text: import argparse
  import os
  import numpy as np
  
  from misc import pyutils
  
  if __name__ == '__main__':
      def str2bool(v):
          if isinstance(v, bool):
              return v
          if v.lower() in ('yes', 'true', 't', 'y', '1'):
              return True
          elif v.lower() in ('no', 'false', 'f', 'n', '0'):
              return False
          else:
              raise argparse.ArgumentTypeError('Boolean value expected.')
  
      parser = argparse.ArgumentParser()
  
      # Environment
      parser.add_argument("--num_workers", default=32, type=int)
      parser.add_argument("--voc12_root", default='../VOCdevkit/VOC2012/', type=str,
                          help="Path to VOC 2012 Devkit, must contain ./JPEGImages as subdirectory.")
  
      # Dataset
      parser.add_argument("--train_list", default="voc12/train_aug.txt", type=str)
      parser.add_argument("--val_list", default="voc12/val.txt", type=str)
      parser.add_argument("--infer_list", default="voc12/train.txt", type=str,
                          help="voc12/train_aug.txt to train a fully supervised model, "
                               "voc12/train.txt or voc12/val.txt to quickly check the quality of the labels.")
      parser.add_argument("--chainer_eval_set", default="train", type=str)
  
      # Class Activation Map
      parser.add_argument("--cam_network", default="net.resnet50_cam", type=str)
      parser.add_argument("--amr_network", default="net.resnet50_amr", type=str)
      parser.add_argument("--cam_crop_size", default=512, type=int)
      parser.add_argument("--cam_batch_size", default=16, type=int) # original: 16
      parser.add_argument("--cam_num_epoches", default=8, type=int)
      parser.add_argument("--cam_learning_rate", default=0.01, type=float)
      parser.add_argument("--cam_weight_decay", default=1e-4, type=float)
      parser.add_argument("--cam_eval_thres", default=0.20, type=float)
      parser.add_argument("--cam_scales", default=(1.0, 0.5, 0.75, 1.25, 1.5, 1.75, 2.0),
                          help="Multi-scale inferences")
  
      parser.add_argument("--target_layer", default="stage4")
      parser.add_argument("--adv_iter", default=27, type=int)
      parser.add_argument("--AD_coeff", default=7, type=int)
      parser.add_argument("--AD_stepsize", default=0.08, type=float)
      parser.add_argument("--score_th", default=0.5, type=float)
      parser.add_argument("--weight", default=0.5, type=float)
  
      # Mining Inter-pixel Relations
      parser.add_argument("--conf_fg_thres", default=0.60, type=float)
      parser.add_argument("--conf_bg_thres", default=0.33, type=float)
  
      # Inter-pixel Relation Network (IRNet)
      parser.add_argument("--irn_network", default="net.resnet50_irn", type=str)
      parser.add_argument("--irn_crop_size", default=512, type=int)
      parser.add_argument("--irn_batch_size", default=16, type=int)
      parser.add_argument("--irn_num_epoches", default=3, type=int)
      parser.add_argument("--irn_learning_rate", default=0.1, type=float)
      parser.add_argument("--irn_weight_decay", default=1e-4, type=float)
  
      # Random Walk Params
      parser.add_argument("--beta", default=10)
      parser.add_argument("--exp_times", default=8,
                          help="Hyper-parameter that controls the number of random walk iterations,"
                               "The random walk is performed 2^{exp_times}.")
      parser.add_argument("--ins_seg_bg_thres", default=0.25)
      parser.add_argument("--sem_seg_bg_thres", default=0.25)
  
      # Output Path
      parser.add_argument("--log_name", default="sample_train_eval", type=str)
      parser.add_argument("--cam_weights_name", default="sess/res50_cam.pth", type=str)
      parser.add_argument("--amr_weights_name", default="sess/res50_amr.pth", type=str)
      parser.add_argument("--irn_weights_name", default="sess/res50_irn.pth", type=str)
      parser.add_argument("--cam_out_dir", default="result/cams", type=str)
      parser.add_argument("--ir_label_out_dir", default="result/ir_label", type=str)
      parser.add_argument("--sem_seg_out_dir", default="result/sem_seg", type=str)
      parser.add_argument("--ins_seg_out_dir", default="result/ins_seg", type=str)
  
      # Step
      parser.add_argument("--train_cam_pass", type=str2bool, default=False)
      parser.add_argument("--train_amr_pass", type=str2bool, default=False)
      parser.add_argument("--make_cam_pass", type=str2bool, default=False)
      parser.add_argument("--eval_cam_pass", type=str2bool, default=False)
      parser.add_argument("--cam_to_ir_label_pass", type=str2bool, default=False)
      parser.add_argument("--train_irn_pass", type=str2bool, default=False)
      parser.add_argument("--make_ins_seg_pass", type=str2bool, default=False)
      parser.add_argument("--eval_ins_seg_pass", type=str2bool, default=False)
      parser.add_argument("--make_sem_seg_pass", type=str2bool, default=False)  
      parser.add_argument("--eval_sem_seg_pass", type=str2bool, default=False)
  
      args = parser.parse_args()
  
      os.makedirs("sess", exist_ok=True)
      os.makedirs(args.cam_out_dir, exist_ok=True)
      os.makedirs(args.ir_label_out_dir, exist_ok=True)
      os.makedirs(args.sem_seg_out_dir, exist_ok=True)
      os.makedirs(args.ins_seg_out_dir, exist_ok=True)
      pyutils.Logger(args.log_name + '.log')
      print(vars(args))
  
      if args.train_cam_pass is True:
          import step.train_cam
  
          timer = pyutils.Timer('step.train_cam:')
          step.train_cam.run(args)
  
      if args.train_amr_pass is True:
          import step.train_amr
  
          timer = pyutils.Timer('step.train_amr:')
          step.train_amr.run(args)
  
      if args.make_cam_pass is True:
          import step.make_cam
  
          timer = pyutils.Timer('step.make_cam:')
          step.make_cam.run(args)
  
  
      if args.eval_cam_pass is True:
          import step.eval_cam
  
          timer = pyutils.Timer('step.eval_cam:')
          step.eval_cam.run(args)
  
  
      if args.cam_to_ir_label_pass is True:
          import step.cam_to_ir_label
  
          timer = pyutils.Timer('step.cam_to_ir_label:')
          step.cam_to_ir_label.run(args)
  
      if args.train_irn_pass is True:
          import step.train_irn
  
          timer = pyutils.Timer('step.train_irn:')
          step.train_irn.run(args)
  
      if args.make_ins_seg_pass is True:
          import step.make_ins_seg_labels
  
          timer = pyutils.Timer('step.make_ins_seg_labels:')
          step.make_ins_seg_labels.run(args)
  
      if args.eval_ins_seg_pass is True:
          import step.eval_ins_seg
  
          timer = pyutils.Timer('step.eval_ins_seg:')
          step.eval_ins_seg.run(args)
  
      if args.make_sem_seg_pass is True:
          import step.make_sem_seg_labels
  
          args.sem_seg_bg_thres = float(args.sem_seg_bg_thres)
          timer = pyutils.Timer('step.make_sem_seg_labels:')
          step.make_sem_seg_labels.run(args)
  
      if args.eval_sem_seg_pass is True:
          import step.eval_sem_seg
  
          timer = pyutils.Timer('step.eval_sem_seg:')
          step.eval_sem_seg.run(args)
  
  