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ModelDataRaw struct part 4 (#22673)

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* convert model data for leads to struct

* make things more consistent

* change names with path to plan

* consistent use of plural
old-commit-hash: 7bb13acc45
commatwo_master
Greg Hogan 4 years ago committed by GitHub
parent 104ba7be84
commit 466288eff0
2 changed files with 91 additions and 83 deletions
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  1. 114
      selfdrive/modeld/models/driving.cc
  2. 60
      selfdrive/modeld/models/driving.h

114
selfdrive/modeld/models/driving.cc
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@ -77,11 +77,10 @@ ModelDataRaw model_eval_frame(ModelState* s, cl_mem yuv_cl, int width, int heigh
// net outputs
ModelDataRaw net_outputs {
.plan = (ModelDataRawPlans*)&s->output[PLAN_IDX],
.plans = (ModelDataRawPlans*)&s->output[PLAN_IDX],
.lane_lines = (ModelDataRawLaneLines*)&s->output[LL_IDX],
.road_edges = (ModelDataRawRoadEdges*)&s->output[RE_IDX],
.lead = &s->output[LEAD_IDX],
.lead_prob = &s->output[LEAD_PROB_IDX],
.leads = (ModelDataRawLeads*)&s->output[LEAD_IDX],
.meta = &s->output[DESIRE_STATE_IDX],
.pose = (ModelDataRawPose*)&s->output[POSE_IDX],
};
@ -92,59 +91,38 @@ void model_free(ModelState* s) {
delete s->frame;
}
static const float *get_best_data(const float *data, int size, int group_size, int weight_idx) {
int max_idx = 0;
for (int i = 1; i < size; i++) {
if (data[i * group_size + weight_idx] >
data[max_idx * group_size + weight_idx]) {
max_idx = i;
}
}
return &data[max_idx * group_size];
}
static const float *get_lead_data(const float *lead, int t_offset) {
return get_best_data(lead, LEAD_MHP_N, LEAD_MHP_GROUP_SIZE, LEAD_MHP_GROUP_SIZE - LEAD_MHP_SELECTION + t_offset);
}
void fill_sigmoid(const float *input, float *output, int len, int stride) {
for (int i=0; i<len; i++) {
output[i] = sigmoid(input[i*stride]);
}
}
void fill_lead_v3(cereal::ModelDataV2::LeadDataV3::Builder lead, const float *lead_data, const float *prob, int t_offset, float prob_t) {
float t[LEAD_TRAJ_LEN] = {0.0, 2.0, 4.0, 6.0, 8.0, 10.0};
const float *data = get_lead_data(lead_data, t_offset);
lead.setProb(sigmoid(prob[t_offset]));
void fill_lead(cereal::ModelDataV2::LeadDataV3::Builder lead, const ModelDataRawLeads &leads, int t_idx, float prob_t) {
std::array<float, LEAD_TRAJ_LEN> lead_t = {0.0, 2.0, 4.0, 6.0, 8.0, 10.0};
auto best_prediction = leads.get_best_prediction(t_idx);
lead.setProb(sigmoid(leads.prob[t_idx]));
lead.setProbTime(prob_t);
float x_arr[LEAD_TRAJ_LEN];
float y_arr[LEAD_TRAJ_LEN];
float v_arr[LEAD_TRAJ_LEN];
float a_arr[LEAD_TRAJ_LEN];
float x_stds_arr[LEAD_TRAJ_LEN];
float y_stds_arr[LEAD_TRAJ_LEN];
float v_stds_arr[LEAD_TRAJ_LEN];
float a_stds_arr[LEAD_TRAJ_LEN];
std::array<float, LEAD_TRAJ_LEN> lead_x, lead_y, lead_v, lead_a;
std::array<float, LEAD_TRAJ_LEN> lead_x_std, lead_y_std, lead_v_std, lead_a_std;
for (int i=0; i<LEAD_TRAJ_LEN; i++) {
x_arr[i] = data[i*LEAD_PRED_DIM+0];
y_arr[i] = data[i*LEAD_PRED_DIM+1];
v_arr[i] = data[i*LEAD_PRED_DIM+2];
a_arr[i] = data[i*LEAD_PRED_DIM+3];
x_stds_arr[i] = exp(data[LEAD_MHP_VALS + i*LEAD_PRED_DIM+0]);
