
D_out_real = model_D(in_real_D)
D_out_real = model_D(y_G)
# format D outputs
if isinstance(D_out_fake, tuple):
@@ -162,7 +159,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
# compute losses
loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
feats_real)
feats_real, y_hat_sub, y_G_sub)
loss_G = loss_G_dict['G_loss']
# optimizer generator
@@ -272,12 +269,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
model_losses=loss_dict)
# compute spectrograms
figures = plot_results(in_fake_D, in_real_D, ap, global_step,
figures = plot_results(y_hat, y_G, ap, global_step,
'train')
tb_logger.tb_train_figures(global_step, figures)
# Sample audio
sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
tb_logger.tb_train_audios(global_step,
{'train/audio': sample_voice},
c.audio["sample_rate"])
@@ -320,23 +317,20 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
# generator pass
y_hat = model_G(c_G)
in_real_D = y_hat
in_fake_D = y_G
y_hat_sub = None
y_G_sub = None
# PQMF formatting
if y_hat.shape[1] > 1:
in_real_D = y_G
in_fake_D = model_G.pqmf_synthesis(y_hat)
y_G = model_G.pqmf_analysis(y_G)
y_hat = y_hat.view(-1, 1, y_hat.shape[2])
y_G = y_G.view(-1, 1, y_G.shape[2])
y_hat_sub = y_hat
y_hat = model_G.pqmf_synthesis(y_hat)
y_G_sub = model_G.pqmf_analysis(y_G)
D_out_fake = model_D(in_fake_D)
D_out_fake = model_D(y_hat)
D_out_real = None
if c.use_feat_match_loss:
with torch.no_grad():
D_out_real = model_D(in_real_D)
D_out_real = model_D(y_G)
# format D outputs
if isinstance(D_out_fake, tuple):
@@ -350,7 +344,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
# compute losses
loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
feats_real)
feats_real, y_hat_sub, y_G_sub)
loss_dict = dict()
for key, value in loss_G_dict.items():
@@ -364,7 +358,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
for key, value in loss_G_dict.items():
update_eval_values['avg_' + key] = value.item()
update_eval_values['avg_loader_time'] = loader_time
update_eval_values['avg_step_time'] = step_time
update_eval_values['avgP_step_time'] = step_time
keep_avg.update_values(update_eval_values)
# print eval stats
@@ -372,18 +366,19 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
# compute spectrograms
figures = plot_results(in_fake_D, in_real_D, ap, global_step, 'eval')
figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
tb_logger.tb_eval_figures(global_step, figures)
# Sample audio
sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
c.audio["sample_rate"])
# synthesize a full voice
data_loader.return_segments = False
tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
tb_logger.tb_evals_stats(global_step, keep_avg.avg_values)
return keep_avg.avg_values