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未验证 提交 05c393d9 编写于 作者: W Walter 提交者： GitHub
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Merge pull request #2045 from HydrogenSulfate/add_cpp_serving 

Add cpp serving chain
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deploy/paddleserving/build_server.sh 0 → 100644
浏览文件 @ 05c393d9
# 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
# 默认编译时的${PWD}=PaddleClas/deploy/paddleserving/
python_name=${1:-'python'}
apt-get update
apt install -y libcurl4-openssl-dev libbz2-dev
wget -nc 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 # PaddleClas/deploy/paddleserving/Serving
export Serving_repo_path=$PWD
git submodule update --init --recursive
${python_name} -m pip install -r python/requirements.txt
# set env
export PYTHON_INCLUDE_DIR=$(${python_name} -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
export PYTHON_LIBRARIES=$(${python_name} -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))")
export PYTHON_EXECUTABLE=`which ${python_name}`
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 ../preprocess/general_clas_op.* ${Serving_repo_path}/core/general-server/op
\cp ../preprocess/preprocess_op.* ${Serving_repo_path}/core/predictor/tools/pp_shitu_tools
# 编译Server
mkdir server-build-gpu-opencv
cd server-build-gpu-opencv
cmake -DPYTHON_INCLUDE_DIR=$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
${python_name} -m pip install python/dist/paddle*
# export SERVING_BIN
export SERVING_BIN=$PWD/core/general-server/serving
cd ../../
\ No newline at end of file
deploy/paddleserving/preprocess/general_clas_op.cpp 0 → 100644
浏览文件 @ 05c393d9
// Copyright (c) 2020 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/general_clas_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::MempoolWrapper;
using baidu::paddle_serving::predictor::general_model::Tensor;
using baidu::paddle_serving::predictor::general_model::Response;
using baidu::paddle_serving::predictor::general_model::Request;
using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
int GeneralClasOp::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);
// RGB2BGR
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
// Resize
cv::Mat resize_img;
resize_op_.Run(img, resize_img, resize_short_size_);
// CenterCrop
crop_op_.Run(resize_img, crop_size_);
// Normalize
normalize_op_.Run(&resize_img, mean_, scale_, is_scale_);
// Permute
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
permute_op_.Run(&resize_img, input.data());
float maxValue = *max_element(input.begin(), input.end());
float minValue = *min_element(input.begin(), input.end());
TensorVector *real_in = new TensorVector();
if (!real_in) {
LOG(ERROR) << "real_in is nullptr,error";
return -1;
}
std::vector<int> input_shape;
int in_num = 0;
void *databuf_data = NULL;
char *databuf_char = NULL;
size_t databuf_size = 0;
input_shape = {1, 3, resize_img.rows, resize_img.cols};
in_num = std::accumulate(input_shape.begin(), input_shape.end(), 1,
std::multiplies<int>());
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 = in->at(0).name;
tensor_in.dtype = paddle::PaddleDType::FLOAT32;
tensor_in.shape = {1, 3, resize_img.rows, resize_img.cols};
tensor_in.lod = in->at(0).lod;
tensor_in.data = paddleBuf;
real_in->push_back(tensor_in);
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;
}
cv::Mat GeneralClasOp::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 GeneralClasOp::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(GeneralClasOp);
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
deploy/paddleserving/preprocess/general_clas_op.h 0 → 100644
浏览文件 @ 05c393d9
// Copyright (c) 2019 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 "core/predictor/tools/pp_shitu_tools/preprocess_op.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 GeneralClasOp
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
public:
typedef std::vector<paddle::PaddleTensor> TensorVector;
DECLARE_OP(GeneralClasOp);
int inference();
private:
// clas 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 resize_short_size_ = 256;
int crop_size_ = 224;
PaddleClas::ResizeImg resize_op_;
PaddleClas::Normalize normalize_op_;
PaddleClas::Permute permute_op_;
PaddleClas::CenterCropImg crop_op_;
// 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/paddleserving/preprocess/preprocess_op.cpp 0 → 100644
浏览文件 @ 05c393d9
// Copyright (c) 2020 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 "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_api.h"
#include "paddle_inference_api.h"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <math.h>
#include <numeric>
#include "preprocess_op.h"
namespace Feature {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
if (size > 0) {
resize_h = size;
resize_w = size;
} else {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace Feature
namespace PaddleClas {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale) {
double e = 1.0;
if (is_scale) {
e /= 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size) {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
int resize_h = round(float(h) * ratio);
int resize_w = round(float(w) * ratio);
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace PaddleClas
deploy/paddleserving/preprocess/preprocess_op.h 0 → 100644
浏览文件 @ 05c393d9
