text: import torch
  from .base_model import BaseModel
  from . import networks
  
  '''siamrpn++'''
  from siamRPNPP import SiamRPNPP
  from data_utils import normalize
  
  '''hyper-parameters, which may need to be tuned'''
  cls_thres = 0.7
  
  class Gsearchresize512regressModel(BaseModel):
      """ This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.
  
      The model training requires '--dataset_mode aligned' dataset.
      By default, it uses a '--netG unet256' U-Net generator,
      a '--netD basic' discriminator (PatchGAN),
      and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
  
      pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
      """
      '''Only use G, and train it with L1 Loss & fooling loss'''
      @staticmethod
      def modify_commandline_options(parser, is_train=True):
          """Add new dataset-specific options, and rewrite default values for existing options.
  
          Parameters:
              parser          -- original option parser
              is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
  
          Returns:
              the modified parser.
  
          For pix2pix, we do not use image buffer
          The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
          By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
          """
          # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
          '''make change here'''
          parser.set_defaults(norm='batch', netG='unet_256', dataset_mode='aligned')
          if is_train:
              parser.set_defaults(pool_size=0, gan_mode='lsgan')
              parser.add_argument('--lambda_L1', type=float, default=1000, help='weight for L1 loss')
  
          return parser
  
      def __init__(self, opt):
          """Initialize the pix2pix class.
  
          Parameters:
              opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
          """
          BaseModel.__init__(self, opt)
          # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
          self.loss_names = ['G_L2', 'cls', 'reg']
          # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
          # self.visual_names = ['real_A', 'fake_B', 'real_B']
          self.visual_names = ['search_clean_vis','search_adv_vis']
          # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
          if self.isTrain:
              self.model_names = ['G']
          else:  # during test time, only load G
              self.model_names = ['G']
          # define networks (both generator and discriminator)
          self.netG = networks.define_G(3, 3, opt.ngf, opt.netG, opt.norm,
                                        not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
  
          if self.isTrain:
              # define loss functions
              self.criterionL2 = torch.nn.MSELoss()
              self.init_weight_L2 = 1000
              self.init_weight_cls = 0.1
              self.init_weight_reg = 1
              self.cls_margin = -5
              self.side_margin1 = -5
              self.side_margin2 = -5
              self.weight_L2 = self.init_weight_L2
              self.weight_cls = self.init_weight_cls
              self.weight_reg = self.init_weight_reg
              # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
              self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
              self.optimizers.append(self.optimizer_G)
          '''siamrpn++'''
          self.siam = SiamRPNPP()
  
      def set_input(self, input):
          """Unpack input data from the dataloader and perform necessary pre-processing steps.
  
          Parameters:
              input (dict): include the data itself and its metadata information.
  
          The option 'direction' can be used to swap images in domain A and domain B.
          """
          self.template = input[0].squeeze(0).cuda()  # pytorch tensor, shape=(1,3,127,127)
  
          self.search_clean255 = input[1].squeeze(0).cuda() # pytorch tensor, shape=(N,3,255,255) [0,255]
          self.search_clean1 = normalize(self.search_clean255)
          self.num_search = self.search_clean1.size(0)
          # print('clean image shape:',self.init_frame_clean.size())
  
  
      def forward(self,target_sz=(255,255)):
          """Run forward pass; called by both functions <optimize_parameters> and <test>."""
          '''resize to (512,512)'''
          search512_clean1 = torch.nn.functional.interpolate(self.search_clean1, size=(512, 512), mode='bilinear')
          search512_adv1 = search512_clean1 + self.netG(search512_clean1)  # Residual form: G(A)+A
          '''Then crop back to (255,255)'''
          self.search_adv1 = torch.nn.functional.interpolate(search512_adv1, size=target_sz, mode='bilinear')
          self.search_adv255 = self.search_adv1 * 127.5 + 127.5
          '''for visualization'''
          self.search_clean_vis = self.search_clean1[0:1]
          self.search_adv_vis = self.search_adv1[0:1]
  
      def transform(self,patch_clean1,target_sz=(255,255)):
          '''resize to (512,512)'''
          patch512_clean1 = torch.nn.functional.interpolate(patch_clean1, size=(512, 512), mode='bilinear')
          patch512_adv1 = patch512_clean1 + self.netG(patch512_clean1)  # Residual form: G(A)+A
          patch_adv1 = torch.nn.functional.interpolate(patch512_adv1, size=target_sz, mode='bilinear')
          patch_adv255 = patch_adv1 * 127.5 + 127.5
          return patch_adv255
  
      def backward_G(self):
          """Calculate GAN and L1 loss for the generator"""
          # Second, G(A) = B
          self.loss_G_L2 = self.criterionL2(self.search_adv1, self.search_clean1) * self.weight_L2
          attention_mask = (self.score_maps_clean > cls_thres)
          num_attention = int(torch.sum(attention_mask))
          if num_attention > 0:
              score_map_adv_att = self.score_maps_adv[attention_mask]
              reg_adv_att = self.reg_res_adv[2:4,attention_mask]
              self.loss_cls = torch.mean(torch.clamp(score_map_adv_att[:, 1] - score_map_adv_att[:, 0], min=self.cls_margin)) * self.weight_cls
              self.loss_reg = (torch.mean(torch.clamp(reg_adv_att[0,:],min=self.side_margin1))+
                               torch.mean(torch.clamp(reg_adv_att[1,:],min=self.side_margin2))) * self.weight_reg
              # combine loss and calculate gradients
              self.loss_G = self.loss_G_L2 + self.loss_cls + self.loss_reg
          else:
              self.loss_G = self.loss_G_L2
          self.loss_G.backward()
  
      def optimize_parameters(self):
          '''One forward & backward pass. One update of parameters'''
          '''forward pass'''
          # 1. predict with clean template
          with torch.no_grad():
              self.siam.model.template(self.template)
              self.score_maps_clean = self.siam.get_heat_map(self.search_clean255,softmax=True)#(5HWN,),with softmax
          # 2. adversarial attack with GAN
          self.forward()  # compute fake image
          # 3. predict with adversarial template
          self.score_maps_adv,self.reg_res_adv = self.siam.get_cls_reg(self.search_adv255,softmax=False)#(5HWN,2)without softmax,(5HWN,4)
          '''backward pass'''
          # update G
          self.optimizer_G.zero_grad()  # set G's gradients to zero
          self.backward_G()  # calculate graidents for G
          self.optimizer_G.step()  # udpate G's weights