Merge pull request #2045 from HydrogenSulfate/add_cpp_serving

Add cpp serving chain

deploy/paddleserving/build_server.sh

+# 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

+// 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

+// 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

+// 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

+// 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

+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

+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

+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

+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

@@ -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

@@ -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

@@ -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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+===========================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

+# 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

 # 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

@@ -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

+#!/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

@@ -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 ###################"