Merge pull request #303 from littletomatodonkey/sta/add_lite_demo

Add lite demo

deploy/cpp_infer/include/preprocess_op.h

@@ -51,8 +51,7 @@ public:
 
 class ResizeImg {
 public:
-  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
-                   float &ratio_h, float &ratio_w);
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
 };
 
 } // namespace PaddleClas
\ No newline at end of file

deploy/cpp_infer/src/cls.cpp

@@ -47,15 +47,12 @@ void Classifier::LoadModel(const std::string &model_dir) {
 }
 
 void Classifier::Run(cv::Mat &img) {
-  float ratio_h{};
-  float ratio_w{};
-
   cv::Mat srcimg;
   cv::Mat resize_img;
   img.copyTo(srcimg);
 
-  this->resize_op_.Run(img, resize_img, this->resize_short_size_, ratio_h,
-                       ratio_w);
+  this->resize_op_.Run(img, resize_img, this->resize_short_size_);
+
   this->crop_op_.Run(resize_img, this->crop_size_);
 
   this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,

deploy/cpp_infer/src/preprocess_op.cpp

@@ -25,6 +25,7 @@
 
 #include <cstring>
 #include <fstream>
+#include <math.h>
 #include <numeric>
 
 #include <include/preprocess_op.h>
@@ -68,25 +69,22 @@ void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
   img = img(rect);
 }
 
-void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
-                    float &ratio_h, float &ratio_w) {
+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(max_size_len) / float(h);
+    ratio = float(resize_short_size) / float(h);
   } else {
-    ratio = float(max_size_len) / float(w);
+    ratio = float(resize_short_size) / float(w);
   }
 
-  int resize_h = int(float(h) * ratio);
-  int resize_w = int(float(w) * ratio);
+  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));
-
-  ratio_h = float(resize_h) / float(h);
-  ratio_w = float(resize_w) / float(w);
 }
 
 } // namespace PaddleClas
\ No newline at end of file

deploy/lite/Makefile

+ARM_ABI = arm8
+export ARM_ABI
+
+include ../Makefile.def
+
+LITE_ROOT=../../../
+
+THIRD_PARTY_DIR=${LITE_ROOT}/third_party
+
+OPENCV_VERSION=opencv4.1.0
+
+OPENCV_LIBS = ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgcodecs.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgproc.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_core.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtegra_hal.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjpeg-turbo.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibwebp.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibpng.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjasper.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibtiff.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libIlmImf.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtbb.a \
+              ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libcpufeatures.a
+
+OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/arm64-v8a/include
+
+CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
+
+CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
+
+###############################################################
+# How to use one of static libaray:                           #
+#  `libpaddle_api_full_bundled.a`                             #
+#  `libpaddle_api_light_bundled.a`                            #
+###############################################################
+# Note: default use lite's shared library.                    #
+###############################################################
+# 1. Comment above line using `libpaddle_light_api_shared.so`
+# 2. Undo comment below line using `libpaddle_api_light_bundled.a`
+
+#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
+
+clas_system: fetch_opencv clas_system.o
+	$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) clas_system.o -o clas_system  $(CXX_LIBS) $(LDFLAGS)
+
+clas_system.o: image_classfication.cpp
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o clas_system.o -c image_classfication.cpp
+
+fetch_opencv:
+	@ test -d ${THIRD_PARTY_DIR} ||  mkdir ${THIRD_PARTY_DIR}
+	@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
+      (echo "fetch opencv libs" && \
+      wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz)
+	@ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \
+      tar -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
+
+
+.PHONY: clean
+clean:
+	rm -f clas_system.o
+	rm -f clas_system

deploy/lite/config.txt

+clas_model_file ./MobileNetV3_large_x1_0.nb
+label_path ./imagenet1k_label_list.txt
+resize_short_size 256
+crop_size 224
+visualize 0

