@ -9,14 +9,17 @@
# include "common/params.h" # include "common/params.h"
# include "driving.h" # include "driving.h"
# define PATH_IDX 0 # define MIN_VALID_LEN 10.0
# define LL_IDX PATH_IDX + MODEL_PATH_DISTANCE*2 + 1 # define TRAJECTORY_SIZE 33
# define RL_IDX LL_IDX + MODEL_PATH_DISTANCE*2 + 2 # define TRAJECTORY_TIME 10.0
# define LEAD_IDX RL_IDX + MODEL_PATH_DISTANCE*2 + 2 # define TRAJECTORY_DISTANCE 192.0
# define LONG_X_IDX LEAD_IDX + MDN_GROUP_SIZE*LEAD_MDN_N + SELECTION # define PLAN_IDX 0
# define LONG_V_IDX LONG_X_IDX + TIME_DISTANCE*2 # define LL_IDX PLAN_IDX + PLAN_MHP_N*(PLAN_MHP_GROUP_SIZE)
# define LONG_A_IDX LONG_V_IDX + TIME_DISTANCE*2 # define LL_PROB_IDX LL_IDX + 4*2*2*33
# define DESIRE_STATE_IDX LONG_A_IDX + TIME_DISTANCE*2 # define RE_IDX LL_PROB_IDX + 4
# define LEAD_IDX RE_IDX + 2*2*2*33
# define LEAD_PROB_IDX LEAD_IDX + LEAD_MHP_N*(LEAD_MHP_GROUP_SIZE)
# define DESIRE_STATE_IDX LEAD_PROB_IDX + 3
# define META_IDX DESIRE_STATE_IDX + DESIRE_LEN # define META_IDX DESIRE_STATE_IDX + DESIRE_LEN
# define POSE_IDX META_IDX + OTHER_META_SIZE + DESIRE_PRED_SIZE # define POSE_IDX META_IDX + OTHER_META_SIZE + DESIRE_PRED_SIZE
# define OUTPUT_SIZE POSE_IDX + POSE_SIZE # define OUTPUT_SIZE POSE_IDX + POSE_SIZE
@ -29,6 +32,8 @@
// #define DUMP_YUV
// #define DUMP_YUV
Eigen : : Matrix < float , MODEL_PATH_DISTANCE , POLYFIT_DEGREE - 1 > vander ; Eigen : : Matrix < float , MODEL_PATH_DISTANCE , POLYFIT_DEGREE - 1 > vander ;
float X_IDXS [ TRAJECTORY_SIZE ] ;
float T_IDXS [ TRAJECTORY_SIZE ] ;
void model_init ( ModelState * s , cl_device_id device_id , cl_context context , int temporal ) { void model_init ( ModelState * s , cl_device_id device_id , cl_context context , int temporal ) {
frame_init ( & s - > frame , MODEL_WIDTH , MODEL_HEIGHT , device_id , context ) ; frame_init ( & s - > frame , MODEL_WIDTH , MODEL_HEIGHT , device_id , context ) ;
@ -63,9 +68,11 @@ void model_init(ModelState* s, cl_device_id device_id, cl_context context, int t
# endif # endif
// Build Vandermonde matrix
// Build Vandermonde matrix
for ( int i = 0 ; i < MODEL_PATH_DISTANC E; i + + ) { for ( int i = 0 ; i < TRAJECTORY_SIZ E; i + + ) {
for ( int j = 0 ; j < POLYFIT_DEGREE - 1 ; j + + ) { for ( int j = 0 ; j < POLYFIT_DEGREE - 1 ; j + + ) {
vander ( i , j ) = pow ( i , POLYFIT_DEGREE - j - 1 ) ; X_IDXS [ i ] = ( TRAJECTORY_DISTANCE / 1024.0 ) * ( pow ( i , 2 ) ) ;
T_IDXS [ i ] = ( TRAJECTORY_TIME / 1024.0 ) * ( pow ( i , 2 ) ) ;
vander ( i , j ) = pow ( X_IDXS [ i ] , POLYFIT_DEGREE - j - 1 ) ;
} }
} }
} }
@ -76,7 +83,7 @@ ModelDataRaw model_eval_frame(ModelState* s, cl_command_queue q,
float * desire_in ) { float * desire_in ) {
# ifdef DESIRE # ifdef DESIRE
if ( desire_in ! = NULL ) { if ( desire_in ! = NULL ) {
for ( int i = 0 ; i < DESIRE_LEN ; i + + ) { for ( int i = 1 ; i < DESIRE_LEN ; i + + ) {
// Model decides when action is completed
