text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import os
  import sys
  
  class opts(object):
    def __init__(self):
      self.parser = argparse.ArgumentParser()
      # basic experiment setting
      self.parser.add_argument('task', default='',
                               help='ctdet | ddd | multi_pose '
                               '| tracking or combined with ,')
      self.parser.add_argument('--dataset', default='coco',
                               help='see lib/dataset/dataset_facotry for ' + 
                              'available datasets')
      self.parser.add_argument('--test_dataset', default='',
                               help='coco | kitti | coco_hp | pascal')
      self.parser.add_argument('--exp_id', default='default')
      self.parser.add_argument('--test', action='store_true')
      self.parser.add_argument('--debug', type=int, default=0,
                               help='level of visualization.'
                                    '1: only show the final detection results'
                                    '2: show the network output features'
                                    '3: use matplot to display' # useful when lunching training with ipython notebook
                                    '4: save all visualizations to disk')
      self.parser.add_argument('--no_pause', action='store_true')
      self.parser.add_argument('--demo', default='', 
                               help='path to image/ image folders/ video. '
                                    'or "webcam"')
      self.parser.add_argument('--load_model', default='',
                               help='path to pretrained model')
      self.parser.add_argument('--resume', action='store_true',
                               help='resume an experiment. '
                                    'Reloaded the optimizer parameter and '
                                    'set load_model to model_last.pth '
                                    'in the exp dir if load_model is empty.') 
  
      # system
      self.parser.add_argument('--gpus', default='0', 
                               help='-1 for CPU, use comma for multiple gpus')
      self.parser.add_argument('--num_workers', type=int, default=4,
                               help='dataloader threads. 0 for single-thread.')
      self.parser.add_argument('--not_cuda_benchmark', action='store_true',
                               help='disable when the input size is not fixed.')
      self.parser.add_argument('--seed', type=int, default=317, 
                               help='random seed') # from CornerNet
      self.parser.add_argument('--not_set_cuda_env', action='store_true',
                               help='used when training in slurm clusters.')
      # Re-ID
      self.parser.add_argument('--reid', action='store_true')
      #self.parser.add_argument('--load_ckpt',default='./AIC2018_iamai/ReID/ReID_CNN/models/model_50_base_res18.ckpt',help='path to load ckpt')
      #self.parser.add_argument('--n_layer',type=int,default=18,help='number of Resnet layers')
      #self.parser.add_argument('--gallery',default='./AIC2018_iamai/ReID/ReID_CNN/VeRi_gallery_info_test.txt',help='path to load gallery')
      self.parser.add_argument('--query',default='./AIC2018_iamai/ReID/ReID_CNN/Veh_ReID_Images/query',help='path to load query')
      self.parser.add_argument('--single_query', action='store_true',help='use if there are more than 1 image of the vehicle to be queried')
      self.parser.add_argument('--dis_mat',default='./AIC2018_iamai/ReID/ReID_CNN',help='path to store distance')
      self.parser.add_argument('--reid_batch_size',default=2,help='batch_size for inferencing')
      self.parser.add_argument('--dist', default='cosine', help='cosine | euclidean, distance calculation')
      self.parser.add_argument('--reid_model', default='resnet50', help='resnet18 | resnet50, choose resnet18 or resnet50 for re-id')
  
      # log
      self.parser.add_argument('--print_iter', type=int, default=0, 
                               help='disable progress bar and print to screen.')
      self.parser.add_argument('--save_all', action='store_true',
                               help='save model to disk every 5 epochs.')
      self.parser.add_argument('--vis_thresh', type=float, default=0.3,
                               help='visualization threshold.')
      self.parser.add_argument('--debugger_theme', default='white', 
                               choices=['white', 'black'])
      self.parser.add_argument('--eval_val', action='store_true')
      self.parser.add_argument('--save_imgs', default='', help='')
      self.parser.add_argument('--save_img_suffix', default='', help='')
      self.parser.add_argument('--skip_first', type=int, default=-1, help='')
      self.parser.add_argument('--save_video', action='store_true')
      self.parser.add_argument('--save_framerate', type=int, default=30)
      self.parser.add_argument('--resize_video', action='store_true')
      self.parser.add_argument('--video_h', type=int, default=512, help='')
      self.parser.add_argument('--video_w', type=int, default=512, help='')
      self.parser.add_argument('--transpose_video', action='store_true')
      self.parser.add_argument('--show_track_color', action='store_true')
      self.parser.add_argument('--not_show_bbox', action='store_true')
      self.parser.add_argument('--not_show_number', action='store_true')
      self.parser.add_argument('--qualitative', action='store_true')
      self.parser.add_argument('--tango_color', action='store_true')
  
