mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
4d1f264d01
* fdtensor support GPU * TRT backend support GPU FDTensor * FDHostAllocator add FASTDEPLOY_DECL * fix FDTensor Data * fix FDTensor dtype Co-authored-by: Jason <jiangjiajun@baidu.com>
93 lines
3.0 KiB
C++
93 lines
3.0 KiB
C++
// 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.
|
|
|
|
#pragma once
|
|
|
|
#include <pybind11/numpy.h>
|
|
#include <pybind11/pybind11.h>
|
|
#include <pybind11/stl.h>
|
|
|
|
#include <type_traits>
|
|
|
|
#include "fastdeploy/fastdeploy_runtime.h"
|
|
|
|
#ifdef ENABLE_VISION
|
|
#include "fastdeploy/vision.h"
|
|
#endif
|
|
|
|
namespace fastdeploy {
|
|
|
|
void BindBackend(pybind11::module&);
|
|
void BindVision(pybind11::module&);
|
|
|
|
pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype);
|
|
|
|
FDDataType NumpyDataTypeToFDDataType(const pybind11::dtype& np_dtype);
|
|
|
|
void PyArrayToTensor(pybind11::array& pyarray, FDTensor* tensor,
|
|
bool share_buffer = false);
|
|
pybind11::array TensorToPyArray(const FDTensor& tensor);
|
|
|
|
#ifdef ENABLE_VISION
|
|
cv::Mat PyArrayToCvMat(pybind11::array& pyarray);
|
|
#endif
|
|
|
|
template <typename T>
|
|
FDDataType CTypeToFDDataType() {
|
|
if (std::is_same<T, int32_t>::value) {
|
|
return FDDataType::INT32;
|
|
} else if (std::is_same<T, int64_t>::value) {
|
|
return FDDataType::INT64;
|
|
} else if (std::is_same<T, float>::value) {
|
|
return FDDataType::FP32;
|
|
} else if (std::is_same<T, double>::value) {
|
|
return FDDataType::FP64;
|
|
}
|
|
FDASSERT(false,
|
|
"CTypeToFDDataType only support int32/int64/float32/float64 now.");
|
|
return FDDataType::FP32;
|
|
}
|
|
|
|
template <typename T>
|
|
std::vector<pybind11::array> PyBackendInfer(
|
|
T& self, const std::vector<std::string>& names,
|
|
std::vector<pybind11::array>& data) {
|
|
std::vector<FDTensor> inputs(data.size());
|
|
for (size_t i = 0; i < data.size(); ++i) {
|
|
// TODO(jiangjiajun) here is considered to use user memory directly
|
|
auto dtype = NumpyDataTypeToFDDataType(data[i].dtype());
|
|
std::vector<int64_t> data_shape;
|
|
data_shape.insert(data_shape.begin(), data[i].shape(),
|
|
data[i].shape() + data[i].ndim());
|
|
inputs[i].Resize(data_shape, dtype);
|
|
memcpy(inputs[i].MutableData(), data[i].mutable_data(), data[i].nbytes());
|
|
inputs[i].name = names[i];
|
|
}
|
|
|
|
std::vector<FDTensor> outputs(self.NumOutputs());
|
|
self.Infer(inputs, &outputs);
|
|
|
|
std::vector<pybind11::array> results;
|
|
results.reserve(outputs.size());
|
|
for (size_t i = 0; i < outputs.size(); ++i) {
|
|
auto numpy_dtype = FDDataTypeToNumpyDataType(outputs[i].dtype);
|
|
results.emplace_back(pybind11::array(numpy_dtype, outputs[i].shape));
|
|
memcpy(results[i].mutable_data(), outputs[i].Data(),
|
|
outputs[i].Numel() * FDDataTypeSize(outputs[i].dtype));
|
|
}
|
|
return results;
|
|
}
|
|
|
|
} // namespace fastdeploy
|