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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>
112 lines
3.3 KiB
C++
112 lines
3.3 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#ifndef MMDEPLOY_SRC_EXPERIMENTAL_PIPELINE_OPERATOR_H_
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#define MMDEPLOY_SRC_EXPERIMENTAL_PIPELINE_OPERATOR_H_
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#include "core/value.h"
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namespace mmdeploy::graph {
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using std::string;
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using std::tuple;
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using std::vector;
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template <class V, std::enable_if_t<is_value_v<std::decay_t<V> >, bool> = true>
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Result<void> Idxs2Keys(V&& array, const vector<string>& keys, Value& object) {
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if (!std::forward<V>(array).is_array() || std::forward<V>(array).size() < keys.size()) {
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return Status(eInvalidArgument);
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}
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if (!(object.is_null() || object.is_object())) {
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return Status(eInvalidArgument);
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}
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for (int i = 0; i < keys.size(); ++i) {
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object[keys[i]] = std::forward<V>(array)[i];
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}
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return success();
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}
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template <class V, std::enable_if_t<is_value_v<std::decay_t<V> >, bool> = true>
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Result<Value> Idxs2Keys(V&& array, const vector<string>& keys) {
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Value object = ValueType::kObject;
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OUTCOME_TRY(Idxs2Keys(std::forward<V>(array), keys, object));
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return object;
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}
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template <class V, std::enable_if_t<is_value_v<std::decay_t<V> >, bool> = true>
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Result<void> Keys2Idxs(V&& object, const vector<string>& keys, Value& array) {
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if (!std::forward<V>(object).is_object()) {
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return Status(eInvalidArgument);
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}
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if (!(array.is_null() || array.is_array())) {
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return Status(eInvalidArgument);
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}
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try {
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for (const auto& key : keys) {
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array.push_back(std::forward<V>(object)[key]);
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}
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} catch (...) {
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// TODO: forward exception
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return Status(eInvalidArgument);
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}
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return success();
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}
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template <class V, std::enable_if_t<is_value_v<std::decay_t<V> >, bool> = true>
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Result<Value> Keys2Idxs(V&& object, const vector<string>& keys) {
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Value array = ValueType::kArray;
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OUTCOME_TRY(Keys2Idxs(std::forward<V>(object), keys, array));
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return array;
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}
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template <class V, std::enable_if_t<is_value_v<std::decay_t<V> >, bool> = true>
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Result<tuple<Value, vector<int> > > Flatten(V&& input) {
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if (!input.is_array()) {
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return Status(eInvalidArgument);
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}
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Value output = ValueType::kArray;
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std::vector<int> idxs;
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for (int i = 0; i < input.size(); ++i) {
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auto inner = std::forward<V>(input)[i];
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if (!inner.is_array()) {
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return Status(eInvalidArgument);
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}
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for (auto& item : inner) {
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output.push_back(std::move(item));
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idxs.push_back(i);
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}
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}
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idxs.push_back(input.size());
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return {output, idxs};
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}
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template <class V, std::enable_if_t<is_value_v<std::decay_t<V> >, bool> = true>
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Result<Value> Unflatten(V&& input, const vector<int>& idxs) {
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if (!input.is_array()) {
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return Status(eInvalidArgument);
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}
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Value output = ValueType::kArray;
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for (int i = 0; i < idxs.back(); ++i) {
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output.push_back(ValueType::kArray);
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}
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for (int i = 0; i < input.size(); ++i) {
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if (idxs[i] >= output.size()) {
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return Status(eInvalidArgument);
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}
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output[idxs[i]].push_back(std::forward<V>(input)[i]);
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}
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return output;
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}
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// object of arrays -> array of objects, all arrays must be of same length
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Result<Value> DistribOA(const Value& oa);
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// array of objects -> object of arrays, all objects must be isomorphic
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Result<Value> DistribAO(const Value& ao);
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// array of arrays -> array of arrays, this is equivalent to transpose
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Result<Value> DistribAA(const Value& a);
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} // namespace mmdeploy::graph
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#endif // MMDEPLOY_SRC_EXPERIMENTAL_PIPELINE_OPERATOR_H_
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