// Copyright (c) 2020 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 "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 Feature {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
};
} // namespace Feature
namespace PaddleClas {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale = true);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
};
} // namespace PaddleClas
deploy/paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt 0 → 100644
浏览文件 @ 05c393d9
feed_var {
name: "x"
alias_name: "x"
is_lod_tensor: false
feed_type: 1
shape: 3
shape: 224
shape: 224
}
feed_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
feed_type: 1
shape: 6
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "features"
is_lod_tensor: false
fetch_type: 1
shape: 512
}
fetch_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
fetch_type: 1
shape: 6
}
deploy/paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/serving_server_conf.prototxt 0 → 100644
浏览文件 @ 05c393d9
feed_var {
name: "x"
alias_name: "x"
is_lod_tensor: false
feed_type: 1
shape: 3
shape: 224
shape: 224
}
feed_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
feed_type: 1
shape: 6
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "features"
is_lod_tensor: false
fetch_type: 1
shape: 512
}
fetch_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
fetch_type: 1
shape: 6
}
deploy/paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt 0 → 100644
浏览文件 @ 05c393d9
feed_var {
name: "im_shape"
alias_name: "im_shape"
is_lod_tensor: false
feed_type: 1
shape: 2
}
feed_var {
name: "image"
alias_name: "image"
is_lod_tensor: false
feed_type: 7
shape: -1
shape: -1
shape: 3
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "save_infer_model/scale_0.tmp_1"
is_lod_tensor: true
fetch_type: 1
shape: -1
}
fetch_var {
name: "save_infer_model/scale_1.tmp_1"
alias_name: "save_infer_model/scale_1.tmp_1"
is_lod_tensor: false
fetch_type: 2
}
deploy/paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/serving_server_conf.prototxt 0 → 100644
浏览文件 @ 05c393d9
feed_var {
name: "im_shape"
alias_name: "im_shape"
is_lod_tensor: false
feed_type: 1
shape: 2
}
feed_var {
name: "image"
alias_name: "image"
is_lod_tensor: false
feed_type: 7
shape: -1
shape: -1
shape: 3
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "save_infer_model/scale_0.tmp_1"
is_lod_tensor: true
fetch_type: 1
shape: -1
}
fetch_var {
name: "save_infer_model/scale_1.tmp_1"
alias_name: "save_infer_model/scale_1.tmp_1"
is_lod_tensor: false
fetch_type: 2
}
deploy/paddleserving/recognition/test_cpp_serving_client.py
浏览文件 @ 05c393d9
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import numpy as np
from paddle_serving_client import Client
......@@ -22,181 +21,101 @@ import faiss
import os
import pickle
class MainbodyDetect():
"""
pp-shitu mainbody detect.
include preprocess, process, postprocess
return detect results
Attention: Postprocess include num limit and box filter; no nms
"""
def __init__(self):
self.preprocess = DetectionSequential([
DetectionFile2Image(), DetectionNormalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
DetectionResize(
(640, 640), False, interpolation=2), DetectionTranspose(
(2, 0, 1))
])
self.client = Client()
self.client.load_client_config(
"../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt"
)
self.client.connect(['127.0.0.1:9293'])
self.max_det_result = 5
self.conf_threshold = 0.2
def predict(self, imgpath):
im, im_info = self.preprocess(imgpath)
im_shape = np.array(im.shape[1:]).reshape(-1)
scale_factor = np.array(list(im_info['scale_factor'])).reshape(-1)
fetch_map = self.client.predict(
feed={
"image": im,
"im_shape": im_shape,
"scale_factor": scale_factor,
},
fetch=["save_infer_model/scale_0.tmp_1"],
batch=False)
return self.postprocess(fetch_map, imgpath)
def postprocess(self, fetch_map, imgpath):
#1. get top max_det_result
det_results = fetch_map["save_infer_model/scale_0.tmp_1"]
if len(det_results) > self.max_det_result:
boxes_reserved = fetch_map[
"save_infer_model/scale_0.tmp_1"][:self.max_det_result]
else:
boxes_reserved = det_results
#2. do conf threshold
boxes_list = []
for i in range(boxes_reserved.shape[0]):
if (boxes_reserved[i, 1]) > self.conf_threshold:
boxes_list.append(boxes_reserved[i, :])
#3. add origin image box
origin_img = cv2.imread(imgpath)
boxes_list.append(
np.array([0, 1.0, 0, 0, origin_img.shape[1], origin_img.shape[0]]))
return np.array(boxes_list)
class ObjectRecognition():
"""
pp-shitu object recognion for all objects detected by MainbodyDetect.
include preprocess, process, postprocess
preprocess include preprocess for each image and batching.
Batch process
postprocess include retrieval and nms
"""
def __init__(self):
self.client = Client()
self.client.load_client_config(
"../../models/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt"
)
self.client.connect(["127.0.0.1:9294"])
self.seq = Sequential([
BGR2RGB(), Resize((224, 224)), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
False), Transpose((2, 0, 1))
])
self.searcher, self.id_map = self.init_index()
self.rec_nms_thresold = 0.05
self.rec_score_thres = 0.5
self.feature_normalize = True
self.return_k = 1
def init_index(self):
index_dir = "../../drink_dataset_v1.0/index"
assert os.path.exists(os.path.join(
index_dir, "vector.index")), "vector.index not found ..."