y_stds_arr[i] = exp(data[LEAD_MHP_VALS + i*LEAD_PRED_DIM+1]);
v_stds_arr[i] = exp(data[LEAD_MHP_VALS + i*LEAD_PRED_DIM+2]);
a_stds_arr[i] = exp(data[LEAD_MHP_VALS + i*LEAD_PRED_DIM+3]);
lead_x[i] = best_prediction.mean[i].x;
lead_y[i] = best_prediction.mean[i].y;
lead_v[i] = best_prediction.mean[i].velocity;
lead_a[i] = best_prediction.mean[i].acceleration;
lead_x_std[i] = exp(best_prediction.std[i].x);
lead_y_std[i] = exp(best_prediction.std[i].y);
lead_v_std[i] = exp(best_prediction.std[i].velocity);
lead_a_std[i] = exp(best_prediction.std[i].acceleration);
}
lead.setT(t);
lead.setX(x_arr);
lead.setY(y_arr);
lead.setV(v_arr);
lead.setA(a_arr);
lead.setXStd(x_stds_arr);
lead.setYStd(y_stds_arr);
lead.setVStd(v_stds_arr);
lead.setAStd(a_stds_arr);
lead.setT(to_kj_array_ptr(lead_t));
lead.setX(to_kj_array_ptr(lead_x));
lead.setY(to_kj_array_ptr(lead_y));
lead.setV(to_kj_array_ptr(lead_v));
lead.setA(to_kj_array_ptr(lead_a));
lead.setXStd(to_kj_array_ptr(lead_x_std));
lead.setYStd(to_kj_array_ptr(lead_y_std));
lead.setVStd(to_kj_array_ptr(lead_v_std));
lead.setAStd(to_kj_array_ptr(lead_a_std));
}
void fill_meta(cereal::ModelDataV2::MetaData::Builder meta, const float *meta_data) {
@ -219,7 +197,7 @@ void fill_xyzt(cereal::ModelDataV2::XYZTData::Builder xyzt, const std::array<flo
xyzt.setZStd(to_kj_array_ptr(z_std));
}
void fill_plan(cereal::ModelDataV2::Builder &framed, const ModelDataRawPlanPath &plan) {
void fill_plan(cereal::ModelDataV2::Builder &framed, const ModelDataRawPlanPrediction &plan) {
std::array<float, TRAJECTORY_SIZE> pos_x, pos_y, pos_z;
std::array<float, TRAJECTORY_SIZE> pos_x_std, pos_y_std, pos_z_std;
std::array<float, TRAJECTORY_SIZE> vel_x, vel_y, vel_z;
@ -250,7 +228,7 @@ void fill_plan(cereal::ModelDataV2::Builder &framed, const ModelDataRawPlanPath
fill_xyzt(framed.initOrientationRate(), T_IDXS_FLOAT, rot_rate_x, rot_rate_y, rot_rate_z);
}
void fill_lane_lines(cereal::ModelDataV2::Builder &framed, const std::array<float, TRAJECTORY_SIZE> &plan_t_arr,
void fill_lane_lines(cereal::ModelDataV2::Builder &framed, const std::array<float, TRAJECTORY_SIZE> &plan_t,
const ModelDataRawLaneLines &lanes) {
std::array<float, TRAJECTORY_SIZE> left_far_y, left_far_z;
std::array<float, TRAJECTORY_SIZE> left_near_y, left_near_z;
@ -268,10 +246,10 @@ void fill_lane_lines(cereal::ModelDataV2::Builder &framed, const std::array<floa
}
auto lane_lines = framed.initLaneLines(4);
fill_xyzt(lane_lines[0], plan_t_arr, X_IDXS_FLOAT, left_far_y, left_far_z);
fill_xyzt(lane_lines[1], plan_t_arr, X_IDXS_FLOAT, left_near_y, left_near_z);
fill_xyzt(lane_lines[2], plan_t_arr, X_IDXS_FLOAT, right_near_y, right_near_z);
fill_xyzt(lane_lines[3], plan_t_arr, X_IDXS_FLOAT, right_far_y, right_far_z);
fill_xyzt(lane_lines[0], plan_t, X_IDXS_FLOAT, left_far_y, left_far_z);
fill_xyzt(lane_lines[1], plan_t, X_IDXS_FLOAT, left_near_y, left_near_z);
fill_xyzt(lane_lines[2], plan_t, X_IDXS_FLOAT, right_near_y, right_near_z);
fill_xyzt(lane_lines[3], plan_t, X_IDXS_FLOAT, right_far_y, right_far_z);
framed.setLaneLineStds({
exp(lanes.std.left_far[0].y),
@ -288,7 +266,7 @@ void fill_lane_lines(cereal::ModelDataV2::Builder &framed, const std::array<floa
