Source code for mindspore.train.callback._loss_monitor

# Copyright 2020 Huawei Technologies Co., Ltd
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"""LossMonitor Callback class."""

import time
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): Print loss every times. Default: 1. lr_init (numpy array): train learning rate. Default: None. Raises: ValueError: If print_step is not int or less than zero. Examples: >>> LossMonitor(100, lr_init=Tensor([0.05]*100).asnumpy()) """ def __init__(self, per_print_times=1, lr_init=None): super(LossMonitor, 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.lr_init = 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) * 1000 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))) print("*" * 60) def step_begin(self, run_context): self.step_time = time.time() def step_end(self, run_context): cb_params = run_context.original_args() 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 = int((cb_params.cur_step_num - 1) % cb_params.batch_num) + 1 if isinstance(step_loss, float) and (np.isnan(step_loss) or np.isinf(step_loss)): raise ValueError("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}]. " "Invalid loss, terminating training.".format( cb_params.cur_epoch_num - 1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num)) if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0: print("epoch: {} step {}, loss is {}".format(cb_params.cur_epoch_num, cur_step_in_epoch, step_loss), flush=True)