pose_local_parameterization.h:8:1: error: expected class-name before ‘{’ token

This error message typically occurs in C++ when there is a syntax error in the code. In this case, it is indicating that there is an issue with the definition of a class in the file "pose_local_parameterization.h" on line 8.

The error message suggests that the opening curly brace '{' is unexpected, which might indicate that there is a missing or incorrect class name before it. This could be due to a typo or a missing header file that defines the class.

To fix this error, you should review the code on or before line 8 of the file and check for any syntax errors. Ensure that all classes are properly defined and that any necessary header files are included.

相关问题

class DownConv(nn.Module): def __init__(self, seq_len=200, hidden_size=64, m_segments=4,k1=10,channel_reduction=16): super().__init__() """ DownConv is implemented by stacked strided convolution layers and more details can be found below. When the parameters k_1 and k_2 are determined, we can soon get m in Eq.2 of the paper. However, we are more concerned with the size of the parameter m, so we searched for a combination of parameter m and parameter k_1 (parameter k_2 can be easily calculated in this process) to find the optimal segment numbers. Args: input_tensor (torch.Tensor): the input of the attention layer Returns: output_conv (torch.Tensor): the convolutional outputs in Eq.2 of the paper """ self.m =m_segments self.k1 = k1 self.channel_reduction = channel_reduction # avoid over-parameterization middle_segment_length = seq_len/k1 k2=math.ceil(middle_segment_length/m_segments) padding = math.ceil((k2*self.m-middle_segment_length)/2.0) # pad the second convolutional layer appropriately self.conv1a = nn.Conv1d(in_channels=hidden_size, out_channels=hidden_size // self.channel_reduction, kernel_size=self.k1, stride=self.k1) self.relu1a = nn.ReLU(inplace=True) self.conv2a = nn.Conv1d(in_channels=hidden_size // self.channel_reduction, out_channels=hidden_size, kernel_size=k2, stride=k2, padding = padding) def forward(self, input_tensor): input_tensor = input_tensor.permute(0, 2, 1) x1a = self.relu1a(self.conv1a(input_tensor)) x2a = self.conv2a(x1a) if x2a.size(2) != self.m: print('size_erroe, x2a.size_{} do not equals to m_segments_{}'.format(x2a.size(2),self.m)) output_conv = x2a.permute(0, 2, 1) return output_conv

这是一个用于实现降采样卷积（DownConv）的PyTorch模型类。在构造函数中，需要指定一些参数，包括序列长度seq_len，隐藏层大小hidden_size，中间段数m_segments，卷积核大小k1和通道缩减channel_reduction。其中，降采样卷积层的实现使用了两个卷积层，第一个卷积层的卷积核大小为k1，步长为k1，将输入张量进行降采样；第二个卷积层的卷积核大小为k2，步长为k2，将第一个卷积层的输出进行进一步的降采样，并按照论文中的公式计算得到输出张量。为了使得第二个卷积层的输出张量大小与中间段数m_segments相等，需要在卷积层中进行适当的padding。在前向传播时，需要将输入张量进行维度变换，使得其可以被卷积层处理，然后将卷积层的输出张量再次进行维度变换，使得其可以作为下一层的输入。如果第二个卷积层的输出张量大小不等于中间段数m_segments，则会输出一条错误信息。

error: ‘LocalParameterization’ is not a member of ‘ceres’

这个错误通常是由于Ceres版本不兼容导致的。在旧版本的Ceres中，LocalParameterization是一个独立的类，但在新版本中，它被移动到了ceres::internal命名空间中。因此，如果你使用的是旧版本的Ceres，则需要将LocalParameterization更改为ceres::LocalParameterization。如果你使用的是新版本的Ceres，则需要确保你的代码中包含了正确的头文件，并且使用了正确的命名空间。

以下是一个使用ceres::LocalParameterization的示例：

#include <ceres/ceres.h>
#include <ceres/local_parameterization.h>

class MyParameterization : public ceres::LocalParameterization {
 public:
  virtual ~MyParameterization() {}
  virtual bool Plus(const double* x,
                    const double* delta,
                    double* x_plus_delta) const {
    // implementation
  }
  virtual bool ComputeJacobian(const double* x, double* jacobian) const {
    // implementation
  }
  virtual int GlobalSize() const { return 3; }
  virtual int LocalSize() const { return 2; }
};

int main(int argc, char** argv) {
  ceres::Problem problem;
  double x[3] = {0.0, 0.0, 0.0};
  ceres::CostFunction* cost_function = new MyCostFunction;
  ceres::LocalParameterization* parameterization = new MyParameterization;
  problem.AddResidualBlock(cost_function, NULL, x);
  problem.SetParameterization(x, parameterization);
  ceres::Solver::Options options;
  ceres::Solver::Summary summary;
  ceres::Solve(options, &problem, &summary);
  return 0;
}