deploy/lite/image_classfication.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 "paddle_api.h" // NOLINT
+#include <arm_neon.h>
+#include <chrono>
+#include <fstream>
+#include <iostream>
+#include <math.h>
+#include <opencv2/opencv.hpp>
+#include <sys/time.h>
+#include <vector>
+
+using namespace paddle::lite_api; // NOLINT
+using namespace std;
+
+struct RESULT {
+  std::string class_name;
+  int class_id;
+  float score;
+};
+
+std::vector<RESULT> PostProcess(const float *output_data, int output_size,
+                                const std::vector<std::string> &word_labels,
+                                cv::Mat &output_image) {
+  const int TOPK = 5;
+  int max_indices[TOPK];
+  double max_scores[TOPK];
+  for (int i = 0; i < TOPK; i++) {
+    max_indices[i] = 0;
+    max_scores[i] = 0;
+  }
+  for (int i = 0; i < output_size; i++) {
+    float score = output_data[i];
+    int index = i;
+    for (int j = 0; j < TOPK; j++) {
+      if (score > max_scores[j]) {
+        index += max_indices[j];
+        max_indices[j] = index - max_indices[j];
+        index -= max_indices[j];
+        score += max_scores[j];
+        max_scores[j] = score - max_scores[j];
+        score -= max_scores[j];
+      }
+    }
+  }
+
+  std::vector<RESULT> results(TOPK);
+  for (int i = 0; i < results.size(); i++) {
+    results[i].class_name = "Unknown";
+    if (max_indices[i] >= 0 && max_indices[i] < word_labels.size()) {
+      results[i].class_name = word_labels[max_indices[i]];
+    }
+    results[i].score = max_scores[i];
+    results[i].class_id = max_indices[i];
+    cv::putText(output_image,
+                "Top" + std::to_string(i + 1) + "." + results[i].class_name +
+                    ":" + std::to_string(results[i].score),
+                cv::Point2d(5, i * 18 + 20), cv::FONT_HERSHEY_PLAIN, 1,
+                cv::Scalar(51, 255, 255));
+  }
+  return results;
+}
+
+// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
+void NeonMeanScale(const float *din, float *dout, int size,
+                   const std::vector<float> mean,
+                   const std::vector<float> scale) {
+  if (mean.size() != 3 || scale.size() != 3) {
+    std::cerr << "[ERROR] mean or scale size must equal to 3\n";
+    exit(1);
+  }
+  float32x4_t vmean0 = vdupq_n_f32(mean[0]);
+  float32x4_t vmean1 = vdupq_n_f32(mean[1]);
+  float32x4_t vmean2 = vdupq_n_f32(mean[2]);
+  float32x4_t vscale0 = vdupq_n_f32(scale[0]);
+  float32x4_t vscale1 = vdupq_n_f32(scale[1]);
+  float32x4_t vscale2 = vdupq_n_f32(scale[2]);
+
+  float *dout_c0 = dout;
+  float *dout_c1 = dout + size;
+  float *dout_c2 = dout + size * 2;
+
+  int i = 0;
+  for (; i < size - 3; i += 4) {
+    float32x4x3_t vin3 = vld3q_f32(din);
+    float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
+    float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
+    float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
+    float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
+    float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
+    float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
+    vst1q_f32(dout_c0, vs0);
+    vst1q_f32(dout_c1, vs1);
+    vst1q_f32(dout_c2, vs2);
+
+    din += 12;
+    dout_c0 += 4;
+    dout_c1 += 4;
+    dout_c2 += 4;
+  }
+  for (; i < size; i++) {
+    *(dout_c0++) = (*(din++) - mean[0]) * scale[0];
+    *(dout_c1++) = (*(din++) - mean[1]) * scale[1];
+    *(dout_c2++) = (*(din++) - mean[2]) * scale[2];
+  }
+}
+
+cv::Mat ResizeImage(const cv::Mat &img, const int &resize_short_size) {
+  int w = img.cols;
+  int h = img.rows;
+
+  cv::Mat resize_img;
+
+  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));
+  return resize_img;
+}
+
+cv::Mat CenterCropImg(const 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);
+  cv::Mat crop_img = img(rect);
+  return crop_img;
+}
+
+std::vector<RESULT>
+RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
+             const std::map<std::string, std::string> &config,
+             const std::vector<std::string> &word_labels) {
+  // Read img
+  int resize_short_size = stoi(config.at("resize_short_size"));
+  int crop_size = stoi(config.at("crop_size"));
+  int visualize = stoi(config.at("visualize"));
+
+  cv::Mat resize_image = ResizeImage(img, resize_short_size);
+
+  cv::Mat crop_image = CenterCropImg(resize_image, crop_size);
+
+  cv::Mat img_fp;
+  double e = 1.0 / 255.0;
+  crop_image.convertTo(img_fp, CV_32FC3, e);
+
+  // Prepare input data from image
+  std::unique_ptr<Tensor> input_tensor(std::move(predictor->GetInput(0)));
+  input_tensor->Resize({1, 3, img_fp.rows, img_fp.cols});
+  auto *data0 = input_tensor->mutable_data<float>();
