Derolol Blog

Generate CAMs

• Feature map
• Class feature map
• Score of class
• CAMs

Pixel-to-Prototype Contrast

• Pseudo mask
• Pixel-wise projected feature
• Pixel-to-prototype contrast
  • Prototype set
  • Temperature
  • Contrast 像素特征与原型的相似度

Prototype Estimation in Batch

• Top K pixels of class c
  • CAM as confidences
  • Estimate prototypes from pixel-wise feature embeddings that are with the top K confidences
• Prototype

Loss

• Cross Prototype Contrast

• Cross CAM Contrast

• Intra-view Contrast

  • Strategy to slove the matter of in accurate pseudo label [50]
    • Semi-hard prototype mining
    • Hard pixel sampling

Code

归一化

归一化

• 作用
  • 保证所有元素之和为 1
  • 将向量转换为概率分布

归一化

# 按通道执行L2归一化
v = v / (torch.norm(v, dim=1, keepdim=True) + 1e-5)
# or
v = torch.nn.functional.normalize(v, dim=1)

• 作用
  • 方向不变性：向量的方向不变，长度变为 1，使得向量表示不再依赖于其大小
  • 数值稳定性：将向量的大小规范在一个相对较小的区间
  • 减小特征尺度的差异
  • 便于执行相似性度量

Max 归一化

• 归一化后向量的最大值为 1

Max-Min 归一化

• 归一化后向量值范围为[0, 1]

Forward

• cam

  # fea是最后一层输出的特征图
  self.fc8 = nn.Conv2d(4096, 21, 1, bias=False)
  cam = self.fc8(fea)
  cam = torch.nn.functional.interpolate(cam, (H, W), mode='bilinear', align_corners=True)

• cam_rv_down

  • 清洗 CAM

    with torch.no_grad():
        cam_d = torch.nn.functional.relu(cam.detach())
        # max norm
        cam_d_max = torch.max(cam_d.view(n, c, -1), dim=-1)[0].view(n, c, 1, 1)+1e-5
        cam_d_norm = torch.nn.functional.relu(cam_d - 1e-5) / cam_d_max
        # 计算保留概率值最大分类，反相为背景概率，其余分类置0
        cam_d_norm[:, 0, :, :] = 1 - torch.max(cam_d_norm[:, 1:, :, :], dim=1)[0]
        cam_max = torch.max(cam_d_norm[:,1:,:,:], dim=1, keepdim=True)[0]
        cam_d_norm[:,1:,:,:][cam_d_norm[:,1:,:,:] < cam_max] = 0

  • 增强 CAM

    # 根据像素相似度调整CAM
    cam_rv_down = self.PCM(cam_d_norm, f)
    
    # PCM
    def PCM(self, cam, f):
        n,c,h,w = f.size()
        cam = torch.nn.functional.interpolate(cam, (h,w), mode='bilinear', align_corners=True).view(n,-1,h*w)
        # 多尺度特征融合
        f = self.f9(f)
        f = f.view(n, -1, h*w)
        # 特征按通道L2归一化
        f = f / (torch.norm(f, dim=1, keepdim=True) + 1e-5)
        # 计算像素相似度矩阵
        aff = torch.nn.functional.relu(torch.matmul(f.transpose(1, 2), f), inplace=True)
        # 相似度矩阵L1归一化
        aff = aff/(torch.sum(aff, dim=1, keepdim=True) + 1e-5)
        # CAM加权
        cam_rv = torch.matmul(cam, aff).view(n, -1, h, w)
    
        return cam_rv

• cam_rv

  cam_rv = torch.nn.functional.interpolate(cam_rv_down, (H,W), mode='bilinear', align_corners=True)

• f_proj

  self.fc_proj = torch.nn.Conv2d(4096, 128, 1, bias=False)
  f_proj = torch.nn.functional.relu(self.fc_proj(fea), inplace=True)