// Model decides when action is completed
// so desire input is just a pulse triggered on rising edge
// so desire input is just a pulse triggered on rising edge
if ( desire_in [ i ] - s - > prev_desire [ i ] > .99 ) { if ( desire_in [ i ] - s - > prev_desire [ i ] > .99 ) {
@ -107,13 +114,12 @@ ModelDataRaw model_eval_frame(ModelState* s, cl_command_queue q,
// net outputs
// net outputs
ModelDataRaw net_outputs ; ModelDataRaw net_outputs ;
net_outputs . path = & s - > output [ PATH_IDX ] ; net_outputs . plan = & s - > output [ PLAN_IDX ] ;
net_outputs . left_lane = & s - > output [ LL_IDX ] ; net_outputs . lane_lines = & s - > output [ LL_IDX ] ;
net_outputs . right_lane = & s - > output [ RL_IDX ] ; net_outputs . lane_lines_prob = & s - > output [ LL_PROB_IDX ] ;
net_outputs . road_edges = & s - > output [ RE_IDX ] ;
net_outputs . lead = & s - > output [ LEAD_IDX ] ; net_outputs . lead = & s - > output [ LEAD_IDX ] ;
net_outputs . long_x = & s - > output [ LONG_X_IDX ] ; net_outputs . lead_prob = & s - > output [ LEAD_PROB_IDX ] ;
net_outputs . long_v = & s - > output [ LONG_V_IDX ] ;
net_outputs . long_a = & s - > output [ LONG_A_IDX ] ;
net_outputs . meta = & s - > output [ DESIRE_STATE_IDX ] ; net_outputs . meta = & s - > output [ DESIRE_STATE_IDX ] ;
net_outputs . pose = & s - > output [ POSE_IDX ] ; net_outputs . pose = & s - > output [ POSE_IDX ] ;
return net_outputs ; return net_outputs ;
@ -151,35 +157,40 @@ void poly_fit(float *in_pts, float *in_stds, float *out, int valid_len) {
out [ 3 ] = y0 ; out [ 3 ] = y0 ;
} }
void fill_path ( cereal : : ModelData : : PathData : : Builder path , const float * data , bool has_prob , const float offset ) { void fill_path ( cereal : : ModelData : : PathData : : Builder path , const float * data , float valid_len , int valid_len_idx ) {
float points_arr [ MODEL_PATH_DISTANC E] ; float points_arr [ TRAJECTORY_SIZ E] ;
float stds_arr [ MODEL_PATH_DISTANC E] ; float stds_arr [ TRAJECTORY_SIZ E] ;
float poly_arr [ POLYFIT_DEGREE ] ; float poly_arr [ POLYFIT_DEGREE ] ;
float std ; float std ;
float prob ;
float valid_len ;
// clamp to 5 and MODEL_PATH_DISTANCE
for ( int i = 0 ; i < TRAJECTORY_SIZE ; i + + ) {
valid_len = fmin ( MODEL_PATH_DISTANCE , fmax ( 5 , data [ MODEL_PATH_DISTANCE * 2 ] ) ) ; // negative sign because mpc has left positive
for ( int i = 0 ; i < MODEL_PATH_DISTANCE ; i + + ) { points_arr [ i ] = - data [ 30 * i + 16 ] ;
points_arr [ i ] = data [ i ] + offset ; stds_arr [ i ] = exp ( data [ 30 * ( 33 + i ) + 16 ] ) ;
stds_arr [ i ] = softplus ( data [ MODEL_PATH_DISTANCE + i ] ) + 1e-6 ;
}
if ( has_prob ) {
prob = sigmoid ( data [ MODEL_PATH_DISTANCE * 2 + 1 ] ) ;
} else {
prob = 1.0 ;
} }
std = softplus ( data [ MODEL_PATH_DISTANCE ] ) + 1e-6 ; std = stds_arr [ 0 ] ;
poly_fit ( points_arr , stds_arr , poly_arr , valid_len ) ; poly_fit ( points_arr , stds_arr , poly_arr , valid_len_idx ) ;
