add raspberry support

deploy/openvino/CMakeLists.txt

@@ -105,10 +105,7 @@ if (NOT WIN32)
 endif()
 
 set(DEPS ${DEPS} ${OpenCV_LIBS})
-add_executable(classifier demo/classifier.cpp src/transforms.cpp src/paddlex.cpp)
+add_executable(classifier src/classifier.cpp src/transforms.cpp src/paddlex.cpp)
 ADD_DEPENDENCIES(classifier ext-yaml-cpp)
 target_link_libraries(classifier ${DEPS})
 
-add_executable(segmenter demo/segmenter.cpp src/transforms.cpp src/paddlex.cpp src/visualize.cpp)
-ADD_DEPENDENCIES(segmenter ext-yaml-cpp)
-target_link_libraries(segmenter ${DEPS})

deploy/openvino/CMakeSettings.json

@@ -37,9 +37,9 @@ namespace PaddleX {
 class Model {
  public:
   void Init(const std::string& model_dir,
-            const std::string& cfg_file,
+            const std::string& cfg_dir,
             std::string device) {
-    create_predictor(model_dir, cfg_file,  device);
+    create_predictor(model_dir, cfg_dir,  device);
   }
 
   void create_predictor(const std::string& model_dir,
@@ -48,18 +48,15 @@ class Model {
 
   bool load_config(const std::string& model_dir);
 
-  bool preprocess(cv::Mat* input_im, ImageBlob* inputs);
+  bool preprocess(cv::Mat* input_im);
 
   bool predict(const cv::Mat& im, ClsResult* result);
-  
-  bool predict(const cv::Mat& im, SegResult* result);
-
 
   std::string type;
   std::string name;
-  std::map<int, std::string> labels;
+  std::vector<std::string> labels;
   Transforms transforms_;
-  ImageBlob inputs_;
+  Blob::Ptr inputs_;
   Blob::Ptr output_;
   CNNNetwork network_;
   ExecutableNetwork executable_network_;

deploy/openvino/include/paddlex/results.h

@@ -61,7 +61,7 @@ class DetResult : public BaseResult {
 
 class SegResult : public BaseResult {
  public:
-  Mask<int> label_map;
+  Mask<int64_t> label_map;
   Mask<float> score_map;
   void clear() {
     label_map.clear();

deploy/openvino/include/paddlex/transforms.h

@@ -31,38 +31,11 @@ using namespace InferenceEngine;
 
 namespace PaddleX {
 
-/*
- * @brief
- * This class represents object for storing all preprocessed data
- * */
-class ImageBlob {
- public:
-  // Original image height and width
-  std::vector<int> ori_im_size_ = std::vector<int>(2);
-  // Newest image height and width after process
-  std::vector<int> new_im_size_ = std::vector<int>(2);
-  // Image height and width before resize
-  std::vector<std::vector<int>> im_size_before_resize_;
-  // Reshape order
-  std::vector<std::string> reshape_order_;
-  // Resize scale
-  float scale = 1.0;
-  // Buffer for image data after preprocessing
-  Blob::Ptr blob;
-
-  void clear() {
-    im_size_before_resize_.clear();
-    reshape_order_.clear();
-  }
-};
-
-
-
 // Abstraction of preprocessing opration class
 class Transform {
  public:
   virtual void Init(const YAML::Node& item) = 0;
-  virtual bool Run(cv::Mat* im, ImageBlob* data) = 0;
+  virtual bool Run(cv::Mat* im) = 0;
 };
 
 class Normalize : public Transform {
@@ -72,7 +45,7 @@ class Normalize : public Transform {
     std_ = item["std"].as<std::vector<float>>();
   }
 
-  virtual bool Run(cv::Mat* im, ImageBlob* data);
+  virtual bool Run(cv::Mat* im);
 
  private:
   std::vector<float> mean_;
@@ -89,7 +62,7 @@ class ResizeByShort : public Transform {
       max_size_ = -1;
     }
   };
-  virtual bool Run(cv::Mat* im, ImageBlob* data);
+  virtual bool Run(cv::Mat* im);
 
  private:
   float GenerateScale(const cv::Mat& im);
@@ -97,55 +70,6 @@ class ResizeByShort : public Transform {
   int max_size_;
 };
 