});
}
void fill_road_edges(cereal::ModelDataV2::Builder &framed, const std::array<float, TRAJECTORY_SIZE> &plan_t_arr,
void fill_road_edges(cereal::ModelDataV2::Builder &framed, const std::array<float, TRAJECTORY_SIZE> &plan_t,
const ModelDataRawRoadEdges &edges) {
std::array<float, TRAJECTORY_SIZE> left_y, left_z;
std::array<float, TRAJECTORY_SIZE> right_y, right_z;
@ -300,8 +278,8 @@ void fill_road_edges(cereal::ModelDataV2::Builder &framed, const std::array<floa
}
auto road_edges = framed.initRoadEdges(2);
fill_xyzt(road_edges[0], plan_t_arr, X_IDXS_FLOAT, left_y, left_z);
fill_xyzt(road_edges[1], plan_t_arr, X_IDXS_FLOAT, right_y, right_z);
fill_xyzt(road_edges[0], plan_t, X_IDXS_FLOAT, left_y, left_z);
fill_xyzt(road_edges[1], plan_t, X_IDXS_FLOAT, right_y, right_z);
framed.setRoadEdgeStds({
exp(edges.std.left[0].y),
@ -310,18 +288,18 @@ void fill_road_edges(cereal::ModelDataV2::Builder &framed, const std::array<floa
}
void fill_model(cereal::ModelDataV2::Builder &framed, const ModelDataRaw &net_outputs) {
auto best_plan = net_outputs.plan->get_best_plan();
std::array<float, TRAJECTORY_SIZE> plan_t_arr;
std::fill_n(plan_t_arr.data(), plan_t_arr.size(), NAN);
plan_t_arr[0] = 0.0;
auto best_plan = net_outputs.plans->get_best_prediction();
std::array<float, TRAJECTORY_SIZE> plan_t;
std::fill_n(plan_t.data(), plan_t.size(), NAN);
plan_t[0] = 0.0;
for (int xidx=1, tidx=0; xidx<TRAJECTORY_SIZE; xidx++) {
// increment tidx until we find an element that's further away than the current xidx
for (int next_tid = tidx + 1; next_tid < TRAJECTORY_SIZE && best_plan.mean[next_tid].position.x < X_IDXS[xidx]; next_tid++) {
tidx++;
}
if (tidx == TRAJECTORY_SIZE - 1) {
// if the plan doesn't extend far enough, set plan_t to the max value (10s), then break
plan_t_arr[xidx] = T_IDXS[TRAJECTORY_SIZE - 1];
// if the Plan doesn't extend far enough, set plan_t to the max value (10s), then break
plan_t[xidx] = T_IDXS[TRAJECTORY_SIZE - 1];
break;
}
@ -329,21 +307,21 @@ void fill_model(cereal::ModelDataV2::Builder &framed, const ModelDataRaw &net_ou
float current_x_val = best_plan.mean[tidx].position.x;
float next_x_val = best_plan.mean[tidx+1].position.x;
float p = (X_IDXS[xidx] - current_x_val) / (next_x_val - current_x_val);
plan_t_arr[xidx] = p * T_IDXS[tidx+1] + (1 - p) * T_IDXS[tidx];
plan_t[xidx] = p * T_IDXS[tidx+1] + (1 - p) * T_IDXS[tidx];
}
fill_plan(framed, best_plan);
fill_lane_lines(framed, plan_t_arr, *net_outputs.lane_lines);
fill_road_edges(framed, plan_t_arr, *net_outputs.road_edges);
fill_lane_lines(framed, plan_t, *net_outputs.lane_lines);
fill_road_edges(framed, plan_t, *net_outputs.road_edges);
// meta
fill_meta(framed.initMeta(), net_outputs.meta);
// leads
auto leads = framed.initLeadsV3(LEAD_MHP_SELECTION);
float t_offsets[LEAD_MHP_SELECTION] = {0.0, 2.0, 4.0};
for (int t_offset=0; t_offset<LEAD_MHP_SELECTION; t_offset++) {
fill_lead_v3(leads[t_offset], net_outputs.lead, net_outputs.lead_prob, t_offset, t_offsets[t_offset]);
std::array<float, LEAD_MHP_SELECTION> t_offsets = {0.0, 2.0, 4.0};
for (int i=0; i<LEAD_MHP_SELECTION; i++) {
fill_lead(leads[i], *net_outputs.leads, i, t_offsets[i]);
}
}

