288 lines
9.8 KiB
C++
288 lines
9.8 KiB
C++
#include <string.h>
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#include <assert.h>
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#include <fcntl.h>
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#include <unistd.h>
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#include "common/timing.h"
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#include "driving.h"
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#define PATH_IDX 0
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#define LL_IDX PATH_IDX + MODEL_PATH_DISTANCE*2
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#define RL_IDX LL_IDX + MODEL_PATH_DISTANCE*2 + 1
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#define LEAD_IDX RL_IDX + MODEL_PATH_DISTANCE*2 + 1
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#define LONG_X_IDX LEAD_IDX + MDN_GROUP_SIZE*LEAD_MDN_N + SELECTION
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#define LONG_V_IDX LONG_X_IDX + TIME_DISTANCE*2
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#define LONG_A_IDX LONG_V_IDX + TIME_DISTANCE*2
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#define META_IDX LONG_A_IDX + TIME_DISTANCE*2
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#define POSE_IDX META_IDX + OTHER_META_SIZE + DESIRE_PRED_SIZE
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#define OUTPUT_SIZE POSE_IDX + POSE_SIZE
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#ifdef TEMPORAL
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#define TEMPORAL_SIZE 512
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#else
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#define TEMPORAL_SIZE 0
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#endif
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// #define DUMP_YUV
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Eigen::Matrix<float, MODEL_PATH_DISTANCE, POLYFIT_DEGREE - 1> vander;
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void model_init(ModelState* s, cl_device_id device_id, cl_context context, int temporal) {
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frame_init(&s->frame, MODEL_WIDTH, MODEL_HEIGHT, device_id, context);
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s->input_frames = (float*)calloc(MODEL_FRAME_SIZE * 2, sizeof(float));
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const int output_size = OUTPUT_SIZE + TEMPORAL_SIZE;
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s->output = (float*)calloc(output_size, sizeof(float));
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s->m = new DefaultRunModel("../../models/supercombo.dlc", s->output, output_size, USE_GPU_RUNTIME);
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#ifdef TEMPORAL
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assert(temporal);
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s->m->addRecurrent(&s->output[OUTPUT_SIZE], TEMPORAL_SIZE);
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#endif
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#ifdef DESIRE
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s->desire = (float*)malloc(DESIRE_SIZE * sizeof(float));
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for (int i = 0; i < DESIRE_SIZE; i++) s->desire[i] = 0.0;
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s->m->addDesire(s->desire, DESIRE_SIZE);
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#endif
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// Build Vandermonde matrix
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for(int i = 0; i < MODEL_PATH_DISTANCE; i++) {
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for(int j = 0; j < POLYFIT_DEGREE - 1; j++) {
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vander(i, j) = pow(i, POLYFIT_DEGREE-j-1);
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}
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}
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}
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ModelDataRaw model_eval_frame(ModelState* s, cl_command_queue q,
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cl_mem yuv_cl, int width, int height,
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mat3 transform, void* sock, float *desire_in) {
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#ifdef DESIRE
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if (desire_in != NULL) {
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for (int i = 0; i < DESIRE_SIZE; i++) s->desire[i] = desire_in[i];
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}
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#endif
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//for (int i = 0; i < OUTPUT_SIZE + TEMPORAL_SIZE; i++) { printf("%f ", s->output[i]); } printf("\n");
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float *new_frame_buf = frame_prepare(&s->frame, q, yuv_cl, width, height, transform);
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memmove(&s->input_frames[0], &s->input_frames[MODEL_FRAME_SIZE], sizeof(float)*MODEL_FRAME_SIZE);
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memmove(&s->input_frames[MODEL_FRAME_SIZE], new_frame_buf, sizeof(float)*MODEL_FRAME_SIZE);
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s->m->execute(s->input_frames);
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#ifdef DUMP_YUV
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FILE *dump_yuv_file = fopen("/sdcard/dump.yuv", "wb");
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fwrite(new_frame_buf, MODEL_HEIGHT*MODEL_WIDTH*3/2, sizeof(float), dump_yuv_file);
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fclose(dump_yuv_file);
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assert(1==2);
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#endif
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// net outputs
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ModelDataRaw net_outputs;
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net_outputs.path = &s->output[PATH_IDX];
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net_outputs.left_lane = &s->output[LL_IDX];
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net_outputs.right_lane = &s->output[RL_IDX];
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net_outputs.lead = &s->output[LEAD_IDX];
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net_outputs.long_x = &s->output[LONG_X_IDX];
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net_outputs.long_v = &s->output[LONG_V_IDX];
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net_outputs.long_a = &s->output[LONG_A_IDX];
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net_outputs.meta = &s->output[META_IDX];
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net_outputs.pose = &s->output[POSE_IDX];
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return net_outputs;
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}
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void model_free(ModelState* s) {
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free(s->output);
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free(s->input_frames);
