code: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # File: train.py
  
  import argparse
  import itertools
  import numpy as np
  import os
  import shutil
  import cv2
  import six
  assert six.PY3, "FasterRCNN requires Python 3!"
  import tensorflow as tf
  import tqdm
  
  import tensorpack.utils.viz as tpviz
  from tensorpack import *
  from tensorpack.tfutils import optimizer, collect_env_info
  from tensorpack.tfutils.common import get_tf_version_tuple
  from tensorpack.tfutils.summary import add_moving_summary
  
  import model_frcnn
  import model_mrcnn
  from basemodel import image_preprocess, resnet_c4_backbone, resnet_conv5, resnet_fpn_backbone
  from dataset import DetectionDataset
  from config import finalize_configs, config as cfg
  from data import get_all_anchors, get_all_anchors_fpn, get_eval_dataflow, get_train_dataflow
  from eval import DetectionResult, predict_image, multithread_predict_dataflow, EvalCallback
  from model_box import RPNAnchors, clip_boxes, crop_and_resize, roi_align
  from model_cascade import CascadeRCNNHead
  from model_fpn import fpn_model, generate_fpn_proposals, multilevel_roi_align, multilevel_rpn_losses
  from model_frcnn import BoxProposals, FastRCNNHead, fastrcnn_outputs, fastrcnn_predictions, sample_fast_rcnn_targets
  from model_mrcnn import maskrcnn_loss, maskrcnn_upXconv_head
  from model_rpn import generate_rpn_proposals, rpn_head, rpn_losses
  from viz import draw_annotation, draw_final_outputs, draw_predictions, draw_proposal_recall
  
  try:
      import horovod.tensorflow as hvd
  except ImportError:
      pass
  
  
  class DetectionModel(ModelDesc):
      def preprocess(self, image):
          image = tf.expand_dims(image, 0)
          image = image_preprocess(image, bgr=True)
          return tf.transpose(image, [0, 3, 1, 2])
  
      @property
      def training(self):
          return get_current_tower_context().is_training
  
      def optimizer(self):
          lr = tf.get_variable('learning_rate', initializer=0.003, trainable=False)
          tf.summary.scalar('learning_rate-summary', lr)
  
          # The learning rate in the config is set for 8 GPUs, and we use trainers with average=False.
          lr = lr / 8.
          opt = tf.train.MomentumOptimizer(lr, 0.9)
          if cfg.TRAIN.NUM_GPUS < 8:
              opt = optimizer.AccumGradOptimizer(opt, 8 // cfg.TRAIN.NUM_GPUS)
          return opt
  
      def get_inference_tensor_names(self):
          """
          Returns two lists of tensor names to be used to create an inference callable.
  
          Returns:
              [str]: input names
              [str]: output names
          """
          out = ['output/boxes', 'output/scores', 'output/labels']
          if cfg.MODE_MASK:
              out.append('output/masks')
          return ['image'], out
  
      def build_graph(self, *inputs):
          inputs = dict(zip(self.input_names, inputs))
  
          image = self.preprocess(inputs['image'])     # 1CHW
  
          features = self.backbone(image)
          anchor_inputs = {k: v for k, v in inputs.items() if k.startswith('anchor_')}
          proposals, rpn_losses = self.rpn(image, features, anchor_inputs)  # inputs?
  
          targets = [inputs[k] for k in ['gt_boxes', 'gt_labels', 'gt_masks'] if k in inputs]
          head_losses = self.roi_heads(image, features, proposals, targets)
  
          if self.training:
              wd_cost = regularize_cost(
                  '.*/W', l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
              total_cost = tf.add_n(
                  rpn_losses + head_losses + [wd_cost], 'total_cost')
              add_moving_summary(total_cost, wd_cost)
              return total_cost
  