+
+  std::vector<float> mean = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+  const float *dimg = reinterpret_cast<const float *>(img_fp.data);
+  NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
+
+  // Run predictor
+  predictor->Run();
+
+  // Get output and post process
+  std::unique_ptr<const Tensor> output_tensor(
+      std::move(predictor->GetOutput(0)));
+  auto *output_data = output_tensor->data<float>();
+
+  int output_size = 1;
+  for (auto dim : output_tensor->shape()) {
+    output_size *= dim;
+  }
+
+  cv::Mat output_image;
+  auto results =
+      PostProcess(output_data, output_size, word_labels, output_image);
+
+  if (visualize) {
+    std::string output_image_path = "./clas_result.png";
+    cv::imwrite(output_image_path, output_image);
+    std::cout << "save output image into " << output_image_path << std::endl;
+  }
+
+  return results;
+}
+
+std::shared_ptr<PaddlePredictor> LoadModel(std::string model_file) {
+  MobileConfig config;
+  config.set_model_from_file(model_file);
+
+  std::shared_ptr<PaddlePredictor> predictor =
+      CreatePaddlePredictor<MobileConfig>(config);
+  return predictor;
+}
+
+std::vector<std::string> split(const std::string &str,
+                               const std::string &delim) {
+  std::vector<std::string> res;
+  if ("" == str)
+    return res;
+  char *strs = new char[str.length() + 1];
+  std::strcpy(strs, str.c_str());
+
+  char *d = new char[delim.length() + 1];
+  std::strcpy(d, delim.c_str());
+
+  char *p = std::strtok(strs, d);
+  while (p) {
+    string s = p;
+    res.push_back(s);
+    p = std::strtok(NULL, d);
+  }
+
+  return res;
+}
+
+std::vector<std::string> ReadDict(std::string path) {
+  std::ifstream in(path);
+  std::string filename;
+  std::string line;
+  std::vector<std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      m_vec.push_back(line);
+    }
+  } else {
+    std::cout << "no such file" << std::endl;
+  }
+  return m_vec;
+}
+
+std::map<std::string, std::string> LoadConfigTxt(std::string config_path) {
+  auto config = ReadDict(config_path);
+
+  std::map<std::string, std::string> dict;
+  for (int i = 0; i < config.size(); i++) {
+    std::vector<std::string> res = split(config[i], " ");
+    dict[res[0]] = res[1];
+  }
+  return dict;
+}
+
+void PrintConfig(const std::map<std::string, std::string> &config) {
+  std::cout << "=======PaddleClas lite demo config======" << std::endl;
+  for (auto iter = config.begin(); iter != config.end(); iter++) {
+    std::cout << iter->first << " : " << iter->second << std::endl;
+  }
+  std::cout << "=======End of PaddleClas lite demo config======" << std::endl;
+}
+
+std::vector<std::string> LoadLabels(const std::string &path) {
+  std::ifstream file;
+  std::vector<std::string> labels;
+  file.open(path);
+  while (file) {
+    std::string line;
+    std::getline(file, line);
+    std::string::size_type pos = line.find(" ");
+    if (pos != std::string::npos) {
+      line = line.substr(pos);
+    }
+    labels.push_back(line);
+  }
+  file.clear();
+  file.close();
+  return labels;
+}
+
+int main(int argc, char **argv) {
+  if (argc < 3) {
+    std::cerr << "[ERROR] usage: " << argv[0] << " config_path img_path\n";
+    exit(1);
+  }
+
+  std::string config_path = argv[1];
+  std::string img_path = argv[2];
+
+  // load config
+  auto config = LoadConfigTxt(config_path);
+  PrintConfig(config);
+
+  std::string clas_model_file = config.at("clas_model_file");
+  std::string label_path = config.at("label_path");
+
+  // Load Labels
+  std::vector<std::string> word_labels = LoadLabels(label_path);
+
+  auto clas_predictor = LoadModel(clas_model_file);
+
+  auto start = std::chrono::system_clock::now();
+
+  cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
+  cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
+
+  std::vector<RESULT> results =
+      RunClasModel(clas_predictor, srcimg, config, word_labels);
+
+  std::cout << "===clas result for image: " << img_path << "===" << std::endl;
+  for (int i = 0; i < results.size(); i++) {
+    std::cout << "\t"
+              << "Top-" << i + 1 << ", class_id: " << results[i].class_id
+              << ", class_name: " << results[i].class_name
+              << ", score: " << results[i].score << std::endl;
+  }
+
+  auto end = std::chrono::system_clock::now();
+  auto duration =
+      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+
+  std::cout << "Cost "
+            << double(duration.count()) *
+                   std::chrono::microseconds::period::num /
+                   std::chrono::microseconds::period::den
+            << " s" << std::endl;
+
+  return 0;
+}

