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https://github.com/open-mmlab/mmdeploy.git
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* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
174 lines
6.3 KiB
C++
174 lines
6.3 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include <cuda_runtime.h>
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#include "core/utils/formatter.h"
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#include "ppl/cv/cuda/cvtcolor.h"
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#include "preprocess/transform/load.h"
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#include "preprocess/transform/transform_utils.h"
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using namespace std;
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using namespace ppl::cv::cuda;
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namespace mmdeploy {
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namespace cuda {
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template <int channels>
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void CastToFloat(const uint8_t* src, int height, int width, float* dst, cudaStream_t stream);
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class PrepareImageImpl : public ::mmdeploy::PrepareImageImpl {
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public:
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explicit PrepareImageImpl(const Value& args) : ::mmdeploy::PrepareImageImpl(args){};
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~PrepareImageImpl() override = default;
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protected:
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Tensor Mat2Tensor(const mmdeploy::Mat& mat) {
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TensorDesc desc{
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mat.buffer().GetDevice(), mat.type(), {1, mat.height(), mat.width(), mat.channel()}, ""};
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shared_ptr<void> data(mat.data<void>(), [mat = mat](void* p) {});
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return Tensor(desc, data);
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}
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protected:
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Result<Tensor> ConvertToBGR(const Mat& img) override {
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auto _img = MakeAvailableOnDevice(img, device_, stream_);
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auto src_mat = _img.value();
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if (img.pixel_format() == PixelFormat::kBGR) {
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return Mat2Tensor(src_mat);
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}
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cudaStream_t stream = ::mmdeploy::GetNative<cudaStream_t>(stream_);
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Mat dst_mat(src_mat.height(), src_mat.width(), PixelFormat::kBGR, src_mat.type(), device_);
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ppl::common::RetCode ret = 0;
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int src_h = src_mat.height();
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int src_w = src_mat.width();
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int src_c = src_mat.channel();
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int src_stride = src_w * src_mat.channel();
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uint8_t* src_ptr = src_mat.data<uint8_t>();
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int dst_w = dst_mat.width();
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int dst_stride = dst_w * dst_mat.channel();
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uint8_t* dst_ptr = dst_mat.data<uint8_t>();
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switch (img.pixel_format()) {
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case PixelFormat::kRGB:
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ret = RGB2BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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case PixelFormat::kGRAYSCALE:
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ret = GRAY2BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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case PixelFormat::kNV12:
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assert(src_c == 1);
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NV122BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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case PixelFormat::kNV21:
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assert(src_c == 1);
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NV212BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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case PixelFormat::kBGRA:
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BGRA2BGR<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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default:
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ERROR("src type: unknown type {}", img.pixel_format());
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return Status(eNotSupported);
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}
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if (ret != 0) {
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ERROR("color transfer from {} to BGR failed, ret {}", img.pixel_format(), ret);
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return Status(eFail);
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}
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if (arg_.to_float32) {
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TensorDesc desc{device_, DataType::kFLOAT, {1, src_h, src_w, dst_mat.channel()}, ""};
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Tensor dst_tensor{desc};
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CastToFloat<3>(dst_ptr, src_h, src_w, dst_tensor.data<float>(), stream);
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return dst_tensor;
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} else {
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return Mat2Tensor(dst_mat);
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}
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}
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Result<Tensor> ConvertToGray(const Mat& img) override {
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OUTCOME_TRY(auto src_mat, MakeAvailableOnDevice(img, device_, stream_));
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if (img.pixel_format() == PixelFormat::kGRAYSCALE) {
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return Mat2Tensor(src_mat);
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}
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cudaStream_t stream = ::mmdeploy::GetNative<cudaStream_t>(stream_);
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Mat dst_mat(src_mat.height(), src_mat.width(), PixelFormat::kGRAYSCALE, src_mat.type(),
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device_);
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ppl::common::RetCode ret = 0;
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int src_h = src_mat.height();
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int src_w = src_mat.width();
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int src_c = src_mat.channel();
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int src_stride = src_w * src_mat.channel();
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uint8_t* src_ptr = src_mat.data<uint8_t>();
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int dst_w = dst_mat.width();
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int dst_stride = dst_w * dst_mat.channel();
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uint8_t* dst_ptr = dst_mat.data<uint8_t>();
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switch (img.pixel_format()) {
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case PixelFormat::kRGB:
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ret = RGB2GRAY<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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case PixelFormat::kBGR:
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ret = BGR2GRAY<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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case PixelFormat::kNV12: {
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assert(src_c == 1);
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Mat rgb_mat(src_mat.height(), src_mat.width(), PixelFormat::kRGB, src_mat.type(), device_);
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NV122RGB<uint8_t>(stream, src_h, src_w, src_stride, src_ptr,
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rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>());
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RGB2GRAY<uint8_t>(stream, rgb_mat.height(), rgb_mat.width(),
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rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>(), dst_stride,
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dst_mat.data<uint8_t>());
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break;
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}
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case PixelFormat::kNV21: {
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assert(src_c == 1);
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Mat rgb_mat(src_mat.height(), src_mat.width(), PixelFormat::kRGB, src_mat.type(), device_);
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NV212RGB<uint8_t>(stream, src_h, src_w, src_stride, src_ptr,
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rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>());
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RGB2GRAY<uint8_t>(stream, rgb_mat.height(), rgb_mat.width(),
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rgb_mat.width() * rgb_mat.channel(), rgb_mat.data<uint8_t>(), dst_stride,
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dst_mat.data<uint8_t>());
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break;
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}
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case PixelFormat::kBGRA:
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BGRA2GRAY<uint8_t>(stream, src_h, src_w, src_stride, src_ptr, dst_stride, dst_ptr);
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break;
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default:
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ERROR("src type: unknown type {}", img.pixel_format());
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throw Status(eNotSupported);
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}
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if (ret != 0) {
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ERROR("color transfer from {} to Gray failed", img.pixel_format());
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throw Status(eFail);
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}
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if (arg_.to_float32) {
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TensorDesc desc{device_, DataType::kFLOAT, {1, src_h, src_w, dst_mat.channel()}, ""};
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Tensor dst_tensor{desc};
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CastToFloat<1>(dst_ptr, src_h, src_w, dst_tensor.data<float>(), stream);
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return dst_tensor;
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} else {
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return Mat2Tensor(dst_mat);
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}
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}
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};
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class PrepareImageImplCreator : public Creator<::mmdeploy::PrepareImageImpl> {
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public:
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const char* GetName() const override { return "cuda"; }
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int GetVersion() const override { return 1; }
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ReturnType Create(const Value& args) override { return make_unique<PrepareImageImpl>(args); }
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};
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} // namespace cuda
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} // namespace mmdeploy
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using mmdeploy::PrepareImageImpl;
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using mmdeploy::cuda::PrepareImageImplCreator;
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REGISTER_MODULE(PrepareImageImpl, PrepareImageImplCreator);
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