roma_unsb/models/roma_unsb_model.py

import numpy as np
import math
import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import GaussianBlur
from .base_model import BaseModel
from . import networks
from .patchnce import PatchNCELoss
import util.util as util

from torchvision.transforms import transforms as tfs

def warp(image, flow): #warp操作
    """
    基于光流的图像变形函数
    Args:
        image: [B, C, H, W] 输入图像
        flow: [B, 2, H, W] 光流场(x/y方向位移)
    Returns:
        warped: [B, C, H, W] 变形后的图像
    """
    B, C, H, W = image.shape
    # 生成网格坐标
    grid_x, grid_y = torch.meshgrid(torch.arange(W), torch.arange(H))
    grid = torch.stack((grid_x, grid_y), dim=0).float().to(image.device)  # [2,H,W]
    grid = grid.unsqueeze(0).repeat(B,1,1,1)  # [B,2,H,W]
    
    # 应用光流位移(归一化到[-1,1])
    new_grid = grid + flow
    new_grid[:,0,:,:] = 2.0 * new_grid[:,0,:,:] / (W-1) - 1.0  # x方向
    new_grid[:,1,:,:] = 2.0 * new_grid[:,1,:,:] / (H-1) - 1.0  # y方向
    new_grid = new_grid.permute(0,2,3,1)  # [B,H,W,2]
    
    # 双线性插值
    return F.grid_sample(image, new_grid, align_corners=True)

# 时序归一化损失计算
def compute_ctn_loss(G, x, F_content): #公式10
    """
    计算内容感知时序归一化损失
    Args:
        G: 生成器
        x: 输入红外图像 [B,C,H,W]
        F_content: 生成的光流场 [B,2,H,W]
    """
    
    # 生成可见光图像
    y_fake = G(x)  # [B,3,H,W]
    
    # 对生成结果应用光流变形
    warped_fake = warp(y_fake, F_content)  # [B,3,H,W]
    
    # 对输入应用相同光流后生成图像
    warped_x = warp(x, F_content)  # [B,C,H,W]
    y_fake_warped = G(warped_x)  # [B,3,H,W]
    
    # 计算L2损失
    loss = F.mse_loss(warped_fake, y_fake_warped)
    return loss

class ContentAwareOptimization(nn.Module):
    def __init__(self, lambda_inc=2.0, eta_ratio=0.4):
        super().__init__()
        self.lambda_inc = lambda_inc  # 权重增强系数
        self.eta_ratio = eta_ratio    # 选择内容区域的比例
        
        # 改为类成员变量，确保钩子函数可访问
        self.gradients_real = []
        self.gradients_fake = []
    
    def compute_cosine_similarity(self, gradients):
        """
        计算每个patch梯度与平均梯度的余弦相似度
        Args:
            gradients: [B, N, D] 判别器输出的每个patch的梯度(N=w*h)
        Returns:
            cosine_sim: [B, N] 每个patch的余弦相似度
        """
        mean_grad = torch.mean(gradients, dim=1, keepdim=True)  # [B, 1, D]
        # 计算余弦相似度
        cosine_sim = F.cosine_similarity(gradients, mean_grad, dim=2)  # [B, N]
        return cosine_sim

    def generate_weight_map(self, gradients_real, gradients_fake):
        """
        生成内容感知权重图
        Args:
            gradients_real: [B, N, D] 真实图像判别器梯度
            gradients_fake: [B, N, D] 生成图像判别器梯度
        Returns:
            weight_real: [B, N] 真实图像权重图
            weight_fake: [B, N] 生成图像权重图
        """
        # 计算真实图像块的余弦相似度
        cosine_real = self.compute_cosine_similarity(gradients_real)  # [B, N]  公式5
        # 计算生成图像块的余弦相似度
        cosine_fake = self.compute_cosine_similarity(gradients_fake)  # [B, N]
        
        # 选择内容丰富的区域（余弦相似度最低的eta_ratio比例）
        k = int(self.eta_ratio * cosine_real.shape[1])
        
        # 对真实图像生成权重图
        _, real_indices = torch.topk(-cosine_real, k, dim=1)  # 选择最不相似的区域
        weight_real = torch.ones_like(cosine_real)
        for b in range(cosine_real.shape[0]):
            weight_real[b, real_indices[b]] = self.lambda_inc / (1e-6 + torch.abs(cosine_real[b, real_indices[b]])) #公式6
        
