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='ctdet',
                               help='ctdet | ddd | multi_pose | exdet')
      self.parser.add_argument('--dataset', default='coco',
                               help='coco | minicoco | 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('--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
  
      # log
      self.parser.add_argument('--print_iter', type=int, default=0, 
                               help='disable progress bar and print to screen.')
      self.parser.add_argument('--hide_data_time', action='store_true',
                               help='not display time during training.')
      self.parser.add_argument('--save_all', action='store_true',
                               help='save model to disk every 5 epochs.')
      self.parser.add_argument('--metric', default='loss', 
                               help='main metric to save best model')
      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'])
      
      # 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 | dlaatt_34 | hourglass')
      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('--down_ratio', type=int, default=4,
                               help='output stride. Currently only supports 4.')
      self.parser.add_argument('--attention', type=str, default=None,
                               help='attention type, channel_add | channel_mul |'
                                    ' channel_conv1d_add |channel_conv1d_mul')
      self.parser.add_argument('--pyconv', type=bool, default=False,
                               help='use PyConv or not')
  
      # 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.')
      
      # train
      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='90,120',
                               help='drop learning rate by 10.')
      self.parser.add_argument('--num_epochs', type=int, default=140,
                               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')
  
      # 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_res', action='store_true',
                               help='fix testing resolution or keep '
                                    'the original resolution')
      self.parser.add_argument('--keep_res', action='store_true',
                               help='keep the original resolution'
                                    ' during validation.')
  
      # 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('--shift', type=float, default=0.1,
                               help='when not using random crop'
                                    'apply shift augmentation.')
      self.parser.add_argument('--scale', type=float, default=0.4,
                               help='when not using random crop'
                                    'apply scale 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')
      # multi_pose
      self.parser.add_argument('--aug_rot', type=float, default=0, 
                               help='probability of applying '
                                    'rotation augmentation.')
      # ddd
      self.parser.add_argument('--aug_ddd', type=float, default=0.5,
                               help='probability of applying crop augmentation.')
      self.parser.add_argument('--rect_mask', action='store_true',
                               help='for ignored object, apply mask on the '
                                    'rectangular region or just center point.')
      self.parser.add_argument('--kitti_split', default='3dop',
                               help='different validation split for kitti: '
                                    '3dop | subcnn')
  
      # loss
      self.parser.add_argument('--mse_loss', action='store_true',
                               help='use mse loss or focal loss to train '
                                    'keypoint heatmaps.')
      # ctdet
      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.')
      # multi_pose
      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.')
      # ddd
      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('--peak_thresh', type=float, default=0.2)
      
      # task
      # ctdet
      self.parser.add_argument('--norm_wh', action='store_true',
                               help='L1(\hat(y) / y, 1) or L1(\hat(y), y)')
      self.parser.add_argument('--dense_wh', action='store_true',
                               help='apply weighted regression near center or '
                                    'just apply regression on center point.')
      self.parser.add_argument('--cat_spec_wh', action='store_true',
                               help='category specific bounding box size.')
      self.parser.add_argument('--not_reg_offset', action='store_true',
                               help='not regress local offset.')
      # exdet
      self.parser.add_argument('--agnostic_ex', action='store_true',
                               help='use category agnostic extreme points.')
      self.parser.add_argument('--scores_thresh', type=float, default=0.1,
                               help='threshold for extreme point heatmap.')
      self.parser.add_argument('--center_thresh', type=float, default=0.1,
                               help='threshold for centermap.')
      self.parser.add_argument('--aggr_weight', type=float, default=0.0,
                               help='edge aggregation weight.')
      # multi_pose
      self.parser.add_argument('--dense_hp', action='store_true',
                               help='apply weighted pose regression near center '
                                    'or just apply regression on center point.')
      self.parser.add_argument('--not_hm_hp', action='store_true',
                               help='not estimate human joint heatmap, '
                                    'directly use the joint offset from center.')
      self.parser.add_argument('--not_reg_hp_offset', action='store_true',
                               help='not regress local offset for '
                                    'human joint heatmaps.')
      self.parser.add_argument('--not_reg_bbox', action='store_true',
                               help='not regression bounding box size.')
      
