Delete logger.py

logger.py

@@ -1,47 +0,0 @@
-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)