repo_description: [ECCV 2022] Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
repo_topics: tracking transformer
code: import os
  # loss function related
  from lib.utils.box_ops import giou_loss
  from torch.nn.functional import l1_loss
  from torch.nn import BCEWithLogitsLoss
  # train pipeline related
  from lib.train.trainers import LTRTrainer
  # distributed training related
  from torch.nn.parallel import DistributedDataParallel as DDP
  # some more advanced functions
  from .base_functions import *
  # network related
  from lib.models.stark import build_starks, build_starkst
  from lib.models.stark import build_stark_lightning_x_trt
  # forward propagation related
  from lib.train.actors import STARKLightningXtrtdistillActor
  # for import modules
  import importlib
  
  
  def build_network(script_name, cfg):
      # Create network
      if script_name == "stark_s":
          net = build_starks(cfg)
      elif script_name == "stark_st1" or script_name == "stark_st2":
          net = build_starkst(cfg)
      elif script_name == "stark_lightning_X_trt":
          net = build_stark_lightning_x_trt(cfg, phase="train")
      else:
          raise ValueError("illegal script name")
      return net
  
  
  def run(settings):
      settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2'
  
      # update the default configs with config file
      if not os.path.exists(settings.cfg_file):
          raise ValueError("%s doesn't exist." % settings.cfg_file)
      config_module = importlib.import_module("lib.config.%s.config" % settings.script_name)
      cfg = config_module.cfg
      config_module.update_config_from_file(settings.cfg_file)
      if settings.local_rank in [-1, 0]:
          print("New configuration is shown below.")
          for key in cfg.keys():
              print("%s configuration:" % key, cfg[key])
              print('\n')
  
      # update the default teacher configs with teacher config file
      if not os.path.exists(settings.cfg_file_teacher):
          raise ValueError("%s doesn't exist." % settings.cfg_file_teacher)
      config_module_teacher = importlib.import_module("lib.config.%s.config" % settings.script_teacher)
      cfg_teacher = config_module_teacher.cfg
      config_module_teacher.update_config_from_file(settings.cfg_file_teacher)
      if settings.local_rank in [-1, 0]:
          print("New teacher configuration is shown below.")
          for key in cfg_teacher.keys():
              print("%s configuration:" % key, cfg_teacher[key])
              print('\n')
  
      # update settings based on cfg
      update_settings(settings, cfg)
  
      # Record the training log
      log_dir = os.path.join(settings.save_dir, 'logs')
      if settings.local_rank in [-1, 0]:
          if not os.path.exists(log_dir):
              os.makedirs(log_dir)
      settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name))
  
      # Build dataloaders
      loader_train, loader_val = build_dataloaders(cfg, settings)
  
      if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE:
          cfg.ckpt_dir = settings.save_dir
      """turn on the distillation mode"""
      cfg.TRAIN.DISTILL = True
      cfg_teacher.TRAIN.DISTILL = True
      net = build_network(settings.script_name, cfg)
      net_teacher = build_network(settings.script_teacher, cfg_teacher)
  
      # wrap networks to distributed one
      net.cuda()
      net_teacher.cuda()
      net_teacher.eval()
  
      if settings.local_rank != -1:
          net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True)
          net_teacher = DDP(net_teacher, device_ids=[settings.local_rank], find_unused_parameters=True)
          settings.device = torch.device("cuda:%d" % settings.local_rank)
      else:
          settings.device = torch.device("cuda:0")
      # settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False)
      # settings.distill = getattr(cfg.TRAIN, "DISTILL", False)
      settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "L1")
      # Loss functions and Actors
      if settings.script_name == "stark_lightning_X_trt":
          objective = {'giou': giou_loss, 'l1': l1_loss}
          loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT}
          actor = STARKLightningXtrtdistillActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings,
                                                 net_teacher=net_teacher)
      else:
          raise ValueError("illegal script name")
  
      # Optimizer, parameters, and learning rates
      optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg)
      use_amp = getattr(cfg.TRAIN, "AMP", False)
      trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp)
  
      # train process
      trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True, distill=True)
  