137 lines
4.1 KiB
C++
137 lines
4.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "segmentor.h"
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#include "codebase/mmseg/mmseg.h"
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#include "core/device.h"
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#include "core/graph.h"
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#include "core/mat.h"
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#include "core/tensor.h"
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#include "core/utils/formatter.h"
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#include "handle.h"
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using namespace std;
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using namespace mmdeploy;
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namespace {
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Value& config_template() {
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// clang-format off
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static Value v{
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{
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"pipeline", {
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{"input", {"img"}},
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{"output", {"mmsegmentation-fcn_output"}},
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{
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"tasks", {
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{
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{"name", "mmsegmentation-fcn"},
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{"type", "Inference"},
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{"params", {{"model", "TBD"}}},
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{"input", {"img"}},
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{"output", {"mmsegmentation-fcn_output"}}
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}
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}
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}
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}
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}
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};
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// clang-format on
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return v;
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}
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template <class ModelType>
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int mmdeploy_segmentor_create_impl(ModelType&& m, const char* device_name, int device_id,
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mm_handle_t* handle) {
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try {
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auto value = config_template();
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value["pipeline"]["tasks"][0]["params"]["model"] = std::forward<ModelType>(m);
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auto segmentor = std::make_unique<Handle>(device_name, device_id, std::move(value));
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*handle = segmentor.release();
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return MM_SUCCESS;
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} catch (const std::exception& e) {
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ERROR("exception caught: {}", e.what());
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} catch (...) {
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ERROR("unknown exception caught");
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}
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return MM_E_FAIL;
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}
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} // namespace
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MM_SDK_API int mmdeploy_segmentor_create(mm_model_t model, const char* device_name, int device_id,
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mm_handle_t* handle) {
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return mmdeploy_segmentor_create_impl(*static_cast<Model*>(model), device_name, device_id,
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handle);
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}
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MM_SDK_API int mmdeploy_segmentor_create_by_path(const char* model_path, const char* device_name,
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int device_id, mm_handle_t* handle) {
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return mmdeploy_segmentor_create_impl(model_path, device_name, device_id, handle);
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}
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MM_SDK_API int mmdeploy_segmentor_apply(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
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mm_segment_t** results) {
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if (handle == nullptr || mats == nullptr || mat_count == 0 || results == nullptr) {
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return MM_E_INVALID_ARG;
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}
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try {
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auto segmentor = static_cast<Handle*>(handle);
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Value input{Value::kArray};
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for (int i = 0; i < mat_count; ++i) {
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mmdeploy::Mat _mat{mats[i].height, mats[i].width, PixelFormat(mats[i].format),
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DataType(mats->type), mats[i].data, Device{"cpu"}};
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input.front().push_back({{"ori_img", _mat}});
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}
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auto output = segmentor->Run(std::move(input)).value().front();
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auto deleter = [&](mm_segment_t* p) { mmdeploy_segmentor_release_result(p, mat_count); };
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unique_ptr<mm_segment_t[], decltype(deleter)> _results(new mm_segment_t[mat_count]{}, deleter);
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auto results_ptr = _results.get();
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for (auto i = 0; i < mat_count; ++i, ++results_ptr) {
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auto& output_item = output[i];
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DEBUG("the {}-th item in output: {}", i, output_item);
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auto segmentor_output = from_value<mmseg::SegmentorOutput>(output_item);
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results_ptr->height = segmentor_output.height;
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results_ptr->width = segmentor_output.width;
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results_ptr->classes = segmentor_output.classes;
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results_ptr->mask = new int[results_ptr->height * results_ptr->width];
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segmentor_output.mask.CopyTo(results_ptr->mask, segmentor->stream()).value();
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}
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segmentor->stream().Wait().value();
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*results = _results.release();
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return MM_SUCCESS;
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} catch (const std::exception& e) {
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ERROR("exception caught: {}", e.what());
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} catch (...) {
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ERROR("unknown exception caught");
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}
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return MM_E_FAIL;
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}
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MM_SDK_API void mmdeploy_segmentor_release_result(mm_segment_t* results, int count) {
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if (results == nullptr) {
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return;
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}
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for (auto i = 0; i < count; ++i) {
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delete[] results[i].mask;
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}
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delete[] results;
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}
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MM_SDK_API void mmdeploy_segmentor_destroy(mm_handle_t handle) {
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if (handle != nullptr) {
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auto segmentor = static_cast<Handle*>(handle);
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delete segmentor;
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}
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}
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