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PaddleDetection
未验证 提交 cfb3699c 编写于 作者: S shangliang Xu 提交者： GitHub
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[TIPC] add serving cpp infer, test=document_fix (#6145)

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deploy/serving/cpp/build_server.sh 0 → 100644
浏览文件 @ cfb3699c
#使用镜像：
#registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82
#编译Serving Server：
#client和app可以直接使用release版本
#server因为加入了自定义OP，需要重新编译
apt-get update
apt install -y libcurl4-openssl-dev libbz2-dev
wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && tar xf centos_ssl.tar && rm -rf centos_ssl.tar && mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k && mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k && ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10 && ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10 && ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so && ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so
# 安装go依赖
rm -rf /usr/local/go
wget -qO- https://paddle-ci.cdn.bcebos.com/go1.17.2.linux-amd64.tar.gz | tar -xz -C /usr/local
export GOROOT=/usr/local/go
export GOPATH=/root/gopath
export PATH=$PATH:$GOPATH/bin:$GOROOT/bin
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
go install github.com/golang/protobuf/protoc-gen-go@v1.4.3
go install google.golang.org/grpc@v1.33.0
go env -w GO111MODULE=auto
# 下载opencv库
wget https://paddle-qa.bj.bcebos.com/PaddleServing/opencv3.tar.gz && tar -xvf opencv3.tar.gz && rm -rf opencv3.tar.gz
export OPENCV_DIR=$PWD/opencv3
# clone Serving
git clone https://github.com/PaddlePaddle/Serving.git -b develop --depth=1
cd Serving
export Serving_repo_path=$PWD
git submodule update --init --recursive
python -m pip install -r python/requirements.txt
# set env
export PYTHON_INCLUDE_DIR=$(python -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
export PYTHON_LIBRARIES=$(python -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))")
export PYTHON_EXECUTABLE=`which python`
export CUDA_PATH='/usr/local/cuda'
export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
export CUDA_CUDART_LIBRARY='/usr/local/cuda/lib64/'
export TENSORRT_LIBRARY_PATH='/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/'
# cp 自定义OP代码
\cp ../deploy/serving/cpp/preprocess/ppyoloe_op.* ${Serving_repo_path}/core/general-server/op
\cp ../deploy/serving/cpp/preprocess/yolov3_op.* ${Serving_repo_path}/core/general-server/op
# 编译Server, export SERVING_BIN
mkdir server-build-gpu-opencv && cd server-build-gpu-opencv
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DCUDA_TOOLKIT_ROOT_DIR=${CUDA_PATH} \
-DCUDNN_LIBRARY=${CUDNN_LIBRARY} \
-DCUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY} \
-DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_OPENCV=ON \
-DSERVER=ON \
-DWITH_GPU=ON ..
make -j32
python -m pip install python/dist/paddle*
export SERVING_BIN=$PWD/core/general-server/serving
cd ../../
deploy/serving/cpp/preprocess/ppyoloe_op.cpp 0 → 100644
浏览文件 @ cfb3699c
// 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 "core/general-server/op/ppyoloe_op.h"
#include "core/predictor/framework/infer.h"
#include "core/predictor/framework/memory.h"
#include "core/predictor/framework/resource.h"
#include "core/util/include/timer.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <sstream>
namespace baidu {
namespace paddle_serving {
namespace serving {
using baidu::paddle_serving::Timer;
using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::MempoolWrapper;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
using baidu::paddle_serving::predictor::general_model::Request;
using baidu::paddle_serving::predictor::general_model::Response;
using baidu::paddle_serving::predictor::general_model::Tensor;
int PPYOLOEOp::inference() {
VLOG(2) << "Going to run inference";
const std::vector<std::string> pre_node_names = pre_names();
if (pre_node_names.size() != 1) {
LOG(ERROR) << "This op(" << op_name()
<< ") can only have one predecessor op, but received "
<< pre_node_names.size();
return -1;
}
const std::string pre_name = pre_node_names[0];
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
if (!input_blob) {
LOG(ERROR) << "input_blob is nullptr,error";
return -1;
}
uint64_t log_id = input_blob->GetLogId();
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
if (!output_blob) {
LOG(ERROR) << "output_blob is nullptr,error";
return -1;
}
output_blob->SetLogId(log_id);
if (!input_blob) {
LOG(ERROR) << "(logid=" << log_id