        # 对生成图像生成权重图（同理）
        _, fake_indices = torch.topk(-cosine_fake, k, dim=1)
        weight_fake = torch.ones_like(cosine_fake)
        for b in range(cosine_fake.shape[0]):
            weight_fake[b, fake_indices[b]] = self.lambda_inc / (1e-6 + torch.abs(cosine_fake[b, fake_indices[b]]))
        
        return weight_real, weight_fake

    def forward(self, D_real, D_fake, real_scores, fake_scores):
        # 清空梯度缓存
        self.gradients_real.clear()
        self.gradients_fake.clear()
        
        # 注册钩子
        hook_real = lambda grad: self.gradients_real.append(grad.detach())
        hook_fake = lambda grad: self.gradients_fake.append(grad.detach())
        
        D_real.register_hook(hook_real)
        D_fake.register_hook(hook_fake)
        
        # 触发梯度计算
        (real_scores.mean() + fake_scores.mean()).backward(retain_graph=True)
        
        # 获取梯度并调整维度
        grad_real = self.gradients_real[0]  # [B, N, D]
        grad_fake = self.gradients_fake[0]
        
        # 生成权重图
        weight_real, weight_fake = self.generate_weight_map(grad_real, grad_fake)
        
        # 计算加权损失
        loss_co_real = (weight_real * torch.log(real_scores + 1e-8)).mean()
        loss_co_fake = (weight_fake * torch.log(1 - fake_scores + 1e-8)).mean()
        
        return -(loss_co_real + loss_co_fake), weight_real, weight_fake

class ContentAwareTemporalNorm(nn.Module):
    def __init__(self, gamma_stride=0.1, kernel_size=21, sigma=5.0):
        super().__init__()
        self.gamma_stride = gamma_stride  # 控制整体运动幅度
        self.smoother = GaussianBlur(kernel_size, sigma=sigma)  # 高斯平滑层

    def upsample_weight_map(self, weight_patch, target_size=(256, 256)):
        """
        将patch级别的权重图上采样到目标分辨率
        Args:
            weight_patch: [B, 1, 24, 24] 来自ViT的patch权重图
            target_size: 目标分辨率 (H, W)
        Returns:
            weight_full: [B, 1, 256, 256] 上采样后的全分辨率权重图
        """
        # 使用双线性插值上采样
        B = weight_patch.shape[0]
        weight_patch = weight_patch.view(B, 1, 24, 24)
        
        weight_full = F.interpolate(
            weight_patch,
            size=target_size,
            mode='bilinear',
            align_corners=False
        )
        
        # 对每个16x16的patch内部保持权重一致（可选）
        # 通过平均池化再扩展，消除插值引入的渐变
        weight_full = F.avg_pool2d(weight_full, kernel_size=16, stride=16)
        weight_full = F.interpolate(weight_full, scale_factor=16, mode='nearest')
        
        return weight_full
    
    def forward(self, weight_map):
        """
        生成内容感知光流
        Args:
            weight_map: [B, 1, H, W] 权重图(来自内容感知优化模块)
        Returns:
            F_content: [B, 2, H, W] 生成的光流场(x/y方向位移)
        """
        # 上采样权重图到全分辨率
        weight_full = self.upsample_weight_map(weight_map)  # [B,1,384,384]     
        
        # 1. 归一化权重图
        # 保持区域相对强度，同时限制数值范围
        weight_norm = F.normalize(weight_full, p=1, dim=(2,3))  # L1归一化 [B,1,H,W]
        
        # 2. 生成高斯噪声
        B, _, H, W = weight_norm.shape
        z = torch.randn(B, 2, H, W, device=weight_norm.device)  # [B,2,H,W]
        
        # 3. 合成基础光流
        # 将权重图扩展为2通道(x/y方向共享权重)
        weight_expanded = weight_norm.expand(-1, 2, -1, -1)  # [B,2,H,W]
        F_raw = self.gamma_stride * weight_expanded * z  # [B,2,H,W]  #公式9
        
