sadmen
/
openpilot_comma
mirror of https://github.com/commaai/openpilot.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Rocket Launcher Model (#25963)

Browse Source 
* 1456d261-d232-4654-8885-4d9fde883894/440 6b7d7cec-ead8-40f3-86cc-86d52c9b03fe/300

* compute only 9 tokens: 1456d261-d232-4654-8885-4d9fde883894/440 6b7d7cec-ead8-40f3-86cc-86d52c9b03fe/300

* tinygrad: cleanup gather

* 1456d261-d232-4654-8885-4d9fde883894/440 6b7d7cec-ead8-40f3-86cc-86d52c9b03fe/700

* empty commit for tests

* bump tinygrad

* dont use tinygrad matmul for now

* bump tinygrad

* 1456d261-d232-4654-8885-4d9fde883894/440 e63ab895-2222-4abd-a9a5-af86bb70e260/700

* float16 1456d261-d232-4654-8885-4d9fde883894/440 e63ab895-2222-4abd-a9a5-af86bb70e260/700

* increase steer rate cost

* Revert "increase steer rate cost"

This reverts commit 74ce9ab9be7ef17ecfec931f96851b12f37f2336.

* fork tinygrad

* empty commit for tests

* basics

* Kinda works

* new lat

* new tuning

* Move LATMPCN so scons compiles

* Update long weights

* Add tinygrad optim

* Update model ref

* update weights

* Update ref

* Try

* Error message for field ignore

* update model regf

* ref commit

* Fix onnx test

Co-authored-by: Yassine Yousfi <yyousfi1@binghamton.edu>
pull/25982/head
HaraldSchafer 3 years ago committed by GitHub
parent a6ba073231
commit cb0b7375b7
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
19 changed files with 93 additions and 76 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      .gitmodules
  2. 1
      release/files_common
  3. 6
      selfdrive/controls/lib/drive_helpers.py
  4. 59
      selfdrive/controls/lib/lateral_mpc_lib/lat_mpc.py
  5. 18
      selfdrive/controls/lib/lateral_planner.py
  6. 2
      selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
  7. 6
      selfdrive/controls/lib/longitudinal_planner.py
  8. 5
      selfdrive/controls/tests/test_lateral_mpc.py
  9. 4
      selfdrive/modeld/SConscript
  10. 16
      selfdrive/modeld/models/driving.cc
  11. 15
      selfdrive/modeld/models/driving.h
  12. 4
      selfdrive/modeld/models/supercombo.onnx
  13. 13
      selfdrive/modeld/runners/onnx_runner.py
  14. 2
      selfdrive/modeld/thneed/thneed_common.cc
  15. 2
      selfdrive/test/process_replay/compare_logs.py
  16. 4
      selfdrive/test/process_replay/model_replay.py
  17. 2
      selfdrive/test/process_replay/model_replay_ref_commit
  18. 2
      selfdrive/test/process_replay/ref_commit
  19. 2
      tinygrad_repo

2
.gitmodules vendored
Unescape View File

@ -18,4 +18,4 @@
url = ../../commaai/body.git
[submodule "tinygrad"]
path = tinygrad_repo
url = https://github.com/geohot/tinygrad.git
url = ../../commaai/tinygrad.git

1
release/files_common
Unescape View File

@ -574,3 +574,4 @@ tinygrad_repo/tinygrad/ops.py
tinygrad_repo/tinygrad/shapetracker.py
tinygrad_repo/tinygrad/tensor.py
tinygrad_repo/tinygrad/nn/__init__.py
tinygrad_repo/tinygrad/nn/optim.py

6
selfdrive/controls/lib/drive_helpers.py
Unescape View File

@ -33,12 +33,6 @@ CRUISE_INTERVAL_SIGN = {
}
class MPC_COST_LAT:
PATH = 1.0
HEADING = 1.0
STEER_RATE = 1.0
def apply_deadzone(error, deadzone):
if error > deadzone:
error -= deadzone