<< ") Failed mutable depended argument, op:" << pre_name;
return -1;
}
const TensorVector *in = &input_blob->tensor_vector;
TensorVector *out = &output_blob->tensor_vector;
int batch_size = input_blob->_batch_size;
output_blob->_batch_size = batch_size;
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
Timer timeline;
int64_t start = timeline.TimeStampUS();
timeline.Start();
// only support string type
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
std::string base64str = total_input_ptr;
cv::Mat img = Base2Mat(base64str);
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
// preprocess
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
im_shape_w, mean_, scale_, is_scale_);
// create real_in
TensorVector *real_in = new TensorVector();
if (!real_in) {
LOG(ERROR) << "real_in is nullptr,error";
return -1;
}
int in_num = 0;
size_t databuf_size = 0;
void *databuf_data = NULL;
char *databuf_char = NULL;
// image
in_num = 1 * 3 * im_shape_h * im_shape_w;
databuf_size = in_num * sizeof(float);
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
if (!databuf_data) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
return -1;
}
memcpy(databuf_data, input.data(), databuf_size);
databuf_char = reinterpret_cast<char *>(databuf_data);
paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
paddle::PaddleTensor tensor_in;
tensor_in.name = "image";
tensor_in.dtype = paddle::PaddleDType::FLOAT32;
tensor_in.shape = {1, 3, im_shape_h, im_shape_w};
tensor_in.lod = in->at(0).lod;
tensor_in.data = paddleBuf;
real_in->push_back(tensor_in);
// scale_factor
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
databuf_size = 2 * sizeof(float);
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
if (!databuf_data) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
return -1;
}
memcpy(databuf_data, scale_factor.data(), databuf_size);
databuf_char = reinterpret_cast<char *>(databuf_data);
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
paddle::PaddleTensor tensor_in_2;
tensor_in_2.name = "scale_factor";
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
tensor_in_2.shape = {1, 2};
tensor_in_2.lod = in->at(0).lod;
tensor_in_2.data = paddleBuf_2;
real_in->push_back(tensor_in_2);
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
batch_size)) {
LOG(ERROR) << "(logid=" << log_id
<< ") Failed do infer in fluid model: " << engine_name().c_str();
return -1;
}
int64_t end = timeline.TimeStampUS();
CopyBlobInfo(input_blob, output_blob);
AddBlobInfo(output_blob, start);
AddBlobInfo(output_blob, end);
return 0;
}
void PPYOLOEOp::preprocess_det(const cv::Mat &img, float *data,
float &scale_factor_h, float &scale_factor_w,
int im_shape_h, int im_shape_w,
const std::vector<float> &mean,
const std::vector<float> &scale,
const bool is_scale) {
// scale_factor
scale_factor_h =
static_cast<float>(im_shape_h) / static_cast<float>(img.rows);
scale_factor_w =
static_cast<float>(im_shape_w) / static_cast<float>(img.cols);
// Resize
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 2);
// Normalize
double e = 1.0;
if (is_scale) {
e /= 255.0;
}
cv::Mat img_fp;
(resize_img).convertTo(img_fp, CV_32FC3, e);
for (int h = 0; h < im_shape_h; h++) {
for (int w = 0; w < im_shape_w; w++) {
img_fp.at<cv::Vec3f>(h, w)[0] =
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
img_fp.at<cv::Vec3f>(h, w)[1] =
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
img_fp.at<cv::Vec3f>(h, w)[2] =
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
}
}
// Permute
int rh = img_fp.rows;
int rw = img_fp.cols;
int rc = img_fp.channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
i);
}
}
cv::Mat PPYOLOEOp::Base2Mat(std::string &base64_data) {
cv::Mat img;
std::string s_mat;
s_mat = base64Decode(base64_data.data(), base64_data.size());
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
return img;
}
std::string PPYOLOEOp::base64Decode(const char *Data, int DataByte) {
const char DecodeTable[] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
62, // '+'
0, 0, 0,
63, // '/'
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
};
std::string strDecode;
int nValue;
int i = 0;
while (i < DataByte) {
if (*Data != '\r' && *Data != '\n') {
nValue = DecodeTable[*Data++] << 18;
nValue += DecodeTable[*Data++] << 12;
strDecode += (nValue & 0x00FF0000) >> 16;
if (*Data != '=') {
nValue += DecodeTable[*Data++] << 6;
strDecode += (nValue & 0x0000FF00) >> 8;
if (*Data != '=') {
nValue += DecodeTable[*Data++];
strDecode += nValue & 0x000000FF;
}
}
i += 4;
} else // 回车换行,跳过
