476 lines
20 KiB
Python
476 lines
20 KiB
Python
import numpy as np
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import math
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import timm
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import torch
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import torchvision.models as models
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import torch.nn as nn
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import torch.nn.functional as F
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from torchvision.transforms import GaussianBlur
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from .base_model import BaseModel
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from . import networks
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from .patchnce import PatchNCELoss
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import util.util as util
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from torchvision.transforms import transforms as tfs
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def warp(image, flow): #warp操作
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"""
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基于光流的图像变形函数
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Args:
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image: [B, C, H, W] 输入图像
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flow: [B, 2, H, W] 光流场(x/y方向位移)
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Returns:
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warped: [B, C, H, W] 变形后的图像
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"""
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B, C, H, W = image.shape
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# 生成网格坐标
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grid_x, grid_y = torch.meshgrid(torch.arange(W), torch.arange(H))
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grid = torch.stack((grid_x, grid_y), dim=0).float().to(image.device) # [2,H,W]
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grid = grid.unsqueeze(0).repeat(B,1,1,1) # [B,2,H,W]
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# 应用光流位移(归一化到[-1,1])
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new_grid = grid + flow
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new_grid[:,0,:,:] = 2.0 * new_grid[:,0,:,:] / (W-1) - 1.0 # x方向
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new_grid[:,1,:,:] = 2.0 * new_grid[:,1,:,:] / (H-1) - 1.0 # y方向
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new_grid = new_grid.permute(0,2,3,1) # [B,H,W,2]
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# 双线性插值
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return F.grid_sample(image, new_grid, align_corners=True)
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# 时序归一化损失计算
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def compute_ctn_loss(G, x, F_content): #公式10
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"""
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计算内容感知时序归一化损失
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Args:
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G: 生成器
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x: 输入红外图像 [B,C,H,W]
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F_content: 生成的光流场 [B,2,H,W]
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"""
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# 生成可见光图像
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y_fake = G(x) # [B,3,H,W]
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# 对生成结果应用光流变形
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warped_fake = warp(y_fake, F_content) # [B,3,H,W]
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# 对输入应用相同光流后生成图像
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warped_x = warp(x, F_content) # [B,C,H,W]
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y_fake_warped = G(warped_x) # [B,3,H,W]
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# 计算L2损失
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loss = F.mse_loss(warped_fake, y_fake_warped)
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return loss
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class ContentAwareOptimization(nn.Module):
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def __init__(self, lambda_inc=2.0, eta_ratio=0.4):
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super().__init__()
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self.lambda_inc = lambda_inc
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self.eta_ratio = eta_ratio
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self.gradients_real = []
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self.gradients_fake = []
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def compute_cosine_similarity(self, gradients):
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mean_grad = torch.mean(gradients, dim=1, keepdim=True)
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return F.cosine_similarity(gradients, mean_grad, dim=2)
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def generate_weight_map(self, gradients_real, gradients_fake):
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# 计算余弦相似度
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cosine_real = self.compute_cosine_similarity(gradients_real)
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cosine_fake = self.compute_cosine_similarity(gradients_fake)
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# 生成权重图（优化实现）
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def _get_weights(cosine):
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k = int(self.eta_ratio * cosine.shape[1])
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_, indices = torch.topk(-cosine, k, dim=1)
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weights = torch.ones_like(cosine)
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weights.scatter_(1, indices, self.lambda_inc / (1e-6 + torch.abs(cosine.gather(1, indices))))
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return weights
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weight_real = _get_weights(cosine_real)
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weight_fake = _get_weights(cosine_fake)
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return weight_real, weight_fake
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def forward(self, D_real, D_fake, real_scores, fake_scores):
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# 清空梯度缓存
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self.gradients_real.clear()
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self.gradients_fake.clear()
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self.criterionGAN=networks.GANLoss('lsgan').cuda()
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# 注册钩子捕获梯度
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hook_real = lambda grad: self.gradients_real.append(grad.detach())
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hook_fake = lambda grad: self.gradients_fake.append(grad.detach())
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D_real.register_hook(hook_real)
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D_fake.register_hook(hook_fake)
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# 触发梯度计算（保留计算图）
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(real_scores.mean() + fake_scores.mean()).backward(retain_graph=True)