  
  class ResNetC4Model(DetectionModel):
      def inputs(self):
          ret = [
              tf.TensorSpec((None, None, 3), tf.float32, 'image'),
              tf.TensorSpec((None, None, cfg.RPN.NUM_ANCHOR), tf.int32, 'anchor_labels'),
              tf.TensorSpec((None, None, cfg.RPN.NUM_ANCHOR, 4), tf.float32, 'anchor_boxes'),
              tf.TensorSpec((None, 4), tf.float32, 'gt_boxes'),
              tf.TensorSpec((None,), tf.int64, 'gt_labels')]  # all > 0
          if cfg.MODE_MASK:
              ret.append(
                  tf.TensorSpec((None, None, None), tf.uint8, 'gt_masks')
              )   # NR_GT x height x width
          return ret
  
      def backbone(self, image):
          return [resnet_c4_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCKS[:3])]
  
      def rpn(self, image, features, inputs):
          featuremap = features[0]
          rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, cfg.RPN.HEAD_DIM, cfg.RPN.NUM_ANCHOR)
          anchors = RPNAnchors(get_all_anchors(), inputs['anchor_labels'], inputs['anchor_boxes'])
          anchors = anchors.narrow_to(featuremap)
  
          image_shape2d = tf.shape(image)[2:]     # h,w
          pred_boxes_decoded = anchors.decode_logits(rpn_box_logits)  # fHxfWxNAx4, floatbox
          proposal_boxes, proposal_scores = generate_rpn_proposals(
              tf.reshape(pred_boxes_decoded, [-1, 4]),
              tf.reshape(rpn_label_logits, [-1]),
              image_shape2d,
              cfg.RPN.TRAIN_PRE_NMS_TOPK if self.training else cfg.RPN.TEST_PRE_NMS_TOPK,
              cfg.RPN.TRAIN_POST_NMS_TOPK if self.training else cfg.RPN.TEST_POST_NMS_TOPK)
  
          if self.training:
              losses = rpn_losses(
                  anchors.gt_labels, anchors.encoded_gt_boxes(), rpn_label_logits, rpn_box_logits)
          else:
              losses = []
  
          return BoxProposals(proposal_boxes), losses
  
      def roi_heads(self, image, features, proposals, targets):
          image_shape2d = tf.shape(image)[2:]     # h,w
          featuremap = features[0]
  
          gt_boxes, gt_labels, *_ = targets
  
          if self.training:
              # sample proposal boxes in training
              proposals = sample_fast_rcnn_targets(proposals.boxes, gt_boxes, gt_labels)
          # The boxes to be used to crop RoIs.
          # Use all proposal boxes in inference
  
          boxes_on_featuremap = proposals.boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
          roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
  
          feature_fastrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCKS[-1])    # nxcx7x7
          # Keep C5 feature to be shared with mask branch
          feature_gap = GlobalAvgPooling('gap', feature_fastrcnn, data_format='channels_first')
          fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs('fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
  
          fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits, gt_boxes,
                                       tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
  
          if self.training:
              all_losses = fastrcnn_head.losses()
  
              if cfg.MODE_MASK:
                  gt_masks = targets[2]
                  # maskrcnn loss
                  # In training, mask branch shares the same C5 feature.
                  fg_feature = tf.gather(feature_fastrcnn, proposals.fg_inds())
                  mask_logits = maskrcnn_upXconv_head(
                      'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY, num_convs=0)   # #fg x #cat x 14x14
  
                  target_masks_for_fg = crop_and_resize(
                      tf.expand_dims(gt_masks, 1),
                      proposals.fg_boxes(),
                      proposals.fg_inds_wrt_gt, 14,
                      pad_border=False)  # nfg x 1x14x14
                  target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1, 'sampled_fg_mask_targets')
                  all_losses.append(maskrcnn_loss(mask_logits, proposals.fg_labels(), target_masks_for_fg))
              return all_losses
          else:
              decoded_boxes = fastrcnn_head.decoded_output_boxes()
              decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
              label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
              final_boxes, final_scores, final_labels = fastrcnn_predictions(
                  decoded_boxes, label_scores, name_scope='output')
  