      # model
      self.parser.add_argument('--arch', default='dla_34', 
                               help='model architecture. Currently tested'
                                    'res_18 | res_101 | resdcn_18 | resdcn_101 |'
                                    'dlav0_34 | dla_34 | hourglass')
      self.parser.add_argument('--dla_node', default='dcn') 
      self.parser.add_argument('--head_conv', type=int, default=-1,
                               help='conv layer channels for output head'
                                    '0 for no conv layer'
                                    '-1 for default setting: '
                                    '64 for resnets and 256 for dla.')
      self.parser.add_argument('--num_head_conv', type=int, default=1)
      self.parser.add_argument('--head_kernel', type=int, default=3, help='')
      self.parser.add_argument('--down_ratio', type=int, default=4,
                               help='output stride. Currently only supports 4.')
      self.parser.add_argument('--not_idaup', action='store_true')
      self.parser.add_argument('--num_classes', type=int, default=-1)
      self.parser.add_argument('--num_layers', type=int, default=101)
      self.parser.add_argument('--backbone', default='dla34')
      self.parser.add_argument('--neck', default='dlaup')
      self.parser.add_argument('--msra_outchannel', type=int, default=256)
      self.parser.add_argument('--efficient_level', type=int, default=0)
      self.parser.add_argument('--prior_bias', type=float, default=-4.6) # -2.19
  
      # input
      self.parser.add_argument('--input_res', type=int, default=-1, 
                               help='input height and width. -1 for default from '
                               'dataset. Will be overriden by input_h | input_w')
      self.parser.add_argument('--input_h', type=int, default=-1, 
                               help='input height. -1 for default from dataset.')
      self.parser.add_argument('--input_w', type=int, default=-1, 
                               help='input width. -1 for default from dataset.')
      self.parser.add_argument('--dataset_version', default='')
  
      # train
      self.parser.add_argument('--optim', default='adam')
      self.parser.add_argument('--lr', type=float, default=1.25e-4, 
                               help='learning rate for batch size 32.')
      self.parser.add_argument('--lr_step', type=str, default='60',
                               help='drop learning rate by 10.')
      self.parser.add_argument('--save_point', type=str, default='90',
                               help='when to save the model to disk.')
      self.parser.add_argument('--num_epochs', type=int, default=70,
                               help='total training epochs.')
      self.parser.add_argument('--batch_size', type=int, default=32,
                               help='batch size')
      self.parser.add_argument('--master_batch_size', type=int, default=-1,
                               help='batch size on the master gpu.')
      self.parser.add_argument('--num_iters', type=int, default=-1,
                               help='default: #samples / batch_size.')
      self.parser.add_argument('--val_intervals', type=int, default=5,
                               help='number of epochs to run validation.')
      self.parser.add_argument('--trainval', action='store_true',
                               help='include validation in training and '
                                    'test on test set')
      self.parser.add_argument('--ltrb', action='store_true',
                               help='')          
      self.parser.add_argument('--ltrb_weight', type=float, default=0.1,
                               help='')
      self.parser.add_argument('--reset_hm', action='store_true')
      self.parser.add_argument('--reuse_hm', action='store_true')
      self.parser.add_argument('--use_kpt_center', action='store_true')
      self.parser.add_argument('--add_05', action='store_true')
      self.parser.add_argument('--dense_reg', type=int, default=1, help='')
  
