# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""LossMonitor Callback class."""
import time
import numpy as np
from mindspore.common.tensor import Tensor
from ._callback import Callback
[docs]class LossMonitor(Callback):
"""
Monitor the loss in training.
If the loss is NAN or INF, it will terminate training.
Note:
If per_print_times is 0 do not print loss.
Args:
per_print_times (int): Print loss every times. Default: 1.
lr_init (numpy array): train learning rate. Default: None.
Raises:
ValueError: If print_step is not int or less than zero.
Examples:
>>> LossMonitor(100, lr_init=Tensor([0.05]*100).asnumpy())
"""
def __init__(self, per_print_times=1, lr_init=None):
super(LossMonitor, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("print_step must be int and >= 0.")
self._per_print_times = per_print_times
self.lr_init = lr_init
def epoch_begin(self, run_context):
self.losses = []
self.epoch_time = time.time()
def epoch_end(self, run_context):
cb_params = run_context.original_args()
epoch_mseconds = (time.time() - self.epoch_time) * 1000
per_step_mseconds = epoch_mseconds / cb_params.batch_num
print("Epoch time: {:5.3f}, per step time: {:5.3f}, "
"avg loss: {:5.3f}".format(epoch_mseconds,
per_step_mseconds,
np.mean(self.losses)))
print("*" * 60)
def step_begin(self, run_context):
self.step_time = time.time()
def step_end(self, run_context):
cb_params = run_context.original_args()
step_loss = cb_params.net_outputs
if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
step_loss = step_loss[0]
if isinstance(step_loss, Tensor):
step_loss = np.mean(step_loss.asnumpy())
self.losses.append(step_loss)
cur_step_in_epoch = int((cb_params.cur_step_num - 1) % cb_params.batch_num) + 1
if isinstance(step_loss, float) and (np.isnan(step_loss) or np.isinf(step_loss)):
raise ValueError("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}]. "
"Invalid loss, terminating training.".format(
cb_params.cur_epoch_num - 1, cb_params.epoch_num,
cur_step_in_epoch, cb_params.batch_num))
if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
print("epoch: {} step {}, loss is {}".format(cb_params.cur_epoch_num,
cur_step_in_epoch,
step_loss), flush=True)