59
selfdrive/controls/lib/lateral_mpc_lib/lat_mpc.py
Unescape View File

@ -5,7 +5,6 @@ import numpy as np
from casadi import SX, vertcat, sin, cos
from common.realtime import sec_since_boot
from selfdrive.controls.lib.drive_helpers import LAT_MPC_N as N
from selfdrive.modeld.constants import T_IDXS
if __name__ == '__main__': # generating code
@ -18,6 +17,9 @@ EXPORT_DIR = os.path.join(LAT_MPC_DIR, "c_generated_code")
JSON_FILE = os.path.join(LAT_MPC_DIR, "acados_ocp_lat.json")
X_DIM = 4
P_DIM = 2
N = 16
COST_E_DIM = 3
COST_DIM = COST_E_DIM + 1
MODEL_NAME = 'lat'
ACADOS_SOLVER_TYPE = 'SQP_RTI'
@ -29,8 +31,8 @@ def gen_lat_model():
x_ego = SX.sym('x_ego')
y_ego = SX.sym('y_ego')
psi_ego = SX.sym('psi_ego')
curv_ego = SX.sym('curv_ego')
model.x = vertcat(x_ego, y_ego, psi_ego, curv_ego)
psi_rate_ego = SX.sym('psi_rate_ego')
model.x = vertcat(x_ego, y_ego, psi_ego, psi_rate_ego)
# parameters
v_ego = SX.sym('v_ego')
@ -38,22 +40,22 @@ def gen_lat_model():
model.p = vertcat(v_ego, rotation_radius)
# controls
curv_rate = SX.sym('curv_rate')
model.u = vertcat(curv_rate)
psi_accel_ego = SX.sym('psi_accel_ego')
model.u = vertcat(psi_accel_ego)
# xdot
x_ego_dot = SX.sym('x_ego_dot')
y_ego_dot = SX.sym('y_ego_dot')
psi_ego_dot = SX.sym('psi_ego_dot')
curv_ego_dot = SX.sym('curv_ego_dot')
psi_rate_ego_dot = SX.sym('psi_rate_ego_dot')
model.xdot = vertcat(x_ego_dot, y_ego_dot, psi_ego_dot, curv_ego_dot)
model.xdot = vertcat(x_ego_dot, y_ego_dot, psi_ego_dot, psi_rate_ego_dot)
# dynamics model
f_expl = vertcat(v_ego * cos(psi_ego) - rotation_radius * sin(psi_ego) * (v_ego * curv_ego),
v_ego * sin(psi_ego) + rotation_radius * cos(psi_ego) * (v_ego * curv_ego),
v_ego * curv_ego,
curv_rate)
f_expl = vertcat(v_ego * cos(psi_ego) - rotation_radius * sin(psi_ego) * psi_rate_ego,
v_ego * sin(psi_ego) + rotation_radius * cos(psi_ego) * psi_rate_ego,
psi_rate_ego,
psi_accel_ego)
model.f_impl_expr = model.xdot - f_expl
model.f_expl_expr = f_expl
return model
@ -72,26 +74,28 @@ def gen_lat_ocp():
ocp.cost.cost_type = 'NONLINEAR_LS'
ocp.cost.cost_type_e = 'NONLINEAR_LS'
Q = np.diag([0.0, 0.0])
QR = np.diag([0.0, 0.0, 0.0])
Q = np.diag(np.zeros(COST_E_DIM))
QR = np.diag(np.zeros(COST_DIM))
ocp.cost.W = QR
ocp.cost.W_e = Q
y_ego, psi_ego = ocp.model.x[1], ocp.model.x[2]
curv_rate = ocp.model.u[0]
y_ego, psi_ego, psi_rate_ego = ocp.model.x[1], ocp.model.x[2], ocp.model.x[3]