assert os.path.exists(os.path.join(
index_dir, "id_map.pkl")), "id_map.pkl not found ... "
searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
id_map = pickle.load(fd)
return searcher, id_map
def predict(self, det_boxes, imgpath):
#1. preprocess
batch_imgs = []
origin_img = cv2.imread(imgpath)
for i in range(det_boxes.shape[0]):
box = det_boxes[i]
x1, y1, x2, y2 = [int(x) for x in box[2:]]
cropped_img = origin_img[y1:y2, x1:x2, :].copy()
tmp = self.seq(cropped_img)
batch_imgs.append(tmp)
batch_imgs = np.array(batch_imgs)
#2. process
fetch_map = self.client.predict(
feed={"x": batch_imgs}, fetch=["features"], batch=True)
batch_features = fetch_map["features"]
#3. postprocess
if self.feature_normalize:
feas_norm = np.sqrt(
np.sum(np.square(batch_features), axis=1, keepdims=True))
batch_features = np.divide(batch_features, feas_norm)
scores, docs = self.searcher.search(batch_features, self.return_k)
results = []
for i in range(scores.shape[0]):
pred = {}
if scores[i][0] >= self.rec_score_thres:
pred["bbox"] = [int(x) for x in det_boxes[i, 2:]]
pred["rec_docs"] = self.id_map[docs[i][0]].split()[1]
pred["rec_scores"] = scores[i][0]
results.append(pred)
return self.nms_to_rec_results(results)
def nms_to_rec_results(self, results):
filtered_results = []
x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
scores = np.array([r["rec_scores"] for r in results])
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
while order.size > 0:
i = order[0]
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= self.rec_nms_thresold)[0]
order = order[inds + 1]
filtered_results.append(results[i])
return filtered_results
rec_nms_thresold = 0.05
rec_score_thres = 0.5
feature_normalize = True
return_k = 1
index_dir = "../../drink_dataset_v1.0/index"
def init_index(index_dir):
assert os.path.exists(os.path.join(
index_dir, "vector.index")), "vector.index not found ..."
assert os.path.exists(os.path.join(
index_dir, "id_map.pkl")), "id_map.pkl not found ... "
searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
id_map = pickle.load(fd)
return searcher, id_map
#get box
def nms_to_rec_results(results, thresh=0.1):
filtered_results = []
x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
scores = np.array([r["rec_scores"] for r in results])
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
while order.size > 0:
i = order[0]
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
filtered_results.append(results[i])
return filtered_results
def postprocess(fetch_dict, feature_normalize, det_boxes, searcher, id_map,
return_k, rec_score_thres, rec_nms_thresold):
batch_features = fetch_dict["features"]
#do feature norm
if feature_normalize:
feas_norm = np.sqrt(
np.sum(np.square(batch_features), axis=1, keepdims=True))
batch_features = np.divide(batch_features, feas_norm)
scores, docs = searcher.search(batch_features, return_k)
results = []
for i in range(scores.shape[0]):
pred = {}
if scores[i][0] >= rec_score_thres:
pred["bbox"] = [int(x) for x in det_boxes[i, 2:]]
pred["rec_docs"] = id_map[docs[i][0]].split()[1]
pred["rec_scores"] = scores[i][0]
results.append(pred)
#do nms
results = nms_to_rec_results(results, rec_nms_thresold)
return results
#do client
if __name__ == "__main__":
det = MainbodyDetect()
rec = ObjectRecognition()
#1. get det_results
imgpath = "../../drink_dataset_v1.0/test_images/001.jpeg"
det_results = det.predict(imgpath)
#2. get rec_results
rec_results = rec.predict(det_results, imgpath)
print(rec_results)
client = Client()
client.load_client_config([
"../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client",
"../../models/general_PPLCNet_x2_5_lite_v1.0_client"
])
client.connect(['127.0.0.1:9400'])
im = cv2.imread("../../drink_dataset_v1.0/test_images/001.jpeg")
im_shape = np.array(im.shape[:2]).reshape(-1)
fetch_map = client.predict(
feed={"image": im,
"im_shape": im_shape},
fetch=["features", "boxes"],
batch=False)
#add retrieval procedure
det_boxes = fetch_map["boxes"]
searcher, id_map = init_index(index_dir)
results = postprocess(fetch_map, feature_normalize, det_boxes, searcher,
id_map, return_k, rec_score_thres, rec_nms_thresold)
print(results)
deploy/paddleserving/test_cpp_serving_client.py
浏览文件 @ 05c393d9
......@@ -12,16 +12,20 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import base64
import time
from paddle_serving_client import Client
#app
from paddle_serving_app.reader import Sequential, URL2Image, Resize
from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
import time
def bytes_to_base64(image: bytes) -> str:
"""encode bytes into base64 string
"""
return base64.b64encode(image).decode('utf8')
client = Client()
client.load_client_config("./ResNet50_vd_serving/serving_server_conf.prototxt")
client.load_client_config("./ResNet50_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9292"])
label_dict = {}
......@@ -31,22 +35,17 @@ with open("imagenet.label") as fin:
label_dict[label_idx] = line.strip()
label_idx += 1
#preprocess
seq = Sequential([
URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
])
start = time.time()
image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
image_file = "./daisy.jpg"
for i in range(1):
img = seq(image_file)
fetch_map = client.predict(
feed={"inputs": img}, fetch=["prediction"], batch=False)
prob = max(fetch_map["prediction"][0])
label = label_dict[fetch_map["prediction"][0].tolist().index(prob)].strip(
).replace(",", "")
print("prediction: {}, probability: {}".format(label, prob))
end = time.time()
print(end - start)
start = time.time()
with open(image_file, 'rb') as img_file:
image_data = img_file.read()
image = bytes_to_base64(image_data)
fetch_dict = client.predict(
feed={"inputs": image}, fetch=["prediction"], batch=False)
prob = max(fetch_dict["prediction"][0])
label = label_dict[fetch_dict["prediction"][0].tolist().index(
prob)].strip().replace(",", "")
print("prediction: {}, probability: {}".format(label, prob))
end = time.time()
print(end - start)
test_tipc/README.md
浏览文件 @ 05c393d9
......@@ -112,4 +112,5 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
- [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md): 测试基于Paddle-Lite的ARM CPU端c++预测部署功能.
