openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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#include "transform.h"
#include <assert.h>
#include <string.h>
#include "selfdrive/common/clutil.h"
void transform_init(Transform* s, cl_context ctx, cl_device_id device_id) {
memset(s, 0, sizeof(*s));
cl_program prg = cl_program_from_file(ctx, device_id, "transforms/transform.cl", "");
s->krnl = CL_CHECK_ERR(clCreateKernel(prg, "warpPerspective", &err));
// done with this
CL_CHECK(clReleaseProgram(prg));
s->m_y_cl = CL_CHECK_ERR(clCreateBuffer(ctx, CL_MEM_READ_WRITE, 3*3*sizeof(float), NULL, &err));
s->m_uv_cl = CL_CHECK_ERR(clCreateBuffer(ctx, CL_MEM_READ_WRITE, 3*3*sizeof(float), NULL, &err));
}
void transform_destroy(Transform* s) {
CL_CHECK(clReleaseMemObject(s->m_y_cl));
CL_CHECK(clReleaseMemObject(s->m_uv_cl));
CL_CHECK(clReleaseKernel(s->krnl));
}
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,
const mat3& projection) {
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);
CL_CHECK(clEnqueueWriteBuffer(q, s->m_y_cl, CL_TRUE, 0, 3*3*sizeof(float), (void*)projection_y.v, 0, NULL, NULL));
CL_CHECK(clEnqueueWriteBuffer(q, s->m_uv_cl, CL_TRUE, 0, 3*3*sizeof(float), (void*)projection_uv.v, 0, NULL, NULL));
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;
CL_CHECK(clSetKernelArg(s->krnl, 0, sizeof(cl_mem), &in_yuv));
CL_CHECK(clSetKernelArg(s->krnl, 1, sizeof(cl_int), &in_y_width));
CL_CHECK(clSetKernelArg(s->krnl, 2, sizeof(cl_int), &in_y_offset));
CL_CHECK(clSetKernelArg(s->krnl, 3, sizeof(cl_int), &in_y_height));
CL_CHECK(clSetKernelArg(s->krnl, 4, sizeof(cl_int), &in_y_width));
CL_CHECK(clSetKernelArg(s->krnl, 5, sizeof(cl_mem), &out_y));
CL_CHECK(clSetKernelArg(s->krnl, 6, sizeof(cl_int), &out_y_width));
CL_CHECK(clSetKernelArg(s->krnl, 7, sizeof(cl_int), &zero));
CL_CHECK(clSetKernelArg(s->krnl, 8, sizeof(cl_int), &out_y_height));
CL_CHECK(clSetKernelArg(s->krnl, 9, sizeof(cl_int), &out_y_width));
CL_CHECK(clSetKernelArg(s->krnl, 10, sizeof(cl_mem), &s->m_y_cl));
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> old-commit-hash: 08846b5c0ec3a5299c8cb73904da15a18d3aaccf
5 years ago
const size_t work_size_y[2] = {(size_t)out_y_width, (size_t)out_y_height};
CL_CHECK(clEnqueueNDRangeKernel(q, s->krnl, 2, NULL,
(const size_t*)&work_size_y, NULL, 0, 0, NULL));
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> old-commit-hash: 08846b5c0ec3a5299c8cb73904da15a18d3aaccf
5 years ago
const size_t work_size_uv[2] = {(size_t)out_uv_width, (size_t)out_uv_height};
CL_CHECK(clSetKernelArg(s->krnl, 1, sizeof(cl_int), &in_uv_width));
CL_CHECK(clSetKernelArg(s->krnl, 2, sizeof(cl_int), &in_u_offset));
CL_CHECK(clSetKernelArg(s->krnl, 3, sizeof(cl_int), &in_uv_height));
CL_CHECK(clSetKernelArg(s->krnl, 4, sizeof(cl_int), &in_uv_width));
CL_CHECK(clSetKernelArg(s->krnl, 5, sizeof(cl_mem), &out_u));
CL_CHECK(clSetKernelArg(s->krnl, 6, sizeof(cl_int), &out_uv_width));
CL_CHECK(clSetKernelArg(s->krnl, 7, sizeof(cl_int), &zero));
CL_CHECK(clSetKernelArg(s->krnl, 8, sizeof(cl_int), &out_uv_height));
CL_CHECK(clSetKernelArg(s->krnl, 9, sizeof(cl_int), &out_uv_width));
CL_CHECK(clSetKernelArg(s->krnl, 10, sizeof(cl_mem), &s->m_uv_cl));
CL_CHECK(clEnqueueNDRangeKernel(q, s->krnl, 2, NULL,
(const size_t*)&work_size_uv, NULL, 0, 0, NULL));
CL_CHECK(clSetKernelArg(s->krnl, 2, sizeof(cl_int), &in_v_offset));
CL_CHECK(clSetKernelArg(s->krnl, 5, sizeof(cl_mem), &out_v));
CL_CHECK(clEnqueueNDRangeKernel(q, s->krnl, 2, NULL,
(const size_t*)&work_size_uv, NULL, 0, 0, NULL));
}