# 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 mindspore._checkparam import Validator
from ._callback import Callback
[文档]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.
Examples:
>>> from mindspore import Model, nn
>>>
>>> net = LeNet5()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim)
>>> data_path = './MNIST_Data'
>>> dataset = create_dataset(data_path)
>>> loss_monitor = LossMonitor()
>>> model.train(10, dataset, callbacks=loss_monitor)
"""
def __init__(self, per_print_times=1):
super(LossMonitor, self).__init__()
Validator.check_non_negative_int(per_print_times)
self._per_print_times = per_print_times
self._last_print_time = 0
[文档] def step_end(self, run_context):
"""
Print training loss at the end of step.
Args:
run_context (RunContext): Include some information of the model.
"""
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))
#In disaster recovery scenario, the cb_params.cur_step_num may be rollback to previous step
# and be less than self._last_print_time, so self._last_print_time need to be updated.
if self._per_print_times != 0 and (cb_params.cur_step_num <= self._last_print_time):
while cb_params.cur_step_num <= self._last_print_time:
self._last_print_time -=\
max(self._per_print_times, cb_params.batch_num if cb_params.dataset_sink_mode else 1)
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)