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import random
import torch.nn.functional as F
from tensorboardX import SummaryWriter
from plotting_utils import plot_alignment_to_numpy, plot_gst_scores_to_numpy, plot_spectrogram_to_numpy
from plotting_utils import plot_gate_outputs_to_numpy
class Tacotron2Logger(SummaryWriter):
def __init__(self, logdir):
super(Tacotron2Logger, self).__init__(logdir)
def log_training(self, reduced_loss, grad_norm, learning_rate, duration,
iteration):
self.add_scalar("training.loss", reduced_loss, iteration)
self.add_scalar("grad.norm", grad_norm, iteration)
self.add_scalar("learning.rate", learning_rate, iteration)
self.add_scalar("duration", duration, iteration)
def log_validation(self, reduced_loss, model, y, y_pred, gst_scores, iteration):
self.add_scalar("validation.loss", reduced_loss, iteration)
_, mel_outputs, gate_outputs, alignments, _ = y_pred
mel_targets, gate_targets = y
# plot distribution of parameters
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
self.add_histogram(tag, value.data.cpu().numpy(), iteration)
# plot alignment, mel target and predicted, gate target and predicted
idx = random.randint(0, alignments.size(0) - 1)
align_idx = alignments[idx].data.cpu().numpy().T
gst_scores = gst_scores.data.cpu().numpy().T
# print("Validation GST scores before plotting to tensorboard: {}".format(gst_scores.shape))
meltarg_idx = mel_targets[idx].data.cpu().numpy()
melout_idx = mel_outputs[idx].data.cpu().numpy()
self.add_image("alignment", plot_alignment_to_numpy(align_idx), iteration)
self.add_image("gst_scores", plot_gst_scores_to_numpy(gst_scores), iteration)
self.add_image("mel_target", plot_spectrogram_to_numpy(meltarg_idx), iteration)
self.add_image("mel_predicted", plot_spectrogram_to_numpy(melout_idx), iteration)
self.add_image(
"gate",
plot_gate_outputs_to_numpy(
gate_targets[idx].data.cpu().numpy(),
F.sigmoid(gate_outputs[idx]).data.cpu().numpy()),
iteration)
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