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callback.py
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import os
import six
import yaml
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import CSVLogger
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.platform import tf_logging as logging
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
from tensorflow.keras.optimizers.schedules import PiecewiseConstantDecay
from tensorflow.keras.experimental import CosineDecay
from common import create_stamp
### ignore logger settings
from tensorflow.python.keras.callbacks import CallbackList
class CustomCallbackList(CallbackList):
"""Ignore the warning message that callbacks take longer than train_step.
"""
def _call_batch_hook(self, mode, hook, batch, logs=None):
if not self.callbacks:
return
hook_name = 'on_{mode}_batch_{hook}'.format(mode=mode, hook=hook)
logs = logs or {}
for callback in self.callbacks:
batch_hook = getattr(callback, hook_name)
batch_hook(batch, logs)
tf.python.keras.callbacks.CallbackList = CustomCallbackList
###
class OptionalLearningRateSchedule(LearningRateSchedule):
"""Set learning rate scheduler.
"""
def __init__(
self,
lr,
lr_mode,
lr_interval,
lr_value,
total_epochs,
steps_per_epoch,
initial_epoch):
super(OptionalLearningRateSchedule, self).__init__()
self.lr = lr
self.lr_mode = lr_mode
self.lr_interval = lr_interval
self.lr_value = lr_value
self.total_epochs = total_epochs
self.steps_per_epoch = steps_per_epoch
self.initial_epoch = initial_epoch
if self.lr_mode == 'exponential':
decay_epochs = [int(e) for e in self.lr_interval.split(',')]
lr_values = [self.lr * (self.lr_value ** k)for k in range(len(decay_epochs) + 1)]
self.lr_scheduler = PiecewiseConstantDecay(decay_epochs, lr_values)
elif self.lr_mode == 'cosine':
self.lr_scheduler = CosineDecay(self.lr, self.total_epochs)
elif self.lr_mode == 'constant':
self.lr_scheduler = lambda x: self.lr
else:
raise ValueError(self.lr_mode)
def get_config(self):
return {
'steps_per_epoch': self.steps_per_epoch,
'init_lr': self.lr,
'lr_mode': self.lr_mode,
'lr_value': self.lr_value,
'lr_interval': self.lr_interval,}
def __call__(self, step):
step = tf.cast(step, tf.float32)
step += self.initial_epoch * self.steps_per_epoch
lr_epoch = (step / self.steps_per_epoch)
if self.lr_mode == 'constant':
return self.lr
else:
return self.lr_scheduler(lr_epoch)
class MomentumUpdate(Callback):
"""Update momentum weights after each step.
"""
def __init__(self, logger, momentum, total_epoch):
super(MomentumUpdate, self).__init__()
self.logger = logger
self.init_momentum = momentum
self.total_epoch = total_epoch
def _recursive_momentum(self, regular, momentum):
for rl, ml in zip(regular.layers, momentum.layers):
if hasattr(rl, 'layers'):
self._recursive_momentum(rl, ml)
else:
ml.set_weights([0.9 * k + 0.1 * q for q, k in zip(rl.get_weights(), ml.get_weights())])
def on_batch_end(self, batch, logs=None):
# self._recursive_momentum(self.model.encoder_regular, self.model.encoder_momentum)
for layer_r, layer_m in zip(self.model.encoder_regular.layers, self.model.encoder_momentum.layers):
r_weights = layer_r.get_weights()
if len(r_weights) > 0:
m_weights = layer_m.get_weights()
layer_m.set_weights([self.momentum * k + (1.-self.momentum) * q for q, k in zip(r_weights, m_weights)])
def on_epoch_begin(self, epoch, logs=None):
self.momentum = self.init_momentum + (1-self.init_momentum) * (float(epoch)/float(self.total_epoch))
self.logger.info(f'Epoch {epoch+1:04d} Momentum : {self.momentum:.4f}')
class CustomCSVLogger(CSVLogger):
"""Save averaged logs during training.
"""
def on_epoch_begin(self, epoch, logs=None):
self.batch_logs = {}
def on_batch_end(self, batch, logs=None):
for k, v in logs.items():
if k not in self.batch_logs:
self.batch_logs[k] = [v]
else:
self.batch_logs[k].append(v)
def on_epoch_end(self, epoch, logs=None):
final_logs = {k: np.mean(v) for k, v in self.batch_logs.items()}
super(CustomCSVLogger, self).on_epoch_end(epoch, final_logs)
def create_callbacks(args, logger, initial_epoch):
if not args.resume:
if args.checkpoint or args.history or args.tensorboard:
if os.path.isdir(f'{args.result_path}/{args.task}/{args.stamp}'):
flag = input(f'\n{args.task}/{args.stamp} is already saved. '
'Do you want new stamp? (y/n) ')
if flag == 'y':
args.stamp = create_stamp()
initial_epoch = 0
logger.info(f'New stamp {args.stamp} will be created.')
elif flag == 'n':
return -1, initial_epoch
else:
logger.info(f'You must select \'y\' or \'n\'.')
return -2, initial_epoch
os.makedirs(f'{args.result_path}/{args.task}/{args.stamp}')
yaml.dump(
vars(args),
open(f'{args.result_path}/{args.task}/{args.stamp}/model_desc.yml', 'w'),
default_flow_style=False)
else:
logger.info(f'{args.stamp} is not created due to '
f'checkpoint - {args.checkpoint} | '
f'history - {args.history} | '
f'tensorboard - {args.tensorboard}')
callbacks = [MomentumUpdate(logger, args.momentum, args.epochs)]
if args.checkpoint:
os.makedirs(f'{args.result_path}/{args.task}/{args.stamp}/checkpoint', exist_ok=True)
callbacks.append(ModelCheckpoint(
filepath=os.path.join(
f'{args.result_path}/{args.task}/{args.stamp}/checkpoint',
'{epoch:04d}_{loss:.4f}_{loss_ij:.4f}_{loss_ji:.4f}'),
monitor='loss',
mode='min',
verbose=1,
save_weights_only=True))
if args.history:
os.makedirs(f'{args.result_path}/{args.task}/{args.stamp}/history', exist_ok=True)
callbacks.append(CustomCSVLogger(
filename=f'{args.result_path}/{args.task}/{args.stamp}/history/epoch.csv',
separator=',', append=True))
if args.tensorboard:
callbacks.append(TensorBoard(
log_dir=f'{args.result_path}/{args.task}/{args.stamp}/logs',
histogram_freq=args.tb_histogram,
write_graph=True,
write_images=True,
update_freq=args.tb_interval,
profile_batch=100,))
return callbacks, initial_epoch