{
Data++;
i++;
}
}
return strDecode;
}
DEFINE_OP(PPYOLOEOp);
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
deploy/serving/cpp/preprocess/ppyoloe_op.h 0 → 100644
浏览文件 @ cfb3699c
// 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 "core/general-server/general_model_service.pb.h"
#include "core/general-server/op/general_infer_helper.h"
#include "paddle_inference_api.h" // NOLINT
#include <string>
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
namespace baidu {
namespace paddle_serving {
namespace serving {
class PPYOLOEOp
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
public:
typedef std::vector<paddle::PaddleTensor> TensorVector;
DECLARE_OP(PPYOLOEOp);
int inference();
private:
// ppyoloe, picodet preprocess
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
bool is_scale_ = true;
int im_shape_h = 640;
int im_shape_w = 640;
float scale_factor_h = 1.0f;
float scale_factor_w = 1.0f;
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
float &scale_factor_w, int im_shape_h,
int im_shape_w, const std::vector<float> &mean,
const std::vector<float> &scale,
const bool is_scale);
// read pics
cv::Mat Base2Mat(std::string &base64_data);
std::string base64Decode(const char *Data, int DataByte);
};
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
deploy/serving/cpp/preprocess/serving_client_conf.prototxt 0 → 100644
浏览文件 @ cfb3699c
feed_var {
name: "input"
alias_name: "input"
is_lod_tensor: false
feed_type: 20
shape: 1
}
fetch_var {
name: "multiclass_nms3_0.tmp_0"
alias_name: "multiclass_nms3_0.tmp_0"
is_lod_tensor: true
fetch_type: 1
shape: -1
}
fetch_var {
name: "multiclass_nms3_0.tmp_2"
alias_name: "multiclass_nms3_0.tmp_2"
is_lod_tensor: false
fetch_type: 2
}
\ No newline at end of file
deploy/serving/cpp/preprocess/yolov3_op.cpp 0 → 100644
浏览文件 @ cfb3699c
// 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 "core/general-server/op/yolov3_op.h"
#include "core/predictor/framework/infer.h"
#include "core/predictor/framework/memory.h"
#include "core/predictor/framework/resource.h"
#include "core/util/include/timer.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <sstream>
namespace baidu {
namespace paddle_serving {
namespace serving {
using baidu::paddle_serving::Timer;
using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::MempoolWrapper;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
using baidu::paddle_serving::predictor::general_model::Request;
using baidu::paddle_serving::predictor::general_model::Response;
using baidu::paddle_serving::predictor::general_model::Tensor;
int YOLOv3Op::inference() {
VLOG(2) << "Going to run inference";
const std::vector<std::string> pre_node_names = pre_names();
if (pre_node_names.size() != 1) {
LOG(ERROR) << "This op(" << op_name()
<< ") can only have one predecessor op, but received "
<< pre_node_names.size();
return -1;
}
const std::string pre_name = pre_node_names[0];
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
if (!input_blob) {
LOG(ERROR) << "input_blob is nullptr,error";
return -1;
}
uint64_t log_id = input_blob->GetLogId();
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
if (!output_blob) {
LOG(ERROR) << "output_blob is nullptr,error";
return -1;
}
output_blob->SetLogId(log_id);
if (!input_blob) {
LOG(ERROR) << "(logid=" << log_id
<< ") Failed mutable depended argument, op:" << pre_name;
return -1;
}
const TensorVector *in = &input_blob->tensor_vector;
TensorVector *out = &output_blob->tensor_vector;
int batch_size = input_blob->_batch_size;
output_blob->_batch_size = batch_size;
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
Timer timeline;
int64_t start = timeline.TimeStampUS();
timeline.Start();
// only support string type
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
std::string base64str = total_input_ptr;
cv::Mat img = Base2Mat(base64str);
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
// preprocess
std::vector<float> input(1 * 3 * im_shape_h * im_shape_w, 0.0f);
preprocess_det(img, input.data(), scale_factor_h, scale_factor_w, im_shape_h,
im_shape_w, mean_, scale_, is_scale_);
// create real_in
TensorVector *real_in = new TensorVector();
if (!real_in) {
LOG(ERROR) << "real_in is nullptr,error";
return -1;
}
int in_num = 0;
size_t databuf_size = 0;
void *databuf_data = NULL;
char *databuf_char = NULL;
// im_shape
std::vector<float> im_shape{static_cast<float>(im_shape_h),
static_cast<float>(im_shape_w)};
databuf_size = 2 * sizeof(float);
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
