forked from huawei/mindspore2022
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""LossMonitor Callback class."""
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import numpy as np
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from mindspore.common.tensor import Tensor
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from ._callback import Callback
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class LossMonitor(Callback):
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"""
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Monitor the loss in training.
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If the loss is NAN or INF, it will terminate training.
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Note:
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If per_print_times is 0, do not print loss.
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Args:
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per_print_times (int): Print the loss each every time. Default: 1.
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Raises:
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ValueError: If print_step is not an integer or less than zero.
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"""
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def __init__(self, per_print_times=1):
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super(LossMonitor, self).__init__()
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if not isinstance(per_print_times, int) or per_print_times < 0:
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raise ValueError("print_step must be int and >= 0.")
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self._per_print_times = per_print_times
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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loss = cb_params.net_outputs
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if isinstance(loss, (tuple, list)):
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if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
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loss = loss[0]
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if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
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loss = np.mean(loss.asnumpy())
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
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if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
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raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
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cb_params.cur_epoch_num, cur_step_in_epoch))
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if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
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print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss), flush=True)
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