deploy/lite/prepare.sh

+#!/bin/bash
+
+if [ $# != 1 ] ; then
+echo "USAGE: $0 your_inference_lite_lib_path"
+exit 1;
+fi
+
+mkdir -p  $1/demo/cxx/clas/debug/
+cp  ../../ppcls/utils/imagenet1k_label_list.txt  $1/demo/cxx/clas/debug/
+cp -r  ./*   $1/demo/cxx/clas/
+cp ./config.txt  $1/demo/cxx/clas/debug/
+cp ./imgs/tabby_cat.jpg  $1/demo/cxx/clas/debug/
+
+echo "Prepare Done"

deploy/lite/readme.md

+# 端侧部署
+
+本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleClas分类模型的详细步骤。
+
+Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。如果希望直接测试速度，可以参考[Paddle-Lite移动端benchmark测试教程](../../docs/zh_CN/extension/paddle_mobile_inference.md)。
+
+
+## 1. 准备环境
+
+### 运行准备
+- 电脑（编译Paddle Lite）
+- 安卓手机（armv7或armv8）
+
+### 1.1 准备交叉编译环境
+交叉编译环境用于编译 Paddle Lite 和 PaddleClas 的C++ demo。
+支持多种开发环境，不同开发环境的编译流程请参考对应文档。
+
+1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
+2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
+3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
+
+### 1.2 准备预测库
+
+预测库有两种获取方式：
+1. [建议]直接下载，预测库下载链接如下：
+      |平台|预测库下载链接|
+      |-|-|
+      |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)|
+      |IOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)|
+
+      注：1. 如果是从下Paddle-Lite[官网文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc)下载的预测库，
+      注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 如果使用量化的模型部署在端侧，建议使用Paddle-Lite develop分支编译预测库。
+
+2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
+```
+git clone https://github.com/PaddlePaddle/Paddle-Lite.git
+cd Paddle-Lite
+# 如果使用编译方式，建议使用develop分支编译预测库
+git checkout develop
+./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
+```
+
+注意：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2)。
+
+直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8/`文件夹，通过编译Paddle-Lite得到的预测库位于
+`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
+预测库的文件目录如下：
+
+```
+inference_lite_lib.android.armv8/
+|-- cxx                                        C++ 预测库和头文件
+|   |-- include                                C++ 头文件
+|   |   |-- paddle_api.h
+|   |   |-- paddle_image_preprocess.h
+|   |   |-- paddle_lite_factory_helper.h
+|   |   |-- paddle_place.h
+|   |   |-- paddle_use_kernels.h
+|   |   |-- paddle_use_ops.h
+|   |   `-- paddle_use_passes.h
+|   `-- lib                                           C++预测库
+|       |-- libpaddle_api_light_bundled.a             C++静态库
+|       `-- libpaddle_light_api_shared.so             C++动态库
+|-- java                                     Java预测库
+|   |-- jar
+|   |   `-- PaddlePredictor.jar
+|   |-- so
+|   |   `-- libpaddle_lite_jni.so
+|   `-- src