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# 获取梯度并调整维度
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grad_real = self.gradients_real[0].flatten(1) # [B, N, D] → [B, N*D]
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grad_fake = self.gradients_fake[0].flatten(1)
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# 生成权重图
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weight_real, weight_fake = self.generate_weight_map(
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grad_real.view(*D_real.shape),
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grad_fake.view(*D_fake.shape)
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)
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# 正确应用权重到对数概率（论文公式7）
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loss_co_real = torch.mean(weight_real * self.criterionGAN(real_scores , True))
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loss_co_fake = torch.mean(weight_fake * self.criterionGAN(fake_scores , False))
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# 总损失（注意符号：判别器需最大化该损失）
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loss_co_adv = (loss_co_real + loss_co_fake)*0.5
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return loss_co_adv, weight_real, weight_fake
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class ContentAwareTemporalNorm(nn.Module):
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def __init__(self, gamma_stride=0.1, kernel_size=21, sigma=5.0):
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super().__init__()
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self.gamma_stride = gamma_stride # 控制整体运动幅度
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self.smoother = GaussianBlur(kernel_size, sigma=sigma) # 高斯平滑层
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def upsample_weight_map(self, weight_patch, target_size=(256, 256)):
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"""
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将patch级别的权重图上采样到目标分辨率
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Args:
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weight_patch: [B, 1, 24, 24] 来自ViT的patch权重图
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target_size: 目标分辨率 (H, W)
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Returns:
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weight_full: [B, 1, 256, 256] 上采样后的全分辨率权重图
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"""
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# 使用双线性插值上采样
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B = weight_patch.shape[0]
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weight_patch = weight_patch.view(B, 1, 24, 24)
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weight_full = F.interpolate(
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weight_patch,
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size=target_size,
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mode='bilinear',
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align_corners=False
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)
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# 对每个16x16的patch内部保持权重一致（可选）
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# 通过平均池化再扩展，消除插值引入的渐变
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weight_full = F.avg_pool2d(weight_full, kernel_size=16, stride=16)
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weight_full = F.interpolate(weight_full, scale_factor=16, mode='nearest')
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return weight_full
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def forward(self, weight_map):
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"""
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生成内容感知光流
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Args:
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weight_map: [B, 1, H, W] 权重图(来自内容感知优化模块)
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Returns:
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F_content: [B, 2, H, W] 生成的光流场(x/y方向位移)
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"""
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# 上采样权重图到全分辨率
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weight_full = self.upsample_weight_map(weight_map) # [B,1,384,384]
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# 1. 归一化权重图
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# 保持区域相对强度，同时限制数值范围
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weight_norm = F.normalize(weight_full, p=1, dim=(2,3)) # L1归一化 [B,1,H,W]
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# 2. 生成高斯噪声
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B, _, H, W = weight_norm.shape
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z = torch.randn(B, 2, H, W, device=weight_norm.device) # [B,2,H,W]
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# 3. 合成基础光流
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# 将权重图扩展为2通道(x/y方向共享权重)
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weight_expanded = weight_norm.expand(-1, 2, -1, -1) # [B,2,H,W]
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F_raw = self.gamma_stride * weight_expanded * z # [B,2,H,W] #公式9
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# 4. 平滑处理(保持结构连续性)
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# 对每个通道独立进行高斯模糊
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F_smooth = self.smoother(F_raw) # [B,2,H,W]
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# 5. 动态范围调整(可选)
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# 限制光流幅值，避免极端位移
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F_content = torch.tanh(F_smooth) # 缩放到[-1,1]范围
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return F_content
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class RomaUnsbModel(BaseModel):
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@staticmethod
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def modify_commandline_options(parser, is_train=True):
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"""配置 CTNx 模型的特定选项"""
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parser.add_argument('--lambda_GAN', type=float, default=1.0, help='weight for GAN loss: GAN(G(X))')
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parser.add_argument('--lambda_ctn', type=float, default=1.0, help='weight for content-aware temporal norm')
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parser.add_argument('--lambda_D_ViT', type=float, default=1.0, help='weight for discriminator')
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parser.add_argument('--lambda_global', type=float, default=1.0, help='weight for Global Structural Consistency')
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parser.add_argument('--lambda_spatial', type=float, default=1.0, help='weight for Local Structural Consistency')
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parser.add_argument('--lambda_inc', type=float, default=1.0, help='incremental weight for content-aware optimization')
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parser.add_argument('--local_nums', type=int, default=64, help='number of local patches')
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parser.add_argument('--side_length', type=int, default=7)
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parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
