Files
FastDeploy/fastdeploy/backends/paddle/util.cc
huangjianhui b565c15bf7 [Model] Add tinypose single && pipeline model (#177)
* Add tinypose model

* Add PPTinypose python API

* Fix picodet preprocess bug && Add Tinypose examples

* Update tinypose example code

* Update ppseg preprocess if condition

* Update ppseg backend support type

* Update permute.h

* Update README.md

* Update code with comments

* Move files dir

* Delete premute.cc

* Add single model pptinypose

* Delete pptinypose old code in ppdet

* Code format

* Add ppdet + pptinypose pipeline model

* Fix bug for posedetpipeline

* Change Frontend to ModelFormat

* Change Frontend to ModelFormat in __init__.py

* Add python posedetpipeline/

* Update pptinypose example dir name

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Create keypointdetection_result.md

* Create README.md

* Create README.md

* Create README.md

* Update README.md

* Update README.md

* Create README.md

* Fix det_keypoint_unite_infer.py bug

* Create README.md

* Update PP-Tinypose by comment

* Update by comment

* Add pipeline directory

* Add pptinypose dir

* Update pptinypose to align accuracy

* Addd warpAffine processor

* Update GetCpuMat to  GetOpenCVMat

* Add comment for pptinypose && pipline

* Update docs/main_page.md

* Add README.md for pptinypose

* Add README for det_keypoint_unite

* Remove ENABLE_PIPELINE option

* Remove ENABLE_PIPELINE option

* Change pptinypose default backend

* PP-TinyPose Pipeline support multi PP-Detection models

* Update pp-tinypose comment

* Update by comments

* Add single test example

Co-authored-by: Jason <jiangjiajun@baidu.com>
2022-10-21 09:28:23 +08:00

131 lines
4.7 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.
#include "fastdeploy/backends/paddle/paddle_backend.h"
#include "fastdeploy/core/float16.h"
namespace fastdeploy {
paddle_infer::PlaceType ConvertFDDeviceToPlace(Device device) {
if (device == Device::GPU) {
return paddle_infer::PlaceType::kGPU;
}
return paddle_infer::PlaceType::kCPU;
}
void ShareTensorFromFDTensor(paddle_infer::Tensor* tensor,
FDTensor& fd_tensor) {
std::vector<int> shape(fd_tensor.shape.begin(), fd_tensor.shape.end());
tensor->Reshape(shape);
auto place = ConvertFDDeviceToPlace(fd_tensor.device);
if (fd_tensor.dtype == FDDataType::FP32) {
if (place == paddle_infer::PlaceType::kGPU) {
tensor->ShareExternalData(static_cast<const float*>(fd_tensor.Data()),
shape, place);
} else {
tensor->CopyFromCpu(static_cast<const float*>(fd_tensor.Data()));
}
return;
} else if (fd_tensor.dtype == FDDataType::INT32) {
if (place == paddle_infer::PlaceType::kGPU) {
tensor->ShareExternalData(static_cast<const int32_t*>(fd_tensor.Data()),
shape, place);
} else {
tensor->CopyFromCpu(static_cast<const int32_t*>(fd_tensor.Data()));
}
return;
} else if (fd_tensor.dtype == FDDataType::INT64) {
if (place == paddle_infer::PlaceType::kGPU) {
tensor->ShareExternalData(static_cast<const int64_t*>(fd_tensor.Data()),
shape, place);
} else {
tensor->CopyFromCpu(static_cast<const int64_t*>(fd_tensor.Data()));
}
return;
} else if (fd_tensor.dtype == FDDataType::UINT8) {
tensor->ShareExternalData(static_cast<const uint8_t*>(fd_tensor.Data()),
shape, paddle_infer::PlaceType::kCPU);
return;
}
FDASSERT(false, "Unexpected data type(%s) while infer with PaddleBackend.",
Str(fd_tensor.dtype).c_str());
}
void CopyTensorToCpu(std::unique_ptr<paddle_infer::Tensor>& tensor,
FDTensor* fd_tensor) {
auto fd_dtype = PaddleDataTypeToFD(tensor->type());
std::vector<int64_t> shape;
auto tmp_shape = tensor->shape();
shape.assign(tmp_shape.begin(), tmp_shape.end());
fd_tensor->Allocate(shape, fd_dtype, tensor->name());
if (fd_tensor->dtype == FDDataType::FP32) {
tensor->CopyToCpu(static_cast<float*>(fd_tensor->MutableData()));
return;
} else if (fd_tensor->dtype == FDDataType::INT32) {
tensor->CopyToCpu(static_cast<int32_t*>(fd_tensor->MutableData()));
return;
} else if (fd_tensor->dtype == FDDataType::INT64) {
tensor->CopyToCpu(static_cast<int64_t*>(fd_tensor->MutableData()));
return;
}
FDASSERT(false, "Unexpected data type(%s) while infer with PaddleBackend.",
Str(fd_tensor->dtype).c_str());
}
FDDataType PaddleDataTypeToFD(const paddle_infer::DataType& dtype) {
auto fd_dtype = FDDataType::FP32;
if (dtype == paddle_infer::FLOAT32) {
fd_dtype = FDDataType::FP32;
} else if (dtype == paddle_infer::INT64) {
fd_dtype = FDDataType::INT64;
} else if (dtype == paddle_infer::INT32) {
fd_dtype = FDDataType::INT32;
} else if (dtype == paddle_infer::UINT8) {
fd_dtype = FDDataType::UINT8;
} else if (dtype == paddle_infer::INT8) {
fd_dtype = FDDataType::INT8;
} else if (dtype == paddle_infer::FLOAT16) {
fd_dtype = FDDataType::FP16;
} else {
FDASSERT(
false,
"Unexpected data type: %d while call CopyTensorToCpu in PaddleBackend.",
int(dtype));
}
return fd_dtype;
}
FDDataType ReaderDataTypeToFD(int32_t dtype) {
auto fd_dtype = FDDataType::FP32;
if (dtype == 0) {
fd_dtype = FDDataType::FP32;
} else if (dtype == 1) {
fd_dtype = FDDataType::FP64;
} else if (dtype == 2) {
fd_dtype = FDDataType::UINT8;
} else if (dtype == 3) {
fd_dtype = FDDataType::INT8;
} else if (dtype == 4) {
fd_dtype = FDDataType::INT32;
} else if (dtype == 5) {
fd_dtype = FDDataType::INT64;
} else if (dtype == 6) {
fd_dtype = FDDataType::FP16;
} else {
FDASSERT(false, "Unexpected data type: %d while call ReaderDataTypeToFD in PaddleBackend.", dtype);
}
return fd_dtype;
}
} // namespace fastdeploy