text: # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import time
  import numpy as np
  import datetime
  from collections import deque
  from tools.configure import print_total_cfg
  
  
  def set_paddle_flags(**kwargs):
      for key, value in kwargs.items():
          if os.environ.get(key, None) is None:
              os.environ[key] = str(value)
  
  
  # NOTE(paddle-dev): All of these flags should be set before 
  # `import paddle`. Otherwise, it would not take any effect.
  set_paddle_flags(
      FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
  )
  
  from paddle import fluid
  from ppdet.core.workspace import load_config, merge_config, create
  from ppdet.data.data_feed import create_reader
  
  from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
  from ppdet.utils.stats import TrainingStats
  from ppdet.utils.cli import ArgsParser
  from ppdet.utils.check import check_gpu
  import ppdet.utils.checkpoint as checkpoint
  from ppdet.modeling.model_input import create_feed
  
  import logging
  FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
  logging.basicConfig(level=logging.INFO, format=FORMAT)
  logger = logging.getLogger(__name__)
  
  
  def main():
      cfg = load_config(FLAGS.config)
      if 'architecture' in cfg:
          main_arch = cfg.architecture
      else:
          raise ValueError("'architecture' not specified in config file.")
  
      merge_config(FLAGS.opt)
      if 'log_iter' not in cfg:
          cfg.log_iter = 20
  
      # check if set use_gpu=True in paddlepaddle cpu version
      check_gpu(cfg.use_gpu)
      print_total_cfg(cfg)
  
      if cfg.use_gpu:
          devices_num = fluid.core.get_cuda_device_count()
      else:
          devices_num = int(os.environ.get('CPU_NUM', 1))
  
      if 'train_feed' not in cfg:
          train_feed = create(main_arch + 'TrainFeed')
      else:
          train_feed = create(cfg.train_feed)
  
      if FLAGS.eval:
          if 'eval_feed' not in cfg:
              eval_feed = create(main_arch + 'EvalFeed')
          else:
              eval_feed = create(cfg.eval_feed)
  
      place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      lr_builder = create('LearningRate')
      optim_builder = create('OptimizerBuilder')
  
      # build program
      startup_prog = fluid.Program()
      train_prog = fluid.Program()
      with fluid.program_guard(train_prog, startup_prog):
          with fluid.unique_name.guard():
              model = create(main_arch)
              train_pyreader, feed_vars = create_feed(train_feed)
              train_fetches = model.train(feed_vars)
              loss = train_fetches['loss']
              lr = lr_builder()
              optimizer = optim_builder(lr)
              optimizer.minimize(loss)
  
      # parse train fetches
      train_keys, train_values, _ = parse_fetches(train_fetches)
      train_values.append(lr)
  
      if FLAGS.eval:
          eval_prog = fluid.Program()
          with fluid.program_guard(eval_prog, startup_prog):
              with fluid.unique_name.guard():
                  model = create(main_arch)
                  eval_pyreader, feed_vars = create_feed(eval_feed)
                  fetches = model.eval(feed_vars)
          eval_prog = eval_prog.clone(True)
  
          eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
          eval_pyreader.decorate_sample_list_generator(eval_reader, place)
  
          # parse eval fetches
          extra_keys = []
          if cfg.metric == 'COCO':
              extra_keys = ['im_info', 'im_id', 'im_shape']
          if cfg.metric == 'VOC':
              extra_keys = ['gt_box', 'gt_label', 'is_difficult']
          eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
                                                           extra_keys)
  
      # compile program for multi-devices
      build_strategy = fluid.BuildStrategy()
      sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
      # only enable sync_bn in multi GPU devices
      build_strategy.sync_batch_norm = sync_bn and devices_num > 1 and cfg.use_gpu
  
      exec_strategy = fluid.ExecutionStrategy()
      # iteration number when CompiledProgram tries to drop local execution scopes.
      # Set it to be 1 to save memory usages, so that unused variables in
      # local execution scopes can be deleted after each iteration.
      exec_strategy.num_iteration_per_drop_scope = 1
  
      train_compile_program = fluid.compiler.CompiledProgram(
          train_prog).with_data_parallel(
              loss_name=loss.name, build_strategy=build_strategy,
              exec_strategy=exec_strategy)
      if FLAGS.eval:
          eval_compile_program = fluid.compiler.CompiledProgram(eval_prog)
  
      exe.run(startup_prog)
  
      fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
      start_iter = 0
      if FLAGS.resume_checkpoint:
          checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
          start_iter = checkpoint.global_step()
      elif cfg.pretrain_weights and fuse_bn:
          checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
      elif cfg.pretrain_weights:
          checkpoint.load_pretrain(exe, train_prog, cfg.pretrain_weights)
  
      train_reader = create_reader(
                      train_feed, 
                      (cfg.max_iters - start_iter) * devices_num,
                      FLAGS.dataset_dir)
      train_pyreader.decorate_sample_list_generator(train_reader, place)
  
