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https://github.com/open-mmlab/mmdeploy.git
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36124f6205
* 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>
98 lines
3.3 KiB
C++
98 lines
3.3 KiB
C++
// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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// Modified from:
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// https://github.com/NVIDIA/TensorRT/blob/master/plugin/common/serialize.hpp
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#ifndef TRT_SERIALIZE_HPP
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#define TRT_SERIALIZE_HPP
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#include <cassert>
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#include <cstring>
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#include <type_traits>
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#include <vector>
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template <typename T>
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inline void serialize_value(void** buffer, T const& value);
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template <typename T>
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inline void deserialize_value(void const** buffer, size_t* buffer_size, T* value);
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namespace {
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template <typename T, class Enable = void>
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struct Serializer {};
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template <typename T>
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struct Serializer<T,
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typename std::enable_if<std::is_arithmetic<T>::value || std::is_enum<T>::value ||
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std::is_pod<T>::value>::type> {
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static size_t serialized_size(T const& value) { return sizeof(T); }
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static void serialize(void** buffer, T const& value) {
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::memcpy(*buffer, &value, sizeof(T));
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reinterpret_cast<char*&>(*buffer) += sizeof(T);
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}
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static void deserialize(void const** buffer, size_t* buffer_size, T* value) {
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assert(*buffer_size >= sizeof(T));
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::memcpy(value, *buffer, sizeof(T));
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reinterpret_cast<char const*&>(*buffer) += sizeof(T);
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*buffer_size -= sizeof(T);
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}
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};
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template <>
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struct Serializer<const char*> {
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static size_t serialized_size(const char* value) { return strlen(value) + 1; }
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static void serialize(void** buffer, const char* value) {
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::strcpy(static_cast<char*>(*buffer), value);
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reinterpret_cast<char*&>(*buffer) += strlen(value) + 1;
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}
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static void deserialize(void const** buffer, size_t* buffer_size, const char** value) {
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*value = static_cast<char const*>(*buffer);
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size_t data_size = strnlen(*value, *buffer_size) + 1;
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assert(*buffer_size >= data_size);
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reinterpret_cast<char const*&>(*buffer) += data_size;
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*buffer_size -= data_size;
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}
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};
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template <typename T>
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struct Serializer<std::vector<T>,
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typename std::enable_if<std::is_arithmetic<T>::value || std::is_enum<T>::value ||
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std::is_pod<T>::value>::type> {
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static size_t serialized_size(std::vector<T> const& value) {
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return sizeof(value.size()) + value.size() * sizeof(T);
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}
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static void serialize(void** buffer, std::vector<T> const& value) {
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serialize_value(buffer, value.size());
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size_t nbyte = value.size() * sizeof(T);
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::memcpy(*buffer, value.data(), nbyte);
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reinterpret_cast<char*&>(*buffer) += nbyte;
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}
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static void deserialize(void const** buffer, size_t* buffer_size, std::vector<T>* value) {
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size_t size;
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deserialize_value(buffer, buffer_size, &size);
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value->resize(size);
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size_t nbyte = value->size() * sizeof(T);
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assert(*buffer_size >= nbyte);
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::memcpy(value->data(), *buffer, nbyte);
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reinterpret_cast<char const*&>(*buffer) += nbyte;
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*buffer_size -= nbyte;
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}
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};
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} // namespace
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template <typename T>
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inline size_t serialized_size(T const& value) {
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return Serializer<T>::serialized_size(value);
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}
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template <typename T>
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inline void serialize_value(void** buffer, T const& value) {
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return Serializer<T>::serialize(buffer, value);
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}
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template <typename T>
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inline void deserialize_value(void const** buffer, size_t* buffer_size, T* value) {
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return Serializer<T>::deserialize(buffer, buffer_size, value);
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}
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#endif // TRT_SERIALIZE_HPP
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