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48 lines
2.0 KiB
48 lines
2.0 KiB
import random
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from plotting_utils import plot_alignment_to_numpy, plot_spectrogram_to_numpy
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from plotting_utils import plot_gate_outputs_to_numpy
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class Tacotron2Logger(SummaryWriter):
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def __init__(self, logdir):
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super(Tacotron2Logger, self).__init__(logdir)
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def log_training(self, reduced_loss, grad_norm, learning_rate, duration,
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iteration):
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self.add_scalar("training.loss", reduced_loss, iteration)
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self.add_scalar("grad.norm", grad_norm, iteration)
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self.add_scalar("learning.rate", learning_rate, iteration)
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self.add_scalar("duration", duration, iteration)
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def log_validation(self, reduced_loss, model, y, y_pred, iteration):
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self.add_scalar("validation.loss", reduced_loss, iteration)
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_, mel_outputs, gate_outputs, alignments = y_pred
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mel_targets, gate_targets = y
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# plot distribution of parameters
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for tag, value in model.named_parameters():
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tag = tag.replace('.', '/')
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self.add_histogram(tag, value.data.cpu().numpy(), iteration)
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# plot alignment, mel target and predicted, gate target and predicted
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idx = random.randint(0, alignments.size(0) - 1)
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self.add_image(
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"alignment",
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plot_alignment_to_numpy(alignments[idx].data.cpu().numpy().T),
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iteration, dataformats='HWC')
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self.add_image(
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"mel_target",
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plot_spectrogram_to_numpy(mel_targets[idx].data.cpu().numpy()),
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iteration, dataformats='HWC')
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self.add_image(
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"mel_predicted",
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plot_spectrogram_to_numpy(mel_outputs[idx].data.cpu().numpy()),
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iteration, dataformats='HWC')
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self.add_image(
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"gate",
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plot_gate_outputs_to_numpy(
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gate_targets[idx].data.cpu().numpy(),
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torch.sigmoid(gate_outputs[idx]).data.cpu().numpy()),
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iteration, dataformats='HWC')
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