      # ground truth validation
      self.parser.add_argument('--eval_oracle_hm', action='store_true', 
                               help='use ground center heatmap.')
      self.parser.add_argument('--eval_oracle_wh', action='store_true', 
                               help='use ground truth bounding box size.')
      self.parser.add_argument('--eval_oracle_offset', action='store_true', 
                               help='use ground truth local heatmap offset.')
      self.parser.add_argument('--eval_oracle_kps', action='store_true', 
                               help='use ground truth human pose offset.')
      self.parser.add_argument('--eval_oracle_hmhp', action='store_true', 
                               help='use ground truth human joint heatmaps.')
      self.parser.add_argument('--eval_oracle_hp_offset', action='store_true', 
                               help='use ground truth human joint local offset.')
      self.parser.add_argument('--eval_oracle_dep', action='store_true', 
                               help='use ground truth depth.')
  
    def parse(self, args=''):
      if args == '':
        opt = self.parser.parse_args()
      else:
        opt = self.parser.parse_args(args)
  
      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.test_scales = [float(i) for i in opt.test_scales.split(',')]
  
      opt.fix_res = not opt.keep_res
      print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.')
      opt.reg_offset = not opt.not_reg_offset
      opt.reg_bbox = not opt.not_reg_bbox
      opt.hm_hp = not opt.not_hm_hp
      opt.reg_hp_offset = (not opt.not_reg_hp_offset) and opt.hm_hp
  
      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.trainval:
        opt.val_intervals = 100000000
  
      if opt.debug > 0:
        opt.num_workers = 0
        opt.batch_size = 1
        opt.gpus = [opt.gpus[0]]
        opt.master_batch_size = -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)
  
      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')
      print('The output will be saved to ', opt.save_dir)
      
      if opt.resume and opt.load_model == '':
        model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \
                    else opt.save_dir
        opt.load_model = os.path.join(model_path, 'model_last.pth')
      return opt
  
    def update_dataset_info_and_set_heads(self, opt, dataset):
      input_h, input_w = dataset.default_resolution
      opt.mean, opt.std = dataset.mean, dataset.std
      opt.num_classes = dataset.num_classes
  
      # input_h(w): opt.input_h overrides opt.input_res overrides dataset default
      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)
      
      if opt.task == 'exdet':
        # assert opt.dataset in ['coco']
        num_hm = 1 if opt.agnostic_ex else opt.num_classes
        opt.heads = {'hm_t': num_hm, 'hm_l': num_hm, 
                     'hm_b': num_hm, 'hm_r': num_hm,
                     'hm_c': opt.num_classes}
        if opt.reg_offset:
          opt.heads.update({'reg_t': 2, 'reg_l': 2, 'reg_b': 2, 'reg_r': 2})
      elif opt.task == 'ddd':
        # assert opt.dataset in ['gta', 'kitti', 'viper']
        opt.heads = {'hm': opt.num_classes, 'dep': 1, 'rot': 8, 'dim': 3}
        if opt.reg_bbox:
          opt.heads.update(
            {'wh': 2})
        if opt.reg_offset:
          opt.heads.update({'reg': 2})
      elif opt.task == 'ctdet':
        # assert opt.dataset in ['pascal', 'coco']
        opt.heads = {'hm': opt.num_classes,
                     'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes}
        if opt.reg_offset:
          opt.heads.update({'reg': 2})
      elif opt.task == 'multi_pose':
        # assert opt.dataset in ['coco_hp']
        opt.flip_idx = dataset.flip_idx
        opt.heads = {'hm': opt.num_classes, 'wh': 2, 'hps': 34}
        if opt.reg_offset:
          opt.heads.update({'reg': 2})
        if opt.hm_hp:
          opt.heads.update({'hm_hp': 17})
        if opt.reg_hp_offset:
          opt.heads.update({'hp_offset': 2})
      else:
        assert 0, 'task not defined!'
      print('heads', opt.heads)
      return opt
  
    def init(self, args=''):
      default_dataset_info = {
        'ctdet': {'default_resolution': [512, 512], 'num_classes': 80, 
                  'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278],
                  'dataset': 'coco'},
        'exdet': {'default_resolution': [512, 512], 'num_classes': 80, 
                  'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278],
                  'dataset': 'coco'},
        'multi_pose': {
          'default_resolution': [512, 512], 'num_classes': 1, 
          'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278],
          'dataset': 'coco_hp', 'num_joints': 17,
          'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], 
                       [11, 12], [13, 14], [15, 16]]},
        'ddd': {'default_resolution': [384, 1280], 'num_classes': 3, 
                  'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225],
                  'dataset': 'kitti'},
      }
      class Struct:
        def __init__(self, entries):
          for k, v in entries.items():
            self.__setattr__(k, v)
      opt = self.parse(args)
      dataset = Struct(default_dataset_info[opt.task])
      opt.dataset = dataset.dataset
      opt = self.update_dataset_info_and_set_heads(opt, dataset)
      return opt
  