kj : : ArrayPtr < const float > poly ( & poly_arr [ 0 ] , ARRAYSIZE ( poly_arr ) ) ;
path . setPoly ( poly ) ;
path . setProb ( 1.0 ) ;
path . setStd ( std ) ;
path . setValidLen ( valid_len ) ;
}
if ( std : : getenv ( " DEBUG " ) ) { void fill_lane_line ( cereal : : ModelData : : PathData : : Builder path , const float * data , int ll_idx , float valid_len , int valid_len_idx , float prob ) {
kj : : ArrayPtr < const float > stds ( & stds_arr [ 0 ] , ARRAYSIZE ( stds_arr ) ) ; float points_arr [ TRAJECTORY_SIZE ] ;
path . setStds ( stds ) ; float stds_arr [ TRAJECTORY_SIZE ] ;
float poly_arr [ POLYFIT_DEGREE ] ;
float std ;
kj : : ArrayPtr < const float > points ( & points_arr [ 0 ] , ARRAYSIZE ( points_arr ) ) ; for ( int i = 0 ; i < TRAJECTORY_SIZE ; i + + ) {
path . setPoints ( points ) ; // negative sign because mpc has left positive
points_arr [ i ] = - data [ 2 * 33 * ll_idx + 2 * i ] ;
stds_arr [ i ] = exp ( data [ 2 * 33 * ( 4 + ll_idx ) + 2 * i ] ) ;
} }
std = stds_arr [ 0 ] ;
poly_fit ( points_arr , stds_arr , poly_arr , valid_len_idx ) ;
kj : : ArrayPtr < const float > poly ( & poly_arr [ 0 ] , ARRAYSIZE ( poly_arr ) ) ; kj : : ArrayPtr < const float > poly ( & poly_arr [ 0 ] , ARRAYSIZE ( poly_arr ) ) ;
path . setPoly ( poly ) ; path . setPoly ( poly ) ;
@ -188,48 +199,197 @@ void fill_path(cereal::ModelData::PathData::Builder path, const float * data, bo
path . setValidLen ( valid_len ) ; path . setValidLen ( valid_len ) ;
} }
void fill_lead ( cereal : : ModelData : : LeadData : : Builder lead , const float * data , int mdn_max_idx , int t_offset ) { void fill_lead_v2 ( cereal : : ModelDataV2 : : LeadDataV2 : : Builder lead , const float * data , float prob , float t ) {
const double x_scale = 10.0 ; lead . setProb ( prob ) ;
const double y_scale = 10.0 ; lead . setT ( t ) ;
float xyva_arr [ LEAD_MHP_VALS ] ;
lead . setProb ( sigmoid ( data [ LEAD_MDN_N * MDN_GROUP_SIZE + t_offset ] ) ) ; float xyva_stds_arr [ LEAD_MHP_VALS ] ;
lead . setDist ( x_scale * data [ mdn_max_idx * MDN_GROUP_SIZE ] ) ; for ( int i = 0 ; i < LEAD_MHP_VALS ; i + + ) {
lead . setStd ( x_scale * softplus ( data [ mdn_max_idx * MDN_GROUP_SIZE + MDN_VALS ] ) ) ; xyva_arr [ i ] = data [ LEAD_MHP_VALS + i ] ;
lead . setRelY ( y_scale * data [ mdn_max_idx * MDN_GROUP_SIZE + 1 ] ) ; xyva_stds_arr [ i ] = exp ( data [ LEAD_MHP_VALS + i ] ) ;
lead . setRelYStd ( y_scale * softplus ( data [ mdn_max_idx * MDN_GROUP_SIZE + MDN_VALS + 1 ] ) ) ; }
lead . setRelVel ( data [ mdn_max_idx * MDN_GROUP_SIZE + 2 ] ) ; kj : : ArrayPtr < const float > xyva ( xyva_arr , LEAD_MHP_VALS ) ;
lead . setRelVelStd ( softplus ( data [ mdn_max_idx * MDN_GROUP_SIZE + MDN_VALS + 2 ] ) ) ; kj : : ArrayPtr < const float > xyva_stds ( xyva_stds_arr , LEAD_MHP_VALS ) ;
