text: # 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
  #
  # 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 CenterNet and get network model files(.ckpt)
  """
  
  import os
  import argparse
  import mindspore.communication.management as D
  from mindspore.communication.management import get_rank
  from mindspore import context
  from mindspore.train.model import Model
  from mindspore.context import ParallelMode
  from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  from mindspore.nn.optim import Adam
  from mindspore import log as logger
  from mindspore.common import set_seed
  from mindspore.profiler import Profiler
  from src.dataset import COCOHP
  from src.centernet_det import CenterNetLossCell, CenterNetWithLossScaleCell
  from src.centernet_det import CenterNetWithoutLossScaleCell
  from src.utils import LossCallBack, CenterNetPolynomialDecayLR, CenterNetMultiEpochsDecayLR
  from src.config import dataset_config, net_config, train_config
  
  _current_dir = os.path.dirname(os.path.realpath(__file__))
  
  parser = argparse.ArgumentParser(description='CenterNet training')
  parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'CPU'],
                      help='device where the code will be implemented. (Default: Ascend)')
  parser.add_argument("--distribute", type=str, default="true", choices=["true", "false"],
                      help="Run distribute, default is true.")
  parser.add_argument("--need_profiler", type=str, default="false", choices=["true", "false"],
                      help="Profiling to parsing runtime info, default is false.")
  parser.add_argument("--profiler_path", type=str, default=" ", help="The path to save profiling data")
  parser.add_argument("--epoch_size", type=int, default="1", help="Epoch size, default is 1.")
  parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1,"
                      "i.e. run all steps according to epoch number.")
  parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
  parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
  parser.add_argument("--enable_save_ckpt", type=str, default="true", choices=["true", "false"],
                      help="Enable save checkpoint, default is true.")
  parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
                      help="Enable shuffle for dataset, default is true.")
  parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
                      help="Enable data sink, default is true.")
  parser.add_argument("--data_sink_steps", type=int, default="-1", help="Sink steps for each epoch, default is -1.")
  parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
  parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
  parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
  parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
  parser.add_argument("--mindrecord_dir", type=str, default="", help="Mindrecord dataset files directory")
  parser.add_argument("--mindrecord_prefix", type=str, default="coco_det.train.mind",
                      help="Prefix of MindRecord dataset filename.")
  parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results")
  
  args_opt = parser.parse_args()
  
  
  def _set_parallel_all_reduce_split():
      """set centernet all_reduce fusion split"""
      if net_config.last_level == 5:
          context.set_auto_parallel_context(all_reduce_fusion_config=[16, 56, 96, 136, 175])
      elif net_config.last_level == 6:
          context.set_auto_parallel_context(all_reduce_fusion_config=[18, 59, 100, 141, 182])
      else:
          raise ValueError("The total num of allreduced grads for last level = {} is unknown,"
                           "please re-split after known the true value".format(net_config.last_level))
  
  
  def _get_params_groups(network, optimizer):
      """
      Get param groups
      """
      params = network.trainable_params()
      decay_params = list(filter(lambda x: not optimizer.decay_filter(x), params))
      other_params = list(filter(optimizer.decay_filter, params))
      group_params = [{'params': decay_params, 'weight_decay': optimizer.weight_decay},
                      {'params': other_params, 'weight_decay': 0.0},
                      {'order_params': params}]
      return group_params
  
  
  def _get_optimizer(network, dataset_size):
      """get optimizer, only support Adam right now."""
      if train_config.optimizer == 'Adam':
          group_params = _get_params_groups(network, train_config.Adam)
          if train_config.lr_schedule == "PolyDecay":
              lr_schedule = CenterNetPolynomialDecayLR(learning_rate=train_config.PolyDecay.learning_rate,
                                                       end_learning_rate=train_config.PolyDecay.end_learning_rate,
                                                       warmup_steps=train_config.PolyDecay.warmup_steps,
                                                       decay_steps=args_opt.train_steps,
                                                       power=train_config.PolyDecay.power)
              optimizer = Adam(group_params, learning_rate=lr_schedule, eps=train_config.PolyDecay.eps, loss_scale=1.0)
          elif train_config.lr_schedule == "MultiDecay":
              multi_epochs = train_config.MultiDecay.multi_epochs
              if not isinstance(multi_epochs, (list, tuple)):
                  raise TypeError("multi_epochs must be list or tuple.")
              if not multi_epochs:
                  multi_epochs = [args_opt.epoch_size]
              lr_schedule = CenterNetMultiEpochsDecayLR(learning_rate=train_config.MultiDecay.learning_rate,
                                                        warmup_steps=train_config.MultiDecay.warmup_steps,
                                                        multi_epochs=multi_epochs,
                                                        steps_per_epoch=dataset_size,
                                                        factor=train_config.MultiDecay.factor)
              optimizer = Adam(group_params, learning_rate=lr_schedule, eps=train_config.MultiDecay.eps, loss_scale=1.0)
          else:
              raise ValueError("Don't support lr_schedule {}, only support [PolynormialDecay, MultiEpochDecay]".
                               format(train_config.optimizer))
      else:
          raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, Adam]".
                           format(train_config.optimizer))
      return optimizer
  
