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masked loss

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Eren Golge
2018-03-22 13:46:52 -07:00
parent 0f3b2ddd7b
commit 33937f54d0
4 changed files with 39 additions and 24 deletions
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train.py
+15 -6
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@@ -26,6 +26,7 @@ from utils.model import get_param_size
from utils.visual import plot_alignment, plot_spectrogram
from datasets.LJSpeech import LJSpeechDataset
from models.tacotron import Tacotron
from losses import
use_cuda = torch.cuda.is_available()
@@ -80,6 +81,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
mel_lengths = data[4]
current_step = num_iter + args.restore_step + epoch * len(data_loader) + 1
@@ -93,6 +95,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
# convert inputs to variables
text_input_var = Variable(text_input)
mel_spec_var = Variable(mel_input)
mel_length_var = Variable(mel_lengths)
linear_spec_var = Variable(linear_input, volatile=True)
# sort sequence by length for curriculum learning
@@ -108,6 +111,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
if use_cuda:
text_input_var = text_input_var.cuda()
mel_spec_var = mel_spec_var.cuda()
mel_lengths_var = mel_lengths_var.cuda()
linear_spec_var = linear_spec_var.cuda()
# forward pass
@@ -115,10 +119,11 @@ def train(model, criterion, data_loader, optimizer, epoch):
model.forward(text_input_var, mel_spec_var)
# loss computation
mel_loss = criterion(mel_output, mel_spec_var)
linear_loss = 0.5 * criterion(linear_output, linear_spec_var) \
mel_loss = criterion(mel_output, mel_spec_var, mel_lengths)
linear_loss = 0.5 * criterion(linear_output, linear_spec_var, mel_lengths) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[: ,: ,:n_priority_freq])
linear_spec_var[: ,: ,:n_priority_freq],
mel_lengths)
loss = mel_loss + linear_loss
# backpass and check the grad norm
@@ -215,26 +220,30 @@ def evaluate(model, criterion, data_loader, current_step):
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
mel_lengths = data[4]
# convert inputs to variables
text_input_var = Variable(text_input)
mel_spec_var = Variable(mel_input)
mel_lengths_var = Variable(mel_lengths)
linear_spec_var = Variable(linear_input, volatile=True)
# dispatch data to GPU
if use_cuda:
text_input_var = text_input_var.cuda()
mel_spec_var = mel_spec_var.cuda()
mel_lengths_var = mel_lengths_var.cuda()
linear_spec_var = linear_spec_var.cuda()
# forward pass
mel_output, linear_output, alignments = model.forward(text_input_var, mel_spec_var)
# loss computation
mel_loss = criterion(mel_output, mel_spec_var)
linear_loss = 0.5 * criterion(linear_output, linear_spec_var) \
mel_loss = criterion(mel_output, mel_spec_var, mel_lengths)
linear_loss = 0.5 * criterion(linear_output, linear_spec_var, mel_lengths) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[: ,: ,:n_priority_freq])
linear_spec_var[: ,: ,:n_priority_freq],
mel_lengths)
loss = mel_loss + linear_loss
step_time = time.time() - start_time