lead . setRelA ( data [ mdn_max_idx * MDN_GROUP_SIZE + 3 ] ) ; lead . setXyva ( xyva ) ;
lead . setRelAStd ( softplus ( data [ mdn_max_idx * MDN_GROUP_SIZE + MDN_VALS + 3 ] ) ) ; lead . setXyvaStd ( xyva_stds ) ;
}
void fill_lead ( cereal : : ModelData : : LeadData : : Builder lead , const float * data , float prob ) {
lead . setProb ( prob ) ;
lead . setDist ( data [ 0 ] ) ;
lead . setStd ( exp ( data [ LEAD_MHP_VALS ] ) ) ;
// TODO make all msgs same format
lead . setRelY ( - data [ 1 ] ) ;
lead . setRelYStd ( exp ( data [ LEAD_MHP_VALS + 1 ] ) ) ;
lead . setRelVel ( data [ 2 ] ) ;
lead . setRelVelStd ( exp ( data [ LEAD_MHP_VALS + 2 ] ) ) ;
lead . setRelA ( data [ 3 ] ) ;
lead . setRelAStd ( exp ( data [ LEAD_MHP_VALS + 3 ] ) ) ;
} }
void fill_meta ( cereal : : ModelData : : MetaData : : Builder meta , const float * meta_data ) { void fill_meta ( cereal : : ModelData : : MetaData : : Builder meta , const float * meta_data ) {
kj : : ArrayPtr < const float > desire_state ( & meta_data [ 0 ] , DESIRE_LEN ) ; float desire_state_softmax [ DESIRE_LEN ] ;
float desire_pred_softmax [ 4 * DESIRE_LEN ] ;
softmax ( & meta_data [ 0 ] , desire_state_softmax , DESIRE_LEN ) ;
for ( int i = 0 ; i < 4 ; i + + ) {
softmax ( & meta_data [ DESIRE_LEN + OTHER_META_SIZE + i * DESIRE_LEN ] ,
& desire_pred_softmax [ i * DESIRE_LEN ] , DESIRE_LEN ) ;
}
kj : : ArrayPtr < const float > desire_state ( desire_state_softmax , DESIRE_LEN ) ;
meta . setDesireState ( desire_state ) ; meta . setDesireState ( desire_state ) ;
meta . setEngagedProb ( meta_data [ DESIRE_LEN ] ) ; meta . setEngagedProb ( sigmoid ( meta_data [ DESIRE_LEN ] ) ) ;
meta . setGasDisengageProb ( meta_data [ DESIRE_LEN + 1 ] ) ; meta . setGasDisengageProb ( sigmoid ( meta_data [ DESIRE_LEN + 1 ] ) ) ;
meta . setBrakeDisengageProb ( meta_data [ DESIRE_LEN + 2 ] ) ; meta . setBrakeDisengageProb ( sigmoid ( meta_data [ DESIRE_LEN + 2 ] ) ) ;
meta . setSteerOverrideProb ( meta_data [ DESIRE_LEN + 3 ] ) ; meta . setSteerOverrideProb ( sigmoid ( meta_data [ DESIRE_LEN + 3 ] ) ) ;
kj : : ArrayPtr < const float > desire_pred ( & meta_data [ DESIRE_LEN + OTHER_META_SIZE ] , DESIRE_PRED_SIZE ) ; kj : : ArrayPtr < const float > desire_pred ( desire_pred_softmax , DESIRE_PRED_SIZE ) ;
meta . setDesirePrediction ( desire_pred ) ; meta . setDesirePrediction ( desire_pred ) ;
} }
void fill_longi ( cereal : : ModelData : : LongitudinalData : : Builder longi , const float * long_x_data , const float * long_v_data , const float * long_a_data ) { void fill_meta_v2 ( cereal : : ModelDataV2 : : MetaData : : Builder meta , const float * meta_data ) {
// just doing 10 vals, 1 every sec for now
float desire_state_softmax [ DESIRE_LEN ] ;
float dist_arr [ TIME_DISTANCE / 10 ] ; float desire_pred_softmax [ 4 * DESIRE_LEN ] ;
float speed_arr [ TIME_DISTANCE / 10 ] ; softmax ( & meta_data [ 0 ] , desire_state_softmax , DESIRE_LEN ) ;