- [test_paddle2onnx 使用](docs/test_paddle2onnx.md)：测试Paddle2ONNX的模型转化功能，并验证正确性。
- [test_serving_infer_python 使用](docs/test_serving_infer_python.md)：测试python serving功能。
- [test_serving_infer_cpp 使用](docs/test_serving_infer_cpp.md)：测试cpp serving功能。
- [test_train_fleet_inference_python 使用](./docs/test_train_fleet_inference_python.md)：测试基于Python的多机多卡训练与推理等基本功能。
test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:MobileNetV3_large_x1_0
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_serving/
--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PP-ShiTu/PPShiTu_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPShiTu
python:python3.7
cls_inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
det_inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./models/general_PPLCNet_x2_5_lite_v1.0_infer/
--dirname:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./models/general_PPLCNet_x2_5_lite_v1.0_serving/
--serving_client:./models/general_PPLCNet_x2_5_lite_v1.0_client/
--serving_server:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/
--serving_client:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
serving_dir:./paddleserving/recognition
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPHGNet_small
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPHGNet_small_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPHGNet_small_serving/
--serving_client:./deploy/paddleserving/PPHGNet_small_client/
serving_dir:./deploy/paddleserving
web_service:classification_web_service.py
--use_gpu:0|null
pipline:pipeline_http_client.py
test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPHGNet_tiny
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPHGNet_tiny_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPHGNet_tiny_serving/
--serving_client:./deploy/paddleserving/PPHGNet_tiny_client/
serving_dir:./deploy/paddleserving
web_service:classification_web_service.py
--use_gpu:0|null
pipline:pipeline_http_client.py
test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x0_25
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_25_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_25_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_25_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x0_35
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_35_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_35_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_35_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x0_5
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_5_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_5_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_5_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x0_75
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_75_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_75_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_75_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x1_0
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x1_0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x1_5
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x1_5_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x1_5_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x1_5_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
浏览文件 @ 05c393d9
===========================serving_params===========================
model_name:PPLCNet_x2_0
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x2_0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x2_0_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x2_0_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================serving_params===========================
model_name:PPLCNet_x2_5
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x2_5_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x2_5_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x2_5_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================serving_params===========================
model_name:PPLCNetV2_base
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNetV2_base_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNetV2_base_serving/
--serving_client:./deploy/paddleserving/PPLCNetV2_base_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================serving_params===========================
model_name:ResNet50
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/ResNet50_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/ResNet50_serving/
--serving_client:./deploy/paddleserving/ResNet50_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================serving_params===========================
model_name:ResNet50_vd
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/ResNet50_vd_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/ResNet50_vd_serving/
--serving_client:./deploy/paddleserving/ResNet50_vd_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================serving_params===========================
model_name:SwinTransformer_tiny_patch4_window7_224
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_serving/
--serving_client:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
test_tipc/docs/test_serving_infer_cpp.md 0 → 100644
浏览文件 @ 05c393d9
# Linux GPU/CPU PYTHON 服务化部署测试
Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer_cpp.sh`，可以测试基于Python的模型服务化部署功能。
## 1. 测试结论汇总
- 推理相关：
| 算法名称 | 模型名称 | device_CPU | device_GPU |
| :----: | :----: | :----: | :----: |
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
| PPHGNet | PPHGNet_small | 支持 | 支持 |
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
| ResNet | ResNet50 | 支持 | 支持 |
| ResNet | ResNet50_vd | 支持 | 支持 |
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
## 2. 测试流程
### 2.1 准备数据
分类模型默认使用`./deploy/paddleserving/daisy.jpg`作为测试输入图片，无需下载
识别模型默认使用`drink_dataset_v1.0/test_images/001.jpeg`作为测试输入图片，在**2.2 准备环境**中会下载好。
### 2.2 准备环境
- 安装PaddlePaddle：如果您已经安装了2.2或者以上版本的paddlepaddle，那么无需运行下面的命令安装paddlepaddle。
```shell
# 需要安装2.2及以上版本的Paddle
# 安装GPU版本的Paddle
python3.7 -m pip install paddlepaddle-gpu==2.2.0
# 安装CPU版本的Paddle
python3.7 -m pip install paddlepaddle==2.2.0
```
- 安装依赖
```shell
python3.7 -m pip install -r requirements.txt
```
- 安装 PaddleServing 相关组件，包括serving_client、serving-app，自动编译并安装带自定义OP的 serving_server 包，以及自动下载并解压推理模型
```bash
bash test_tipc/prepare.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt serving_infer
```
### 2.3 功能测试
测试方法如下所示，希望测试不同的模型文件，只需更换为自己的参数配置文件，即可完成对应模型的测试。
```bash
bash test_tipc/test_serving_infer_cpp.sh ${your_params_file}
```
以`PPLCNet_x1_0`的`Linux GPU/CPU C++ 服务化部署测试`为例，命令如下所示。
```bash
bash test_tipc/test_serving_infer_cpp.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt
```
输出结果如下，表示命令运行成功。
```
Run successfully with command - PPLCNet_x1_0 - python3.7 test_cpp_serving_client.py > ../../test_tipc/output/PPLCNet_x1_0/server_infer_cpp_gpu_pipeline_batchsize_1.log 2>&1 !
