own model input without internsity,how to modify this code
angryhen opened this issue · comments
Hi, my model input only has x, y, z, andwithou intensity.Feature shape is 3. By modifying the relevant parameters of exporter.py, onnx was successfully converted and got params.h.
But don't know how to modify the code in the .cu file to fit my model
Looking forward to your reply
params.h :
#ifndef PARAMS_H_
#define PARAMS_H_
const int MAX_VOXELS = 40000;
class Params
{
public:
static const int num_classes = 1;
const char *class_name [num_classes] = { "Car",};
const float min_x_range = -5.12;
const float max_x_range = 15.36;
const float min_y_range = -5.12;
const float max_y_range = 15.36;
const float min_z_range = -2.0;
const float max_z_range = 2.0;
// the size of a pillar
const float pillar_x_size = 0.16;
const float pillar_y_size = 0.16;
const float pillar_z_size = 4.0;
const int max_num_points_per_pillar = 32;
const int num_point_values = 3;
// the number of feature maps for pillar scatter
const int num_feature_scatter = 64;
const float dir_offset = 0.78539;
const float dir_limit_offset = 0.0;
// the num of direction classes(bins)
const int num_dir_bins = 2;
// anchors decode by (x, y, z, dir)
static const int num_anchors = num_classes * 2;
static const int len_per_anchor = 4;
const float anchors[num_anchors * len_per_anchor] = {
3.9,1.6,1.56,0.0,
3.9,1.6,1.56,1.57,
};
const float anchor_bottom_heights[num_classes] = {-1.78,};
// the score threshold for classification
const float score_thresh = 0.1;
const float nms_thresh = 0.01;
const int max_num_pillars = MAX_VOXELS;
const int pillarPoints_bev = max_num_points_per_pillar * max_num_pillars;
// the detected boxes result decode by (x, y, z, w, l, h, yaw)
const int num_box_values = 7;
// the input size of the 2D backbone network
const int grid_x_size = (max_x_range - min_x_range) / pillar_x_size;
const int grid_y_size = (max_y_range - min_y_range) / pillar_y_size;
const int grid_z_size = (max_z_range - min_z_range) / pillar_z_size;
// the output size of the 2D backbone network
const int feature_x_size = grid_x_size / 2;
const int feature_y_size = grid_y_size / 2;
Params() {};
};
#endif