if (!databuf_data) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
return -1;
}
memcpy(databuf_data, im_shape.data(), databuf_size);
databuf_char = reinterpret_cast<char *>(databuf_data);
paddle::PaddleBuf paddleBuf_0(databuf_char, databuf_size);
paddle::PaddleTensor tensor_in_0;
tensor_in_0.name = "im_shape";
tensor_in_0.dtype = paddle::PaddleDType::FLOAT32;
tensor_in_0.shape = {1, 2};
tensor_in_0.lod = in->at(0).lod;
tensor_in_0.data = paddleBuf_0;
real_in->push_back(tensor_in_0);
// image
in_num = 1 * 3 * im_shape_h * im_shape_w;
databuf_size = in_num * sizeof(float);
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
if (!databuf_data) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
return -1;
}
memcpy(databuf_data, input.data(), databuf_size);
databuf_char = reinterpret_cast<char *>(databuf_data);
paddle::PaddleBuf paddleBuf_1(databuf_char, databuf_size);
paddle::PaddleTensor tensor_in_1;
tensor_in_1.name = "image";
tensor_in_1.dtype = paddle::PaddleDType::FLOAT32;
tensor_in_1.shape = {1, 3, im_shape_h, im_shape_w};
tensor_in_1.lod = in->at(0).lod;
tensor_in_1.data = paddleBuf_1;
real_in->push_back(tensor_in_1);
// scale_factor
std::vector<float> scale_factor{scale_factor_h, scale_factor_w};
databuf_size = 2 * sizeof(float);
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
if (!databuf_data) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
return -1;
}
memcpy(databuf_data, scale_factor.data(), databuf_size);
databuf_char = reinterpret_cast<char *>(databuf_data);
paddle::PaddleBuf paddleBuf_2(databuf_char, databuf_size);
paddle::PaddleTensor tensor_in_2;
tensor_in_2.name = "scale_factor";
tensor_in_2.dtype = paddle::PaddleDType::FLOAT32;
tensor_in_2.shape = {1, 2};
tensor_in_2.lod = in->at(0).lod;
tensor_in_2.data = paddleBuf_2;
real_in->push_back(tensor_in_2);
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
batch_size)) {
LOG(ERROR) << "(logid=" << log_id
<< ") Failed do infer in fluid model: " << engine_name().c_str();
return -1;
}
int64_t end = timeline.TimeStampUS();
CopyBlobInfo(input_blob, output_blob);
AddBlobInfo(output_blob, start);
AddBlobInfo(output_blob, end);
return 0;
}
void YOLOv3Op::preprocess_det(const cv::Mat &img, float *data,
float &scale_factor_h, float &scale_factor_w,
int im_shape_h, int im_shape_w,
const std::vector<float> &mean,
const std::vector<float> &scale,
const bool is_scale) {
// scale_factor
scale_factor_h =
static_cast<float>(im_shape_h) / static_cast<float>(img.rows);
scale_factor_w =
static_cast<float>(im_shape_w) / static_cast<float>(img.cols);
// Resize
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(im_shape_w, im_shape_h), 0, 0, 2);
// Normalize
double e = 1.0;
if (is_scale) {
e /= 255.0;
}
cv::Mat img_fp;
(resize_img).convertTo(img_fp, CV_32FC3, e);
for (int h = 0; h < im_shape_h; h++) {
for (int w = 0; w < im_shape_w; w++) {
img_fp.at<cv::Vec3f>(h, w)[0] =
(img_fp.at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
img_fp.at<cv::Vec3f>(h, w)[1] =
(img_fp.at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
img_fp.at<cv::Vec3f>(h, w)[2] =
(img_fp.at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
}
}
// Permute
int rh = img_fp.rows;
int rw = img_fp.cols;
int rc = img_fp.channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(img_fp, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw),
i);
}
}
cv::Mat YOLOv3Op::Base2Mat(std::string &base64_data) {
cv::Mat img;
std::string s_mat;
s_mat = base64Decode(base64_data.data(), base64_data.size());
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
return img;
}
std::string YOLOv3Op::base64Decode(const char *Data, int DataByte) {
const char DecodeTable[] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
62, // '+'
0, 0, 0,
63, // '/'
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
};
std::string strDecode;
int nValue;
int i = 0;
while (i < DataByte) {
if (*Data != '\r' && *Data != '\n') {
nValue = DecodeTable[*Data++] << 18;
nValue += DecodeTable[*Data++] << 12;
strDecode += (nValue & 0x00FF0000) >> 16;
if (*Data != '=') {
nValue += DecodeTable[*Data++] << 6;
strDecode += (nValue & 0x0000FF00) >> 8;
if (*Data != '=') {
nValue += DecodeTable[*Data++];
strDecode += nValue & 0x000000FF;
}
}
i += 4;
} else // 回车换行,跳过
{
Data++;
i++;
}
}
return strDecode;
}
DEFINE_OP(YOLOv3Op);
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
deploy/serving/cpp/preprocess/yolov3_op.h 0 → 100644
浏览文件 @ cfb3699c
// 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 "core/general-server/general_model_service.pb.h"
