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import glob
import os
import shutil
import torch
from torch.nn.utils import weight_norm
import json
def plot_spectrogram(spectrogram):
import matplotlib.pylab as plt
import matplotlib
matplotlib.use("Agg")
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 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]
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
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