              if cfg.MODE_MASK:
                  roi_resized = roi_align(featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE), 14)
                  feature_maskrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCKS[-1])
                  mask_logits = maskrcnn_upXconv_head(
                      'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY, 0)   # #result x #cat x 14x14
                  indices = tf.stack([tf.range(tf.size(final_labels)), tf.cast(final_labels, tf.int32) - 1], axis=1)
                  final_mask_logits = tf.gather_nd(mask_logits, indices)   # #resultx14x14
                  tf.sigmoid(final_mask_logits, name='output/masks')
              return []
  
  
  class ResNetFPNModel(DetectionModel):
  
      def inputs(self):
          ret = [
              tf.TensorSpec((None, None, 3), tf.float32, 'image')]
          num_anchors = len(cfg.RPN.ANCHOR_RATIOS)
          for k in range(len(cfg.FPN.ANCHOR_STRIDES)):
              ret.extend([
                  tf.TensorSpec((None, None, num_anchors), tf.int32,
                                'anchor_labels_lvl{}'.format(k + 2)),
                  tf.TensorSpec((None, None, num_anchors, 4), tf.float32,
                                'anchor_boxes_lvl{}'.format(k + 2))])
          ret.extend([
              tf.TensorSpec((None, 4), tf.float32, 'gt_boxes'),
              tf.TensorSpec((None,), tf.int64, 'gt_labels')])  # all > 0
          if cfg.MODE_MASK:
              ret.append(
                  tf.TensorSpec((None, None, None), tf.uint8, 'gt_masks')
              )   # NR_GT x height x width
          return ret
  
      def slice_feature_and_anchors(self, p23456, anchors):
          for i, stride in enumerate(cfg.FPN.ANCHOR_STRIDES):
              with tf.name_scope('FPN_slice_lvl{}'.format(i)):
                  anchors[i] = anchors[i].narrow_to(p23456[i])
  
      def backbone(self, image):
          c2345 = resnet_fpn_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCKS)
          p23456 = fpn_model('fpn', c2345)
          return p23456
  
      def rpn(self, image, features, inputs):
          assert len(cfg.RPN.ANCHOR_SIZES) == len(cfg.FPN.ANCHOR_STRIDES)
  
          image_shape2d = tf.shape(image)[2:]     # h,w
          all_anchors_fpn = get_all_anchors_fpn()
          multilevel_anchors = [RPNAnchors(
              all_anchors_fpn[i],
              inputs['anchor_labels_lvl{}'.format(i + 2)],
              inputs['anchor_boxes_lvl{}'.format(i + 2)]) for i in range(len(all_anchors_fpn))]
          self.slice_feature_and_anchors(features, multilevel_anchors)
  
          # Multi-Level RPN Proposals
          rpn_outputs = [rpn_head('rpn', pi, cfg.FPN.NUM_CHANNEL, len(cfg.RPN.ANCHOR_RATIOS))
                         for pi in features]
          multilevel_label_logits = [k[0] for k in rpn_outputs]
          multilevel_box_logits = [k[1] for k in rpn_outputs]
          multilevel_pred_boxes = [anchor.decode_logits(logits)
                                   for anchor, logits in zip(multilevel_anchors, multilevel_box_logits)]
  
          proposal_boxes, proposal_scores = generate_fpn_proposals(
              multilevel_pred_boxes, multilevel_label_logits, image_shape2d)
  
          if self.training:
              losses = multilevel_rpn_losses(
                  multilevel_anchors, multilevel_label_logits, multilevel_box_logits)
          else:
              losses = []
  
          return BoxProposals(proposal_boxes), losses
  
      def roi_heads(self, image, features, proposals, targets):
          image_shape2d = tf.shape(image)[2:]     # h,w
          assert len(features) == 5, "Features have to be P23456!"
          gt_boxes, gt_labels, *_ = targets
  
          if self.training:
              proposals = sample_fast_rcnn_targets(proposals.boxes, gt_boxes, gt_labels)
  
          fastrcnn_head_func = getattr(model_frcnn, cfg.FPN.FRCNN_HEAD_FUNC)
          if not cfg.FPN.CASCADE:
              roi_feature_fastrcnn = multilevel_roi_align(features[:4], proposals.boxes, 7)
  