psi_rate_ego_dot = ocp.model.u[0]
v_ego = ocp.model.p[0]
ocp.parameter_values = np.zeros((P_DIM, ))
ocp.cost.yref = np.zeros((3, ))
ocp.cost.yref_e = np.zeros((2, ))
ocp.cost.yref = np.zeros((COST_DIM, ))
ocp.cost.yref_e = np.zeros((COST_E_DIM, ))
# TODO hacky weights to keep behavior the same
ocp.model.cost_y_expr = vertcat(y_ego,
((v_ego + 5.0) * psi_ego),
((v_ego + 5.0) * 4.0 * curv_rate))
((v_ego + 5.0) * psi_rate_ego),
((v_ego + 5.0) * psi_rate_ego_dot))
ocp.model.cost_y_expr_e = vertcat(y_ego,
((v_ego +5.0) * psi_ego))
((v_ego + 5.0) * psi_ego),
((v_ego + 5.0) * psi_rate_ego))
# set constraints
ocp.constraints.constr_type = 'BGH'
@ -124,10 +128,10 @@ class LateralMpc():
def reset(self, x0=np.zeros(X_DIM)):
self.x_sol = np.zeros((N+1, X_DIM))
self.u_sol = np.zeros((N, 1))
self.yref = np.zeros((N+1, 3))
self.yref = np.zeros((N+1, COST_DIM))
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
self.solver.cost_set(N, "yref", self.yref[N][:2])
self.solver.cost_set(N, "yref", self.yref[N][:COST_E_DIM])
# Somehow needed for stable init
for i in range(N+1):
@ -140,14 +144,13 @@ class LateralMpc():
self.solve_time = 0.0
self.cost = 0
def set_weights(self, path_weight, heading_weight, steer_rate_weight):
W = np.asfortranarray(np.diag([path_weight, heading_weight, steer_rate_weight]))
def set_weights(self, path_weight, heading_weight, yaw_rate_weight, yaw_accel_cost):
W = np.asfortranarray(np.diag([path_weight, heading_weight, yaw_rate_weight, yaw_accel_cost]))
for i in range(N):
self.solver.cost_set(i, 'W', W)
#TODO hacky weights to keep behavior the same
self.solver.cost_set(N, 'W', (3/20.)*W[:2,:2])
self.solver.cost_set(N, 'W', W[:COST_E_DIM,:COST_E_DIM])
def run(self, x0, p, y_pts, heading_pts, curv_rate_pts):
def run(self, x0, p, y_pts, heading_pts, yaw_rate_pts):
x0_cp = np.copy(x0)
p_cp = np.copy(p)
self.solver.constraints_set(0, "lbx", x0_cp)
@ -156,12 +159,12 @@ class LateralMpc():
v_ego = p_cp[0]
# rotation_radius = p_cp[1]
self.yref[:,1] = heading_pts * (v_ego+5.0)
self.yref[:,2] = curv_rate_pts * (v_ego+5.0) * 4.0
self.yref[:,2] = yaw_rate_pts * (v_ego+5.0)
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
self.solver.set(i, "p", p_cp)
self.solver.set(N, "p", p_cp)
self.solver.cost_set(N, "yref", self.yref[N][:2])
self.solver.cost_set(N, "yref", self.yref[N][:COST_E_DIM])
t = sec_since_boot()
self.solution_status = self.solver.solve()