Run successfully with command - PPLCNet_x1_0 - python3.7 test_cpp_serving_client.py > ../../test_tipc/output/PPLCNet_x1_0/server_infer_cpp_cpu_pipeline_batchsize_1.log 2>&1 !
```
预测结果会自动保存在 `./test_tipc/output/PPLCNet_x1_0/server_infer_gpu_pipeline_http_batchsize_1.log` ，可以看到 PaddleServing 的运行结果：
```
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0612 09:55:16.109890 38303 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9292"): added 1
I0612 09:55:16.172924 38303 general_model.cpp:490] [client]logid=0,client_cost=60.772ms,server_cost=57.6ms.
prediction: daisy, probability: 0.9099399447441101
0.06275796890258789
```
如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。
test_tipc/docs/test_serving_infer_python.md
浏览文件 @ 05c393d9
# Linux GPU/CPU PYTHON 服务化部署测试
Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer.sh`，可以测试基于Python的模型服务化部署功能。
Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer_python.sh`，可以测试基于Python的模型服务化部署功能。
## 1. 测试结论汇总
......@@ -60,14 +60,14 @@ Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer.sh
测试方法如下所示，希望测试不同的模型文件，只需更换为自己的参数配置文件，即可完成对应模型的测试。
```bash
bash test_tipc/test_serving_infer_python.sh ${your_params_file} lite_train_lite_infer
bash test_tipc/test_serving_infer_python.sh ${your_params_file}
```
以`ResNet50`的`Linux GPU/CPU PYTHON 服务化部署测试`为例，命令如下所示。
```bash
bash test_tipc/test_serving_infer_python.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt serving_infer
bash test_tipc/test_serving_infer_python.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
```
输出结果如下，表示命令运行成功。
......
test_tipc/prepare.sh
浏览文件 @ 05c393d9
......@@ -200,15 +200,25 @@ fi
if [[ ${MODE} = "serving_infer" ]]; then
# prepare serving env
python_name=$(func_parser_value "${lines[2]}")
${python_name} -m pip install paddle-serving-server-gpu==0.7.0.post102
${python_name} -m pip install paddle_serving_client==0.7.0
${python_name} -m pip install paddle-serving-app==0.7.0
${python_name} -m pip install paddle_serving_client==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
${python_name} -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
python_name=$(func_parser_value "${lines[2]}")
if [[ ${FILENAME} =~ "cpp" ]]; then
pushd ./deploy/paddleserving
bash build_server.sh ${python_name}
popd
else
${python_name} -m pip install install paddle-serving-server-gpu==0.9.0.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
fi
if [[ ${model_name} =~ "ShiTu" ]]; then
${python_name} -m pip install faiss-cpu==1.7.1post2 -i https://pypi.tuna.tsinghua.edu.cn/simple
cls_inference_model_url=$(func_parser_value "${lines[3]}")
cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}")
det_inference_model_url=$(func_parser_value "${lines[4]}")
det_tar_name=$(func_get_url_file_name "${det_inference_model_url}")
cd ./deploy
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar --no-check-certificate
tar -xf drink_dataset_v1.0.tar
mkdir models
cd models
wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name}
......
test_tipc/test_serving_infer_cpp.sh 0 → 100644
浏览文件 @ 05c393d9
#!/bin/bash
source test_tipc/common_func.sh
FILENAME=$1
dataline=$(awk 'NR==1, NR==19{print}' $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
function func_get_url_file_name(){
strs=$1
IFS="/"
array=(${strs})
tmp=${array[${#array[@]}-1]}
echo ${tmp}
}
# parser serving
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
trans_model_py=$(func_parser_value "${lines[4]}")
infer_model_dir_key=$(func_parser_key "${lines[5]}")
infer_model_dir_value=$(func_parser_value "${lines[5]}")
model_filename_key=$(func_parser_key "${lines[6]}")
model_filename_value=$(func_parser_value "${lines[6]}")
params_filename_key=$(func_parser_key "${lines[7]}")
params_filename_value=$(func_parser_value "${lines[7]}")
serving_server_key=$(func_parser_key "${lines[8]}")
serving_server_value=$(func_parser_value "${lines[8]}")
serving_client_key=$(func_parser_key "${lines[9]}")
serving_client_value=$(func_parser_value "${lines[9]}")
serving_dir_value=$(func_parser_value "${lines[10]}")
web_service_py=$(func_parser_value "${lines[11]}")
web_use_gpu_key=$(func_parser_key "${lines[12]}")
web_use_gpu_list=$(func_parser_value "${lines[12]}")
pipeline_py=$(func_parser_value "${lines[13]}")
function func_serving_cls(){
LOG_PATH="test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
LOG_PATH="../../${LOG_PATH}"
status_log="${LOG_PATH}/results_serving.log"
IFS='|'
# pdserving
set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
for python_ in ${python[*]}; do
if [[ ${python_} =~ "python" ]]; then
trans_model_cmd="${python_} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${trans_model_cmd}
break
fi
done
# modify the alias_name of fetch_var to "outputs"
server_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_server_value}/serving_server_conf.prototxt"
eval ${server_fetch_var_line_cmd}
client_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_client_value}/serving_client_conf.prototxt"
eval ${client_fetch_var_line_cmd}