      # test
      self.parser.add_argument('--flip_test', action='store_true',
                               help='flip data augmentation.')
      self.parser.add_argument('--test_scales', type=str, default='1',
                               help='multi scale test augmentation.')
      self.parser.add_argument('--nms', action='store_true',
                               help='run nms in testing.')
      self.parser.add_argument('--K', type=int, default=100,
                               help='max number of output objects.') 
      self.parser.add_argument('--not_prefetch_test', action='store_true',
                               help='not use parallal data pre-processing.')
      self.parser.add_argument('--fix_short', type=int, default=-1)
      self.parser.add_argument('--keep_res', action='store_true',
                               help='keep the original resolution'
                                    ' during validation.')
      self.parser.add_argument('--map_argoverse_id', action='store_true',
                               help='if trained on nuscenes and eval on kitti')
      self.parser.add_argument('--out_thresh', type=float, default=-1,
                               help='')
      self.parser.add_argument('--depth_scale', type=float, default=1,
                               help='')
      self.parser.add_argument('--save_results', action='store_true')
      self.parser.add_argument('--load_results', default='')
      self.parser.add_argument('--use_loaded_results', action='store_true')
      self.parser.add_argument('--ignore_loaded_cats', default='')
      self.parser.add_argument('--model_output_list', action='store_true',
                               help='Used when convert to onnx')
      self.parser.add_argument('--non_block_test', action='store_true')
      self.parser.add_argument('--vis_gt_bev', default='', help='')
      self.parser.add_argument('--kitti_split', default='3dop',
                               help='different validation split for kitti: '
                                    '3dop | subcnn')
  
      # dataset
      self.parser.add_argument('--not_rand_crop', action='store_true',
                               help='not use the random crop data augmentation'
                                    'from CornerNet.')
      self.parser.add_argument('--not_max_crop', action='store_true',
                               help='used when the training dataset has'
                                    'inbalanced aspect ratios.')
      self.parser.add_argument('--shift', type=float, default=0,
                               help='when not using random crop, 0.1'
                                    'apply shift augmentation.')
      self.parser.add_argument('--scale', type=float, default=0,
                               help='when not using random crop, 0.4'
                                    'apply scale augmentation.')
      self.parser.add_argument('--aug_rot', type=float, default=0, 
                               help='probability of applying '
                                    'rotation augmentation.')
      self.parser.add_argument('--rotate', type=float, default=0,
                               help='when not using random crop'
                                    'apply rotation augmentation.')
      self.parser.add_argument('--flip', type=float, default=0.5,
                               help='probability of applying flip augmentation.')
      self.parser.add_argument('--no_color_aug', action='store_true',
                               help='not use the color augmenation '
                                    'from CornerNet')
  
      # Tracking
      self.parser.add_argument('--tracking', action='store_true')
      self.parser.add_argument('--pre_hm', action='store_true')
      self.parser.add_argument('--same_aug_pre', action='store_true')
      self.parser.add_argument('--zero_pre_hm', action='store_true')
      self.parser.add_argument('--hm_disturb', type=float, default=0)
      self.parser.add_argument('--lost_disturb', type=float, default=0)
      self.parser.add_argument('--fp_disturb', type=float, default=0)
      self.parser.add_argument('--pre_thresh', type=float, default=-1)
      self.parser.add_argument('--track_thresh', type=float, default=0.3)
      self.parser.add_argument('--new_thresh', type=float, default=0.3)
      self.parser.add_argument('--max_frame_dist', type=int, default=3)
      self.parser.add_argument('--ltrb_amodal', action='store_true')
      self.parser.add_argument('--ltrb_amodal_weight', type=float, default=0.1)
      self.parser.add_argument('--public_det', action='store_true')
      self.parser.add_argument('--no_pre_img', action='store_true')
      self.parser.add_argument('--zero_tracking', action='store_true')
      self.parser.add_argument('--hungarian', action='store_true')
      self.parser.add_argument('--max_age', type=int, default=-1)
  