-/*
- * @brief
- * This class execute resize by long operation on image matrix. At first, it resizes
- * the long side of image matrix to specified length. Accordingly, the short side
- * will be resized in the same proportion.
- * */
-class ResizeByLong : public Transform {
- public:
-  virtual void Init(const YAML::Node& item) {
-    long_size_ = item["long_size"].as<int>();
-  }
-  virtual bool Run(cv::Mat* im, ImageBlob* data);
-
- private:
-  int long_size_;
-};
-
-/*
- * @brief
- * This class execute resize operation on image matrix. It resizes width and height
- * to specified length.
- * */
-class Resize : public Transform {
- public:
-  virtual void Init(const YAML::Node& item) {
-    if (item["interp"].IsDefined()) {
-      interp_ = item["interp"].as<std::string>();
-    }
-    if (item["target_size"].IsScalar()) {
-      height_ = item["target_size"].as<int>();
-      width_ = item["target_size"].as<int>();
-    } else if (item["target_size"].IsSequence()) {
-      std::vector<int> target_size = item["target_size"].as<std::vector<int>>();
-      width_ = target_size[0];
-      height_ = target_size[1];
-    }
-    if (height_ <= 0 || width_ <= 0) {
-      std::cerr << "[Resize] target_size should greater than 0" << std::endl;
-      exit(-1);
-    }
-  }
-  virtual bool Run(cv::Mat* im, ImageBlob* data);
-
- private:
-  int height_;
-  int width_;
-  std::string interp_;
-};
-
 
 class CenterCrop : public Transform {
  public:
@@ -159,53 +83,18 @@ class CenterCrop : public Transform {
       height_ = crop_size[1];
     }
   }
-  virtual bool Run(cv::Mat* im, ImageBlob* data);
+  virtual bool Run(cv::Mat* im);
 
  private:
   int height_;
   int width_;
 };
 
-
-/*
- * @brief
- * This class execute padding operation on image matrix. It makes border on edge
- * of image matrix.
- * */
-class Padding : public Transform {
- public:
-  virtual void Init(const YAML::Node& item) {
-    if (item["coarsest_stride"].IsDefined()) {
-      coarsest_stride_ = item["coarsest_stride"].as<int>();
-      if (coarsest_stride_ < 1) {
-        std::cerr << "[Padding] coarest_stride should greater than 0"
-                  << std::endl;
-        exit(-1);
-      }
-    }
-    if (item["target_size"].IsDefined()) {
-      if (item["target_size"].IsScalar()) {
-        width_ = item["target_size"].as<int>();
-        height_ = item["target_size"].as<int>();
-      } else if (item["target_size"].IsSequence()) {
-        width_ = item["target_size"].as<std::vector<int>>()[0];
-        height_ = item["target_size"].as<std::vector<int>>()[1];
-      }
-    }
-  }
-  virtual bool Run(cv::Mat* im, ImageBlob* data);
-
- private:
-  int coarsest_stride_ = -1;
-  int width_ = 0;
-  int height_ = 0;
-};
-
 class Transforms {
  public:
   void Init(const YAML::Node& node, bool to_rgb = true);
   std::shared_ptr<Transform> CreateTransform(const std::string& name);
-  bool Run(cv::Mat* im, ImageBlob* data);
+  bool Run(cv::Mat* im, Blob::Ptr blob);
 
  private:
   std::vector<std::shared_ptr<Transform>> transforms_;