prototxt_dataline=$(awk 'NR==1, NR==3{print}' ${serving_server_value}/serving_server_conf.prototxt)
IFS=$'\n'
prototxt_lines=(${prototxt_dataline})
feed_var_name=$(func_parser_value "${prototxt_lines[2]}")
IFS='|'
cd ${serving_dir_value}
unset https_proxy
unset http_proxy
for item in ${python[*]}; do
if [[ ${item} =~ "python" ]]; then
python_=${item}
break
fi
done
serving_client_dir_name=$(func_get_url_file_name "$serving_client_value")
set_client_feed_type_cmd="sed -i '/feed_type/,/: .*/s/feed_type: .*/feed_type: 20/' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_feed_type_cmd}
set_client_shape_cmd="sed -i '/shape: 3/,/shape: 3/s/shape: 3/shape: 1/' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_shape_cmd}
set_client_shape224_cmd="sed -i '/shape: 224/,/shape: 224/s/shape: 224//' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_shape224_cmd}
set_client_shape224_cmd="sed -i '/shape: 224/,/shape: 224/s/shape: 224//' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_shape224_cmd}
set_pipeline_load_config_cmd="sed -i '/load_client_config/,/.prototxt/s/.\/.*\/serving_client_conf.prototxt/.\/${serving_client_dir_name}\/serving_client_conf.prototxt/' ${pipeline_py}"
eval ${set_pipeline_load_config_cmd}
set_pipeline_feed_var_cmd="sed -i '/feed=/,/: image}/s/feed={.*: image}/feed={${feed_var_name}: image}/' ${pipeline_py}"
eval ${set_pipeline_feed_var_cmd}
serving_server_dir_name=$(func_get_url_file_name "$serving_server_value")
for use_gpu in ${web_use_gpu_list[*]}; do
if [[ ${use_gpu} = "null" ]]; then
web_service_cpp_cmd="${python_} -m paddle_serving_server.serve --model ${serving_server_dir_name} --op GeneralClasOp --port 9292 &"
eval ${web_service_cpp_cmd}
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_batchsize_1.log"
pipeline_cmd="${python_} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
eval "${python_} -m paddle_serving_server.serve stop"
sleep 5s
else
web_service_cpp_cmd="${python_} -m paddle_serving_server.serve --model ${serving_server_dir_name} --op GeneralClasOp --port 9292 --gpu_id=${use_gpu} &"
eval ${web_service_cpp_cmd}
sleep 8s
_save_log_path="${LOG_PATH}/server_infer_cpp_gpu_pipeline_batchsize_1.log"
pipeline_cmd="${python_} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
eval "${python_} -m paddle_serving_server.serve stop"
fi
done
}
function func_serving_rec(){
LOG_PATH="test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
LOG_PATH="../../../${LOG_PATH}"
status_log="${LOG_PATH}/results_serving.log"
trans_model_py=$(func_parser_value "${lines[5]}")
cls_infer_model_dir_key=$(func_parser_key "${lines[6]}")
cls_infer_model_dir_value=$(func_parser_value "${lines[6]}")
det_infer_model_dir_key=$(func_parser_key "${lines[7]}")
det_infer_model_dir_value=$(func_parser_value "${lines[7]}")
model_filename_key=$(func_parser_key "${lines[8]}")
model_filename_value=$(func_parser_value "${lines[8]}")
params_filename_key=$(func_parser_key "${lines[9]}")
params_filename_value=$(func_parser_value "${lines[9]}")
cls_serving_server_key=$(func_parser_key "${lines[10]}")
cls_serving_server_value=$(func_parser_value "${lines[10]}")
cls_serving_client_key=$(func_parser_key "${lines[11]}")
cls_serving_client_value=$(func_parser_value "${lines[11]}")
det_serving_server_key=$(func_parser_key "${lines[12]}")
det_serving_server_value=$(func_parser_value "${lines[12]}")
det_serving_client_key=$(func_parser_key "${lines[13]}")
det_serving_client_value=$(func_parser_value "${lines[13]}")
serving_dir_value=$(func_parser_value "${lines[14]}")
web_service_py=$(func_parser_value "${lines[15]}")
web_use_gpu_key=$(func_parser_key "${lines[16]}")
web_use_gpu_list=$(func_parser_value "${lines[16]}")
pipeline_py=$(func_parser_value "${lines[17]}")
IFS='|'
for python_ in ${python[*]}; do
if [[ ${python_} =~ "python" ]]; then
python_interp=${python_}
break
fi
done
# pdserving
cd ./deploy
set_dirname=$(func_set_params "${cls_infer_model_dir_key}" "${cls_infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${cls_serving_server_key}" "${cls_serving_server_value}")
set_serving_client=$(func_set_params "${cls_serving_client_key}" "${cls_serving_client_value}")
cls_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${cls_trans_model_cmd}
set_dirname=$(func_set_params "${det_infer_model_dir_key}" "${det_infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${det_serving_server_key}" "${det_serving_server_value}")
set_serving_client=$(func_set_params "${det_serving_client_key}" "${det_serving_client_value}")
det_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${det_trans_model_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/*.prototxt ${cls_serving_server_value}"
eval ${cp_prototxt_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/*.prototxt ${cls_serving_client_value}"
eval ${cp_prototxt_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/*.prototxt ${det_serving_client_value}"
eval ${cp_prototxt_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/*.prototxt ${det_serving_server_value}"
eval ${cp_prototxt_cmd}
