repo_description: [ICCV'21] Learning Spatio-Temporal Transformer for Visual Tracking
repo_topics: transformer
code: import torch
  from torch.utils.data.distributed import DistributedSampler
  # datasets related
  from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet
  from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb
  from lib.train.data import sampler, opencv_loader, processing, LTRLoader
  import lib.train.data.transforms as tfm
  from lib.utils.misc import is_main_process
  
  
  def update_settings(settings, cfg):
      settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
      settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
                                     'search': cfg.DATA.SEARCH.FACTOR}
      settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
                            'search': cfg.DATA.SEARCH.SIZE}
      settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
                                       'search': cfg.DATA.SEARCH.CENTER_JITTER}
      settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
                                      'search': cfg.DATA.SEARCH.SCALE_JITTER}
      settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
      settings.print_stats = None
      settings.batchsize = cfg.TRAIN.BATCH_SIZE
      settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
  
  
  def names2datasets(name_list: list, settings, image_loader):
      assert isinstance(name_list, list)
      datasets = []
      for name in name_list:
          assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "COCO17", "VID", "TRACKINGNET"]
          if name == "LASOT":
              if settings.use_lmdb:
                  print("Building lasot dataset from lmdb")
                  datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))
              else:
                  datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))
          if name == "GOT10K_vottrain":
              if settings.use_lmdb:
                  print("Building got10k from lmdb")
                  datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))
              else:
                  datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader))
          if name == "GOT10K_train_full":
              if settings.use_lmdb:
                  print("Building got10k_train_full from lmdb")
                  datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader))
              else:
                  datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader))
          if name == "GOT10K_votval":
              if settings.use_lmdb:
                  print("Building got10k from lmdb")
                  datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader))
              else:
                  datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader))
          if name == "COCO17":
              if settings.use_lmdb:
                  print("Building COCO2017 from lmdb")
                  datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader))
              else:
                  datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader))
          if name == "VID":
              if settings.use_lmdb:
                  print("Building VID from lmdb")
                  datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader))
              else:
                  datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader))
          if name == "TRACKINGNET":
              if settings.use_lmdb:
                  print("Building TrackingNet from lmdb")
                  datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader))
              else:
                  # raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB")
                  datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader))
      return datasets
  
  
  def build_dataloaders(cfg, settings):
      # Data transform
      transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),
                                      tfm.RandomHorizontalFlip(probability=0.5))
  
      transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
                                      tfm.RandomHorizontalFlip_Norm(probability=0.5),
                                      tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
  
      transform_val = tfm.Transform(tfm.ToTensor(),
                                    tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
  
      # The tracking pairs processing module
      output_sz = settings.output_sz
      search_area_factor = settings.search_area_factor
  
      data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor,
                                                         output_sz=output_sz,
                                                         center_jitter_factor=settings.center_jitter_factor,
                                                         scale_jitter_factor=settings.scale_jitter_factor,
                                                         mode='sequence',
                                                         transform=transform_train,
                                                         joint_transform=transform_joint,
                                                         settings=settings)
  
      data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor,
                                                       output_sz=output_sz,
                                                       center_jitter_factor=settings.center_jitter_factor,
                                                       scale_jitter_factor=settings.scale_jitter_factor,
                                                       mode='sequence',
                                                       transform=transform_val,
                                                       joint_transform=transform_joint,
                                                       settings=settings)
  
      # Train sampler and loader
      settings.num_template = getattr(cfg.DATA.TEMPLATE, "NUMBER", 1)
      settings.num_search = getattr(cfg.DATA.SEARCH, "NUMBER", 1)
      sampler_mode = getattr(cfg.DATA, "SAMPLER_MODE", "causal")
      train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False)
      print("sampler_mode", sampler_mode)
      dataset_train = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader),
                                              p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO,
                                              samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH,
                                              max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search,
                                              num_template_frames=settings.num_template, processing=data_processing_train,
                                              frame_sample_mode=sampler_mode, train_cls=train_cls)
  
      train_sampler = DistributedSampler(dataset_train) if settings.local_rank != -1 else None
      shuffle = False if settings.local_rank != -1 else True
  
      loader_train = LTRLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=shuffle,
                               num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=train_sampler)
  
      # Validation samplers and loaders
      dataset_val = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.VAL.DATASETS_NAME, settings, opencv_loader),
                                            p_datasets=cfg.DATA.VAL.DATASETS_RATIO,
                                            samples_per_epoch=cfg.DATA.VAL.SAMPLE_PER_EPOCH,
                                            max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search,
                                            num_template_frames=settings.num_template, processing=data_processing_val,
                                            frame_sample_mode=sampler_mode, train_cls=train_cls)
      val_sampler = DistributedSampler(dataset_val) if settings.local_rank != -1 else None
      loader_val = LTRLoader('val', dataset_val, training=False, batch_size=cfg.TRAIN.BATCH_SIZE,
                             num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=val_sampler,
                             epoch_interval=cfg.TRAIN.VAL_EPOCH_INTERVAL)
  
      return loader_train, loader_val
  
  
  def get_optimizer_scheduler(net, cfg):
      train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False)
      if train_cls:
          print("Only training classification head. Learnable parameters are shown below.")
          param_dicts = [
              {"params": [p for n, p in net.named_parameters() if "cls" in n and p.requires_grad]}
          ]
  
          for n, p in net.named_parameters():
              if "cls" not in n:
                  p.requires_grad = False
              else:
                  print(n)
      else:
          param_dicts = [
              {"params": [p for n, p in net.named_parameters() if "backbone" not in n and p.requires_grad]},
              {
                  "params": [p for n, p in net.named_parameters() if "backbone" in n and p.requires_grad],
                  "lr": cfg.TRAIN.LR * cfg.TRAIN.BACKBONE_MULTIPLIER,
              },
          ]
          if is_main_process():
              print("Learnable parameters are shown below.")
              for n, p in net.named_parameters():
                  if p.requires_grad:
                      print(n)
  
      if cfg.TRAIN.OPTIMIZER == "ADAMW":
          optimizer = torch.optim.AdamW(param_dicts, lr=cfg.TRAIN.LR,
                                        weight_decay=cfg.TRAIN.WEIGHT_DECAY)
      else:
          raise ValueError("Unsupported Optimizer")
      if cfg.TRAIN.SCHEDULER.TYPE == 'step':
          lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, cfg.TRAIN.LR_DROP_EPOCH)
      elif cfg.TRAIN.SCHEDULER.TYPE == "Mstep":
          lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
                                                              milestones=cfg.TRAIN.SCHEDULER.MILESTONES,
                                                              gamma=cfg.TRAIN.SCHEDULER.GAMMA)
      else:
          raise ValueError("Unsupported scheduler")
      return optimizer, lr_scheduler
  