float accel_arr [ TIME_DISTANCE / 10 ] ; for ( int i = 0 ; i < 4 ; i + + ) {
for ( int i = 0 ; i < TIME_DISTANCE / 10 ; i + + ) { softmax ( & meta_data [ DESIRE_LEN + OTHER_META_SIZE + i * DESIRE_LEN ] ,
dist_arr [ i ] = long_x_data [ i * 10 ] ; & desire_pred_softmax [ i * DESIRE_LEN ] , DESIRE_LEN ) ;
speed_arr [ i ] = long_v_data [ i * 10 ] ; }
accel_arr [ i ] = long_a_data [ i * 10 ] ; kj : : ArrayPtr < const float > desire_state ( desire_state_softmax , DESIRE_LEN ) ;
meta . setDesireState ( desire_state ) ;
meta . setEngagedProb ( sigmoid ( meta_data [ DESIRE_LEN ] ) ) ;
meta . setGasDisengageProb ( sigmoid ( meta_data [ DESIRE_LEN + 1 ] ) ) ;
meta . setBrakeDisengageProb ( sigmoid ( meta_data [ DESIRE_LEN + 2 ] ) ) ;
meta . setSteerOverrideProb ( sigmoid ( meta_data [ DESIRE_LEN + 3 ] ) ) ;
kj : : ArrayPtr < const float > desire_pred ( desire_pred_softmax , DESIRE_PRED_SIZE ) ;
meta . setDesirePrediction ( desire_pred ) ;
}
void fill_xyzt ( cereal : : ModelDataV2 : : XYZTData : : Builder xyzt , const float * data ,
int columns , int column_offset , float * plan_t_arr ) {
float x_arr [ TRAJECTORY_SIZE ] ;
float y_arr [ TRAJECTORY_SIZE ] ;
float z_arr [ TRAJECTORY_SIZE ] ;
//float x_std_arr[TRAJECTORY_SIZE];
//float y_std_arr[TRAJECTORY_SIZE];
//float z_std_arr[TRAJECTORY_SIZE];
float t_arr [ TRAJECTORY_SIZE ] ;
for ( int i = 0 ; i < TRAJECTORY_SIZE ; i + + ) {
// column_offset == -1 means this data is X indexed not T indexed
if ( column_offset > = 0 ) {
t_arr [ i ] = T_IDXS [ i ] ;
x_arr [ i ] = data [ i * columns + 0 + column_offset ] ;
//x_std_arr[i] = data[columns*(TRAJECTORY_SIZE + i) + 0 + column_offset];
} else {
t_arr [ i ] = plan_t_arr [ i ] ;
x_arr [ i ] = X_IDXS [ i ] ;
//x_std_arr[i] = NAN;
}
y_arr [ i ] = data [ i * columns + 1 + column_offset ] ;
//y_std_arr[i] = data[columns*(TRAJECTORY_SIZE + i) + 1 + column_offset];
z_arr [ i ] = data [ i * columns + 2 + column_offset ] ;
//z_std_arr[i] = data[columns*(TRAJECTORY_SIZE + i) + 2 + column_offset];
}
kj : : ArrayPtr < const float > x ( x_arr , TRAJECTORY_SIZE ) ;
kj : : ArrayPtr < const float > y ( y_arr , TRAJECTORY_SIZE ) ;
kj : : ArrayPtr < const float > z ( z_arr , TRAJECTORY_SIZE ) ;
//kj::ArrayPtr<const float> x_std(x_std_arr, TRAJECTORY_SIZE);
//kj::ArrayPtr<const float> y_std(y_std_arr, TRAJECTORY_SIZE);
//kj::ArrayPtr<const float> z_std(z_std_arr, TRAJECTORY_SIZE);
kj : : ArrayPtr < const float > t ( t_arr , TRAJECTORY_SIZE ) ;
xyzt . setX ( x ) ;
xyzt . setY ( y ) ;
xyzt . setZ ( z ) ;
//xyzt.setXStd(x_std);
//xyzt.setYStd(y_std);
//xyzt.setZStd(z_std);
xyzt . setT ( t ) ;
}
void model_publish_v2 ( PubMaster & pm , uint32_t vipc_frame_id , uint32_t frame_id ,
uint32_t vipc_dropped_frames , float frame_drop ,
const ModelDataRaw & net_outputs , uint64_t timestamp_eof ) {
// make msg
MessageBuilder msg ;
auto framed = msg . initEvent ( frame_drop < MAX_FRAME_DROP ) . initModelV2 ( ) ;
