mirror of
https://github.com/open-mmlab/mmdeploy.git
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* minor changes * support windows * fix GCC build * fix lint * reformat * fix Windows build * fix GCC build * search backend ops for onnxruntime * fix lint * fix lint * code clean-up * code clean-up * fix clang build * fix trt support * fix cmake for ncnn * fix cmake for openvino * fix SDK Python API * handle ops for other backends (ncnn, trt) * handle SDK Python API library location * robustify linkage * fix cuda * minor fix for openvino & ncnn * use CMAKE_CUDA_ARCHITECTURES if set * fix cuda preprocessor * fix misc * fix pplnn & pplcv, drop support for pplcv<0.6.0 * robustify cmake * update build.md (#2) * build dynamic modules as module library & fix demo (partially) * fix candidate path for mmdeploy_python * move "enable CUDA" to cmake config for demo * refine demo cmake * add comment * fix ubuntu build * revert docs/en/build.md * fix C API * fix lint * Windows build doc (#3) * check in docs related to mmdeploy build on windows * update build guide on windows platform * update build guide on windows platform * make path of thirdparty libraries consistent * make path consistency * correct build command for custom ops * correct build command for sdk * update sdk build instructions * update doc * correct build command * fix lint * correct build command and fix lint Co-authored-by: lvhan <lvhan@pjlab.org> * trailing whitespace (#4) * minor fix * fix sr sdk model * fix type deduction * fix cudaFree after driver shutting down * update ppl.cv installation warning (#5) * fix device allocator threshold & fix lint * update doc (#6) * update ppl.cv installation warning * missing 'git clone' Co-authored-by: chenxin <chenxin2@sensetime.com> Co-authored-by: zhangli <zhangli@sensetime.com> Co-authored-by: lvhan028 <lvhan_028@163.com> Co-authored-by: lvhan <lvhan@pjlab.org>
114 lines
3.8 KiB
C++
114 lines
3.8 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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MMDEPLOY_API Result<void> Gather(const Value::Array& array, const vector<int>& idxs,
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Value::Array& output);
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MMDEPLOY_API Result<void> Gather(Value::Array&& array, const vector<int>& idxs,
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Value::Array& output);
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MMDEPLOY_API Result<void> Gather(const Value::Object& object, const vector<std::string>& keys,
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Value::Array& output);
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MMDEPLOY_API Result<void> Gather(Value::Object&& object, const vector<std::string>& keys,
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Value::Array& output);
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MMDEPLOY_API Result<void> Scatter(Value::Array array, const vector<int>& idxs,
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Value::Array& output);
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MMDEPLOY_API Result<void> Scatter(Value::Array array, const vector<std::string>& keys,
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Value::Object& output);
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inline Result<Value::Array> Gather(const Value::Array& array, const vector<int>& idxs) {
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Value::Array output;
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OUTCOME_TRY(Gather(array, idxs, output));
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return output;
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}
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inline Result<Value::Array> Gather(Value::Array&& array, const vector<int>& idxs) {
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Value::Array output;
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OUTCOME_TRY(Gather(std::move(array), idxs, output));
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return output;
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}
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inline Result<Value::Array> Gather(const Value::Object& object, const vector<std::string>& keys) {
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Value::Array output;
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OUTCOME_TRY(Gather(object, keys, output));
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return output;
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}
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inline Result<Value::Array> Gather(Value::Object&& object, const vector<std::string>& keys) {
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Value::Array output;
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OUTCOME_TRY(Gather(std::move(object), keys, output));
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return output;
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}
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inline Result<Value::Array> Scatter(Value::Array array, const vector<int>& idxs) {
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Value::Array output(idxs.size(), Value::kNull);
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OUTCOME_TRY(Scatter(std::move(array), idxs, output));
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return output;
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}
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inline Result<Value::Object> Scatter(Value::Array array, const vector<std::string>& keys) {
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Value::Object output;
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OUTCOME_TRY(Scatter(std::move(array), keys, output));
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return output;
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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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MMDEPLOY_API 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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MMDEPLOY_API 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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MMDEPLOY_API 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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