text: # 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.
  # ============================================================================
  """train_imagenet."""
  import os
  import time
  import argparse
  import random
  import numpy as np
  from mindspore import context
  from mindspore import Tensor
  from mindspore import nn
  from mindspore.parallel._auto_parallel_context import auto_parallel_context
  from mindspore.nn.optim.momentum import Momentum
  from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
  from mindspore.nn.loss.loss import _Loss
  from mindspore.ops import operations as P
  from mindspore.ops import functional as F
  from mindspore.common import dtype as mstype
  from mindspore.train.model import Model, ParallelMode
  from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
  from mindspore.train.loss_scale_manager import FixedLossScaleManager
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  import mindspore.dataset.engine as de
  from mindspore.communication.management import init, get_group_size
  from src.dataset import create_dataset
  from src.lr_generator import get_lr
  from src.config import config_gpu, config_ascend
  from src.mobilenetV3 import mobilenet_v3_large
  
  random.seed(1)
  np.random.seed(1)
  de.config.set_seed(1)
  
  parser = argparse.ArgumentParser(description='Image classification')
  parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
  parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
  parser.add_argument('--platform', type=str, default=None, help='run platform')
  args_opt = parser.parse_args()
  
  if args_opt.platform == "Ascend":
      device_id = int(os.getenv('DEVICE_ID'))
      rank_id = int(os.getenv('RANK_ID'))
      rank_size = int(os.getenv('RANK_SIZE'))
      run_distribute = rank_size > 1
      device_id = int(os.getenv('DEVICE_ID'))
      context.set_context(mode=context.GRAPH_MODE,
                          device_target="Ascend",
                          device_id=device_id, save_graphs=False)
  elif args_opt.platform == "GPU":
      context.set_context(mode=context.GRAPH_MODE,
                          device_target="GPU", save_graphs=False)
  else:
      raise ValueError("Unsupport platform.")
  
  
  class CrossEntropyWithLabelSmooth(_Loss):
      """
      CrossEntropyWith LabelSmooth.
  
      Args:
          smooth_factor (float): smooth factor, default=0.
          num_classes (int): num classes
  
      Returns:
          None.
  
      Examples:
          >>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
      """
  
      def __init__(self, smooth_factor=0., num_classes=1000):
          super(CrossEntropyWithLabelSmooth, self).__init__()
          self.onehot = P.OneHot()
          self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
          self.off_value = Tensor(1.0 * smooth_factor /
                                  (num_classes - 1), mstype.float32)
          self.ce = nn.SoftmaxCrossEntropyWithLogits()
          self.mean = P.ReduceMean(False)
          self.cast = P.Cast()
  
      def construct(self, logit, label):
          one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
                                      self.on_value, self.off_value)
          out_loss = self.ce(logit, one_hot_label)
          out_loss = self.mean(out_loss, 0)
          return out_loss
  
  
  class Monitor(Callback):
      """
      Monitor loss and time.
  
      Args:
          lr_init (numpy array): train lr
  
      Returns:
          None
  
      Examples:
          >>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
      """
  
      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) * 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)))
  
      def step_begin(self, run_context):
          self.step_time = time.time()
  
      def step_end(self, run_context):
          cb_params = run_context.original_args()
          step_mseconds = (time.time() - self.step_time) * 1000
          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:[{:5.3f}]".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]))
  
  
  if __name__ == '__main__':
      if args_opt.platform == "GPU":
          # train on gpu
          print("train args: ", args_opt, "\ncfg: ", config_gpu)
  
          init('nccl')
          context.set_auto_parallel_context(parallel_mode="data_parallel",
                                            mirror_mean=True,
                                            device_num=get_group_size())
  