uint32_t frame_age = ( frame_id > vipc_frame_id ) ? ( frame_id - vipc_frame_id ) : 0 ;
framed . setFrameId ( vipc_frame_id ) ;
framed . setFrameAge ( frame_age ) ;
framed . setFrameDropPerc ( frame_drop * 100 ) ;
framed . setTimestampEof ( timestamp_eof ) ;
// plan
int plan_mhp_max_idx = 0 ;
for ( int i = 1 ; i < PLAN_MHP_N ; i + + ) {
if ( net_outputs . plan [ ( i + 1 ) * ( PLAN_MHP_GROUP_SIZE ) - 1 ] >
net_outputs . plan [ ( plan_mhp_max_idx + 1 ) * ( PLAN_MHP_GROUP_SIZE ) - 1 ] ) {
plan_mhp_max_idx = i ;
}
}
float valid_len = net_outputs . plan [ plan_mhp_max_idx * ( PLAN_MHP_GROUP_SIZE ) + 30 * 32 ] ;
valid_len = fmin ( MODEL_PATH_DISTANCE , fmax ( MIN_VALID_LEN , valid_len ) ) ;
int valid_len_idx = 0 ;
for ( int i = 1 ; i < TRAJECTORY_SIZE ; i + + ) {
if ( valid_len > = X_IDXS [ valid_len_idx ] ) {
valid_len_idx = i ;
}
}
float * best_plan = & net_outputs . plan [ plan_mhp_max_idx * ( PLAN_MHP_GROUP_SIZE ) ] ;
float plan_t_arr [ TRAJECTORY_SIZE ] ;
for ( int i = 0 ; i < TRAJECTORY_SIZE ; i + + ) {
plan_t_arr [ i ] = best_plan [ i * PLAN_MHP_COLUMNS + 15 ] ;
} }
kj : : ArrayPtr < const float > dist ( & dist_arr [ 0 ] , ARRAYSIZE ( dist_arr ) ) ;
longi . setDistances ( dist ) ; auto position = framed . initPosition ( ) ;
kj : : ArrayPtr < const float > speed ( & speed_arr [ 0 ] , ARRAYSIZE ( speed_arr ) ) ; fill_xyzt ( position , best_plan , PLAN_MHP_COLUMNS , 0 , plan_t_arr ) ;
longi . setSpeeds ( speed ) ; auto orientation = framed . initOrientation ( ) ;
kj : : ArrayPtr < const float > accel ( & accel_arr [ 0 ] , ARRAYSIZE ( accel_arr ) ) ; fill_xyzt ( orientation , best_plan , PLAN_MHP_COLUMNS , 3 , plan_t_arr ) ;
longi . setAccelerations ( accel ) ; auto velocity = framed . initVelocity ( ) ;
fill_xyzt ( velocity , best_plan , PLAN_MHP_COLUMNS , 6 , plan_t_arr ) ;
auto orientation_rate = framed . initOrientationRate ( ) ;
fill_xyzt ( orientation_rate , best_plan , PLAN_MHP_COLUMNS , 9 , plan_t_arr ) ;
// lane lines
auto lane_lines = framed . initLaneLines ( 4 ) ;
float lane_line_probs_arr [ 4 ] ;
float lane_line_stds_arr [ 4 ] ;
for ( int i = 0 ; i < 4 ; i + + ) {
fill_xyzt ( lane_lines [ i ] , & net_outputs . lane_lines [ i * TRAJECTORY_SIZE * 2 ] , 2 , - 1 , plan_t_arr ) ;
lane_line_probs_arr [ i ] = sigmoid ( net_outputs . lane_lines_prob [ i ] ) ;
lane_line_stds_arr [ i ] = exp ( net_outputs . lane_lines [ 2 * TRAJECTORY_SIZE * ( 4 + i ) ] ) ;
}
kj : : ArrayPtr < const float > lane_line_probs ( lane_line_probs_arr , 4 ) ;
framed . setLaneLineProbs ( lane_line_probs ) ;
framed . setLaneLineStds ( lane_line_stds_arr ) ;
// road edges
auto road_edges = framed . initRoadEdges ( 2 ) ;
float road_edge_stds_arr [ 2 ] ;
for ( int i = 0 ; i < 2 ; i + + ) {
fill_xyzt ( road_edges [ i ] , & net_outputs . road_edges [ i * TRAJECTORY_SIZE * 2 ] , 2 , - 1 , plan_t_arr ) ;
