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| 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] | |