  
      # loss
      self.parser.add_argument('--tracking_weight', type=float, default=1)
      self.parser.add_argument('--reg_loss', default='l1',
                               help='regression loss: sl1 | l1 | l2')
      self.parser.add_argument('--hm_weight', type=float, default=1,
                               help='loss weight for keypoint heatmaps.')
      self.parser.add_argument('--off_weight', type=float, default=1,
                               help='loss weight for keypoint local offsets.')
      self.parser.add_argument('--wh_weight', type=float, default=0.1,
                               help='loss weight for bounding box size.')
      self.parser.add_argument('--hp_weight', type=float, default=1,
                               help='loss weight for human pose offset.')
      self.parser.add_argument('--hm_hp_weight', type=float, default=1,
                               help='loss weight for human keypoint heatmap.')
      self.parser.add_argument('--amodel_offset_weight', type=float, default=1,
                               help='Please forgive the typo.')
      self.parser.add_argument('--dep_weight', type=float, default=1,
                               help='loss weight for depth.')
      self.parser.add_argument('--dim_weight', type=float, default=1,
                               help='loss weight for 3d bounding box size.')
      self.parser.add_argument('--rot_weight', type=float, default=1,
                               help='loss weight for orientation.')
      self.parser.add_argument('--nuscenes_att', action='store_true')
      self.parser.add_argument('--nuscenes_att_weight', type=float, default=1)
      self.parser.add_argument('--velocity', action='store_true')
      self.parser.add_argument('--velocity_weight', type=float, default=1)
  
      # custom dataset
      self.parser.add_argument('--custom_dataset_img_path', default='')
      self.parser.add_argument('--custom_dataset_ann_path', default='')
  
    def parse(self, args=''):
      if args == '':
        opt = self.parser.parse_args()
      else:
        opt = self.parser.parse_args(args)
    
      if opt.test_dataset == '':
        opt.test_dataset = opt.dataset
      
      opt.gpus_str = opt.gpus
      opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')]
      opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1]
      opt.lr_step = [int(i) for i in opt.lr_step.split(',')]
      opt.save_point = [int(i) for i in opt.save_point.split(',')]
      opt.test_scales = [float(i) for i in opt.test_scales.split(',')]
      opt.save_imgs = [i for i in opt.save_imgs.split(',')] \
        if opt.save_imgs != '' else []
      opt.ignore_loaded_cats = \
        [int(i) for i in opt.ignore_loaded_cats.split(',')] \
        if opt.ignore_loaded_cats != '' else []
  
      opt.num_workers = max(opt.num_workers, 2 * len(opt.gpus))
      opt.pre_img = False
      if 'tracking' in opt.task:
        print('Running tracking')
        opt.tracking = True
        opt.out_thresh = max(opt.track_thresh, opt.out_thresh)
        opt.pre_thresh = max(opt.track_thresh, opt.pre_thresh)
        opt.new_thresh = max(opt.track_thresh, opt.new_thresh)
        opt.pre_img = not opt.no_pre_img
        print('Using tracking threshold for out threshold!', opt.track_thresh)
        if 'ddd' in opt.task:
          opt.show_track_color = True
  
      opt.fix_res = not opt.keep_res
      print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.')
  
      if opt.head_conv == -1: # init default head_conv
        opt.head_conv = 256 if 'dla' in opt.arch else 64
  
      opt.pad = 127 if 'hourglass' in opt.arch else 31
      opt.num_stacks = 2 if opt.arch == 'hourglass' else 1
  
      if opt.master_batch_size == -1:
        opt.master_batch_size = opt.batch_size // len(opt.gpus)
      rest_batch_size = (opt.batch_size - opt.master_batch_size)
      opt.chunk_sizes = [opt.master_batch_size]
      for i in range(len(opt.gpus) - 1):
        slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1)
        if i < rest_batch_size % (len(opt.gpus) - 1):
          slave_chunk_size += 1
        opt.chunk_sizes.append(slave_chunk_size)
      print('training chunk_sizes:', opt.chunk_sizes)
  
      if opt.debug > 0:
        opt.num_workers = 0
        opt.batch_size = 1
        opt.gpus = [opt.gpus[0]]
        opt.master_batch_size = -1
  