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parser.add_argument('--eta_ratio', type=float, default=0.4, help='ratio of content-rich regions')
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parser.add_argument('--gamma_stride', type=float, default=20, help='ratio of stride for computing the similarity matrix')
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parser.add_argument('--atten_layers', type=str, default='5', help='compute Cross-Similarity on which layers')
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parser.add_argument('--tau', type=float, default=0.01, help='Entropy parameter')
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parser.add_argument('--num_timesteps', type=int, default=5, help='# of discrim filters in the first conv layer')
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parser.add_argument('--n_mlp', type=int, default=3, help='only used if netD==n_layers')
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opt, _ = parser.parse_known_args()
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return parser
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def __init__(self, opt):
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"""初始化 CTNx 模型"""
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BaseModel.__init__(self, opt)
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# 指定需要打印的训练损失
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self.loss_names = ['G_GAN', 'D_ViT', 'G', 'global', 'spatial','ctn']
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self.visual_names = ['real_A0', 'fake_B0', 'real_B0','real_A1', 'fake_B1', 'real_B1']
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self.atten_layers = [int(i) for i in self.opt.atten_layers.split(',')]
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if self.opt.phase == 'test':
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self.visual_names = ['real']
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for NFE in range(self.opt.num_timesteps):
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fake_name = 'fake_' + str(NFE+1)
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self.visual_names.append(fake_name)
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self.nce_layers = [int(i) for i in self.opt.nce_layers.split(',')]
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if self.isTrain:
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self.model_names = ['G', 'D_ViT']
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else:
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self.model_names = ['G']
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# 创建网络
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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)
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if self.isTrain:
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self.resize = tfs.Resize(size=(384,384), antialias=True)
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self.netD_ViT = networks.MLPDiscriminator().to(self.device)
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# 加入预训练VIT
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self.netPreViT = timm.create_model("vit_base_patch16_384", pretrained=True).to(self.device)
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# 定义损失函数
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self.criterionL1 = torch.nn.L1Loss().to(self.device)
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self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
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self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
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self.optimizer_D = torch.optim.Adam(self.netD_ViT.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
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self.optimizers = [self.optimizer_G, self.optimizer_D]
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self.cao = ContentAwareOptimization(opt.lambda_inc, opt.eta_ratio) #损失函数
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self.ctn = ContentAwareTemporalNorm() #生成的伪光流
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def data_dependent_initialize(self, data):
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"""
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The feature network netF is defined in terms of the shape of the intermediate, extracted
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features of the encoder portion of netG. Because of this, the weights of netF are
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initialized at the first feedforward pass with some input images.
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Please also see PatchSampleF.create_mlp(), which is called at the first forward() call.
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"""
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pass
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def optimize_parameters(self):
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# forward
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self.forward()
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self.netG.train()
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self.netD_ViT.train()
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# update D
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self.set_requires_grad(self.netD_ViT, True)
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self.optimizer_D.zero_grad()
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self.loss_D = self.compute_D_loss()
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self.loss_D.backward()
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self.optimizer_D.step()
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# update G
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self.set_requires_grad(self.netD_ViT, False)
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self.optimizer_G.zero_grad()
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self.loss_G = self.compute_G_loss()
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self.loss_G.backward()
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self.optimizer_G.step()
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def set_input(self, input):
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"""Unpack input data from the dataloader and perform necessary pre-processing steps.
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Parameters:
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input (dict): include the data itself and its metadata information.
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The option 'direction' can be used to swap domain A and domain B.
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"""
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AtoB = self.opt.direction == 'AtoB'
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self.real_A0 = input['A0' if AtoB else 'B0'].to(self.device)
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self.real_A1 = input['A1' if AtoB else 'B1'].to(self.device)
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self.real_B0 = input['B0' if AtoB else 'A0'].to(self.device)
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self.real_B1 = input['B1' if AtoB else 'A1'].to(self.device)