+|-- demo                                     C++和Java示例代码
+|   |-- cxx                                  C++  预测库demo
+|   `-- java                                 Java 预测库demo
+```
+
+## 2 开始运行
+
+### 2.1 模型优化
+
+Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-lite的opt工具可以自动对inference模型进行优化，优化后的模型更轻量，模型运行速度更快。有2种方式对inference模型进行优化。
+
+**注意**：如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。
+
+#### 2.1.1 [建议]pip安装paddlelite并进行转换
+
+* python下安装paddlelite
+
+```shell
+pip install paddlelite
+```
+
+之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下
+
+|选项|说明|
+|-|-|
+|--model_dir|待优化的PaddlePaddle模型（非combined形式）的路径|
+|--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径|
+|--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径|
+|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现。若您需要在mobile端执行模型预测，请将此选项设置为naive_buffer。默认为protobuf|
+|--optimize_out|优化模型的输出路径|
+|--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm|
+|--record_tailoring_info|当使用 根据模型裁剪库文件 功能时，则设置该选项为true，以记录优化后模型含有的kernel和OP信息，默认为false|
+
+`--model_file`表示inference模型的model文件地址，`--param_file`表示inference模型的param文件地址；`optimize_out`用于指定输出文件的名称（不需要添加`.nb`的后缀）。直接在命令行中运行`paddle_lite_opt`，也可以查看所有参数及其说明。
+
+
+#### 2.1.2 源码编译Paddle-Lite生成opt工具
+
+模型优化需要Paddle-Lite的opt可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下：
+```
+# 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
+git clone https://github.com/PaddlePaddle/Paddle-Lite.git
+cd Paddle-Lite
+git checkout develop
+# 启动编译
+./lite/tools/build.sh build_optimize_tool
+```
+
+编译完成后，opt文件位于`build.opt/lite/api/`下，可通过如下方式查看opt的运行选项和使用方式；
+```
+cd build.opt/lite/api/
+./opt
+```
+
+`opt`的使用方式与参数与上面的`paddle_lite_opt`完全一致。
+
+
+#### 2.1.3 转换示例
+
+下面以PaddleClas的`MobileNetV3_large_x1_0`模型为例，介绍使用`paddle_lite_opt`完成预训练模型到inference模型，再到Paddle-Lite优化模型的转换。
+
+```shell
+# 进入PaddleClas根目录
+cd PaddleClas_root_path
+# 下载并解压预训练模型
+wget https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar
+tar -xf MobileNetV3_large_x1_0_pretrained
+# 将预训练模型导出为inference模型
+python tools/export_model.py -m MobileNetV3_large_x1_0 -p ./MobileNetV3_large_x1_0_pretrained/ -o ./MobileNetV3_large_x1_0_inference/
+# 将inference模型转化为Paddle-Lite优化模型
+paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_inference/model --param_file=./MobileNetV3_large_x1_0_inference/params --optimize_out=./MobileNetV3_large_x1_0
+```
+
+最终在当前文件夹下生成`MobileNetV3_large_x1_0.nb`的文件。
+
+
+<a name="2.2与手机联调"></a>
+### 2.2 与手机联调
+
+首先需要进行一些准备工作。
+1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI = arm7`。
+2. 打开手机的USB调试选项，选择文件传输模式，连接电脑。
+3. 电脑上安装adb工具，用于调试。 adb安装方式如下：
+
+    3.1. MAC电脑安装ADB:
+    ```