          # define net
          net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
          # define loss
          if config_gpu.label_smooth > 0:
              loss = CrossEntropyWithLabelSmooth(
                  smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
          else:
              loss = SoftmaxCrossEntropyWithLogits(
                  is_grad=False, sparse=True, reduction='mean')
          # define dataset
          epoch_size = config_gpu.epoch_size
          dataset = create_dataset(dataset_path=args_opt.dataset_path,
                                   do_train=True,
                                   config=config_gpu,
                                   platform=args_opt.platform,
                                   repeat_num=epoch_size,
                                   batch_size=config_gpu.batch_size)
          step_size = dataset.get_dataset_size()
          # resume
          if args_opt.pre_trained:
              param_dict = load_checkpoint(args_opt.pre_trained)
              load_param_into_net(net, param_dict)
          # define optimizer
          loss_scale = FixedLossScaleManager(
              config_gpu.loss_scale, drop_overflow_update=False)
          lr = Tensor(get_lr(global_step=0,
                             lr_init=0,
                             lr_end=0,
                             lr_max=config_gpu.lr,
                             warmup_epochs=config_gpu.warmup_epochs,
                             total_epochs=epoch_size,
                             steps_per_epoch=step_size))
          opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
                         config_gpu.weight_decay, config_gpu.loss_scale)
          # define model
          model = Model(net, loss_fn=loss, optimizer=opt,
                        loss_scale_manager=loss_scale)
  
          cb = [Monitor(lr_init=lr.asnumpy())]
          if config_gpu.save_checkpoint:
              config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
                                           keep_checkpoint_max=config_gpu.keep_checkpoint_max)
              ckpt_cb = ModelCheckpoint(
                  prefix="mobilenetV3", directory=config_gpu.save_checkpoint_path, config=config_ck)
              cb += [ckpt_cb]
          # begine train
          model.train(epoch_size, dataset, callbacks=cb)
      elif args_opt.platform == "Ascend":
          # train on ascend
          print("train args: ", args_opt, "\ncfg: ", config_ascend,
                "\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
  
          if run_distribute:
              context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
                                                parameter_broadcast=True, mirror_mean=True)
              auto_parallel_context().set_all_reduce_fusion_split_indices([140])
              init()
  
          epoch_size = config_ascend.epoch_size
          net = mobilenet_v3_large(num_classes=config_ascend.num_classes)
          net.to_float(mstype.float16)
          for _, cell in net.cells_and_names():
              if isinstance(cell, nn.Dense):
                  cell.to_float(mstype.float32)
          if config_ascend.label_smooth > 0:
              loss = CrossEntropyWithLabelSmooth(
                  smooth_factor=config_ascend.label_smooth, num_classes=config.num_classes)
          else:
              loss = SoftmaxCrossEntropyWithLogits(
                  is_grad=False, sparse=True, reduction='mean')
          dataset = create_dataset(dataset_path=args_opt.dataset_path,
                                   do_train=True,
                                   config=config_ascend,
                                   platform=args_opt.platform,
                                   repeat_num=epoch_size,
                                   batch_size=config_ascend.batch_size)
          step_size = dataset.get_dataset_size()
          if args_opt.pre_trained:
              param_dict = load_checkpoint(args_opt.pre_trained)
              load_param_into_net(net, param_dict)
  
          loss_scale = FixedLossScaleManager(
              config_ascend.loss_scale, drop_overflow_update=False)
          lr = Tensor(get_lr(global_step=0,
                             lr_init=0,
                             lr_end=0,
                             lr_max=config_ascend.lr,
                             warmup_epochs=config_ascend.warmup_epochs,
                             total_epochs=epoch_size,
                             steps_per_epoch=step_size))
          opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_ascend.momentum,
                         config_ascend.weight_decay, config_ascend.loss_scale)
  
          model = Model(net, loss_fn=loss, optimizer=opt,
                        loss_scale_manager=loss_scale)
  
          cb = None
          if rank_id == 0:
              cb = [Monitor(lr_init=lr.asnumpy())]
              if config_ascend.save_checkpoint:
                  config_ck = CheckpointConfig(save_checkpoint_steps=config_ascend.save_checkpoint_epochs * step_size,
                                               keep_checkpoint_max=config_ascend.keep_checkpoint_max)
                  ckpt_cb = ModelCheckpoint(
                      prefix="mobilenetV3", directory=config_ascend.save_checkpoint_path, config=config_ck)
                  cb += [ckpt_cb]
          model.train(epoch_size, dataset, callbacks=cb)
      else:
          raise Exception
  