        # 4. 平滑处理(保持结构连续性)
        # 对每个通道独立进行高斯模糊
        F_smooth = self.smoother(F_raw)  # [B,2,H,W]
        
        # 5. 动态范围调整(可选)
        # 限制光流幅值，避免极端位移
        F_content = torch.tanh(F_smooth)  # 缩放到[-1,1]范围
        
        return F_content

class RomaUnsbModel(BaseModel):
    @staticmethod
    def modify_commandline_options(parser, is_train=True):
        """配置 CTNx 模型的特定选项"""
        
        parser.add_argument('--lambda_GAN', type=float, default=1.0, help='weight for GAN loss: GAN(G(X))')
        parser.add_argument('--lambda_SB', type=float, default=0.1, help='weight for SB loss')
        parser.add_argument('--lambda_ctn', type=float, default=1.0, help='weight for content-aware temporal norm')
        parser.add_argument('--lambda_D_ViT', type=float, default=1.0, help='weight for discriminator')
        parser.add_argument('--lambda_global', type=float, default=1.0, help='weight for Global Structural Consistency')
        parser.add_argument('--lambda_inc', type=float, default=1.0, help='incremental weight for content-aware optimization')
        
        parser.add_argument('--nce_idt', type=util.str2bool, nargs='?', const=True, default=False, help='use NCE loss for identity mapping: NCE(G(Y), Y))')
        parser.add_argument('--nce_includes_all_negatives_from_minibatch',
                            type=util.str2bool, nargs='?', const=True, default=False,
                            help='(used for single image translation) If True, include the negatives from the other samples of the minibatch when computing the contrastive loss. Please see models/patchnce.py for more details.')
        parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
        
        parser.add_argument('--netF', type=str, default='mlp_sample', choices=['sample', 'reshape', 'mlp_sample'], help='how to downsample the feature map')
        
        parser.add_argument('--flip_equivariance',
                            type=util.str2bool, nargs='?', const=True, default=False,
                            help="Enforce flip-equivariance as additional regularization. It's used by FastCUT, but not CUT")
        
        parser.add_argument('--eta_ratio', type=float, default=0.4, help='ratio of content-rich regions')

        parser.add_argument('--atten_layers', type=str, default='5', help='compute Cross-Similarity on which layers')
        
        parser.add_argument('--tau', type=float, default=0.01, help='Entropy parameter')
        parser.add_argument('--num_timesteps', type=int, default=5, help='# of discrim filters in the first conv layer')
        
        parser.add_argument('--n_mlp', type=int, default=3, help='only used if netD==n_layers')

        opt, _ = parser.parse_known_args()
        
        return parser
    
    def __init__(self, opt):
        """初始化 CTNx 模型"""
        BaseModel.__init__(self, opt)
        
        # 指定需要打印的训练损失
        self.loss_names = ['G_GAN', 'D_real_ViT', 'D_fake_ViT', 'G', 'SB', 'global', 'ctn']
        self.visual_names = ['real_A0', 'real_A_noisy', 'fake_B0', 'real_B0']
        self.atten_layers = [int(i) for i in self.opt.atten_layers.split(',')]
        
        if self.opt.phase == 'test':
            self.visual_names = ['real']
            for NFE in range(self.opt.num_timesteps):
                fake_name = 'fake_' + str(NFE+1)
                self.visual_names.append(fake_name)
        self.nce_layers = [int(i) for i in self.opt.nce_layers.split(',')]

        if opt.nce_idt and self.isTrain:
            self.loss_names += ['NCE_Y']
            self.visual_names += ['idt_B']

        if self.isTrain:
            self.model_names = ['G', 'D_ViT', 'E']
            
        else:
            self.model_names = ['G']
        
        # 创建网络
        self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.normG, not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, opt.no_antialias_up, self.gpu_ids, opt)
        
        
        if self.isTrain:
            self.netE = networks.define_D(opt.output_nc*4, opt.ndf, opt.netD, opt.n_layers_D, opt.normD, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids, opt)
            
            self.resize = tfs.Resize(size=(384,384), antialias=True)
            
            self.netD_ViT = networks.MLPDiscriminator().to(self.device)
            