              head_feature = fastrcnn_head_func('fastrcnn', roi_feature_fastrcnn)
              fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
                  'fastrcnn/outputs', head_feature, cfg.DATA.NUM_CLASS)
              fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits,
                                           gt_boxes, tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
          else:
              def roi_func(boxes):
                  return multilevel_roi_align(features[:4], boxes, 7)
  
              fastrcnn_head = CascadeRCNNHead(
                  proposals, roi_func, fastrcnn_head_func,
                  (gt_boxes, gt_labels), image_shape2d, cfg.DATA.NUM_CLASS)
  
          if self.training:
              all_losses = fastrcnn_head.losses()
  
              if cfg.MODE_MASK:
                  gt_masks = targets[2]
                  # maskrcnn loss
                  roi_feature_maskrcnn = multilevel_roi_align(
                      features[:4], proposals.fg_boxes(), 14,
                      name_scope='multilevel_roi_align_mask')
                  maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
                  mask_logits = maskrcnn_head_func(
                      'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY)   # #fg x #cat x 28 x 28
  
                  target_masks_for_fg = crop_and_resize(
                      tf.expand_dims(gt_masks, 1),
                      proposals.fg_boxes(),
                      proposals.fg_inds_wrt_gt, 28,
                      pad_border=False)  # fg x 1x28x28
                  target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1, 'sampled_fg_mask_targets')
                  all_losses.append(maskrcnn_loss(mask_logits, proposals.fg_labels(), target_masks_for_fg))
              return all_losses
          else:
              decoded_boxes = fastrcnn_head.decoded_output_boxes()
              decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
              label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
              final_boxes, final_scores, final_labels = fastrcnn_predictions(
                  decoded_boxes, label_scores, name_scope='output')
              if cfg.MODE_MASK:
                  # Cascade inference needs roi transform with refined boxes.
                  roi_feature_maskrcnn = multilevel_roi_align(features[:4], final_boxes, 14)
                  maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
                  mask_logits = maskrcnn_head_func(
                      'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY)   # #fg x #cat x 28 x 28
                  indices = tf.stack([tf.range(tf.size(final_labels)), tf.cast(final_labels, tf.int32) - 1], axis=1)
                  final_mask_logits = tf.gather_nd(mask_logits, indices)   # #resultx28x28
                  tf.sigmoid(final_mask_logits, name='output/masks')
              return []
  
  
  def do_visualize(model, model_path, nr_visualize=100, output_dir='output'):
      """
      Visualize some intermediate results (proposals, raw predictions) inside the pipeline.
      """
      df = get_train_dataflow()   # we don't visualize mask stuff
      df.reset_state()
  
      pred = OfflinePredictor(PredictConfig(
          model=model,
          session_init=get_model_loader(model_path),
          input_names=['image', 'gt_boxes', 'gt_labels'],
          output_names=[
              'generate_{}_proposals/boxes'.format('fpn' if cfg.MODE_FPN else 'rpn'),
              'generate_{}_proposals/scores'.format('fpn' if cfg.MODE_FPN else 'rpn'),
              'fastrcnn_all_scores',
              'output/boxes',
              'output/scores',
              'output/labels',
          ]))
  
      if os.path.isdir(output_dir):
          shutil.rmtree(output_dir)
      utils.fs.mkdir_p(output_dir)
      with tqdm.tqdm(total=nr_visualize) as pbar:
          for idx, dp in itertools.islice(enumerate(df), nr_visualize):
              img, gt_boxes, gt_labels = dp['image'], dp['gt_boxes'], dp['gt_labels']
  
              rpn_boxes, rpn_scores, all_scores, \
                  final_boxes, final_scores, final_labels = pred(img, gt_boxes, gt_labels)
  
