mmdeploy/csrc/apis/c/restorer.cpp
lvhan028 36124f6205
Merge sdk (#251)
* 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>
2021-12-07 10:57:55 +08:00

122 lines
3.7 KiB
C++

// Copyright (c) OpenMMLab. All rights reserved.
#include "restorer.h"
#include "codebase/mmedit/mmedit.h"
#include "core/device.h"
#include "core/graph.h"
#include "core/mat.h"
#include "core/utils/formatter.h"
#include "handle.h"
using namespace mmdeploy;
namespace {
const Value &config_template() {
// clang-format off
static Value v {
{
"pipeline", {
{
"tasks", {
{
{"name", "det"},
{"type", "Inference"},
{"params", {{"model", "TBD"}}},
{"input", {"img"}},
{"output", {"out"}}
}
}
},
{"input", {"img"}},
{"output", {"out"}}
}
}
};
// clang-format on
return v;
}
template <class ModelType>
int mmdeploy_restorer_create_impl(ModelType &&m, const char *device_name, int device_id,
mm_handle_t *handle) {
try {
auto config = config_template();
config["pipeline"]["tasks"][0]["params"]["model"] = std::forward<ModelType>(m);
auto restorer = std::make_unique<Handle>(device_name, device_id, std::move(config));
*handle = restorer.release();
return MM_SUCCESS;
} catch (const std::exception &e) {
ERROR("exception caught: {}", e.what());
} catch (...) {
ERROR("unknown exception caught");
}
return MM_E_FAIL;
}
} // namespace
int mmdeploy_restorer_create(mm_model_t model, const char *device_name, int device_id,
mm_handle_t *handle) {
return mmdeploy_restorer_create_impl(*static_cast<Model *>(model), device_name, device_id,
handle);
}
int mmdeploy_restorer_create_by_path(const char *model_path, const char *device_name, int device_id,
mm_handle_t *handle) {
return mmdeploy_restorer_create_impl(model_path, device_name, device_id, handle);
}
int mmdeploy_restorer_apply(mm_handle_t handle, const mm_mat_t *images, int count,
mm_mat_t **results) {
if (handle == nullptr || images == nullptr || count == 0 || results == nullptr) {
return MM_E_INVALID_ARG;
}
try {
auto restorer = static_cast<Handle *>(handle);
Value input{Value::kArray};
for (int i = 0; i < count; ++i) {
Mat _mat{images[i].height, images[i].width, PixelFormat(images[i].format),
DataType(images[i].type), images[i].data, Device{"cpu"}};
input.front().push_back({{"ori_img", _mat}});
}
auto output = restorer->Run(std::move(input)).value().front();
auto restorer_output = from_value<std::vector<mmedit::RestorerOutput>>(output);
auto deleter = [&](mm_mat_t *p) { mmdeploy_restorer_release_result(p, count); };
std::unique_ptr<mm_mat_t[], decltype(deleter)> _results(new mm_mat_t[count]{}, deleter);
for (int i = 0; i < count; ++i) {
auto upscale = restorer_output[i];
auto &res = _results[i];
res.data = new uint8_t[upscale.byte_size()];
memcpy(res.data, upscale.data<uint8_t>(), upscale.byte_size());
res.format = (mm_pixel_format_t)upscale.pixel_format();
res.height = upscale.height();
res.width = upscale.width();
res.type = (mm_data_type_t)upscale.type();
}
*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;
}
void mmdeploy_restorer_release_result(mm_mat_t *results, int count) {
for (int i = 0; i < count; ++i) {
delete[] results[i].data;
}
delete[] results;
}
void mmdeploy_restorer_destroy(mm_handle_t handle) { delete static_cast<Handle *>(handle); }