road_edge_stds_arr [ i ] = exp ( net_outputs . road_edges [ 2 * TRAJECTORY_SIZE * ( 2 + i ) ] ) ;
}
framed . setRoadEdgeStds ( road_edge_stds_arr ) ;
// meta
auto meta = framed . initMeta ( ) ;
fill_meta_v2 ( meta , net_outputs . meta ) ;
// leads
auto leads = framed . initLeads ( LEAD_MHP_SELECTION ) ;
int mdn_max_idx = 0 ;
float t_offsets [ LEAD_MHP_SELECTION ] = { 0.0 , 2.0 , 4.0 } ;
for ( int t_offset = 0 ; t_offset < LEAD_MHP_SELECTION ; t_offset + + ) {
for ( int i = 1 ; i < LEAD_MHP_N ; i + + ) {
if ( net_outputs . lead [ ( i + 1 ) * ( LEAD_MHP_GROUP_SIZE ) + t_offset - LEAD_MHP_SELECTION ] >
net_outputs . lead [ ( mdn_max_idx + 1 ) * ( LEAD_MHP_GROUP_SIZE ) + t_offset - LEAD_MHP_SELECTION ] ) {
mdn_max_idx = i ;
fill_lead_v2 ( leads [ t_offset ] , & net_outputs . lead [ mdn_max_idx * ( LEAD_MHP_GROUP_SIZE ) ] ,
sigmoid ( net_outputs . lead_prob [ t_offset ] ) , t_offsets [ t_offset ] ) ;
}
}
}
pm . send ( " modelV2 " , msg ) ;
} }
void model_publish ( PubMaster & pm , uint32_t vipc_frame_id , uint32_t frame_id , void model_publish ( PubMaster & pm , uint32_t vipc_frame_id , uint32_t frame_id ,
@ -243,29 +403,58 @@ void model_publish(PubMaster &pm, uint32_t vipc_frame_id, uint32_t frame_id,
framed . setFrameDropPerc ( frame_drop * 100 ) ; framed . setFrameDropPerc ( frame_drop * 100 ) ;
framed . setTimestampEof ( timestamp_eof ) ; framed . setTimestampEof ( timestamp_eof ) ;
fill_path ( framed . initPath ( ) , net_outputs . path , false , 0 ) ; // Find the distribution that corresponds to the most probable plan
fill_path ( framed . initLeftLane ( ) , net_outputs . left_lane , true , 1.8 ) ; int plan_mhp_max_idx = 0 ;
fill_path ( framed . initRightLane ( ) , net_outputs . right_lane , true , - 1.8 ) ; for ( int i = 1 ; i < PLAN_MHP_N ; i + + ) {
fill_longi ( framed . initLongitudinal ( ) , net_outputs . long_x , net_outputs . long_v , net_outputs . long_a ) ; if ( net_outputs . plan [ ( i + 1 ) * ( PLAN_MHP_GROUP_SIZE ) - 1 ] >
net_outputs . plan [ ( plan_mhp_max_idx + 1 ) * ( PLAN_MHP_GROUP_SIZE ) - 1 ] ) {
plan_mhp_max_idx = i ;
}
}
// x pos at 10s is a good valid_len
float valid_len = net_outputs . plan [ plan_mhp_max_idx * ( PLAN_MHP_GROUP_SIZE ) + 30 * 32 ] ;
// clamp to 5 and MODEL_PATH_DISTANCE
valid_len = fmin ( MODEL_PATH_DISTANCE , fmax ( MIN_VALID_LEN , valid_len ) ) ;
int valid_len_idx = 0 ;
for ( int i = 1 ; i < TRAJECTORY_SIZE ; i + + ) {
if ( valid_len > = X_IDXS [ valid_len_idx ] ) {
valid_len_idx = i ;
}
}
auto lpath = framed . initPath ( ) ;
fill_path ( lpath , & net_outputs . plan [ plan_mhp_max_idx * ( PLAN_MHP_GROUP_SIZE ) ] , valid_len , valid_len_idx ) ;
auto left_lane = framed . initLeftLane ( ) ;
int ll_idx = 1 ;
fill_lane_line ( left_lane , net_outputs . lane_lines , ll_idx , valid_len , valid_len_idx ,
sigmoid ( net_outputs . lane_lines_prob [ ll_idx ] ) ) ;
auto right_lane = framed . initRightLane ( ) ;