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frame_free(&s->frame);
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delete s->m;
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}
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void poly_fit(float *in_pts, float *in_stds, float *out) {
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// References to inputs
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Eigen::Map<Eigen::Matrix<float, MODEL_PATH_DISTANCE, 1> > pts(in_pts, MODEL_PATH_DISTANCE);
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Eigen::Map<Eigen::Matrix<float, MODEL_PATH_DISTANCE, 1> > std(in_stds, MODEL_PATH_DISTANCE);
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Eigen::Map<Eigen::Matrix<float, POLYFIT_DEGREE - 1, 1> > p(out, POLYFIT_DEGREE - 1);
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float y0 = pts[0];
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pts = pts.array() - y0;
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// Build Least Squares equations
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Eigen::Matrix<float, MODEL_PATH_DISTANCE, POLYFIT_DEGREE - 1> lhs = vander.array().colwise() / std.array();
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Eigen::Matrix<float, MODEL_PATH_DISTANCE, 1> rhs = pts.array() / std.array();
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// Improve numerical stability
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Eigen::Matrix<float, POLYFIT_DEGREE - 1, 1> scale = 1. / (lhs.array()*lhs.array()).sqrt().colwise().sum();
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lhs = lhs * scale.asDiagonal();
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// Solve inplace
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Eigen::ColPivHouseholderQR<Eigen::Ref<Eigen::MatrixXf> > qr(lhs);
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p = qr.solve(rhs);
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// Apply scale to output
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p = p.transpose() * scale.asDiagonal();
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out[3] = y0;
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}
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void fill_path(cereal::ModelData::PathData::Builder path, const float * data, bool has_prob, const float offset) {
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float points_arr[MODEL_PATH_DISTANCE];
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float stds_arr[MODEL_PATH_DISTANCE];
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float poly_arr[POLYFIT_DEGREE];
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float std;
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float prob;
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for (int i=0; i<MODEL_PATH_DISTANCE; i++) {
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points_arr[i] = data[i] + offset;
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stds_arr[i] = softplus(data[MODEL_PATH_DISTANCE + i]);
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}
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if (has_prob) {
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prob = sigmoid(data[MODEL_PATH_DISTANCE*2]);
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} else {
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prob = 1.0;
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}
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std = softplus(data[MODEL_PATH_DISTANCE]);
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poly_fit(points_arr, stds_arr, poly_arr);
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if (std::getenv("DEBUG")){
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kj::ArrayPtr<const float> stds(&stds_arr[0], ARRAYSIZE(stds_arr));
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path.setStds(stds);
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kj::ArrayPtr<const float> points(&points_arr[0], ARRAYSIZE(points_arr));
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path.setPoints(points);
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}
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kj::ArrayPtr<const float> poly(&poly_arr[0], ARRAYSIZE(poly_arr));
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path.setPoly(poly);
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path.setProb(prob);
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path.setStd(std);
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}
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void fill_lead(cereal::ModelData::LeadData::Builder lead, const float * data, int mdn_max_idx) {
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const double x_scale = 10.0;
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const double y_scale = 10.0;
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lead.setProb(sigmoid(data[LEAD_MDN_N*MDN_GROUP_SIZE]));
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lead.setDist(x_scale * data[mdn_max_idx*MDN_GROUP_SIZE]);
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lead.setStd(x_scale * softplus(data[mdn_max_idx*MDN_GROUP_SIZE + MDN_VALS]));
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lead.setRelY(y_scale * data[mdn_max_idx*MDN_GROUP_SIZE + 1]);
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lead.setRelYStd(y_scale * softplus(data[mdn_max_idx*MDN_GROUP_SIZE + MDN_VALS + 1]));
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lead.setRelVel(data[mdn_max_idx*MDN_GROUP_SIZE + 2]);
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lead.setRelVelStd(softplus(data[mdn_max_idx*MDN_GROUP_SIZE + MDN_VALS + 2]));
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lead.setRelA(data[mdn_max_idx*MDN_GROUP_SIZE + 3]);
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lead.setRelAStd(softplus(data[mdn_max_idx*MDN_GROUP_SIZE + MDN_VALS + 3]));
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}
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void fill_meta(cereal::ModelData::MetaData::Builder meta, const float * meta_data) {
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meta.setEngagedProb(meta_data[0]);
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meta.setGasDisengageProb(meta_data[1]);
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meta.setBrakeDisengageProb(meta_data[2]);
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meta.setSteerOverrideProb(meta_data[3]);
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kj::ArrayPtr<const float> desire_pred(&meta_data[OTHER_META_SIZE], DESIRE_PRED_SIZE);
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meta.setDesirePrediction(desire_pred);
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}
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void fill_longi(cereal::ModelData::LongitudinalData::Builder longi, const float * long_v_data, const float * long_a_data) {
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// just doing 10 vals, 1 every sec for now
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float speed_arr[TIME_DISTANCE/10];
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float accel_arr[TIME_DISTANCE/10];
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for (int i=0; i<TIME_DISTANCE/10; i++) {
