Files
sunnypilot/selfdrive/modeld/transforms/transform.cc
HaraldSchafer 08846b5c0e Torch model (#2452)
* refactor draw model

* rebase master

* correct valid_len

* rename function

* rename variables

* white space

* rebase to master

* e16c13ac-927d-455e-ae0a-81b482a2c787

* start rewriting

* save proress

* compiles!

* oops

* many fixes

* seems to work

* fix desires

* finally cleaned

* wrong std for ll

* dont pulse none

* compiles!

* ready to test

* WIP does not compile

* compiles

* various fixes

* does something!

* full 3d

* not needed

* draw up to 100m

* fix segfault

* wrong sign

* fix flicker

* add road edges

* finish v2 packet

* Added pytorch supercombo

* fix rebase

* no more keras

* Hacky solution to the NCHW/NHWC incompatibility between SNPE and our frame data

* dont break dmonitoringd, final model 229e3ce1-7259-412b-85e6-cc646d70f1d8/430

* fix hack

* Revert "fix hack"

This reverts commit 5550fc01a7881d065a5eddbbb42dac55ef7ec36c.

* Removed axis permutation hack

* Folded padding layers into conv layers

* Removed the last pad layer from the dlc

* Revert "Removed the last pad layer from the dlc"

This reverts commit b85f24b9e1d04abf64e85901a7ff49e00d82020a.

* Revert "Folded padding layers into conv layers"

This reverts commit b8d1773e4e76dea481acebbfad6a6235fbb58463.

* vision model: 5034ac8b-5703-4a49-948b-11c064d10880/780  temporal model: 229e3ce1-7259-412b-85e6-cc646d70f1d8/430  with permute + pool opt

* fix ui drawing with clips

* ./compile_torch.py 5034ac8b-5703-4a49-948b-11c064d10880/780 dfcd2375-81d8-49df-95bf-1d2d6ad86010/450 with variable history length

* std::clamp

* not sure how this compiled before

* 2895ace6-a296-47ac-86e6-17ea800a74e5/550

* db090195-8810-42de-ab38-bb835d775d87/601

* 5m is very little

* onnx runner

* add onnxruntime to pipfile

* run in real time without using the whole CPU

* bump cereal;

* add stds

* set road edge opacity based on stddev

* don't access the model packet in paint

* convert mat.h to a c++ header file (#2499)

* update tests

* safety first

Co-authored-by: deanlee <deanlee3@gmail.com>
Co-authored-by: mitchell <mitchell@comma.ai>
Co-authored-by: Comma Device <device@comma.ai>
Co-authored-by: George Hotz <george@comma.ai>
Co-authored-by: Adeeb Shihadeh <adeebshihadeh@gmail.com>
2020-11-11 20:31:46 -08:00

