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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>
82 lines
2.8 KiB
C++
82 lines
2.8 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 "preprocess/transform/normalize.h"
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#include "preprocess/transform/transform_utils.h"
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using namespace std;
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namespace mmdeploy::cuda {
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template <typename T, int channels>
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void Normalize(const T* src, int height, int width, int stride, float* output, const float* mean,
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const float* std, bool to_rgb, cudaStream_t stream);
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class NormalizeImpl : public ::mmdeploy::NormalizeImpl {
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public:
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explicit NormalizeImpl(const Value& args) : ::mmdeploy::NormalizeImpl(args) {}
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protected:
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Result<Tensor> NormalizeImage(const Tensor& tensor) override {
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OUTCOME_TRY(auto src_tensor, MakeAvailableOnDevice(tensor, device_, stream_));
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auto src_desc = src_tensor.desc();
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int h = (int)src_desc.shape[1];
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int w = (int)src_desc.shape[2];
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int c = (int)src_desc.shape[3];
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int stride = w * c;
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TensorDesc dst_desc{device_, DataType::kFLOAT, src_desc.shape, src_desc.name};
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Tensor dst_tensor{dst_desc};
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auto output = dst_tensor.data<float>();
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auto stream = ::mmdeploy::GetNative<cudaStream_t>(stream_);
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if (DataType::kINT8 == src_desc.data_type) {
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auto input = src_tensor.data<uint8_t>();
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if (3 == c) {
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Normalize<uint8_t, 3>(input, h, w, stride, output, arg_.mean.data(), arg_.std.data(),
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arg_.to_rgb, stream);
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} else if (1 == c) {
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Normalize<uint8_t, 1>(input, h, w, stride, output, arg_.mean.data(), arg_.std.data(),
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arg_.to_rgb, stream);
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} else {
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ERROR("unsupported channels {}", c);
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return Status(eNotSupported);
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}
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} else if (DataType::kFLOAT == src_desc.data_type) {
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auto input = src_tensor.data<float>();
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if (3 == c) {
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Normalize<float, 3>(input, h, w, stride, output, arg_.mean.data(), arg_.std.data(),
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arg_.to_rgb, stream);
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} else if (1 == c) {
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Normalize<float, 1>(input, h, w, stride, output, arg_.mean.data(), arg_.std.data(),
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arg_.to_rgb, stream);
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} else {
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ERROR("unsupported channels {}", c);
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return Status(eNotSupported);
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}
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} else {
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ERROR("unsupported data type {}", src_desc.data_type);
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assert(0);
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return Status(eNotSupported);
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}
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return dst_tensor;
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}
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};
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class NormalizeImplCreator : public Creator<::mmdeploy::NormalizeImpl> {
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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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std::unique_ptr<::mmdeploy::NormalizeImpl> Create(const Value& args) override {
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return make_unique<NormalizeImpl>(args);
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}
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};
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} // namespace mmdeploy::cuda
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using mmdeploy::NormalizeImpl;
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using mmdeploy::cuda::NormalizeImplCreator;
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REGISTER_MODULE(NormalizeImpl, NormalizeImplCreator);
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