mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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101 lines
3.2 KiB
C++
101 lines
3.2 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <pybind11/numpy.h>
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include <type_traits>
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#include "fastdeploy/fastdeploy_runtime.h"
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#ifdef ENABLE_VISION
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#include "fastdeploy/vision.h"
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#endif
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#ifdef ENABLE_TEXT
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#include "fastdeploy/text.h"
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#endif
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namespace fastdeploy {
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void BindBackend(pybind11::module&);
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void BindVision(pybind11::module&);
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void BindText(pybind11::module& m);
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pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype);
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FDDataType NumpyDataTypeToFDDataType(const pybind11::dtype& np_dtype);
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void PyArrayToTensor(pybind11::array& pyarray, FDTensor* tensor,
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bool share_buffer = false);
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void PyArrayToTensorList(std::vector<pybind11::array>& pyarray,
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std::vector<FDTensor>* tensor,
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bool share_buffer = false);
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pybind11::array TensorToPyArray(const FDTensor& tensor);
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#ifdef ENABLE_VISION
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cv::Mat PyArrayToCvMat(pybind11::array& pyarray);
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#endif
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template <typename T>
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FDDataType CTypeToFDDataType() {
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if (std::is_same<T, int32_t>::value) {
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return FDDataType::INT32;
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} else if (std::is_same<T, int64_t>::value) {
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return FDDataType::INT64;
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} else if (std::is_same<T, float>::value) {
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return FDDataType::FP32;
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} else if (std::is_same<T, double>::value) {
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return FDDataType::FP64;
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}
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FDASSERT(false,
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"CTypeToFDDataType only support int32/int64/float32/float64 now.");
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return FDDataType::FP32;
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}
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template <typename T>
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std::vector<pybind11::array> PyBackendInfer(
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T& self, const std::vector<std::string>& names,
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std::vector<pybind11::array>& data) {
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std::vector<FDTensor> inputs(data.size());
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for (size_t i = 0; i < data.size(); ++i) {
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// TODO(jiangjiajun) here is considered to use user memory directly
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auto dtype = NumpyDataTypeToFDDataType(data[i].dtype());
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std::vector<int64_t> data_shape;
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data_shape.insert(data_shape.begin(), data[i].shape(),
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data[i].shape() + data[i].ndim());
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inputs[i].Resize(data_shape, dtype);
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memcpy(inputs[i].MutableData(), data[i].mutable_data(), data[i].nbytes());
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inputs[i].name = names[i];
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}
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std::vector<FDTensor> outputs(self.NumOutputs());
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self.Infer(inputs, &outputs);
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std::vector<pybind11::array> results;
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results.reserve(outputs.size());
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for (size_t i = 0; i < outputs.size(); ++i) {
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auto numpy_dtype = FDDataTypeToNumpyDataType(outputs[i].dtype);
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results.emplace_back(pybind11::array(numpy_dtype, outputs[i].shape));
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memcpy(results[i].mutable_data(), outputs[i].Data(),
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outputs[i].Numel() * FDDataTypeSize(outputs[i].dtype));
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}
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return results;
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}
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} // namespace fastdeploy
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