prototxt_dataline=$(awk 'NR==1, NR==3{print}' ${cls_serving_server_value}/serving_server_conf.prototxt)
IFS=$'\n'
prototxt_lines=(${prototxt_dataline})
feed_var_name=$(func_parser_value "${prototxt_lines[2]}")
IFS='|'
cd ${serving_dir_value}
unset https_proxy
unset http_proxy
export SERVING_BIN=${PWD}/../Serving/server-build-gpu-opencv/core/general-server/serving
for use_gpu in ${web_use_gpu_list[*]}; do
if [ ${use_gpu} = "null" ]; then
det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 &"
eval ${web_service_cpp_cmd}
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_batchsize_1.log"
pipeline_cmd="${python_interp} ${pipeline_py} > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
eval "${python_} -m paddle_serving_server.serve stop"
sleep 5s
else
det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 --gpu_id=${use_gpu} &"
eval ${web_service_cpp_cmd}
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_gpu_batchsize_1.log"
pipeline_cmd="${python_interp} ${pipeline_py} > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
eval "${python_} -m paddle_serving_server.serve stop"
sleep 5s
fi
done
}
# set cuda device
GPUID=$2
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
set CUDA_VISIBLE_DEVICES
eval ${env}
echo "################### run test ###################"
export Count=0
IFS="|"
if [[ ${model_name} =~ "ShiTu" ]]; then
func_serving_rec
else
func_serving_cls
fi
test_tipc/test_serving_infer.sh → test_tipc/test_serving_infer_python.sh
浏览文件 @ 05c393d9
......@@ -38,8 +38,9 @@ pipeline_py=$(func_parser_value "${lines[13]}")
function func_serving_cls(){
LOG_PATH="../../test_tipc/output/${model_name}"
LOG_PATH="test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
LOG_PATH="../../${LOG_PATH}"
status_log="${LOG_PATH}/results_serving.log"
IFS='|'
......@@ -50,12 +51,18 @@ function func_serving_cls(){
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $trans_model_cmd
for python_ in ${python[*]}; do
if [[ ${python_} =~ "python" ]]; then
trans_model_cmd="${python_} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${trans_model_cmd}
break
fi
done
# modify the alias_name of fetch_var to "outputs"
server_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_server_value}/serving_server_conf.prototxt"
eval ${server_fetch_var_line_cmd}
client_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_client_value}/serving_client_conf.prototxt"
eval ${client_fetch_var_line_cmd}
......@@ -69,109 +76,82 @@ function func_serving_cls(){
unset https_proxy
unset http_proxy
# python serving
# modify the input_name in "classification_web_service.py" to be consistent with feed_var.name in prototxt
set_web_service_feet_var_cmd="sed -i '/preprocess/,/input_imgs}/s/{.*: input_imgs}/{${feed_var_name}: input_imgs}/' ${web_service_py}"
eval ${set_web_service_feet_var_cmd}
set_web_service_feed_var_cmd="sed -i '/preprocess/,/input_imgs}/s/{.*: input_imgs}/{${feed_var_name}: input_imgs}/' ${web_service_py}"
eval ${set_web_service_feed_var_cmd}
model_config=21
serving_server_dir_name=$(func_get_url_file_name "$serving_server_value")
set_model_config_cmd="sed -i '${model_config}s/model_config: .*/model_config: ${serving_server_dir_name}/' config.yml"
eval ${set_model_config_cmd}
for python in ${python[*]}; do
if [[ ${python} = "cpp" ]]; then
for use_gpu in ${web_use_gpu_list[*]}; do
if [ ${use_gpu} = "null" ]; then
web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
eval $web_service_cmd
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
else
web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
eval $web_service_cmd
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
fi
for use_gpu in ${web_use_gpu_list[*]}; do
if [[ ${use_gpu} = "null" ]]; then
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 0/' config.yml"
eval ${set_device_type_cmd}
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"\"/' config.yml"
eval ${set_devices_cmd}
web_service_cmd="${python_} ${web_service_py} &"
eval ${web_service_cmd}
sleep 5s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python_} ${pipeline} > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
done
else
# python serving
for use_gpu in ${web_use_gpu_list[*]}; do
if [[ ${use_gpu} = "null" ]]; then
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 0/' config.yml"
eval $set_device_type_cmd
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"\"/' config.yml"
eval $set_devices_cmd
web_service_cmd="${python} ${web_service_py} &"
eval $web_service_cmd
sleep 5s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1 "
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
elif [ ${use_gpu} -eq 0 ]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 1/' config.yml"
eval $set_device_type_cmd
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"${use_gpu}\"/' config.yml"
eval $set_devices_cmd
web_service_cmd="${python} ${web_service_py} & "
eval $web_service_cmd
sleep 5s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
else
echo "Does not support hardware [${use_gpu}] other than CPU and GPU Currently!"
