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https://github.com/infiniteCable2/openpilot.git
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DevTekVE
1a8dd310ae
* Add support for TinyGrad model runner processing Introduced a new function `is_tinygrad_model` to detect TinyGrad as an active model runner. Updated the `is_stock_model` logic to account for TinyGrad models and added a new process entry for TinyGrad in the model manager. This enables handling TinyGrad models alongside existing configurations. adding modeld back Add support for `modeld_v2` and update paths for consistency Updated `SConscript` files to integrate `modeld_v2` alongside `modeld` and adjusted script paths for correct metadata handling. Adjusted various configurations and scripts, such as `labeler.yaml` and `build_release.sh`, to include `modeld_v2` and ensure cohesive project structure. Refactor imports to use updated `modeld_v2` paths. Replaced outdated `modeld` references with their `modeld_v2` counterparts for consistency and clarity across the codebase. Also updated `.gitignore` to accommodate new directory structure. This change ensures better maintainability and alignment with the new directory schema. Refactor and reorganize modeld to sunnypilot/modeld_v2 structure. Moved and renamed `modeld` components to the new `sunnypilot/modeld_v2` directory for better organization and modularity. Updated imports and file references to align with the new structure, ensuring compatibility and functionality. Streamlined project structure to improve maintainability and future development. * typo * Use `stock` model runner and refactor model checks. Replaces outdated model detection logic with unified `stock` runner integration, simplifying the decision flow for model selection. Includes `stock` as a new enum in the `Runner` type and updates affected references accordingly. * Handle missing 'sim_pose' in model outputs gracefully. Added conditional checks to ensure the code handles cases where 'sim_pose' is absent in the model outputs. Fallback behaviors use 'plan' data when 'sim_pose' is unavailable, preventing potential errors and enhancing robustness.
98 lines
3.3 KiB
C++
98 lines
3.3 KiB
C++
#pragma once
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#include <cfloat>
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#include <cstdlib>
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#include <cassert>
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#include <memory>
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#define CL_USE_DEPRECATED_OPENCL_1_2_APIS
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#ifdef __APPLE__
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#include <OpenCL/cl.h>
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#else
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#include <CL/cl.h>
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#endif
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#include "common/mat.h"
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#include "sunnypilot/modeld_v2/transforms/loadyuv.h"
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#include "sunnypilot/modeld_v2/transforms/transform.h"
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class ModelFrame {
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public:
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ModelFrame(cl_device_id device_id, cl_context context) {
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q = CL_CHECK_ERR(clCreateCommandQueue(context, device_id, 0, &err));
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}
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virtual ~ModelFrame() {}
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virtual cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) { return NULL; }
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uint8_t* buffer_from_cl(cl_mem *in_frames, int buffer_size) {
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CL_CHECK(clEnqueueReadBuffer(q, *in_frames, CL_TRUE, 0, buffer_size, input_frames.get(), 0, nullptr, nullptr));
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clFinish(q);
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return &input_frames[0];
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}
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int MODEL_WIDTH;
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int MODEL_HEIGHT;
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int MODEL_FRAME_SIZE;
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int buf_size;
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protected:
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cl_mem y_cl, u_cl, v_cl;
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Transform transform;
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cl_command_queue q;
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std::unique_ptr<uint8_t[]> input_frames;
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void init_transform(cl_device_id device_id, cl_context context, int model_width, int model_height) {
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y_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, model_width * model_height, NULL, &err));
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u_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, (model_width / 2) * (model_height / 2), NULL, &err));
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v_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, (model_width / 2) * (model_height / 2), NULL, &err));
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transform_init(&transform, context, device_id);
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}
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void deinit_transform() {
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transform_destroy(&transform);
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CL_CHECK(clReleaseMemObject(v_cl));
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CL_CHECK(clReleaseMemObject(u_cl));
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CL_CHECK(clReleaseMemObject(y_cl));
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}
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void run_transform(cl_mem yuv_cl, int model_width, int model_height, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) {
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transform_queue(&transform, q,
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yuv_cl, frame_width, frame_height, frame_stride, frame_uv_offset,
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y_cl, u_cl, v_cl, model_width, model_height, projection);
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}
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};
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class DrivingModelFrame : public ModelFrame {
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public:
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DrivingModelFrame(cl_device_id device_id, cl_context context, uint8_t buffer_length);
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~DrivingModelFrame();
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cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection);
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const int MODEL_WIDTH = 512;
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const int MODEL_HEIGHT = 256;
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const int MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 / 2;
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const int buf_size = MODEL_FRAME_SIZE * 2;
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const size_t frame_size_bytes = MODEL_FRAME_SIZE * sizeof(uint8_t);
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const uint8_t buffer_length;
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private:
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LoadYUVState loadyuv;
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cl_mem img_buffer_20hz_cl, last_img_cl, input_frames_cl;
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cl_buffer_region region;
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};
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class MonitoringModelFrame : public ModelFrame {
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public:
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MonitoringModelFrame(cl_device_id device_id, cl_context context);
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~MonitoringModelFrame();
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cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection);
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const int MODEL_WIDTH = 1440;
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const int MODEL_HEIGHT = 960;
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const int MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT;
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const int buf_size = MODEL_FRAME_SIZE;
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private:
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cl_mem input_frame_cl;
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};
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