              # draw groundtruth boxes
              gt_viz = draw_annotation(img, gt_boxes, gt_labels)
              # draw best proposals for each groundtruth, to show recall
              proposal_viz, good_proposals_ind = draw_proposal_recall(img, rpn_boxes, rpn_scores, gt_boxes)
              # draw the scores for the above proposals
              score_viz = draw_predictions(img, rpn_boxes[good_proposals_ind], all_scores[good_proposals_ind])
  
              results = [DetectionResult(*args) for args in
                         zip(final_boxes, final_scores, final_labels,
                             [None] * len(final_labels))]
              final_viz = draw_final_outputs(img, results)
  
              viz = tpviz.stack_patches([
                  gt_viz, proposal_viz,
                  score_viz, final_viz], 2, 2)
  
              if os.environ.get('DISPLAY', None):
                  tpviz.interactive_imshow(viz)
              cv2.imwrite("{}/{:03d}.png".format(output_dir, idx), viz)
              pbar.update()
  
  
  def do_evaluate(pred_config, output_file):
      num_gpu = cfg.TRAIN.NUM_GPUS
      graph_funcs = MultiTowerOfflinePredictor(
          pred_config, list(range(num_gpu))).get_predictors()
  
      for dataset in cfg.DATA.VAL:
          logger.info("Evaluating {} ...".format(dataset))
          dataflows = [
              get_eval_dataflow(dataset, shard=k, num_shards=num_gpu)
              for k in range(num_gpu)]
          all_results = multithread_predict_dataflow(dataflows, graph_funcs)
          output = output_file + '-' + dataset
          DetectionDataset().eval_or_save_inference_results(all_results, dataset, output)
  
  
  def do_predict(pred_func, input_file):
      img = cv2.imread(input_file, cv2.IMREAD_COLOR)
      results = predict_image(img, pred_func)
      final = draw_final_outputs(img, results)
      viz = np.concatenate((img, final), axis=1)
      cv2.imwrite("output.png", viz)
      logger.info("Inference output for {} written to output.png".format(input_file))
      tpviz.interactive_imshow(viz)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--load', help='load a model for evaluation or training. Can overwrite BACKBONE.WEIGHTS')
      parser.add_argument('--logdir', help='log directory', default='train_log/maskrcnn')
      parser.add_argument('--visualize', action='store_true', help='visualize intermediate results')
      parser.add_argument('--evaluate', help="Run evaluation. "
                                             "This argument is the path to the output json evaluation file")
      parser.add_argument('--predict', help="Run prediction on a given image. "
                                            "This argument is the path to the input image file", nargs='+')
      parser.add_argument('--config', help="A list of KEY=VALUE to overwrite those defined in config.py",
                          nargs='+')
  
      if get_tf_version_tuple() < (1, 6):
          # https://github.com/tensorflow/tensorflow/issues/14657
          logger.warn("TF<1.6 has a bug which may lead to crash in FasterRCNN if you're unlucky.")
  
      args = parser.parse_args()
      if args.config:
          cfg.update_args(args.config)
  
      MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
      DetectionDataset()  # initialize the config with information from our dataset
  
      if args.visualize or args.evaluate or args.predict:
          if not tf.test.is_gpu_available():
              from tensorflow.python.framework import test_util
              assert get_tf_version_tuple() >= (1, 7) and test_util.IsMklEnabled(), \
                  "Inference requires either GPU support or MKL support!"
          assert args.load
          finalize_configs(is_training=False)
  
          if args.predict or args.visualize:
              cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
  
          if args.visualize:
              do_visualize(MODEL, args.load)
          else:
              predcfg = PredictConfig(
                  model=MODEL,
                  session_init=get_model_loader(args.load),
                  input_names=MODEL.get_inference_tensor_names()[0],
                  output_names=MODEL.get_inference_tensor_names()[1])
              if args.predict:
                  predictor = OfflinePredictor(predcfg)
                  for image_file in args.predict:
                      do_predict(predictor, image_file)
              elif args.evaluate:
                  assert args.evaluate.endswith('.json'), args.evaluate
                  do_evaluate(predcfg, args.evaluate)
      else:
          is_horovod = cfg.TRAINER == 'horovod'
          if is_horovod:
              hvd.init()
              logger.info("Horovod Rank={}, Size={}".format(hvd.rank(), hvd.size()))
  