  
      # whether output bbox is normalized in model output layer
      is_bbox_normalized = False
      if hasattr(model, 'is_bbox_normalized') and \
              callable(model.is_bbox_normalized):
          is_bbox_normalized = model.is_bbox_normalized()
  
      # if map_type not set, use default 11point, only use in VOC eval
      map_type = cfg.map_type if 'map_type' in cfg else '11point'
  
      train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
      train_pyreader.start()
      start_time = time.time()
      end_time = time.time()
  
      cfg_name = os.path.basename(FLAGS.config).split('.')[0]
      save_dir = os.path.join(cfg.save_dir, cfg_name)
      time_stat = deque(maxlen=cfg.log_iter)
      best_box_ap_list = [0.0, 0]  #[map, iter]
  
      # use tb-paddle to log data
      if FLAGS.use_tb:
          from tb_paddle import SummaryWriter
          tb_writer = SummaryWriter(FLAGS.tb_log_dir)
          tb_loss_step = 0
          tb_mAP_step = 0
  
      for it in range(start_iter, cfg.max_iters):
          start_time = end_time
          end_time = time.time()
          time_stat.append(end_time - start_time)
          time_cost = np.mean(time_stat)
          eta_sec = (cfg.max_iters - it) * time_cost
          eta = str(datetime.timedelta(seconds=int(eta_sec)))
          outs = exe.run(train_compile_program, fetch_list=train_values)
          stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
  
          # use tb-paddle to log loss
          if FLAGS.use_tb:
              if it % cfg.log_iter == 0:
                  for loss_name, loss_value in stats.items():
                      tb_writer.add_scalar(loss_name, loss_value, tb_loss_step)
                  tb_loss_step += 1
  
          train_stats.update(stats)
          logs = train_stats.log()
          if it % cfg.log_iter == 0:
              strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
                  it, np.mean(outs[-1]), logs, time_cost, eta)
              logger.info(strs)
  
          if it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1:
              save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
              checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
  
              if FLAGS.eval:
                  # evaluation
                  results = eval_run(exe, eval_compile_program, eval_pyreader,
                                     eval_keys, eval_values, eval_cls)
                  resolution = None
                  if 'mask' in results[0]:
                      resolution = model.mask_head.resolution
                  box_ap_stats = eval_results(
                      results, eval_feed, cfg.metric, cfg.num_classes, resolution,
                      is_bbox_normalized, FLAGS.output_eval, map_type)
                  
                  # use tb_paddle to log mAP
                  if FLAGS.use_tb:
                      tb_writer.add_scalar("mAP", box_ap_stats[0], tb_mAP_step)
                      tb_mAP_step += 1
                  
                  if box_ap_stats[0] > best_box_ap_list[0]:
                      best_box_ap_list[0] = box_ap_stats[0]
                      best_box_ap_list[1] = it
                      checkpoint.save(exe, train_prog,
                                      os.path.join(save_dir, "best_model"))
                  logger.info("Best test box ap: {}, in iter: {}".format(
                      best_box_ap_list[0], best_box_ap_list[1]))
  
      train_pyreader.reset()
  
  
  if __name__ == '__main__':
      parser = ArgsParser()
      parser.add_argument(
          "-r",
          "--resume_checkpoint",
          default=None,
          type=str,
          help="Checkpoint path for resuming training.")
      parser.add_argument(
          "--eval",
          action='store_true',
          default=False,
          help="Whether to perform evaluation in train")
      parser.add_argument(
          "--output_eval",
          default=None,
          type=str,
          help="Evaluation directory, default is current directory.")
      parser.add_argument(
          "-d",
          "--dataset_dir",
          default=None,
          type=str,
          help="Dataset path, same as DataFeed.dataset.dataset_dir")
      parser.add_argument(
          "--use_tb",
          type=bool,
          default=False,
          help="whether to record the data to Tensorboard.")
      parser.add_argument(
          '--tb_log_dir',
          type=str,
          default="tb_log_dir/scalar",
          help='Tensorboard logging directory for scalar.')
      FLAGS = parser.parse_args()
      main()
  