#include "core/general-server/op/general_infer_helper.h"
#include "paddle_inference_api.h" // NOLINT
#include <string>
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
namespace baidu {
namespace paddle_serving {
namespace serving {
class YOLOv3Op
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
public:
typedef std::vector<paddle::PaddleTensor> TensorVector;
DECLARE_OP(YOLOv3Op);
int inference();
private:
// yolov3, ppyolo preprocess
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
bool is_scale_ = true;
int im_shape_h = 608;
int im_shape_w = 608;
float scale_factor_h = 1.0f;
float scale_factor_w = 1.0f;
void preprocess_det(const cv::Mat &img, float *data, float &scale_factor_h,
float &scale_factor_w, int im_shape_h, int im_shape_w,
const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale);
// read pics
cv::Mat Base2Mat(std::string &base64_data);
std::string base64Decode(const char *Data, int DataByte);
};
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
deploy/serving/cpp/serving_client.py 0 → 100644
浏览文件 @ cfb3699c
# 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.
import base64
import glob
import os
from paddle_serving_client import Client
from paddle_serving_client.proto import general_model_config_pb2 as m_config
import google.protobuf.text_format
import argparse
parser = argparse.ArgumentParser(description="args for paddleserving")
parser.add_argument(
"--serving_client", type=str, help="the directory of serving_client")
parser.add_argument("--image_dir", type=str)
parser.add_argument("--image_file", type=str)
parser.add_argument(
"--threshold", type=float, default=0.5, help="Threshold of score.")
args = parser.parse_args()
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--image_file or --image_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
assert infer_dir is None or os.path.isdir(infer_dir), \
"{} is not a directory".format(infer_dir)
# infer_img has a higher priority
if infer_img and os.path.isfile(infer_img):
return [infer_img]
images = set()
infer_dir = os.path.abspath(infer_dir)
assert os.path.isdir(infer_dir), \
"infer_dir {} is not a directory".format(infer_dir)
exts = ['jpg', 'jpeg', 'png', 'bmp']
exts += [ext.upper() for ext in exts]
for ext in exts:
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
images = list(images)
assert len(images) > 0, "no image found in {}".format(infer_dir)
print("Found {} inference images in total.".format(len(images)))
return images
def postprocess(fetch_dict, draw_threshold=0.5):
bboxes = fetch_dict["multiclass_nms3_0.tmp_0"]
bboxes_num = fetch_dict["multiclass_nms3_0.tmp_2"]
for bbox in bboxes:
if bbox[0] > -1 and bbox[1] > draw_threshold:
print(f"{int(bbox[0])} {bbox[1]} "
f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
return fetch_dict
def get_model_vars(client_config_dir):
# read original serving_client_conf.prototxt
client_config_file = os.path.join(client_config_dir,
"serving_client_conf.prototxt")
with open(client_config_file, 'r') as f:
model_var = google.protobuf.text_format.Merge(
str(f.read()), m_config.GeneralModelConfig())
# modify feed_var to run core/general-server/op/
[model_var.feed_var.pop() for _ in range(len(model_var.feed_var))]
feed_var = m_config.FeedVar()
feed_var.name = "input"
feed_var.alias_name = "input"
feed_var.is_lod_tensor = False
feed_var.feed_type = 20
feed_var.shape.extend([1])
model_var.feed_var.extend([feed_var])
with open(
os.path.join(client_config_dir, "serving_client_conf_cpp.prototxt"),
"w") as f:
f.write(str(model_var))
# get feed_vars/fetch_vars
feed_vars = [var.name for var in model_var.feed_var]
fetch_vars = [var.name for var in model_var.fetch_var]
return feed_vars, fetch_vars
if __name__ == '__main__':
url = "127.0.0.1:9997"
logid = 10000
img_list = get_test_images(args.image_dir, args.image_file)
feed_vars, fetch_vars = get_model_vars(args.serving_client)
client = Client()
client.load_client_config(
os.path.join(args.serving_client, "serving_client_conf_cpp.prototxt"))
client.connect([url])
for img_file in img_list:
with open(img_file, 'rb') as file:
image_data = file.read()
image = base64.b64encode(image_data).decode('utf8')
fetch_dict = client.predict(
feed={feed_vars[0]: image}, fetch=fetch_vars)
result = postprocess(fetch_dict, args.threshold)
deploy/serving/python/pipeline_http_client.py
浏览文件 @ cfb3699c
......@@ -56,19 +56,6 @@ def get_test_images(infer_dir, infer_img):
return images
def cv2_to_base64(image):
"""cv2_to_base64
Convert an numpy array to a base64 object.
