import numpy as np import torch from .base_model import BaseModel from . import networks from .patchnce import PatchNCELoss from .cnt import * import util.util as util import timm import time import torch.nn.functional as F import sys from functools import partial import torch.nn as nn import math from torchvision.transforms import transforms as tfs class ROMAUNSBModel(BaseModel): @staticmethod def modify_commandline_options(parser, is_train=True): """ Configures options specific for CUT model """ parser.add_argument('--adj_size_list', type=list, default=[2, 4, 6, 8, 12], help='different scales of perception field') parser.add_argument('--lambda_mlp', type=float, default=1.0, help='weight of lr for discriminator') parser.add_argument('--lambda_D_ViT', type=float, default=1.0, help='weight for discriminator') parser.add_argument('--lambda_GAN', type=float, default=1.0, help='weight for GAN loss: GAN(G(X))') parser.add_argument('--lambda_global', type=float, default=1.0, help='weight for Global Structural Consistency') parser.add_argument('--lambda_spatial', type=float, default=1.0, help='weight for Local Structural Consistency') parser.add_argument('--lambda_inc', type=float, default=2.0, help='weight for Content Aware Optimization') parser.add_argument('--eta_ratio', type=float, default=0.4, help='ratio for selecting content region') parser.add_argument('--atten_layers', type=str, default='1,3,5', help='compute Cross-Similarity on which layers') parser.add_argument('--local_nums', type=int, default=256) parser.add_argument('--which_D_layer', type=int, default=-1) parser.add_argument('--side_length', type=int, default=7) parser.set_defaults(pool_size=0) opt, _ = parser.parse_known_args() return parser def __init__(self, opt): BaseModel.__init__(self, opt) self.loss_names = ['G_GAN_ViT', 'D_real_ViT', 'D_fake_ViT', 'global', 'spatial'] self.visual_names = ['real_A0', 'real_A1', 'fake_B0', 'fake_B1', 'real_B0', 'real_B1'] self.atten_layers = [int(i) for i in self.opt.atten_layers.split(',')] if self.isTrain: self.model_names = ['G', 'D_ViT', 'G_2'] else: # during test time, only load G self.model_names = ['G'] # define networks (both generator and discriminator) 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.netD_ViT = networks.MLPDiscriminator().to(self.device) self.netPreViT = timm.create_model("vit_base_patch16_384",pretrained=True).to(self.device) # From UNSB 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.norm = F.softmax self.resize = tfs.Resize(size=(384,384)) self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) self.criterionNCE = [] for atten_layer in self.atten_layers: self.criterionNCE.append(PatchNCELoss(opt).to(self.device)) self.criterionL1 = 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_ViT = torch.optim.Adam(self.netD_ViT.parameters(), lr=opt.lr * opt.lambda_mlp, betas=(opt.beta1, opt.beta2)) self.optimizer_E = torch.optim.Adam(self.netE.parameters(), lr=opt.lr * opt.lambda_mlp, betas=(opt.beta1, opt.beta2)) self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_D_ViT) self.optimizers.append(self.optimizer_E) self.cao = ContentAwareOptimization(opt.lambda_inc, opt.eta_ratio) #损失函数 self.ctn = ContentAwareTemporalNorm() #生成的伪光流场 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. """ pass def optimize_parameters(self): # forward self.forward() # update D self.set_requires_grad(self.netD_ViT, True) self.optimizer_D_ViT.zero_grad() self.loss_D = self.compute_D_loss() self.loss_D.backward() self.optimizer_D_ViT.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.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 forward(self): """Run forward pass; called by both functions and .""" # ============ 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) z_in = torch.randn(size=[2 * bs, 4 * self.opt.ngf]).to(self.real_A0.device) 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.fake_B1 = self.netG(self.real_A1) if self.opt.phase == 'train': # 生成图像的梯度 fake_gradient = torch.autograd.grad(self.fake_B0.sum(), self.fake_B0, create_graph=True)[0] # 梯度图 self.weight_fake = self.cao.generate_weight_map(fake_gradient) # 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, z_in) if self.opt.isTrain: 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 warped_fake_B0_2=self.warped_fake_B0_2 warped_fake_B0=self.warped_fake_B0 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.warped_fake_B0_2_resize = self.resize(warped_fake_B0_2) self.warped_fake_B0_resize = self.resize(warped_fake_B0) 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) self.mutil_warped_fake_B0_tokens = self.netPreViT(self.warped_fake_B0_resize, self.atten_layers, get_tokens=True) self.mutil_fake_B0_2_tokens = self.netPreViT(self.warped_fake_B0_2_resize, self.atten_layers, get_tokens=True) 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.detach() fake_B1_tokens = self.mutil_fake_B1_tokens.detach() real_B0_tokens = self.mutil_real_B0_tokens real_B1_tokens = self.mutil_real_B1_tokens pre_fake0_ViT = self.netD_ViT(fake_B0_tokens) pre_fake1_ViT = self.netD_ViT(fake_B1_tokens) self.loss_D_fake_ViT = (self.criterionGAN(pre_fake0_ViT, False).mean() + self.criterionGAN(pre_fake1_ViT, False).mean()) * 0.5 * lambda_D_ViT pred_real0_ViT = self.netD_ViT(real_B0_tokens) pred_real1_ViT = self.netD_ViT(real_B1_tokens) self.loss_D_real_ViT = (self.criterionGAN(pred_real0_ViT, True).mean() + self.criterionGAN(pred_real1_ViT, True).mean()) * 0.5 * lambda_D_ViT self.loss_D_ViT = (self.loss_D_fake_ViT + self.loss_D_real_ViT) * 0.5 return self.loss_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): if self.opt.lambda_GAN > 0.0: fake_B0_tokens = self.mutil_fake_B0_tokens fake_B1_tokens = self.mutil_fake_B1_tokens pred_fake0_ViT = self.netD_ViT(fake_B0_tokens) pred_fake1_ViT = self.netD_ViT(fake_B1_tokens) self.loss_G_GAN_ViT = (self.criterionGAN(pred_fake0_ViT, True) + self.criterionGAN(pred_fake1_ViT, True)) * 0.5 * self.opt.lambda_GAN else: self.loss_G_GAN_ViT = 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() - torch.logsumexp(self.netE(XtXt_1, self.time, XtXt_2).reshape(-1), dim=0) 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 or self.opt.lambda_spatial > 0.0: self.loss_global, self.loss_spatial = self.calculate_attention_loss() else: self.loss_global, self.loss_spatial = 0.0, 0.0 if self.opt.lambda_ctn > 0.0: warped_fake_B1 = warp(self.fake_B0, self.f_content) # use updated self.f_content self.l2_loss = F.mse_loss(self.warped_fake_B0_2, warped_fake_B1) * self.opt.lambda_ctn else: self.l2_loss = 0.0 self.loss_G = self.loss_G_GAN_ViT + self.loss_global + self.loss_spatial + self.l2_loss # include l2_loss in total loss 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.real_A0_resize, self.atten_layers, get_tokens=True, local_id=local_id, side_length = self.opt.side_length) mutil_real_A1_local_tokens = self.netPreViT(self.real_A1_resize, self.atten_layers, get_tokens=True, local_id=local_id, side_length = self.opt.side_length) mutil_fake_B0_local_tokens = self.netPreViT(self.fake_B0_resize, self.atten_layers, get_tokens=True, local_id=local_id, side_length = self.opt.side_length) mutil_fake_B1_local_tokens = self.netPreViT(self.fake_B1_resize, 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