            # 加入预训练VIT
            self.netPreViT = timm.create_model("vit_base_patch16_384", pretrained=True).to(self.device)
            
            # 定义损失函数
            self.criterionL1 = torch.nn.L1Loss().to(self.device)
            self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
            self.criterionIdt = torch.nn.L1Loss().to(self.device)
            self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
            self.optimizer_D = torch.optim.Adam(self.netD_ViT.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
            self.optimizer_E = torch.optim.Adam(self.netE.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
            self.optimizers = [self.optimizer_G, self.optimizer_D, self.optimizer_E]
            
            self.cao = ContentAwareOptimization(opt.lambda_inc, opt.eta_ratio) #损失函数
            self.ctn = ContentAwareTemporalNorm()  #生成的伪光流
            
    def data_dependent_initialize(self, data):
        """
        The feature network netF is defined in terms of the shape of the intermediate, extracted
        features of the encoder portion of netG. Because of this, the weights of netF are
        initialized at the first feedforward pass with some input images.
        Please also see PatchSampleF.create_mlp(), which is called at the first forward() call.
        """
        #bs_per_gpu = data["A"].size(0) // max(len(self.opt.gpu_ids), 1)
        #self.set_input(data)
        #self.real_A = self.real_A[:bs_per_gpu]
        #self.real_B = self.real_B[:bs_per_gpu]
        #self.forward()                     # compute fake images: G(A)
        #if self.opt.isTrain:
        #    
        #    self.compute_G_loss().backward()
        #    self.compute_D_loss().backward()
        #    self.compute_E_loss().backward()
        #    if self.opt.lambda_NCE > 0.0:
        #        self.optimizer_F = torch.optim.Adam(self.netF.parameters(), lr=self.opt.lr, betas=(self.opt.beta1, self.opt.beta2))
        #        self.optimizers.append(self.optimizer_F)
        pass
            
    def optimize_parameters(self):
        # forward
        self.forward()
        
        self.netG.train()
        self.netE.train()
        self.netD_ViT.train()
        
        # update D
        self.set_requires_grad(self.netD_ViT, True)
        self.optimizer_D.zero_grad()
        self.loss_D = self.compute_D_loss()
        self.loss_D.backward()
        self.optimizer_D.step()
        
        # update E
        self.set_requires_grad(self.netE, True)
        self.optimizer_E.zero_grad()
        self.loss_E = self.compute_E_loss()
        self.loss_E.backward()
        self.optimizer_E.step()
        
        # update G
        self.set_requires_grad(self.netD_ViT, False)
        self.set_requires_grad(self.netE, False)
        
        self.optimizer_G.zero_grad()

        self.loss_G = self.compute_G_loss()
        self.loss_G.backward()
        self.optimizer_G.step()
 
    
    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 domain A and domain B.
        """
        AtoB = self.opt.direction == 'AtoB'
        self.real_A0 = input['A0' if AtoB else 'B0'].to(self.device)
        self.real_A1 = input['A1' if AtoB else 'B1'].to(self.device)
        self.real_B0 = input['B0' if AtoB else 'A0'].to(self.device)
        self.real_B1 = input['B1' if AtoB else 'A1'].to(self.device)
        self.image_paths = input['A_paths' if AtoB else 'B_paths']
        
        
    def tokens_concat(self, origin_tokens, adjacent_size):
        adj_size = adjacent_size
        B, token_num, C = origin_tokens.shape[0], origin_tokens.shape[1], origin_tokens.shape[2]
        S = int(math.sqrt(token_num))
        if S * S != token_num:
            print('Error! Not a square!')
        token_map = origin_tokens.clone().reshape(B,S,S,C)
        cut_patch_list = []
        for i in range(0, S, adj_size):
            for j in range(0, S, adj_size):
                i_left = i
                i_right = i + adj_size + 1 if i + adj_size <= S else S + 1
                j_left = j
                j_right = j + adj_size if j + adj_size <= S else S + 1