18
selfdrive/controls/lib/lateral_planner.py
Unescape View File

@ -3,7 +3,8 @@ from common.realtime import sec_since_boot, DT_MDL
from common.numpy_fast import interp
from system.swaglog import cloudlog
from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc
from selfdrive.controls.lib.drive_helpers import CONTROL_N, MPC_COST_LAT, LAT_MPC_N
from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import N as LAT_MPC_N
from selfdrive.controls.lib.drive_helpers import CONTROL_N
from selfdrive.controls.lib.desire_helper import DesireHelper
import cereal.messaging as messaging
from cereal import log
@ -23,7 +24,7 @@ class LateralPlanner:
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
self.plan_curv_rate = np.zeros((TRAJECTORY_SIZE,))
self.plan_yaw_rate = np.zeros((TRAJECTORY_SIZE,))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
@ -44,6 +45,7 @@ class LateralPlanner:
self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z])
self.t_idxs = np.array(md.position.t)
self.plan_yaw = np.array(md.orientation.z)
self.plan_yaw_rate = np.array(md.orientationRate.z)
# Lane change logic
desire_state = md.meta.desireState
@ -55,24 +57,24 @@ class LateralPlanner:
d_path_xyz = self.path_xyz
# Heading cost is useful at low speed, otherwise end of plan can be off-heading
heading_cost = interp(v_ego, [5.0, 10.0], [MPC_COST_LAT.HEADING, 0.15])
self.lat_mpc.set_weights(MPC_COST_LAT.PATH, heading_cost, MPC_COST_LAT.STEER_RATE)
heading_cost = interp(v_ego, [5.0, 10.0], [1.0, 0.15])
self.lat_mpc.set_weights(1.0, heading_cost, 0.0, .075)
y_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:, 1])
heading_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(self.path_xyz, axis=1), self.plan_yaw)
curv_rate_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(self.path_xyz, axis=1), self.plan_curv_rate)
yaw_rate_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(self.path_xyz, axis=1), self.plan_yaw_rate)
self.y_pts = y_pts
assert len(y_pts) == LAT_MPC_N + 1
assert len(heading_pts) == LAT_MPC_N + 1
assert len(curv_rate_pts) == LAT_MPC_N + 1
assert len(yaw_rate_pts) == LAT_MPC_N + 1
lateral_factor = max(0, self.factor1 - (self.factor2 * v_ego**2))
p = np.array([v_ego, lateral_factor])
self.lat_mpc.run(self.x0,
p,
y_pts,
heading_pts,
curv_rate_pts)
yaw_rate_pts)
# init state for next
# mpc.u_sol is the desired curvature rate given x0 curv state.
# with x0[3] = measured_curvature, this would be the actual desired rate.
@ -103,7 +105,7 @@ class LateralPlanner:
lateralPlan.modelMonoTime = sm.logMonoTime['modelV2']
lateralPlan.dPathPoints = self.y_pts.tolist()
lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist()
lateralPlan.curvatures = self.lat_mpc.x_sol[0:CONTROL_N, 3].tolist()
lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3]/sm['carState'].vEgo).tolist()
lateralPlan.curvatureRates = [float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
lateralPlan.mpcSolutionValid = bool(plan_solution_valid)

2
selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
Unescape View File

@ -253,7 +253,7 @@ class LongitudinalMpc:
cost_weights = [X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, a_change_cost, J_EGO_COST]
constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST]
elif self.mode == 'blended':
cost_weights = [0., 0.2, 0.25, 1.0, 0.0, 1.0]
cost_weights = [0., 0.1, 0.2, 5.0, 0.0, 1.0]
constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, 50.0]
else:
raise NotImplementedError(f'Planner mode {self.mode} not recognized in planner cost set')

6
selfdrive/controls/lib/longitudinal_planner.py
Unescape View File

@ -58,7 +58,6 @@ class LongitudinalPlanner:
self.a_desired = init_a
self.v_desired_filter = FirstOrderFilter(init_v, 2.0, DT_MDL)
self.t_uniform = np.arange(0.0, T_IDXS_MPC[-1] + 0.5, 0.5)
self.v_desired_trajectory = np.zeros(CONTROL_N)
self.a_desired_trajectory = np.zeros(CONTROL_N)
@ -76,10 +75,7 @@ class LongitudinalPlanner:
x = np.interp(T_IDXS_MPC, T_IDXS, model_msg.position.x)
v = np.interp(T_IDXS_MPC, T_IDXS, model_msg.velocity.x)
a = np.interp(T_IDXS_MPC, T_IDXS, model_msg.acceleration.x)
# Uniform interp so gradient is less noisy
a_sparse = np.interp(self.t_uniform, T_IDXS, model_msg.acceleration.x)
j_sparse = np.gradient(a_sparse, self.t_uniform)
j = np.interp(T_IDXS_MPC, self.t_uniform, j_sparse)
j = np.zeros(len(T_IDXS_MPC))
else:
x = np.zeros(len(T_IDXS_MPC))
v = np.zeros(len(T_IDXS_MPC))

5
selfdrive/controls/tests/test_lateral_mpc.py
Unescape View File

@ -1,7 +1,8 @@
import unittest
import numpy as np
from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc
from selfdrive.controls.lib.drive_helpers import LAT_MPC_N, CAR_ROTATION_RADIUS
from selfdrive.controls.lib.drive_helpers import CAR_ROTATION_RADIUS
from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import N as LAT_MPC_N
def run_mpc(lat_mpc=None, v_ref=30., x_init=0., y_init=0., psi_init=0., curvature_init=0.,
@ -9,7 +10,7 @@ def run_mpc(lat_mpc=None, v_ref=30., x_init=0., y_init=0., psi_init=0., curvatur
if lat_mpc is None:
lat_mpc = LateralMpc()
lat_mpc.set_weights(1., 1., 1.)
lat_mpc.set_weights(1., 1., 0.0, 1.)
y_pts = poly_shift * np.ones(LAT_MPC_N + 1)
heading_pts = np.zeros(LAT_MPC_N + 1)

