mindspore/model_zoo/research/audio/deepspeech2/src/callback.py

# Copyright 2021 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
#
# httpwww.apache.orglicensesLICENSE-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.
# ============================================================================

"""
Defined callback for DeepSpeech.
"""
import time
from mindspore.train.callback import Callback
from mindspore import Tensor
import numpy as np


class TimeMonitor(Callback):
    """
    Time monitor for calculating cost of each epoch.
    Args
        data_size (int) step size of an epoch.
    """

    def __init__(self, data_size):
        super(TimeMonitor, self).__init__()
        self.data_size = data_size

    def epoch_begin(self, run_context):
        self.epoch_time = time.time()

    def epoch_end(self, run_context):
        epoch_mseconds = (time.time() - self.epoch_time) * 1000
        per_step_mseconds = epoch_mseconds / self.data_size
        print("epoch time: {0}, per step time: {1}".format(epoch_mseconds, per_step_mseconds), flush=True)

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

    def step_end(self, run_context):
        step_mseconds = (time.time() - self.step_time) * 1000
        print(f"step time {step_mseconds}", flush=True)


class Monitor(Callback):
    """
    Monitor loss and time.

    Args:
        lr_init (numpy array): train lr

    Returns:
        None
    """

    def __init__(self, lr_init=None):
        super(Monitor, self).__init__()
        self.lr_init = lr_init
        self.lr_init_len = len(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)
        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)))

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

    def step_end(self, run_context):
        """

        Args:
            run_context:

        Returns:

        """
        cb_params = run_context.original_args()
        step_mseconds = (time.time() - self.step_time)
        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 = (cb_params.cur_step_num - 1) % cb_params.batch_num

        print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:.9f}]".format(
            cb_params.cur_epoch_num -
            1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
            np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1].asnumpy()))