  
  def train():
      """training CenterNet"""
      context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
      context.set_context(reserve_class_name_in_scope=False)
      context.set_context(save_graphs=False)
  
      ckpt_save_dir = args_opt.save_checkpoint_path
      rank = 0
      device_num = 1
      num_workers = 8
      if args_opt.device_target == "Ascend":
          context.set_context(enable_auto_mixed_precision=False)
          context.set_context(device_id=args_opt.device_id)
          if args_opt.distribute == "true":
              D.init()
              device_num = args_opt.device_num
              rank = args_opt.device_id % device_num
              ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(get_rank()) + '/'
  
              context.reset_auto_parallel_context()
              context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
                                                device_num=device_num)
              _set_parallel_all_reduce_split()
      else:
          args_opt.distribute = "false"
          args_opt.need_profiler = "false"
          args_opt.enable_data_sink = "false"
  
      # Start create dataset!
      # mindrecord files will be generated at args_opt.mindrecord_dir such as centernet.mindrecord0, 1, ... file_num.
      logger.info("Begin creating dataset for CenterNet")
      coco = COCOHP(dataset_config, run_mode="train", net_opt=net_config, save_path=args_opt.save_result_dir)
      dataset = coco.create_train_dataset(args_opt.mindrecord_dir, args_opt.mindrecord_prefix,
                                          batch_size=train_config.batch_size, device_num=device_num, rank=rank,
                                          num_parallel_workers=num_workers, do_shuffle=args_opt.do_shuffle == 'true')
      dataset_size = dataset.get_dataset_size()
      logger.info("Create dataset done!")
  
      net_with_loss = CenterNetLossCell(net_config)
  
      args_opt.train_steps = args_opt.epoch_size * dataset_size
      logger.info("train steps: {}".format(args_opt.train_steps))
  
      optimizer = _get_optimizer(net_with_loss, dataset_size)
  
      enable_static_time = args_opt.device_target == "CPU"
      callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack(dataset_size, enable_static_time)]
      if args_opt.enable_save_ckpt == "true" and args_opt.device_id % min(8, device_num) == 0:
          config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
                                       keep_checkpoint_max=args_opt.save_checkpoint_num)
          ckpoint_cb = ModelCheckpoint(prefix='checkpoint_centernet',
                                       directory=None if ckpt_save_dir == "" else ckpt_save_dir, config=config_ck)
          callback.append(ckpoint_cb)
  
      if args_opt.load_checkpoint_path:
          param_dict = load_checkpoint(args_opt.load_checkpoint_path)
          load_param_into_net(net_with_loss, param_dict)
      if args_opt.device_target == "Ascend":
          net_with_grads = CenterNetWithLossScaleCell(net_with_loss, optimizer=optimizer,
                                                      sens=train_config.loss_scale_value)
      else:
          net_with_grads = CenterNetWithoutLossScaleCell(net_with_loss, optimizer=optimizer)
  
      model = Model(net_with_grads)
      model.train(args_opt.epoch_size, dataset, callbacks=callback,
                  dataset_sink_mode=(args_opt.enable_data_sink == "true"), sink_size=args_opt.data_sink_steps)
  
  
  if __name__ == '__main__':
      if args_opt.need_profiler == "true":
          profiler = Profiler(output_path=args_opt.profiler_path)
      set_seed(317)
      train()
      if args_opt.need_profiler == "true":
          profiler.analyse()
  