4
selfdrive/modeld/SConscript
Unescape View File

@ -71,9 +71,9 @@ if use_thneed and arch == "larch64" or GetOption('pc_thneed'):
fn = File("models/supercombo").abspath
if GetOption('pc_thneed'):
cmd = f"cd {Dir('#').abspath}/tinygrad_repo && NATIVE_EXPLOG=1 OPTWG=1 UNSAFE_FLOAT4=1 DEBUGCL=1 python3 openpilot/compile.py {fn}.onnx {fn}.thneed"
cmd = f"cd {Dir('#').abspath}/tinygrad_repo && GPU=1 NATIVE_EXPLOG=1 OPTWG=1 UNSAFE_FLOAT4=1 DEBUGCL=1 python3 openpilot/compile.py {fn}.onnx {fn}.thneed"
else:
cmd = f"cd {Dir('#').abspath}/tinygrad_repo && FLOAT16=1 PYOPENCL_NO_CACHE=1 MATMUL=1 NATIVE_EXPLOG=1 OPTWG=1 UNSAFE_FLOAT4=1 DEBUGCL=1 python3 openpilot/compile.py {fn}.onnx {fn}.thneed"
cmd = f"cd {Dir('#').abspath}/tinygrad_repo && FLOAT16=1 MATMUL=1 PYOPENCL_NO_CACHE=1 NATIVE_EXPLOG=1 OPTWG=1 UNSAFE_FLOAT4=1 DEBUGCL=1 python3 openpilot/compile.py {fn}.onnx {fn}.thneed"
# is there a better way then listing all of tinygrad?
lenv.Command(fn + ".thneed", [fn + ".onnx",

16
selfdrive/modeld/models/driving.cc
Unescape View File

@ -41,11 +41,11 @@ void model_init(ModelState* s, cl_device_id device_id, cl_context context) {
&s->output[0], NET_OUTPUT_SIZE, USE_GPU_RUNTIME, true, false, context);
#ifdef TEMPORAL
s->m->addRecurrent(&s->output[OUTPUT_SIZE], TEMPORAL_SIZE);
s->m->addRecurrent(&s->feature_buffer[0], TEMPORAL_SIZE);
#endif
#ifdef DESIRE
s->m->addDesire(s->pulse_desire, DESIRE_LEN);
s->m->addDesire(s->pulse_desire, DESIRE_LEN*(HISTORY_BUFFER_LEN+1));
#endif
#ifdef TRAFFIC_CONVENTION
@ -56,18 +56,20 @@ void model_init(ModelState* s, cl_device_id device_id, cl_context context) {
ModelOutput* model_eval_frame(ModelState* s, VisionBuf* buf, VisionBuf* wbuf,
const mat3 &transform, const mat3 &transform_wide, float *desire_in, bool is_rhd, bool prepare_only) {
#ifdef DESIRE
std::memmove(&s->pulse_desire[0], &s->pulse_desire[DESIRE_LEN], sizeof(float) * DESIRE_LEN*HISTORY_BUFFER_LEN);
if (desire_in != NULL) {
for (int i = 1; i < DESIRE_LEN; i++) {
// Model decides when action is completed
// so desire input is just a pulse triggered on rising edge
if (desire_in[i] - s->prev_desire[i] > .99) {
s->pulse_desire[i] = desire_in[i];
s->pulse_desire[DESIRE_LEN*(HISTORY_BUFFER_LEN-1)+i] = desire_in[i];
} else {
s->pulse_desire[i] = 0.0;
s->pulse_desire[DESIRE_LEN*(HISTORY_BUFFER_LEN-1)+i] = 0.0;
}
s->prev_desire[i] = desire_in[i];
}
}
LOGT("Desire enqueued");
#endif
int rhd_idx = is_rhd;
@ -92,6 +94,12 @@ ModelOutput* model_eval_frame(ModelState* s, VisionBuf* buf, VisionBuf* wbuf,
s->m->execute();
LOGT("Execution finished");
#ifdef TEMPORAL
std::memmove(&s->feature_buffer[0], &s->feature_buffer[FEATURE_LEN], sizeof(float) * FEATURE_LEN*(HISTORY_BUFFER_LEN-1));
std::memcpy(&s->feature_buffer[FEATURE_LEN*(HISTORY_BUFFER_LEN-1)], &s->output[OUTPUT_SIZE], sizeof(float) * FEATURE_LEN);
LOGT("Features enqueued");
#endif
return (ModelOutput*)&s->output;
}