150 lines
4.7 KiB
C++

#include <string.h>
#include <assert.h>
#include "clutil.h"
#include "transform.h"
void transform_init(Transform* s, cl_context ctx, cl_device_id device_id) {
int err = 0;
memset(s, 0, sizeof(*s));
cl_program prg = CLU_LOAD_FROM_FILE(ctx, device_id, "transforms/transform.cl", "");
s->krnl = clCreateKernel(prg, "warpPerspective", &err);
assert(err == 0);
// done with this
err = clReleaseProgram(prg);
assert(err == 0);
s->m_y_cl = clCreateBuffer(ctx, CL_MEM_READ_WRITE, 3*3*sizeof(float), NULL, &err);
assert(err == 0);
s->m_uv_cl = clCreateBuffer(ctx, CL_MEM_READ_WRITE, 3*3*sizeof(float), NULL, &err);
assert(err == 0);
}
void transform_destroy(Transform* s) {
int err = 0;
err = clReleaseMemObject(s->m_y_cl);
assert(err == 0);
err = clReleaseMemObject(s->m_uv_cl);
assert(err == 0);
err = clReleaseKernel(s->krnl);
assert(err == 0);
}
void transform_queue(Transform* s,
cl_command_queue q,
cl_mem in_yuv, int in_width, int in_height,
cl_mem out_y, cl_mem out_u, cl_mem out_v,
int out_width, int out_height,
mat3 projection) {
int err = 0;
const int zero = 0;
// sampled using pixel center origin
// (because thats how fastcv and opencv does it)
mat3 projection_y = projection;
// in and out uv is half the size of y.
mat3 projection_uv = transform_scale_buffer(projection, 0.5);
err = clEnqueueWriteBuffer(q, s->m_y_cl, CL_TRUE, 0, 3*3*sizeof(float), (void*)projection_y.v, 0, NULL, NULL);
assert(err == 0);
err = clEnqueueWriteBuffer(q, s->m_uv_cl, CL_TRUE, 0, 3*3*sizeof(float), (void*)projection_uv.v, 0, NULL, NULL);
assert(err == 0);
const int in_y_width = in_width;
const int in_y_height = in_height;
const int in_uv_width = in_width/2;
const int in_uv_height = in_height/2;
const int in_y_offset = 0;
const int in_u_offset = in_y_offset + in_y_width*in_y_height;
const int in_v_offset = in_u_offset + in_uv_width*in_uv_height;
const int out_y_width = out_width;
const int out_y_height = out_height;
const int out_uv_width = out_width/2;
const int out_uv_height = out_height/2;
err = clSetKernelArg(s->krnl, 0, sizeof(cl_mem), &in_yuv);
assert(err == 0);
err = clSetKernelArg(s->krnl, 1, sizeof(cl_int), &in_y_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 2, sizeof(cl_int), &in_y_offset);
assert(err == 0);
err = clSetKernelArg(s->krnl, 3, sizeof(cl_int), &in_y_height);
assert(err == 0);
err = clSetKernelArg(s->krnl, 4, sizeof(cl_int), &in_y_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 5, sizeof(cl_mem), &out_y);
assert(err == 0);
err = clSetKernelArg(s->krnl, 6, sizeof(cl_int), &out_y_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 7, sizeof(cl_int), &zero);
assert(err == 0);
err = clSetKernelArg(s->krnl, 8, sizeof(cl_int), &out_y_height);
assert(err == 0);
err = clSetKernelArg(s->krnl, 9, sizeof(cl_int), &out_y_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 10, sizeof(cl_mem), &s->m_y_cl);
assert(err == 0);
const size_t work_size_y[2] = {(size_t)out_y_width, (size_t)out_y_height};
err = clEnqueueNDRangeKernel(q, s->krnl, 2, NULL,
(const size_t*)&work_size_y, NULL, 0, 0, NULL);
assert(err == 0);
const size_t work_size_uv[2] = {(size_t)out_uv_width, (size_t)out_uv_height};
err = clSetKernelArg(s->krnl, 1, sizeof(cl_int), &in_uv_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 2, sizeof(cl_int), &in_u_offset);
assert(err == 0);
err = clSetKernelArg(s->krnl, 3, sizeof(cl_int), &in_uv_height);
assert(err == 0);
err = clSetKernelArg(s->krnl, 4, sizeof(cl_int), &in_uv_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 5, sizeof(cl_mem), &out_u);
assert(err == 0);
err = clSetKernelArg(s->krnl, 6, sizeof(cl_int), &out_uv_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 7, sizeof(cl_int), &zero);
assert(err == 0);
err = clSetKernelArg(s->krnl, 8, sizeof(cl_int), &out_uv_height);
assert(err == 0);
err = clSetKernelArg(s->krnl, 9, sizeof(cl_int), &out_uv_width);
assert(err == 0);
err = clSetKernelArg(s->krnl, 10, sizeof(cl_mem), &s->m_uv_cl);
assert(err == 0);
err = clEnqueueNDRangeKernel(q, s->krnl, 2, NULL,
(const size_t*)&work_size_uv, NULL, 0, 0, NULL);
assert(err == 0);
err = clSetKernelArg(s->krnl, 2, sizeof(cl_int), &in_v_offset);
assert(err == 0);
err = clSetKernelArg(s->krnl, 5, sizeof(cl_mem), &out_v);
assert(err == 0);
err = clEnqueueNDRangeKernel(q, s->krnl, 2, NULL,
(const size_t*)&work_size_uv, NULL, 0, 0, NULL);
assert(err == 0);
}