Files
FastDeploy/fastdeploy/pybind/main.cc.in
Zheng-Bicheng 458523fc29 update pybind
2023-03-07 15:50:49 +08:00

187 lines
6.1 KiB
C++
Executable File

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/pybind/main.h"
namespace fastdeploy {
void BindFDTensor(pybind11::module&);
void BindRuntime(pybind11::module&);
void BindFDModel(pybind11::module&);
void BindVision(pybind11::module&);
void BindEncryption(pybind11::module&);
void BindText(pybind11::module&);
void BindPipeline(pybind11::module&);
pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype) {
pybind11::dtype dt;
if (fd_dtype == FDDataType::INT32) {
dt = pybind11::dtype::of<int32_t>();
} else if (fd_dtype == FDDataType::INT64) {
dt = pybind11::dtype::of<int64_t>();
} else if (fd_dtype == FDDataType::FP32) {
dt = pybind11::dtype::of<float>();
} else if (fd_dtype == FDDataType::FP64) {
dt = pybind11::dtype::of<double>();
} else if (fd_dtype == FDDataType::UINT8) {
dt = pybind11::dtype::of<uint8_t>();
} else if (fd_dtype == FDDataType::INT8) {
dt = pybind11::dtype::of<int8_t>();
} else if (fd_dtype == FDDataType::FP16) {
dt = pybind11::dtype::of<float16>();
} else {
FDASSERT(false, "The function doesn't support data type of %s.",
Str(fd_dtype).c_str());
}
return dt;
}
FDDataType NumpyDataTypeToFDDataType(const pybind11::dtype& np_dtype) {
if (np_dtype.is(pybind11::dtype::of<int32_t>())) {
return FDDataType::INT32;
} else if (np_dtype.is(pybind11::dtype::of<int64_t>())) {
return FDDataType::INT64;
} else if (np_dtype.is(pybind11::dtype::of<float>())) {
return FDDataType::FP32;
} else if (np_dtype.is(pybind11::dtype::of<double>())) {
return FDDataType::FP64;
} else if (np_dtype.is(pybind11::dtype::of<uint8_t>())) {
return FDDataType::UINT8;
} else if (np_dtype.is(pybind11::dtype::of<int8_t>())) {
return FDDataType::INT8;
} else if (np_dtype.is(pybind11::dtype::of<float16>())) {
return FDDataType::FP16;
}
FDASSERT(false,
"NumpyDataTypeToFDDataType() only support "
"int8/int32/int64/float32/float64/float16 now.");
return FDDataType::FP32;
}
void PyArrayToTensor(pybind11::array& pyarray, FDTensor* tensor,
bool share_buffer) {
auto dtype = NumpyDataTypeToFDDataType(pyarray.dtype());
std::vector<int64_t> data_shape;
data_shape.insert(data_shape.begin(), pyarray.shape(),
pyarray.shape() + pyarray.ndim());
if (share_buffer) {
tensor->SetExternalData(data_shape, dtype,
pyarray.mutable_data());
} else {
tensor->Resize(data_shape, dtype);
memcpy(tensor->MutableData(), pyarray.mutable_data(), pyarray.nbytes());
}
}
void PyArrayToTensorList(std::vector<pybind11::array>& pyarrays, std::vector<FDTensor>* tensors,
bool share_buffer) {
tensors->resize(pyarrays.size());
for(auto i = 0; i < pyarrays.size(); ++i) {
PyArrayToTensor(pyarrays[i], &(*tensors)[i], share_buffer);
}
}
pybind11::array TensorToPyArray(const FDTensor& tensor) {
auto numpy_dtype = FDDataTypeToNumpyDataType(tensor.dtype);
auto out = pybind11::array(numpy_dtype, tensor.shape);
memcpy(out.mutable_data(), tensor.CpuData(), tensor.Nbytes());
return out;
}
#ifdef ENABLE_VISION
int NumpyDataTypeToOpenCvType(const pybind11::dtype& np_dtype) {
if (np_dtype.is(pybind11::dtype::of<int32_t>())) {
return CV_32S;
} else if (np_dtype.is(pybind11::dtype::of<int8_t>())) {
return CV_8S;
} else if (np_dtype.is(pybind11::dtype::of<uint8_t>())) {
return CV_8U;
} else if (np_dtype.is(pybind11::dtype::of<float>())) {
return CV_32F;
} else {
FDASSERT(
false,
"NumpyDataTypeToOpenCvType() only support int32/int8/uint8/float32 "
"now.");
}
return CV_8U;
}
int NumpyDataTypeToOpenCvTypeV2(pybind11::array& pyarray) {
if (pybind11::isinstance<pybind11::array_t<std::int32_t>>(pyarray)) {
return CV_32S;
} else if (pybind11::isinstance<pybind11::array_t<std::int8_t>>(pyarray)) {
return CV_8S;
} else if (pybind11::isinstance<pybind11::array_t<std::uint8_t>>(pyarray)) {
return CV_8U;
} else if (pybind11::isinstance<pybind11::array_t<std::float_t>>(pyarray)) {
return CV_32F;
} else {
FDASSERT(
false,
"NumpyDataTypeToOpenCvTypeV2() only support int32/int8/uint8/float32 "
"now.");
}
return CV_8U;
}
cv::Mat PyArrayToCvMat(pybind11::array& pyarray) {
// auto cv_type = NumpyDataTypeToOpenCvType(pyarray.dtype());
auto cv_type = NumpyDataTypeToOpenCvTypeV2(pyarray);
FDASSERT(
pyarray.ndim() == 3,
"Require rank of array to be 3 with HWC format while converting it to "
"cv::Mat.");
int channel = *(pyarray.shape() + 2);
int height = *(pyarray.shape());
int width = *(pyarray.shape() + 1);
return cv::Mat(height, width, CV_MAKETYPE(cv_type, channel),
pyarray.mutable_data());
}
#endif
PYBIND11_MODULE(@PY_LIBRARY_NAME@, m) {
m.doc() =
"Make programer easier to deploy deeplearning model, save time to save "
"the world!";
m.def("set_logger", &SetLogger);
BindFDTensor(m);
BindRuntime(m);
BindFDModel(m);
#ifdef ENABLE_VISION
auto vision_module =
m.def_submodule("vision", "Vision module of FastDeploy.");
BindVision(vision_module);
auto pipeline_module =
m.def_submodule("pipeline", "Pipeline module of FastDeploy.");
BindPipeline(pipeline_module);
#endif
#ifdef ENABLE_ENCRYPTION
auto encryption_module =
m.def_submodule("encryption", "Encryption module of FastDeploy.");
BindEncryption(encryption_module);
#endif
#ifdef ENABLE_TEXT
auto text_module =
m.def_submodule("text", "Text module of FastDeploy.");
BindText(text_module);
#endif
}
} // namespace fastdeploy