15
selfdrive/modeld/models/driving.h
Unescape View File

@ -16,6 +16,8 @@
#include "selfdrive/modeld/models/commonmodel.h"
#include "selfdrive/modeld/runners/run.h"
constexpr int FEATURE_LEN = 2048;
constexpr int HISTORY_BUFFER_LEN = 99;
constexpr int DESIRE_LEN = 8;
constexpr int DESIRE_PRED_LEN = 4;
constexpr int TRAFFIC_CONVENTION_LEN = 2;
@ -233,6 +235,11 @@ struct ModelOutputMeta {
};
static_assert(sizeof(ModelOutputMeta) == sizeof(ModelOutputDesireProb) + sizeof(float) + (sizeof(ModelOutputDisengageProb)*DISENGAGE_LEN) + (sizeof(ModelOutputBlinkerProb)*BLINKER_LEN) + (sizeof(ModelOutputDesireProb)*DESIRE_PRED_LEN));
struct ModelOutputFeatures {
std::array<float, FEATURE_LEN> feature;
};
static_assert(sizeof(ModelOutputFeatures) == (sizeof(float)*FEATURE_LEN));
struct ModelOutput {
const ModelOutputPlans plans;
const ModelOutputLaneLines lane_lines;
@ -244,22 +251,24 @@ struct ModelOutput {
};
constexpr int OUTPUT_SIZE = sizeof(ModelOutput) / sizeof(float);
#ifdef TEMPORAL
constexpr int TEMPORAL_SIZE = 512;
constexpr int TEMPORAL_SIZE = HISTORY_BUFFER_LEN * FEATURE_LEN;
#else
constexpr int TEMPORAL_SIZE = 0;
#endif
constexpr int NET_OUTPUT_SIZE = OUTPUT_SIZE + TEMPORAL_SIZE;
constexpr int NET_OUTPUT_SIZE = OUTPUT_SIZE + FEATURE_LEN;
// TODO: convert remaining arrays to std::array and update model runners
struct ModelState {
ModelFrame *frame = nullptr;
ModelFrame *wide_frame = nullptr;
std::array<float, HISTORY_BUFFER_LEN * FEATURE_LEN> feature_buffer = {};
std::array<float, NET_OUTPUT_SIZE> output = {};
std::unique_ptr<RunModel> m;
#ifdef DESIRE
float prev_desire[DESIRE_LEN] = {};
float pulse_desire[DESIRE_LEN] = {};
float pulse_desire[DESIRE_LEN*(HISTORY_BUFFER_LEN+1)] = {};
#endif
#ifdef TRAFFIC_CONVENTION
float traffic_convention[TRAFFIC_CONVENTION_LEN] = {};

4
selfdrive/modeld/models/supercombo.onnx
Unescape View File

@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:50c7fc8565ac69a4b9a0de122e961326820e78bf13659255a89d0ed04be030d5
size 95167481
oid sha256:30f30bc1251c03db135564ecbf7dc0bc96cbb07be0ebd3691edd8d555dc087fa
size 58539693