                cut_patch = token_map[:, i_left:i_right, j_left: j_right, :]
                cut_patch= cut_patch.reshape(B,-1,C)
                cut_patch = torch.mean(cut_patch, dim=1, keepdim=True)
                cut_patch_list.append(cut_patch)


        result = torch.cat(cut_patch_list,dim=1)
        return result
        
    def cat_results(self, origin_tokens, adj_size_list):
        res_list = [origin_tokens]
        for ad_s in adj_size_list:
            cat_result = self.tokens_concat(origin_tokens, ad_s)
            res_list.append(cat_result)
        
        result = torch.cat(res_list, dim=1)

        return result
    
    def forward(self):
        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
        
        # ============ 第一步：对 real_A / real_A2 进行多步随机生成过程 ============
        tau = self.opt.tau
        T = self.opt.num_timesteps
        incs = np.array([0] + [1/(i+1) for i in range(T-1)])
        times = np.cumsum(incs)
        times = times / times[-1]
        times = 0.5 * times[-1] + 0.5 * times #[0.5,1]
        times = np.concatenate([np.zeros(1), times])
        times = torch.tensor(times).float().cuda()
        self.times = times
        bs = self.real_A0.size(0)
        time_idx = (torch.randint(T, size=[1]).cuda() * torch.ones(size=[1]).cuda()).long()
        self.time_idx = time_idx

        with torch.no_grad():
            self.netG.eval()
            # ============ 第二步：对 real_A / real_A2 进行多步随机生成过程 ============
            for t in range(self.time_idx.int().item() + 1):
                # 计算增量 delta 与 inter/scale，用于每个时间步的插值等
                if t > 0:
                    delta = times[t] - times[t - 1]
                    denom = times[-1] - times[t - 1]
                    inter = (delta / denom).reshape(-1, 1, 1, 1)
                    scale = (delta * (1 - delta / denom)).reshape(-1, 1, 1, 1)

                # 对 Xt、Xt2 进行随机噪声更新
                Xt = self.real_A0 if (t == 0) else (1 - inter) * Xt + inter * Xt_1.detach() + \
                    (scale * tau).sqrt() * torch.randn_like(Xt).to(self.real_A0.device)
                time_idx = (t * torch.ones(size=[self.real_A0.shape[0]]).to(self.real_A0.device)).long()
                z = torch.randn(size=[self.real_A0.shape[0], 4 * self.opt.ngf]).to(self.real_A0.device)
                self.time     = times[time_idx]
                Xt_1 = self.netG(Xt, self.time, z)

                Xt2 = self.real_A1 if (t == 0) else (1 - inter) * Xt2 + inter * Xt_12.detach() + \
                    (scale * tau).sqrt() * torch.randn_like(Xt2).to(self.real_A1.device)
                time_idx = (t * torch.ones(size=[self.real_A1.shape[0]]).to(self.real_A1.device)).long()
                z = torch.randn(size=[self.real_A1.shape[0], 4 * self.opt.ngf]).to(self.real_A1.device)
                Xt_12 = self.netG(Xt2, self.time, z)

            # 保存去噪后的中间结果 (real_A_noisy 等)，供下一步做拼接
            self.real_A_noisy = Xt.detach()
            self.real_A_noisy2 = Xt2.detach()

        # ============ 第三步：拼接输入并执行网络推理 =============
        bs = self.real_A0.size(0)
        self.z_in = torch.randn(size=[bs, 4 * self.opt.ngf]).to(self.real_A0.device)
        self.z_in2 = torch.randn(size=[bs, 4 * self.opt.ngf]).to(self.real_A1.device)
        # 将 real_A, real_B 拼接 (如 nce_idt=True)，并同样处理 real_A_noisy 与 XtB
        self.real = self.real_A0
        self.realt = self.real_A_noisy

        if self.opt.flip_equivariance:
            self.flipped_for_equivariance = self.opt.isTrain and (np.random.random() < 0.5)
            if self.flipped_for_equivariance:
                self.real = torch.flip(self.real, [3])
                self.realt = torch.flip(self.realt, [3])
        
        self.fake_B0 = self.netG(self.real_A0, self.time, self.z_in)
        self.fake_B1 = self.netG(self.real_A1, self.time, self.z_in2)