          if not is_horovod or hvd.rank() == 0:
              logger.set_logger_dir(args.logdir, 'd')
          logger.info("Environment Information:\n" + collect_env_info())
  
          finalize_configs(is_training=True)
          stepnum = cfg.TRAIN.STEPS_PER_EPOCH
  
          # warmup is step based, lr is epoch based
          init_lr = cfg.TRAIN.WARMUP_INIT_LR * min(8. / cfg.TRAIN.NUM_GPUS, 1.)
          warmup_schedule = [(0, init_lr), (cfg.TRAIN.WARMUP, cfg.TRAIN.BASE_LR)]
          warmup_end_epoch = cfg.TRAIN.WARMUP * 1. / stepnum
          lr_schedule = [(int(warmup_end_epoch + 0.5), cfg.TRAIN.BASE_LR)]
  
          factor = 8. / cfg.TRAIN.NUM_GPUS
          for idx, steps in enumerate(cfg.TRAIN.LR_SCHEDULE[:-1]):
              mult = 0.1 ** (idx + 1)
              lr_schedule.append(
                  (steps * factor // stepnum, cfg.TRAIN.BASE_LR * mult))
          logger.info("Warm Up Schedule (steps, value): " + str(warmup_schedule))
          logger.info("LR Schedule (epochs, value): " + str(lr_schedule))
          train_dataflow = get_train_dataflow()
          # This is what's commonly referred to as "epochs"
          total_passes = cfg.TRAIN.LR_SCHEDULE[-1] * 8 / train_dataflow.size()
          logger.info("Total passes of the training set is: {:.5g}".format(total_passes))
  
          callbacks = [
              PeriodicCallback(
                  ModelSaver(max_to_keep=10, keep_checkpoint_every_n_hours=1),
                  every_k_epochs=20),
              # linear warmup
              ScheduledHyperParamSetter(
                  'learning_rate', warmup_schedule, interp='linear', step_based=True),
              ScheduledHyperParamSetter('learning_rate', lr_schedule),
              PeakMemoryTracker(),
              EstimatedTimeLeft(median=True),
              SessionRunTimeout(60000).set_chief_only(True),   # 1 minute timeout
          ]
          if cfg.TRAIN.EVAL_PERIOD > 0:
              callbacks.extend([
                  EvalCallback(dataset, *MODEL.get_inference_tensor_names(), args.logdir)
                  for dataset in cfg.DATA.VAL
              ])
          if not is_horovod:
              callbacks.append(GPUUtilizationTracker())
  
          if is_horovod and hvd.rank() > 0:
              session_init = None
          else:
              if args.load:
                  session_init = get_model_loader(args.load)
              else:
                  session_init = get_model_loader(cfg.BACKBONE.WEIGHTS) if cfg.BACKBONE.WEIGHTS else None
  
          traincfg = TrainConfig(
              model=MODEL,
              data=QueueInput(train_dataflow),
              callbacks=callbacks,
              steps_per_epoch=stepnum,
              max_epoch=cfg.TRAIN.LR_SCHEDULE[-1] * factor // stepnum,
              session_init=session_init,
              starting_epoch=cfg.TRAIN.STARTING_EPOCH
          )
          if is_horovod:
              trainer = HorovodTrainer(average=False)
          else:
              # nccl mode appears faster than cpu mode
              trainer = SyncMultiGPUTrainerReplicated(cfg.TRAIN.NUM_GPUS, average=False, mode='nccl')
          launch_train_with_config(traincfg, trainer)
  
apis: tensorflow.size tensorflow.summary.scalar tensorflow.add_n tensorflow.shape tensorflow.train.MomentumOptimizer tensorflow.reshape tensorflow.gather_nd tensorflow.sigmoid tensorflow.expand_dims tensorflow.squeeze tensorflow.cast tensorflow.python.framework.test_util.IsMklEnabled tensorflow.TensorSpec tensorflow.constant numpy.concatenate tensorflow.transpose tensorflow.get_variable tensorflow.test.is_gpu_available