      # log dirs
      opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..')
      opt.data_dir = os.path.join(opt.root_dir, 'data')
      opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task)
      opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id)
      opt.debug_dir = os.path.join(opt.save_dir, 'debug')
      
      if opt.resume and opt.load_model == '':
        opt.load_model = os.path.join(opt.save_dir, 'model_last.pth')
      return opt
  
  
    def update_dataset_info_and_set_heads(self, opt, dataset):
      opt.num_classes = dataset.num_categories \
                        if opt.num_classes < 0 else opt.num_classes
      # input_h(w): opt.input_h overrides opt.input_res overrides dataset default
      input_h, input_w = dataset.default_resolution
      input_h = opt.input_res if opt.input_res > 0 else input_h
      input_w = opt.input_res if opt.input_res > 0 else input_w
      opt.input_h = opt.input_h if opt.input_h > 0 else input_h
      opt.input_w = opt.input_w if opt.input_w > 0 else input_w
      opt.output_h = opt.input_h // opt.down_ratio
      opt.output_w = opt.input_w // opt.down_ratio
      opt.input_res = max(opt.input_h, opt.input_w)
      opt.output_res = max(opt.output_h, opt.output_w)
    
      opt.heads = {'hm': opt.num_classes, 'reg': 2, 'wh': 2}
  
      if 'tracking' in opt.task:
        opt.heads.update({'tracking': 2})
  
      if 'ddd' in opt.task:
        opt.heads.update({'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2})
      
      if 'multi_pose' in opt.task:
        opt.heads.update({
          'hps': dataset.num_joints * 2, 'hm_hp': dataset.num_joints,
          'hp_offset': 2})
  
      if opt.ltrb:
        opt.heads.update({'ltrb': 4})
      if opt.ltrb_amodal:
        opt.heads.update({'ltrb_amodal': 4})
      if opt.nuscenes_att:
        opt.heads.update({'nuscenes_att': 8})
      if opt.velocity:
        opt.heads.update({'velocity': 3})
  
      weight_dict = {'hm': opt.hm_weight, 'wh': opt.wh_weight,
                     'reg': opt.off_weight, 'hps': opt.hp_weight,
                     'hm_hp': opt.hm_hp_weight, 'hp_offset': opt.off_weight,
                     'dep': opt.dep_weight, 'rot': opt.rot_weight,
                     'dim': opt.dim_weight,
                     'amodel_offset': opt.amodel_offset_weight,
                     'ltrb': opt.ltrb_weight,
                     'tracking': opt.tracking_weight,
                     'ltrb_amodal': opt.ltrb_amodal_weight,
                     'nuscenes_att': opt.nuscenes_att_weight,
                     'velocity': opt.velocity_weight}
      opt.weights = {head: weight_dict[head] for head in opt.heads}
      for head in opt.weights:
        if opt.weights[head] == 0:
          del opt.heads[head]
      opt.head_conv = {head: [opt.head_conv \
        for i in range(opt.num_head_conv if head != 'reg' else 1)] for head in opt.heads}
      
      print('input h w:', opt.input_h, opt.input_w)
      print('heads', opt.heads)
      print('weights', opt.weights)
      print('head conv', opt.head_conv)
  
      return opt
  
    def init(self, args=''):
      # only used in demo
      default_dataset_info = {
        'ctdet': 'coco', 'multi_pose': 'coco_hp', 'ddd': 'nuscenes',
        'tracking,ctdet': 'kitti_tracking', 'tracking,multi_pose': 'coco_hp', 
        'tracking,ddd': 'nuscenes', 'tracking': 'kitti_tracking'
      }
      opt = self.parse()
      from dataset.dataset_factory import dataset_factory
      train_dataset = default_dataset_info[opt.task] \
        if opt.task in default_dataset_info else 'coco'
      dataset = dataset_factory[train_dataset]
      opt = self.update_dataset_info_and_set_heads(opt, dataset)
      return opt
  