| import random |
| import torch |
| from torch.utils.tensorboard import SummaryWriter |
| from plotting_utils import plot_alignment_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, iteration): |
| self.add_scalar("validation.loss", reduced_loss, iteration) |
| _, mel_outputs, gate_outputs, alignments = y_pred |
| mel_targets, gate_targets = y |
|
|
| |
| for tag, value in model.named_parameters(): |
| tag = tag.replace('.', '/') |
| self.add_histogram(tag, value.data.cpu().numpy(), iteration) |
|
|
| |
| idx = random.randint(0, alignments.size(0) - 1) |
| self.add_image( |
| "alignment", |
| plot_alignment_to_numpy(alignments[idx].data.cpu().numpy().T), |
| iteration, dataformats='HWC') |
| self.add_image( |
| "mel_target", |
| plot_spectrogram_to_numpy(mel_targets[idx].data.cpu().numpy()), |
| iteration, dataformats='HWC') |
| self.add_image( |
| "mel_predicted", |
| plot_spectrogram_to_numpy(mel_outputs[idx].data.cpu().numpy()), |
| iteration, dataformats='HWC') |
| self.add_image( |
| "gate", |
| plot_gate_outputs_to_numpy( |
| gate_targets[idx].data.cpu().numpy(), |
| torch.sigmoid(gate_outputs[idx]).data.cpu().numpy()), |
| iteration, dataformats='HWC') |
|
|
