mmdeploy/csrc/apis/c/segmentor.cpp

137 lines
4.1 KiB
C++

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