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speed_arr[i] = long_v_data[i*10];
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accel_arr[i] = long_a_data[i*10];
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}
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kj::ArrayPtr<const float> speed(&speed_arr[0], ARRAYSIZE(speed_arr));
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longi.setSpeeds(speed);
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kj::ArrayPtr<const float> accel(&accel_arr[0], ARRAYSIZE(accel_arr));
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longi.setAccelerations(accel);
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}
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void model_publish(PubSocket *sock, uint32_t frame_id,
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const ModelDataRaw net_outputs, uint64_t timestamp_eof) {
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// make msg
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capnp::MallocMessageBuilder msg;
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cereal::Event::Builder event = msg.initRoot<cereal::Event>();
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event.setLogMonoTime(nanos_since_boot());
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auto framed = event.initModel();
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framed.setFrameId(frame_id);
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framed.setTimestampEof(timestamp_eof);
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auto lpath = framed.initPath();
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fill_path(lpath, net_outputs.path, false, 0);
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auto left_lane = framed.initLeftLane();
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fill_path(left_lane, net_outputs.left_lane, true, 1.8);
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auto right_lane = framed.initRightLane();
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fill_path(right_lane, net_outputs.right_lane, true, -1.8);
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auto longi = framed.initLongitudinal();
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fill_longi(longi, net_outputs.long_v, net_outputs.long_a);
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// Find the distribution that corresponds to the current lead
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int mdn_max_idx = 0;
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for (int i=1; i<LEAD_MDN_N; i++) {
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if (net_outputs.lead[i*MDN_GROUP_SIZE + 8] > net_outputs.lead[mdn_max_idx*MDN_GROUP_SIZE + 8]) {
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mdn_max_idx = i;
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}
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}
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auto lead = framed.initLead();
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fill_lead(lead, net_outputs.lead, mdn_max_idx);
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// Find the distribution that corresponds to the lead in 2s
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mdn_max_idx = 0;
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for (int i=1; i<LEAD_MDN_N; i++) {
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if (net_outputs.lead[i*MDN_GROUP_SIZE + 9] > net_outputs.lead[mdn_max_idx*MDN_GROUP_SIZE + 9]) {
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mdn_max_idx = i;
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}
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}
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auto lead_future = framed.initLeadFuture();
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fill_lead(lead_future, net_outputs.lead, mdn_max_idx);
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auto meta = framed.initMeta();
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fill_meta(meta, net_outputs.meta);
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// send message
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auto words = capnp::messageToFlatArray(msg);
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auto bytes = words.asBytes();
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sock->send((char*)bytes.begin(), bytes.size());
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}
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void posenet_publish(PubSocket *sock, uint32_t frame_id,
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const ModelDataRaw net_outputs, uint64_t timestamp_eof) {
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capnp::MallocMessageBuilder msg;
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cereal::Event::Builder event = msg.initRoot<cereal::Event>();
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event.setLogMonoTime(nanos_since_boot());
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float trans_arr[3];
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float trans_std_arr[3];
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float rot_arr[3];
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float rot_std_arr[3];
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for (int i =0; i < 3; i++) {
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trans_arr[i] = net_outputs.pose[i];
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trans_std_arr[i] = softplus(net_outputs.pose[6 + i]) + 1e-6;
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rot_arr[i] = M_PI * net_outputs.pose[3 + i] / 180.0;
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rot_std_arr[i] = M_PI * (softplus(net_outputs.pose[9 + i]) + 1e-6) / 180.0;
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}
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auto posenetd = event.initCameraOdometry();
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kj::ArrayPtr<const float> trans_vs(&trans_arr[0], 3);
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posenetd.setTrans(trans_vs);
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kj::ArrayPtr<const float> rot_vs(&rot_arr[0], 3);
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posenetd.setRot(rot_vs);
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kj::ArrayPtr<const float> trans_std_vs(&trans_std_arr[0], 3);
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posenetd.setTransStd(trans_std_vs);
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kj::ArrayPtr<const float> rot_std_vs(&rot_std_arr[0], 3);
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posenetd.setRotStd(rot_std_vs);
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posenetd.setTimestampEof(timestamp_eof);
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posenetd.setFrameId(frame_id);
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auto words = capnp::messageToFlatArray(msg);
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auto bytes = words.asBytes();
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sock->send((char*)bytes.begin(), bytes.size());
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}
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