mindspore/model_zoo/official/cv/unet/src/utils.py

# 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
#
# less 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.
# ============================================================================

import time
import numpy as np
from mindspore.train.callback import Callback
from mindspore.common.tensor import Tensor

class StepLossTimeMonitor(Callback):

    def __init__(self, batch_size, per_print_times=1):
        super(StepLossTimeMonitor, 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.batch_size = batch_size

    def step_begin(self, run_context):
        self.step_time = time.time()

    def step_end(self, run_context):

        step_seconds = time.time() - self.step_time
        step_fps = self.batch_size*1.0/step_seconds

        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 = 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._per_print_times == 0:
            # TEST
            print("step: %s, loss is %s, fps is %s" % (cur_step_in_epoch, loss, step_fps), flush=True)