        if self.opt.phase == 'train':
            real_A0 = self.real_A0
            real_A1 = self.real_A1
            real_B0 = self.real_B0
            real_B1 = self.real_B1
            fake_B0 = self.fake_B0
            fake_B1 = self.fake_B1

            self.real_A0_resize = self.resize(real_A0)
            self.real_A1_resize = self.resize(real_A1)
            real_B0 = self.resize(real_B0)
            real_B1 = self.resize(real_B1)
            self.fake_B0_resize = self.resize(fake_B0)
            self.fake_B1_resize = self.resize(fake_B1)

            self.mutil_real_A0_tokens = self.netPreViT(self.real_A0_resize, self.atten_layers, get_tokens=True)
            self.mutil_real_A1_tokens = self.netPreViT(self.real_A1_resize, self.atten_layers, get_tokens=True)
            self.mutil_real_B0_tokens = self.netPreViT(real_B0, self.atten_layers, get_tokens=True)
            self.mutil_real_B1_tokens = self.netPreViT(real_B1, self.atten_layers, get_tokens=True)
            self.mutil_fake_B0_tokens = self.netPreViT(self.fake_B0_resize, self.atten_layers, get_tokens=True)
            self.mutil_fake_B1_tokens = self.netPreViT(self.fake_B1_resize, self.atten_layers, get_tokens=True)
            # [[1,576,768],[1,576,768],[1,576,768]]
            #  [3,576,768]
            

            
    def compute_D_loss(self):      #判别器还是没有改
        """Calculate GAN loss for the discriminator"""

        lambda_D_ViT = self.opt.lambda_D_ViT
        fake_B0_tokens = self.mutil_fake_B0_tokens[0].detach()
       

        real_B0_tokens = self.mutil_real_B0_tokens[0]

        pre_fake0_ViT = self.netD_ViT(fake_B0_tokens)
        self.loss_D_fake_ViT = self.criterionGAN(pre_fake0_ViT, False)

        pred_real0_ViT = self.netD_ViT(real_B0_tokens)       
        self.loss_D_real_ViT = self.criterionGAN(pred_real0_ViT, True)

        self.losscao, self.weight_real, self.weight_fake = self.cao(pred_real0_ViT, pre_fake0_ViT, self.loss_D_real_ViT, self.loss_D_fake_ViT)
        
        return self.losscao* lambda_D_ViT

    def compute_E_loss(self):
        """计算判别器 E 的损失"""
        
        XtXt_1 = torch.cat([self.real_A_noisy, self.fake_B0.detach()], dim=1)
        XtXt_2 = torch.cat([self.real_A_noisy2, self.fake_B1.detach()], dim=1)
        temp = torch.logsumexp(self.netE(XtXt_1, self.time, XtXt_2).reshape(-1), dim=0).mean()
        self.loss_E = -self.netE(XtXt_1, self.time, XtXt_1).mean() + temp + temp**2
        
        return self.loss_E
        
    def compute_G_loss(self):
        """计算生成器的 GAN 损失"""
        if self.opt.lambda_ctn > 0.0:
            # 生成图像的CTN光流图
            self.f_content = self.ctn(self.weight_fake)
            
            # 变换后的图片
            self.warped_real_A_noisy2 = warp(self.real_A_noisy, self.f_content)
            self.warped_fake_B0       = warp(self.fake_B0,self.f_content)
            
            # 经过第二次生成器
            self.warped_fake_B0_2 = self.netG(self.warped_real_A_noisy2, self.time, self.z_in)   
            
            warped_fake_B0_2=self.warped_fake_B0_2
            warped_fake_B0=self.warped_fake_B0
            # 计算L2损失
            self.loss_ctn = F.mse_loss(warped_fake_B0_2, warped_fake_B0)    

        if self.opt.lambda_GAN > 0.0:
            pred_fake = self.netD_ViT(self.mutil_fake_B0_tokens[0])
            self.loss_G_GAN = self.criterionGAN(pred_fake, True).mean() 
        else:
            self.loss_G_GAN = 0.0
            
