regmap_config 中 .use_single_read=true
时间: 2024-05-17 14:18:01 浏览: 12
z - z_bar_mean;
P_zz.mat[0][0] += weights_cov.mat[i][i]*z_diff*z_diff;
}
Matrix K = {{0.0}};
for (i=0; i<N; i++) {
for (int j这个参数表示是否使用单个读操作来访问寄存器。当这个参数设置为 true 时,使用单=0; j<1; j++) {
K.mat[i][j] = P_zx.mat[i][j]/P_zz次读取操作来访问寄存器,否则使用多次读取和写入操作来访问寄存器。.mat[j][j];
}
}
Vector z_diff = {{z->z - sin(x_bar_mean.x[0])}};
使用单个读操作可以提高效率,但是需要硬件支持。如果硬件不支持,那么将会出现错误。
相关问题
给下列程序添加注释: void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) { if (setup_ && config.restore_defaults) { config = default_config_; config.restore_defaults = false; } if ( ! setup_) { default_config_ = config; setup_ = true; } // update generic local planner params base_local_planner::LocalPlannerLimits limits; limits.max_vel_trans = config.max_vel_trans; limits.min_vel_trans = config.min_vel_trans; limits.max_vel_x = config.max_vel_x; limits.min_vel_x = config.min_vel_x; limits.max_vel_y = config.max_vel_y; limits.min_vel_y = config.min_vel_y; limits.max_vel_theta = config.max_vel_theta; limits.min_vel_theta = config.min_vel_theta; limits.acc_lim_x = config.acc_lim_x; limits.acc_lim_y = config.acc_lim_y; limits.acc_lim_theta = config.acc_lim_theta; limits.acc_lim_trans = config.acc_lim_trans; limits.xy_goal_tolerance = config.xy_goal_tolerance; limits.yaw_goal_tolerance = config.yaw_goal_tolerance; limits.prune_plan = config.prune_plan; limits.trans_stopped_vel = config.trans_stopped_vel; limits.theta_stopped_vel = config.theta_stopped_vel; planner_util_.reconfigureCB(limits, config.restore_defaults); // update dwa specific configuration dp_->reconfigure(config); }
/**
* @brief Callback function for dynamic reconfiguration of DWA planner parameters
*
* @param config Reference to the configuration object that stores the updated parameters
* @param level The level of reconfiguration, unused in this function
*/
void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) {
// If the setup has been completed and restore_defaults flag is set, restore default configuration
if (setup_ && config.restore_defaults) {
config = default_config_;
config.restore_defaults = false;
}
// If setup has not been completed, store default configuration and set the setup flag to true
if ( ! setup_) {
default_config_ = config;
setup_ = true;
}
// Update generic local planner parameters
base_local_planner::LocalPlannerLimits limits;
limits.max_vel_trans = config.max_vel_trans;
limits.min_vel_trans = config.min_vel_trans;
limits.max_vel_x = config.max_vel_x;
limits.min_vel_x = config.min_vel_x;
limits.max_vel_y = config.max_vel_y;
limits.min_vel_y = config.min_vel_y;
limits.max_vel_theta = config.max_vel_theta;
limits.min_vel_theta = config.min_vel_theta;
limits.acc_lim_x = config.acc_lim_x;
limits.acc_lim_y = config.acc_lim_y;
limits.acc_lim_theta = config.acc_lim_theta;
limits.acc_lim_trans = config.acc_lim_trans;
limits.xy_goal_tolerance = config.xy_goal_tolerance;
limits.yaw_goal_tolerance = config.yaw_goal_tolerance;
limits.prune_plan = config.prune_plan;
limits.trans_stopped_vel = config.trans_stopped_vel;
limits.theta_stopped_vel = config.theta_stopped_vel;
// Call reconfigureCB function of the planner_util_ object with updated limits and restore_defaults flag
planner_util_.reconfigureCB(limits, config.restore_defaults);
// Call reconfigure function of the dp_ object with updated configuration
dp_->reconfigure(config);
}
self.learning_rate = config.learning_rate self.learning_rates = [self.learning_rate, self.learning_rate * 0.5, self.learning_rate * 0.1] self.learning_rate_boundaries = [50000, 75000] self.max_iter = 10000 self.test_iter = 5000 self.disp_iter = 100 self.snapshot_iter = 5000 self.test_initialization = False self.reg_constant = 0.0 self.data_format = 'channels_first' self.network_parameters = OrderedDict(num_filters_base=config.num_filters_base, activation=config.activation, dropout_ratio=config.dropout_ratio, num_levels=config.num_levels, heatmap_initialization=True, data_format=self.data_format)
这段代码是在MainLoop类的初始化方法中设置了一些训练参数和网络参数。
- self.learning_rate:学习率,根据配置文件中的learning_rate来设置。
- self.learning_rates:学习率列表,根据self.learning_rate计算得到,包括三个值,分别为self.learning_rate、self.learning_rate * 0.5、self.learning_rate * 0.1。
- self.learning_rate_boundaries:学习率变化的边界,根据配置文件中的50000和75000来设置。
- self.max_iter:最大迭代次数,设置为10000。
- self.test_iter:测试迭代次数,设置为5000。
- self.disp_iter:显示迭代次数,每迭代100次显示一次。
- self.snapshot_iter:保存模型迭代次数,每迭代5000次保存一次模型。
- self.test_initialization:测试初始化标志,设置为False。
- self.reg_constant:正则化系数,设置为0.0。
- self.data_format:数据格式,设置为'channels_first'。
- self.network_parameters:网络参数,使用有序字典OrderedDict保存。包括num_filters_base、activation、dropout_ratio、num_levels、heatmap_initialization和data_format等参数。
这些参数和网络参数将在训练过程中使用。如果你还有其他问题,请继续提问。
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