60
selfdrive/modeld/models/driving.h
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@ -68,36 +68,36 @@ struct ModelDataRawYZ {
};
static_assert(sizeof(ModelDataRawYZ) == sizeof(float)*2);
struct ModelDataRawPlanTimeStep {
struct ModelDataRawPlanElement {
ModelDataRawXYZ position;
ModelDataRawXYZ velocity;
ModelDataRawXYZ acceleration;
ModelDataRawXYZ rotation;
ModelDataRawXYZ rotation_rate;
};
static_assert(sizeof(ModelDataRawPlanTimeStep) == sizeof(ModelDataRawXYZ)*5);
static_assert(sizeof(ModelDataRawPlanElement) == sizeof(ModelDataRawXYZ)*5);
struct ModelDataRawPlanPath {
std::array<ModelDataRawPlanTimeStep, TRAJECTORY_SIZE> mean;
std::array<ModelDataRawPlanTimeStep, TRAJECTORY_SIZE> std;
struct ModelDataRawPlanPrediction {
std::array<ModelDataRawPlanElement, TRAJECTORY_SIZE> mean;
std::array<ModelDataRawPlanElement, TRAJECTORY_SIZE> std;
float prob;
};
static_assert(sizeof(ModelDataRawPlanPath) == (sizeof(ModelDataRawPlanTimeStep)*TRAJECTORY_SIZE*2) + sizeof(float));
static_assert(sizeof(ModelDataRawPlanPrediction) == (sizeof(ModelDataRawPlanElement)*TRAJECTORY_SIZE*2) + sizeof(float));
struct ModelDataRawPlans {
std::array<ModelDataRawPlanPath, PLAN_MHP_N> path;
std::array<ModelDataRawPlanPrediction, PLAN_MHP_N> prediction;
constexpr const ModelDataRawPlanPath &get_best_plan() const {
constexpr const ModelDataRawPlanPrediction &get_best_prediction() const {
int max_idx = 0;
for (int i = 1; i < path.size(); i++) {
if (path[i].prob > path[max_idx].prob) {
for (int i = 1; i < prediction.size(); i++) {
if (prediction[i].prob > prediction[max_idx].prob) {
max_idx = i;
}
}
return path[max_idx];
return prediction[max_idx];
}
};
static_assert(sizeof(ModelDataRawPlans) == sizeof(ModelDataRawPlanPath)*PLAN_MHP_N);
static_assert(sizeof(ModelDataRawPlans) == sizeof(ModelDataRawPlanPrediction)*PLAN_MHP_N);
struct ModelDataRawLinesXY {
std::array<ModelDataRawYZ, TRAJECTORY_SIZE> left_far;
@ -140,6 +140,37 @@ struct ModelDataRawRoadEdges {
};
static_assert(sizeof(ModelDataRawRoadEdges) == (sizeof(ModelDataRawEdgessXY)*2));
struct ModelDataRawLeadElement {
float x;
float y;
float velocity;
float acceleration;
};
static_assert(sizeof(ModelDataRawLeadElement) == sizeof(float)*4);
struct ModelDataRawLeadPrediction {
std::array<ModelDataRawLeadElement, LEAD_TRAJ_LEN> mean;
std::array<ModelDataRawLeadElement, LEAD_TRAJ_LEN> std;
std::array<float, LEAD_MHP_SELECTION> prob;
};
static_assert(sizeof(ModelDataRawLeadPrediction) == (sizeof(ModelDataRawLeadElement)*LEAD_TRAJ_LEN*2) + (sizeof(float)*LEAD_MHP_SELECTION));
struct ModelDataRawLeads {
std::array<ModelDataRawLeadPrediction, LEAD_MHP_N> prediction;
std::array<float, LEAD_MHP_SELECTION> prob;
constexpr const ModelDataRawLeadPrediction &get_best_prediction(int t_idx) const {
int max_idx = 0;
for (int i = 1; i < prediction.size(); i++) {
if (prediction[i].prob[t_idx] > prediction[max_idx].prob[t_idx]) {
max_idx = i;
}
}
return prediction[max_idx];
}
};
static_assert(sizeof(ModelDataRawLeads) == (sizeof(ModelDataRawLeadPrediction)*LEAD_MHP_N) + (sizeof(float)*LEAD_MHP_SELECTION));
struct ModelDataRawPose {
ModelDataRawXYZ velocity_mean;
ModelDataRawXYZ rotation_mean;
@ -149,11 +180,10 @@ struct ModelDataRawPose {
static_assert(sizeof(ModelDataRawPose) == sizeof(ModelDataRawXYZ)*4);
struct ModelDataRaw {
const ModelDataRawPlans *const plan;
const ModelDataRawPlans *const plans;
const ModelDataRawLaneLines *const lane_lines;
const ModelDataRawRoadEdges *const road_edges;
const float *const lead;
const float *const lead_prob;
const ModelDataRawLeads *const leads;
const float *const desire_state;
const float *const meta;
const float *const desire_pred;

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