        self.loss_SB = 0
        if self.opt.lambda_SB > 0.0:
            XtXt_1 = torch.cat([self.real_A_noisy, self.fake_B0], dim=1)
            XtXt_2 = torch.cat([self.real_A_noisy2, self.fake_B1], dim=1)
            
            bs = self.opt.batch_size

            # eq.9
            ET_XY    = self.netE(XtXt_1, self.time, XtXt_1).mean() - self.netE(XtXt_1, self.time, XtXt_2).mean()
            self.loss_SB = -(self.opt.num_timesteps - self.time[0]) / self.opt.num_timesteps * self.opt.tau * ET_XY
            self.loss_SB += torch.mean((self.real_A_noisy - self.fake_B0) ** 2)
        
        if self.opt.lambda_global > 0.0:
            self.loss_global = self.calculate_similarity(self.real_A0, self.fake_B0) + self.calculate_similarity(self.real_A1, self.fake_B1)
            self.loss_global *= 0.5
        else:
            self.loss_global = 0.0
        
        self.loss_G = self.opt.lambda_GAN * self.loss_G_GAN + \
                      self.opt.lambda_SB * self.loss_SB + \
                      self.opt.lambda_ctn * self.loss_ctn + \
                      self.loss_global * self.opt.lambda_global
        return self.loss_G
    
    def calculate_attention_loss(self):
        n_layers = len(self.atten_layers)
        mutil_real_A0_tokens = self.mutil_real_A0_tokens
        mutil_real_A1_tokens = self.mutil_real_A1_tokens
        mutil_fake_B0_tokens = self.mutil_fake_B0_tokens
        mutil_fake_B1_tokens = self.mutil_fake_B1_tokens

            
        if self.opt.lambda_global > 0.0:
            loss_global = self.calculate_similarity(mutil_real_A0_tokens, mutil_fake_B0_tokens) + self.calculate_similarity(mutil_real_A1_tokens, mutil_fake_B1_tokens)
            loss_global *= 0.5

        else:
            loss_global = 0.0

        if self.opt.lambda_spatial > 0.0:
            loss_spatial = 0.0
            local_nums = self.opt.local_nums
            tokens_cnt = 576
            local_id = np.random.permutation(tokens_cnt)
            local_id = local_id[:int(min(local_nums, tokens_cnt))]

            mutil_real_A0_local_tokens = self.netPreViT(self.resize(self.real_A0), self.atten_layers, get_tokens=True, local_id=local_id, side_length=self.opt.side_length)
            mutil_real_A1_local_tokens = self.netPreViT(self.resize(self.real_A1), self.atten_layers, get_tokens=True, local_id=local_id, side_length=self.opt.side_length)

            mutil_fake_B0_local_tokens = self.netPreViT(self.resize(self.fake_B0), self.atten_layers, get_tokens=True, local_id=local_id, side_length=self.opt.side_length)
            mutil_fake_B1_local_tokens = self.netPreViT(self.resize(self.fake_B1), self.atten_layers, get_tokens=True, local_id=local_id, side_length=self.opt.side_length)

            loss_spatial = self.calculate_similarity(mutil_real_A0_local_tokens, mutil_fake_B0_local_tokens) + self.calculate_similarity(mutil_real_A1_local_tokens, mutil_fake_B1_local_tokens)
            loss_spatial *= 0.5
        
        else:
            loss_spatial = 0.0

        return loss_global * self.opt.lambda_global, loss_spatial * self.opt.lambda_spatial
    
    def calculate_similarity(self, mutil_src_tokens, mutil_tgt_tokens):
        loss = 0.0
        n_layers = len(self.atten_layers)

        for src_tokens, tgt_tokens in zip(mutil_src_tokens, mutil_tgt_tokens):
            src_tgt = src_tokens.bmm(tgt_tokens.permute(0,2,1))
            tgt_src = tgt_tokens.bmm(src_tokens.permute(0,2,1))
            cos_dis_global =  F.cosine_similarity(src_tgt, tgt_src, dim=-1)
            loss += self.criterionL1(torch.ones_like(cos_dis_global), cos_dis_global).mean()

        loss = loss / n_layers
        return loss