fi
eval "${python_} -m paddle_serving_server.serve stop"
elif [ ${use_gpu} -eq 0 ]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 1/' config.yml"
eval ${set_device_type_cmd}
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"${use_gpu}\"/' config.yml"
eval ${set_devices_cmd}
web_service_cmd="${python_} ${web_service_py} & "
eval ${web_service_cmd}
sleep 5s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python_} ${pipeline} > ${_save_log_path} 2>&1"
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
done
eval "${python_} -m paddle_serving_server.serve stop"
else
echo "Does not support hardware [${use_gpu}] other than CPU and GPU Currently!"
fi
done
}
function func_serving_rec(){
LOG_PATH="../../../test_tipc/output/${model_name}"
LOG_PATH="test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
LOG_PATH="../../../${LOG_PATH}"
status_log="${LOG_PATH}/results_serving.log"
trans_model_py=$(func_parser_value "${lines[5]}")
cls_infer_model_dir_key=$(func_parser_key "${lines[6]}")
......@@ -200,6 +180,12 @@ function func_serving_rec(){
pipeline_py=$(func_parser_value "${lines[17]}")
IFS='|'
for python_ in ${python[*]}; do
if [[ ${python_} =~ "python" ]]; then
python_interp=${python_}
break
fi
done
# pdserving
cd ./deploy
......@@ -208,16 +194,16 @@ function func_serving_rec(){
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${cls_serving_server_key}" "${cls_serving_server_value}")
set_serving_client=$(func_set_params "${cls_serving_client_key}" "${cls_serving_client_value}")
cls_trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $cls_trans_model_cmd
cls_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${cls_trans_model_cmd}
set_dirname=$(func_set_params "${det_infer_model_dir_key}" "${det_infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${det_serving_server_key}" "${det_serving_server_value}")
set_serving_client=$(func_set_params "${det_serving_client_key}" "${det_serving_client_value}")
det_trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $det_trans_model_cmd
det_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${det_trans_model_cmd}
# modify the alias_name of fetch_var to "outputs"
server_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"features\"/' $cls_serving_server_value/serving_server_conf.prototxt"
......@@ -236,96 +222,66 @@ function func_serving_rec(){
unset http_proxy
# modify the input_name in "recognition_web_service.py" to be consistent with feed_var.name in prototxt
set_web_service_feet_var_cmd="sed -i '/preprocess/,/input_imgs}/s/{.*: input_imgs}/{${feed_var_name}: input_imgs}/' ${web_service_py}"
eval ${set_web_service_feet_var_cmd}
for python in ${python[*]}; do
if [[ ${python} = "cpp" ]]; then
for use_gpu in ${web_use_gpu_list[*]}; do
if [ ${use_gpu} = "null" ]; then
web_service_cpp_cmd="${python} web_service_py"
eval $web_service_cmd
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
else
web_service_cpp_cmd="${python} web_service_py"
eval $web_service_cmd
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
eval $pipeline_cmd
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
fi
set_web_service_feed_var_cmd="sed -i '/preprocess/,/input_imgs}/s/{.*: input_imgs}/{${feed_var_name}: input_imgs}/' ${web_service_py}"
eval ${set_web_service_feed_var_cmd}
# python serving
for use_gpu in ${web_use_gpu_list[*]}; do
if [[ ${use_gpu} = "null" ]]; then
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 0/' config.yml"
eval ${set_device_type_cmd}
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"\"/' config.yml"
eval ${set_devices_cmd}
web_service_cmd="${python} ${web_service_py} &"
eval ${web_service_cmd}
sleep 5s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
done
else
# python serving
for use_gpu in ${web_use_gpu_list[*]}; do
if [[ ${use_gpu} = "null" ]]; then
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 0/' config.yml"
eval $set_device_type_cmd
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"\"/' config.yml"
eval $set_devices_cmd
web_service_cmd="${python} ${web_service_py} &"
eval $web_service_cmd
sleep 5s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1 "
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
elif [ ${use_gpu} -eq 0 ]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 1/' config.yml"
eval $set_device_type_cmd
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"${use_gpu}\"/' config.yml"
eval $set_devices_cmd
web_service_cmd="${python} ${web_service_py} & "
eval $web_service_cmd
sleep 10s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 10s
done
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
else
echo "Does not support hardware [${use_gpu}] other than CPU and GPU Currently!"
fi
eval "${python_} -m paddle_serving_server.serve stop"
elif [ ${use_gpu} -eq 0 ]; then
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
continue
fi
device_type_line=24
set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 1/' config.yml"
eval ${set_device_type_cmd}
devices_line=27
set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"${use_gpu}\"/' config.yml"
eval ${set_devices_cmd}
web_service_cmd="${python} ${web_service_py} & "
eval ${web_service_cmd}
sleep 10s
for pipeline in ${pipeline_py[*]}; do
_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1"
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 10s
done
eval "${python_} -m paddle_serving_server.serve stop"
else
echo "Does not support hardware [${use_gpu}] other than CPU and GPU Currently!"
fi
done
}
......@@ -339,7 +295,7 @@ else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
set CUDA_VISIBLE_DEVICES
eval $env
eval ${env}
echo "################### run test ###################"
......
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