• prototype

  f_proj1 = torch.nn.functional.interpolate(f_proj1, size=(128 // 8, 128 // 8), mode='bilinear', align_corners=True)
  cam_rv1_down = torch.nn.functional.interpolate(cam_rv1_down, size=(128 // 8, 128 // 8), mode='bilinear', align_corners=True)
  cam_rv2_down = cam_rv2_down
  
  with torch.no_grad():
      fea1 = f_proj1.detach()
      c_fea1 = fea1.shape[1]
      cam_rv1_down = torch.nn.functional.relu(cam_rv1_down.detach())
      # CAM Max-min归一化
      n1, c1, h1, w1 = cam_rv1_down.shape
      max1 = torch.max(cam_rv1_down.view(n1, c1, -1), dim=-1)[0].view(n1, c1, 1, 1)
      min1 = torch.min(cam_rv1_down.view(n1, c1, -1), dim=-1)[0].view(n1, c1, 1, 1)
      cam_rv1_down[cam_rv1_down < min1 + 1e-5] = 0.
      norm_cam1 = (cam_rv1_down - min1 - 1e-5) / (max1 - min1 + 1e-5)
      cam_rv1_down = norm_cam1
      # 设置背景阈值
      cam_rv1_down[:, 0, :, :] = args.bg_threshold
      # 根据图像级标签保留相应的类别
      scores1 = torch.nn.functional.softmax(cam_rv1_down * label, dim=1)
  
      # 计算伪标签
      pseudo_label1 = scores1.argmax(dim=1, keepdim=True)
      n_sc1, c_sc1, h_sc1, w_sc1 = scores1.shape
      scores1 = scores1.transpose(0, 1)
      fea1 = fea1.permute(0, 2, 3, 1).reshape(-1, c_fea1)
  
      # 获取各个分类CAM值最高的值与索引
      top_values, top_indices = torch.topk(cam_rv1_down.transpose(0, 1).reshape(c_sc1, -1), k=h_sc1 * w_sc1 // 8, dim=-1)
      prototypes1 = torch.zeros(c_sc1, c_fea1).cuda()  # [21, 128]
      # 遍历各个分类
      for i in range(c_sc1):
          # 获取k个像素对应的特征
          top_fea = fea1[top_indices[i]]
          # CAM值加权平均k个特征得到分类原型
          prototypes1[i] = torch.sum(top_values[i].unsqueeze(-1) * top_fea, dim=0) / torch.sum(top_values[i])
      # 各个原型L2归一化
      prototypes1 = torch.nn.functional.normalize(prototypes1, dim=-1)

• prototype similarity

  n_f, c_f, h_f, w_f = f_proj1.shape
  # [N, H, W, C] -> [N x H x W, C]
  f_proj1 = f_proj1.permute(0, 2, 3, 1).reshape(n_f * h_f * w_f, c_f)
  # 特征L2归一化
  f_proj1 = torch.nn.functional.normalize(f_proj1, dim=-1)
  pseudo_label1 = pseudo_label1.reshape(-1)
  positives1 = prototypes2[pseudo_label1]
  negitives1 = prototypes2
  
  # for target
  n_f, c_f, h_f, w_f = f_proj2.shape
  f_proj2 = f_proj2.permute(0, 2, 3, 1).reshape(n_f * h_f * w_f, c_f)
  f_proj2 = torch.nn.functional.normalize(f_proj2, dim=-1)
  pseudo_label2 = pseudo_label2.reshape(-1)
  positives2 = prototypes1[pseudo_label2]
  negitives2 = prototypes1
  A1 = torch.exp(torch.sum(f_proj1 * positives1, dim=-1) / 0.1)
  A2 = torch.sum(torch.exp(torch.matmul(f_proj1, negitives1.transpose(0, 1)) / 0.1), dim=-1)
  loss_nce1 = torch.mean(-1 * torch.log(A1 / A2))
  
  A3 = torch.exp(torch.sum(f_proj2 * positives2, dim=-1) / 0.1)
  A4 = torch.sum(torch.exp(torch.matmul(f_proj2, negitives2.transpose(0, 1)) / 0.1), dim=-1)
  loss_nce2 = torch.mean(-1 * torch.log(A3 / A4))
  
  loss_cross_nce = 0.1 * (loss_nce1 + loss_nce2) / 2