13
selfdrive/modeld/runners/onnx_runner.py
Unescape View File

@ -9,6 +9,8 @@ os.environ["OMP_WAIT_POLICY"] = "PASSIVE"
import onnxruntime as ort # pylint: disable=import-error
ORT_TYPES_TO_NP_TYPES = {'tensor(float16)': np.float16, 'tensor(float)': np.float32, 'tensor(uint8)': np.uint8}
def read(sz, tf8=False):
dd = []
gt = 0
@ -18,7 +20,7 @@ def read(sz, tf8=False):
assert(len(st) > 0)
dd.append(st)
gt += len(st)
r = np.frombuffer(b''.join(dd), dtype=np.uint8 if tf8 else np.float32).astype(np.float32)
r = np.frombuffer(b''.join(dd), dtype=np.uint8 if tf8 else np.float32)
if tf8:
r = r / 255.
return r
@ -29,22 +31,23 @@ def write(d):
def run_loop(m, tf8_input=False):
ishapes = [[1]+ii.shape[1:] for ii in m.get_inputs()]
keys = [x.name for x in m.get_inputs()]
itypes = [ORT_TYPES_TO_NP_TYPES[x.type] for x in m.get_inputs()]
# run once to initialize CUDA provider
if "CUDAExecutionProvider" in m.get_providers():
m.run(None, dict(zip(keys, [np.zeros(shp, dtype=np.float32) for shp in ishapes])))
m.run(None, dict(zip(keys, [np.zeros(shp, dtype=itp) for shp, itp in zip(ishapes, itypes)])))
print("ready to run onnx model", keys, ishapes, file=sys.stderr)
while 1:
inputs = []
for k, shp in zip(keys, ishapes):
for k, shp, itp in zip(keys, ishapes, itypes):
ts = np.product(shp)
#print("reshaping %s with offset %d" % (str(shp), offset), file=sys.stderr)
inputs.append(read(ts, (k=='input_img' and tf8_input)).reshape(shp))
inputs.append(read(ts, (k=='input_img' and tf8_input)).reshape(shp).astype(itp))
ret = m.run(None, dict(zip(keys, inputs)))
#print(ret, file=sys.stderr)
for r in ret:
write(r)
write(r.astype(np.float32))
if __name__ == "__main__":

2
selfdrive/modeld/thneed/thneed_common.cc
Unescape View File

@ -12,7 +12,7 @@ map<pair<cl_kernel, int>, int> g_args_size;
map<cl_program, string> g_program_source;
void Thneed::stop() {
printf("Thneed::stop: recorded %lu commands\n", cmds.size());
//printf("Thneed::stop: recorded %lu commands\n", cmds.size());
record = false;
}

2
selfdrive/test/process_replay/compare_logs.py
Unescape View File

@ -41,7 +41,7 @@ def remove_ignored_fields(msg, ignore):
elif isinstance(v, numbers.Number):
val = 0
else:
raise NotImplementedError
raise NotImplementedError('Error ignoring field')
setattr(attr, keys[-1], val)
return msg.as_reader()

4
selfdrive/test/process_replay/model_replay.py
Unescape View File

@ -22,7 +22,7 @@ from tools.lib.logreader import LogReader
TEST_ROUTE = "4cf7a6ad03080c90|2021-09-29--13-46-36"
SEGMENT = 0
MAX_FRAMES = 10 if PC else 1300
MAX_FRAMES = 100 if PC else 1300
SEND_EXTRA_INPUTS = bool(os.getenv("SEND_EXTRA_INPUTS", "0"))
@ -174,7 +174,7 @@ if __name__ == "__main__":
'driverStateV2.dspExecutionTime'
]
# TODO this tolerance is absurdly large
tolerance = 5e-1 if PC else None
tolerance = 2.0 if PC else None
results: Any = {TEST_ROUTE: {}}
log_paths: Any = {TEST_ROUTE: {"models": {'ref': BASE_URL + log_fn, 'new': log_fn}}}
results[TEST_ROUTE]["models"] = compare_logs(cmp_log, log_msgs, tolerance=tolerance, ignore_fields=ignore)

2
selfdrive/test/process_replay/model_replay_ref_commit
Unescape View File

@ -1 +1 @@
c40319a454840d8a2196ec1227d27b536ee14375
008c0a622b7471c6234690d668f76bcb5dc8d999

2
selfdrive/test/process_replay/ref_commit
Unescape View File

@ -1 +1 @@
1989d8c2a5de94faa3756b7d10fc94e6c063afa5
e0ffcae8def2fd9c82c547d1f257d4f06a48a3c3

2
tinygrad_repo

@ -1 +1 @@
Subproject commit 2e9b7637b3c3c8895fda9f964215db3a35fe3441
Subproject commit 82ca9c66664b5f3739e1881f62cb94b72cf0b1fa
Write Preview
Loading…
Cancel
Save