# 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 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): How many steps to print once loss. During sink mode, it will print loss in the
nearest step. Default: 1.
Raises:
ValueError: If per_print_times is not an integer or less than zero.
"""
def __init__(self, per_print_times=1):
super(LossMonitor, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("The argument 'per_print_times' must be int and >= 0, "
"but got {}".format(per_print_times))
self._per_print_times = per_print_times
self._last_print_time = 0
[docs] def step_end(self, run_context):
"""
Print training loss at the end of step.
Args:
run_context (RunContext): Context of the train running.
"""
cb_params = run_context.original_args()
loss = cb_params.net_outputs
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = float(np.mean(loss.asnumpy()))
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
cb_params.cur_epoch_num, cur_step_in_epoch))
if self._per_print_times != 0 and (cb_params.cur_step_num - self._last_print_time) >= self._per_print_times:
self._last_print_time = cb_params.cur_step_num
print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss), flush=True)