ll_idx = 2 ;
fill_lane_line ( right_lane , net_outputs . lane_lines , ll_idx , valid_len , valid_len_idx ,
sigmoid ( net_outputs . lane_lines_prob [ ll_idx ] ) ) ;
// Find the distribution that corresponds to the current lead
// Find the distribution that corresponds to the current lead
int mdn_max_idx = 0 ; int mdn_max_idx = 0 ;
int t_offset = 0 ; int t_offset = 0 ;
for ( int i = 1 ; i < LEAD_MDN_N ; i + + ) { for ( int i = 1 ; i < LEAD_MHP_N ; i + + ) {
if ( net_outputs . lead [ i * MDN_GROUP_SIZE + 8 + t_offset ] > net_outputs . lead [ mdn_max_idx * MDN_GROUP_SIZE + 8 + t_offset ] ) { if ( net_outputs . lead [ ( i + 1 ) * ( LEAD_MHP_GROUP_SIZE ) + t_offset - 3 ] >
net_outputs . lead [ ( mdn_max_idx + 1 ) * ( LEAD_MHP_GROUP_SIZE ) + t_offset - 3 ] ) {
mdn_max_idx = i ; mdn_max_idx = i ;
} }
} }
fill_lead ( framed . initLead ( ) , net_outputs . lead , mdn_max_idx , t_offset ) ; fill_lead ( framed . initLead ( ) , & net_outputs . lead [ mdn_max_idx * ( LEAD_MHP_GROUP_SIZE ) ] , sigmoid ( net_outputs . lead_prob [ t_offset ] ) ) ;
// Find the distribution that corresponds to the lead in 2s
// Find the distribution that corresponds to the lead in 2s
mdn_max_idx = 0 ; mdn_max_idx = 0 ;
t_offset = 1 ; t_offset = 1 ;
for ( int i = 1 ; i < LEAD_MDN_N ; i + + ) { for ( int i = 1 ; i < LEAD_MHP_N ; i + + ) {
if ( net_outputs . lead [ i * MDN_GROUP_SIZE + 8 + t_offset ] > net_outputs . lead [ mdn_max_idx * MDN_GROUP_SIZE + 8 + t_offset ] ) { if ( net_outputs . lead [ ( i + 1 ) * ( LEAD_MHP_GROUP_SIZE ) + t_offset - 3 ] >
net_outputs . lead [ ( mdn_max_idx + 1 ) * ( LEAD_MHP_GROUP_SIZE ) + t_offset - 3 ] ) {
mdn_max_idx = i ; mdn_max_idx = i ;
} }
} }
fill_lead ( framed . initLeadFuture ( ) , net_outputs . lead , mdn_max_idx , t_offset ) ; fill_lead ( framed . initLeadFuture ( ) , & net_outputs . lead [ mdn_max_idx * ( LEAD_MHP_GROUP_SIZE ) ] , sigmoid ( net_outputs . lead_prob [ t_offset ] ) ) ;
fill_meta ( framed . initMeta ( ) , net_outputs . meta ) ; fill_meta ( framed . initMeta ( ) , net_outputs . meta ) ;
pm . send ( " model " , msg ) ; pm . send ( " model " , msg ) ;
@ -280,10 +469,10 @@ void posenet_publish(PubMaster &pm, uint32_t vipc_frame_id, uint32_t frame_id,
for ( int i = 0 ; i < 3 ; i + + ) { for ( int i = 0 ; i < 3 ; i + + ) {
trans_arr [ i ] = net_outputs . pose [ i ] ; trans_arr [ i ] = net_outputs . pose [ i ] ;
trans_std_arr [ i ] = softplus ( net_outputs . pose [ 6 + i ] ) + 1e-6 ; trans_std_arr [ i ] = exp ( net_outputs . pose [ 6 + i ] ) ;
rot_arr [ i ] = M_PI * net_outputs . pose [ 3 + i ] / 180.0 ; rot_arr [ i ] = net_outputs . pose [ 3 + i ] ;
rot_std_arr [ i ] = M_PI * ( softplus ( net_outputs . pose [ 9 + i ] ) + 1e-6 ) / 180.0 ; rot_std_arr [ i ] = exp ( net_outputs . pose [ 9 + i ] ) ;
} }
MessageBuilder msg ; MessageBuilder msg ;
HaraldSchafer
5 years ago
committed by
GitHub