import glob import os import matplotlib import torch from torch.nn.utils import weight_norm matplotlib.use("Agg") import matplotlib.pylab as plt def plot_spectrogram(spectrogram): fig, ax = plt.subplots(figsize=(10, 2)) im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation='none') plt.colorbar(im, ax=ax) fig.canvas.draw() plt.close() return fig def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) def apply_weight_norm(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: weight_norm(m) def get_padding(kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) def load_checkpoint(filepath, device): assert os.path.isfile(filepath) print("Loading '{}'".format(filepath)) checkpoint_dict = torch.load(filepath, map_location=device) print("Complete.") return checkpoint_dict def save_checkpoint(filepath, obj): print("Saving checkpoint to {}".format(filepath)) torch.save(obj, filepath) print("Complete.") def del_old_checkpoints(cp_dir, prefix, n_models=2): pattern = os.path.join(cp_dir, prefix + '????????') cp_list = glob.glob(pattern) # get checkpoint paths cp_list = sorted(cp_list) # sort by iter if len(cp_list) > n_models: # if more than n_models models are found for cp in cp_list[:-n_models]: # delete the oldest models other than lastest n_models open(cp, 'w').close() # empty file contents os.unlink(cp) # delete file (move to trash when using Colab) def scan_checkpoint(cp_dir, prefix): pattern = os.path.join(cp_dir, prefix + '????????') cp_list = glob.glob(pattern) if len(cp_list) == 0: return None return sorted(cp_list)[-1]