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self.image_paths = input['A_paths' if AtoB else 'B_paths']
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def forward(self):
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"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
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self.fake_B0 = self.netG(self.real_A0)
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self.fake_B1 = self.netG(self.real_A1)
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if self.opt.isTrain:
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real_A0 = self.real_A0
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real_A1 = self.real_A1
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real_B0 = self.real_B0
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real_B1 = self.real_B1
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fake_B0 = self.fake_B0
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fake_B1 = self.fake_B1
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self.real_A0_resize = self.resize(real_A0)
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self.real_A1_resize = self.resize(real_A1)
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real_B0 = self.resize(real_B0)
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real_B1 = self.resize(real_B1)
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self.fake_B0_resize = self.resize(fake_B0)
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self.fake_B1_resize = self.resize(fake_B1)
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self.mutil_real_A0_tokens = self.netPreViT(self.real_A0_resize, self.atten_layers, get_tokens=True)
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self.mutil_real_A1_tokens = self.netPreViT(self.real_A1_resize, self.atten_layers, get_tokens=True)
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self.mutil_real_B0_tokens = self.netPreViT(real_B0, self.atten_layers, get_tokens=True)
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self.mutil_real_B1_tokens = self.netPreViT(real_B1, self.atten_layers, get_tokens=True)
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self.mutil_fake_B0_tokens = self.netPreViT(self.fake_B0_resize, self.atten_layers, get_tokens=True)
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self.mutil_fake_B1_tokens = self.netPreViT(self.fake_B1_resize, self.atten_layers, get_tokens=True)
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def compute_D_loss(self):
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"""Calculate GAN loss with Content-Aware Optimization"""
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lambda_D_ViT = self.opt.lambda_D_ViT
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loss_cao = 0.0
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real_B0_tokens = self.mutil_real_B0_tokens[0]
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pred_real0, real_features0 = self.netD_ViT(real_B0_tokens) # scores, features
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real_B1_tokens = self.mutil_real_B1_tokens[0]
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pred_real1, real_features1 = self.netD_ViT(real_B1_tokens) # scores, features
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pre_fake0, fake_features0 = self.netD_ViT(self.mutil_fake_B0_tokens[0].detach())
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pre_fake1, fake_features1 = self.netD_ViT(self.mutil_fake_B1_tokens[0].detach())
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loss_cao0, self.weight_real0, self.weight_fake0 = self.cao(
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D_real=real_features0,
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D_fake=fake_features0,
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real_scores=pred_real0,
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fake_scores=pre_fake0
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)
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loss_cao1, self.weight_real1, self.weight_fake1 = self.cao(
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D_real=real_features1,
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D_fake=fake_features1,
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real_scores=pred_real1,
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fake_scores=pre_fake1
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)
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loss_cao += loss_cao0 + loss_cao1
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# ===== 综合损失 =====
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self.loss_D_ViT = loss_cao * 0.5 * lambda_D_ViT
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# 记录损失值供可视化
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# self.loss_D_real = loss_D_real.item()
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# self.loss_D_fake = loss_D_fake.item()
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# self.loss_cao = (loss_cao0 + loss_cao1).item() * 0.5
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return self.loss_D_ViT
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def compute_G_loss(self):
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"""计算生成器的 GAN 损失"""
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if self.opt.lambda_ctn > 0.0:
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# 生成图像的CTN光流图
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self.f_content0 = self.ctn(self.weight_fake0.detach())
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self.f_content1 = self.ctn(self.weight_fake1.detach())
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# 变换后的图片
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self.warped_real_A0 = warp(self.real_A0, self.f_content0)
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self.warped_real_A1 = warp(self.real_A1, self.f_content1)
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self.warped_fake_B0 = warp(self.fake_B0,self.f_content0)
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self.warped_fake_B1 = warp(self.fake_B1,self.f_content1)
|
||
|
||
# 经过第二次生成器
|
||
self.warped_fake_B0_2 = self.netG(self.warped_real_A0)
|
||
self.warped_fake_B1_2 = self.netG(self.warped_real_A1)
|
||
|
||
warped_fake_B0_2=self.warped_fake_B0_2
|
||
warped_fake_B1_2=self.warped_fake_B1_2
|
||
warped_fake_B0=self.warped_fake_B0
|
||
warped_fake_B1=self.warped_fake_B1
|
||
# 计算L2损失
|
||
self.loss_ctn0 = F.mse_loss(warped_fake_B0_2, warped_fake_B0)
|
||
self.loss_ctn1 = F.mse_loss(warped_fake_B1_2, warped_fake_B1)
|
||
self.loss_ctn = (self.loss_ctn0 + self.loss_ctn1)*0.5
|
||
|
||
if self.opt.lambda_GAN > 0.0:
|
||
|
||
pred_fake0,_ = self.netD_ViT(self.mutil_fake_B0_tokens[0])
|
||
pred_fake1,_ = self.netD_ViT(self.mutil_fake_B1_tokens[0])
|
||
self.loss_G_GAN0 = self.criterionGAN(pred_fake0, True).mean()
|
||
self.loss_G_GAN1 = self.criterionGAN(pred_fake1, True).mean()
|
||
self.loss_G_GAN = (self.loss_G_GAN0 + self.loss_G_GAN1)*0.5
|
||
else:
|
||
self.loss_G_GAN = 0.0
|
||
|
||
|
||
if self.opt.lambda_global 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
|
||
|
||
self.loss_G = self.opt.lambda_GAN * self.loss_G_GAN + \
|
||
self.opt.lambda_ctn * self.loss_ctn + \
|
||
self.loss_global * self.opt.lambda_global+\
|
||
self.loss_spatial * self.opt.lambda_spatial
|
||
|
||
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 , loss_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
|
||
|