Args:
image: Input array.
Returns: Base64 output of the input.
"""
return base64.b64encode(image).decode('utf8')
if __name__ == "__main__":
url = "http://127.0.0.1:18093/ppdet/prediction"
logid = 10000
......@@ -76,9 +63,10 @@ if __name__ == "__main__":
for img_file in img_list:
with open(img_file, 'rb') as file:
image_data1 = file.read()
image_data = file.read()
image = cv2_to_base64(image_data1)
# base64 encode
image = base64.b64encode(image_data).decode('utf8')
data = {"key": ["image_0"], "value": [image], "logid": logid}
# send requests
......
deploy/serving/python/web_service.py
浏览文件 @ cfb3699c
......@@ -207,7 +207,7 @@ class DetectorOp(Op):
result = []
for line in bbox:
if line[0] > -1 and line[1] > draw_threshold:
result.append(f"{label_list[int(line[0])]} {line[1]} "
result.append(f"{int(line[0])} {line[1]} "
f"{line[2]} {line[3]} {line[4]} {line[5]}")
return result
......@@ -222,10 +222,11 @@ def get_model_vars(model_dir, service_config):
# rewrite model_config
service_config['op']['ppdet']['local_service_conf'][
'model_config'] = serving_server_dir
f = open(
os.path.join(serving_server_dir, "serving_server_conf.prototxt"), 'r')
model_var = google.protobuf.text_format.Merge(
str(f.read()), m_config.GeneralModelConfig())
serving_server_conf = os.path.join(serving_server_dir,
"serving_server_conf.prototxt")
with open(serving_server_conf, 'r') as f:
model_var = google.protobuf.text_format.Merge(
str(f.read()), m_config.GeneralModelConfig())
feed_vars = [var.name for var in model_var.feed_var]
fetch_vars = [var.name for var in model_var.fetch_var]
return feed_vars, fetch_vars
......
deploy/third_engine/onnx/infer.py
浏览文件 @ cfb3699c
......@@ -45,7 +45,7 @@ SUPPORT_MODELS = {
}
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("-c", "--config", type=str, help="infer_cfg.yml")
parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml")
parser.add_argument(
'--onnx_file', type=str, default="model.onnx", help="onnx model file path")
parser.add_argument("--image_dir", type=str)
......@@ -86,7 +86,7 @@ def get_test_images(infer_dir, infer_img):
class PredictConfig(object):
"""set config of preprocess, postprocess and visualize
Args:
model_dir (str): root path of infer_cfg.yml
infer_config (str): path of infer_cfg.yml
"""
def __init__(self, infer_config):
......@@ -145,7 +145,7 @@ def predict_image(infer_config, predictor, img_list):
bboxes = np.array(outputs[0])
for bbox in bboxes:
if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
print(f"{infer_config.label_list[int(bbox[0])]} {bbox[1]} "
print(f"{int(bbox[0])} {bbox[1]} "
f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
......@@ -156,6 +156,6 @@ if __name__ == '__main__':
# load predictor
predictor = InferenceSession(FLAGS.onnx_file)
# load infer config
infer_config = PredictConfig(FLAGS.config)
infer_config = PredictConfig(FLAGS.infer_cfg)
predict_image(infer_config, predictor, img_list)
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