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'''
author: wayn391@mastertones
'''
import datetime
import os
import time
import matplotlib.pyplot as plt
import torch
import yaml
from torch.utils.tensorboard import SummaryWriter
class Saver(object):
def __init__(
self,
args,
initial_global_step=-1):
self.expdir = args.env.expdir
self.sample_rate = args.data.sampling_rate
# cold start
self.global_step = initial_global_step
self.init_time = time.time()
self.last_time = time.time()
# makedirs
os.makedirs(self.expdir, exist_ok=True)
# path
self.path_log_info = os.path.join(self.expdir, 'log_info.txt')
# ckpt
os.makedirs(self.expdir, exist_ok=True)
# writer
self.writer = SummaryWriter(os.path.join(self.expdir, 'logs'))
# save config
path_config = os.path.join(self.expdir, 'config.yaml')
with open(path_config, "w") as out_config:
yaml.dump(dict(args), out_config)
def log_info(self, msg):
'''log method'''
if isinstance(msg, dict):
msg_list = []
for k, v in msg.items():
tmp_str = ''
if isinstance(v, int):
tmp_str = '{}: {:,}'.format(k, v)
else:
tmp_str = '{}: {}'.format(k, v)
msg_list.append(tmp_str)
msg_str = '\n'.join(msg_list)
else:
msg_str = msg
# dsplay
print(msg_str)
# save
with open(self.path_log_info, 'a') as fp:
fp.write(msg_str+'\n')
def log_value(self, dict):
for k, v in dict.items():
self.writer.add_scalar(k, v, self.global_step)
def log_spec(self, name, spec, spec_out, vmin=-14, vmax=3.5):
spec_cat = torch.cat([(spec_out - spec).abs() + vmin, spec, spec_out], -1)
spec = spec_cat[0]
if isinstance(spec, torch.Tensor):
spec = spec.cpu().numpy()
fig = plt.figure(figsize=(12, 9))
plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
plt.tight_layout()
self.writer.add_figure(name, fig, self.global_step)
def log_audio(self, dict):
for k, v in dict.items():
self.writer.add_audio(k, v, global_step=self.global_step, sample_rate=self.sample_rate)
def get_interval_time(self, update=True):
cur_time = time.time()
time_interval = cur_time - self.last_time
if update:
self.last_time = cur_time
return time_interval
def get_total_time(self, to_str=True):
total_time = time.time() - self.init_time
if to_str:
total_time = str(datetime.timedelta(
seconds=total_time))[:-5]
return total_time
def save_model(
self,
model,
optimizer,
name='model',
postfix='',
to_json=False):
# path
if postfix:
postfix = '_' + postfix
path_pt = os.path.join(
self.expdir , name+postfix+'.pt')
# check
print(' [*] model checkpoint saved: {}'.format(path_pt))
# save
if optimizer is not None:
torch.save({
'global_step': self.global_step,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()}, path_pt)
else:
torch.save({
'global_step': self.global_step,
'model': model.state_dict()}, path_pt)
def delete_model(self, name='model', postfix=''):
# path
if postfix:
postfix = '_' + postfix
path_pt = os.path.join(
self.expdir , name+postfix+'.pt')
# delete
if os.path.exists(path_pt):
os.remove(path_pt)
print(' [*] model checkpoint deleted: {}'.format(path_pt))
def global_step_increment(self):
self.global_step += 1
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