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