+    brew cask install android-platform-tools
+    ```
+    3.2. Linux安装ADB
+    ```
+    sudo apt update
+    sudo apt install -y wget adb
+    ```
+    3.3. Window安装ADB
+
+    win上安装需要去谷歌的安卓平台下载adb软件包进行安装：[链接](https://developer.android.com/studio)
+
+    打开终端，手机连接电脑，在终端中输入
+    ```
+    adb devices
+    ```
+    如果有device输出，则表示安装成功。
+    ```
+       List of devices attached
+       744be294    device
+    ```
+
+4. 准备优化后的模型、预测库文件、测试图像和类别映射文件。
+
+```shell
+cd PaddleClas_root_path
+cd deploy/lite/
+# 运行prepare.sh，准备预测库文件、测试图像和使用的字典文件，并放置在预测库中的demo/cxx/clas文件夹下
+sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
+
+# 进入lite demo的工作目录
+cd /{lite prediction library path}/inference_lite_lib.android.armv8/
+cd demo/cxx/clas/
+# 将C++预测动态库so文件复制到debug文件夹中
+cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+```
+
+ 准备测试图像，以`PaddleClas/deploy/lite/imgs/tabby_cat.jpg`为例，将测试的图像复制到`demo/cxx/clas/debug/`文件夹下。
+ 准备`paddle_lite_opt`工具优化后的模型文件，比如使用`MobileNetV3_large_x1_0.nb`，模型文件放置在`demo/cxx/clas/debug/`文件夹下。
+
+ 执行完成后，clas文件夹下将有如下文件格式：
+
+```shell
+demo/cxx/clas/
+|-- debug/
+|   |--MobileNetV3_large_x1_0.nb                优化后的文字方向分类器模型文件
+|   |--tabby_cat.jpg                           待测试图像
+|   |--imagenet1k_label_list.txt                类别映射文件
+|   |--libpaddle_light_api_shared.so    C++预测库文件
+|   |--config.txt                       分类预测超参数配置
+|-- config.txt                  分类预测超参数配置
+|-- image_classfication.cpp            图像分类代码文件
+|-- Makefile                    编译文件
+```
+
+#### 注意：
+1. `imagenet1k_label_list.txt`是ImageNet1k数据集的类别映射文件，如果是自定义的类别，需要更换该类别映射文件。
+
+2.  `config.txt` 包含了检测器、分类器的超参数，如下：
+```
+clas_model_file ./MobileNetV3_large_x1_0.nb # 模型文件地址
+label_path ./imagenet1k_label_list.txt # 类别映射文本文件
+resize_short_size 256 # resize之后的短边边长
+crop_size 224 # 裁剪后用于预测的边长
+visualize 0 # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为clas_result.png的图像文件。
+```
+
+3. 启动调试：上述步骤完成后就可以使用adb将文件push到手机上运行，步骤如下：
+
+```
+# 执行编译，得到可执行文件clas_system
+# clas_system可执行文件的使用方式为:
+# ./clas_system 配置文件路径  测试图像路径
+make -j
+# 将编译的可执行文件移动到debug文件夹中
+mv clas_system ./debug/
+# 将debug文件夹push到手机上
+adb push debug /data/local/tmp/
+adb shell
+cd /data/local/tmp/debug
+export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
+./clas_system ./config.txt ./tabby_cat.jpg
+ ```
+
+ 如果对代码做了修改，则需要重新编译并push到手机上。
+
+ 运行效果如下：
+
+<div align="center">
+    <img src="./imgs/lite_demo_result.png" width="600">
+</div>
+
+
+## FAQ
+Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？  
+A1：如果已经走通了上述步骤，更换模型只需要替换`.nb`模型文件即可，同时要注意修改下配置文件中的nb文件路径以及类别映射文件（如有必要）。
+
+Q2：换一个图测试怎么做？  
+A2：替换debug下的测试图像为你想要测试的图像，adb push 到手机上即可。