deploy/openvino/python/deploy.py

@@ -1,160 +0,0 @@
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import sys
-import os
-import os.path as osp
-import cv2
-import numpy as np
-import yaml
-from six import text_type as _text_type
-from openvino.inference_engine import IECore
-from utils import logging 
-
-
-
-
-class Predictor:
-    def __init__(self,
-                 model_xml,
-                 model_yaml,
-                 device="CPU"):
-        self.device = device
-        if not osp.exists(model_xml):
-            logging.error("model xml file is not exists in {}".format(model_xml))
-        self.model_xml = model_xml
-        self.model_bin = osp.splitext(model_xml)[0] + ".bin"
-        if not osp.exists(model_yaml):
-            logging,error("model yaml file is not exists in {}".format(model_yaml))
-        with open(model_yaml) as f:
-            self.info = yaml.load(f.read(), Loader=yaml.Loader)
-        self.model_type = self.info['_Attributes']['model_type']
-        self.model_name = self.info['Model']
-        self.num_classes = self.info['_Attributes']['num_classes']
-        self.labels = self.info['_Attributes']['labels']
-        if self.info['Model'] == 'MaskRCNN':
-            if self.info['_init_params']['with_fpn']:
-                self.mask_head_resolution = 28
-            else:
-                self.mask_head_resolution = 14
-        transforms_mode = self.info.get('TransformsMode', 'RGB')
-        if transforms_mode == 'RGB':
-            to_rgb = True
-        else:
-            to_rgb = False
-        self.transforms = self.build_transforms(self.info['Transforms'], to_rgb)
-        self.predictor, self.net = self.create_predictor()
-
-
-
-
-    def create_predictor(self):
-
-        #initialization for specified device
-        logging.info("Creating Inference Engine")
-        ie = IECore()
-        logging.info("Loading network files:\n\t{}\n\t{}".format(self.model_xml, self.model_bin))
-        net = ie.read_network(model=self.model_xml, weights=self.model_bin)
-        net.batch_size = 1
-        exec_net = ie.load_network(network=net, device_name=self.device)
-        return exec_net, net
-
-
-    def build_transforms(self, transforms_info, to_rgb=True):
-        if self.model_type == "classifier":
-            import transforms.cls_transforms as transforms
-        elif self.model_type == "detector":
-            import transforms.det_transforms as transforms
-        elif self.model_type == "segmenter":
-            import transforms.seg_transforms as transforms
-        op_list = list()
-        for op_info in transforms_info:
-            op_name = list(op_info.keys())[0]
-            op_attr = op_info[op_name]
-            if not hasattr(transforms, op_name):
-                raise Exception(
-                    "There's no operator named '{}' in transforms of {}".
-                    format(op_name, self.model_type))
-            op_list.append(getattr(transforms, op_name)(**op_attr))
-        eval_transforms = transforms.Compose(op_list)
-        if hasattr(eval_transforms, 'to_rgb'):
-            eval_transforms.to_rgb = to_rgb
-        self.arrange_transforms(eval_transforms)
-        return eval_transforms    
- 
-    def arrange_transforms(self, eval_transforms):
-        if self.model_type == 'classifier':
-            import transforms.cls_transforms as transforms
-            arrange_transform = transforms.ArrangeClassifier
-        elif self.model_type == 'segmenter':
-            import transforms.det_transforms as transforms
-            arrange_transform = transforms.ArrangeSegmenter
-        elif self.model_type == 'detector':
-            import transforms.seg_transforms as transforms
-            arrange_name = 'Arrange{}'.format(self.model_name)
-            arrange_transform = getattr(transforms, arrange_name)
-        else:
-            raise Exception("Unrecognized model type: {}".format(
-                self.model_type))
-        if type(eval_transforms.transforms[-1]).__name__.startswith('Arrange'):
-            eval_transforms.transforms[-1] = arrange_transform(mode='test')
-        else:
-            eval_transforms.transforms.append(arrange_transform(mode='test'))
-
-
-    def raw_predict(self, images):
-        input_blob = next(iter(self.net.inputs))
-        out_blob = next(iter(self.net.outputs)) 
-        #Start sync inference
-        logging.info("Starting inference in synchronous mode")
-        res = self.predictor.infer(inputs={input_blob:images})
-    
-        #Processing output blob
-        logging.info("Processing output blob")
-        res = res[out_blob]
-        print("res: ",res)
-
-    def preprocess(self, image):
-        
-        if self.model_type == "classifier":
-            im, = self.transforms(image)
-            im = np.expand_dims(im, axis=0).copy()
-            #res['image'] = im
-        '''elif self.model_type == "detector":
-            if self.model_name == "YOLOv3":
-                im, im_shape = self.transforms(image)
-                im = np.expand_dims(im, axis=0).copy()
-                im_shape = np.expand_dims(im_shape, axis=0).copy()
-                res['image'] = im
-                res['im_size'] = im_shape
-            if self.model_name.count('RCNN') > 0:
-                im, im_resize_info, im_shape = self.transforms(image)
-                im = np.expand_dims(im, axis=0).copy()
-                im_resize_info = np.expand_dims(im_resize_info, axis=0).copy()
-                im_shape = np.expand_dims(im_shape, axis=0).copy()
-                res['image'] = im
-                res['im_info'] = im_resize_info
-                res['im_shape'] = im_shape
-        elif self.model_type == "segmenter":
-            im, im_info = self.transforms(image)
-            im = np.expand_dims(im, axis=0).copy()
-            res['image'] = im
-            res['im_info'] = im_info'''
-        return im
-
-
-    def predict(self, image, topk=1, threshold=0.5):
-        preprocessed_input = self.preprocess(image)
-        model_pred = self.raw_predict(preprocessed_input)
-

deploy/openvino/scripts/bootstrap.sh

@@ -1,9 +1,9 @@
 # openvino预编译库的路径
-OPENVINO_DIR=$INTEL_OPENVINO_DIR/inference_engine
+OPENVINO_DIR=/path/to/inference_engine/
 # gflags预编译库的路径
-GFLAGS_DIR=/wangsiyuan06/gflags/build
+GFLAGS_DIR=/path/to/gflags
 # ngraph lib的路径，编译openvino时通常会生成
-NGRAPH_LIB=$INTEL_OPENVINO_DIR/deployment_tools/ngraph/lib
+NGRAPH_LIB=/path/to/ngraph/lib/
 
 # opencv预编译库的路径, 如果使用自带预编译版本可不修改
 OPENCV_DIR=$(pwd)/deps/opencv3gcc4.8/

deploy/openvino/demo/classifier.cpp → deploy/openvino/src/classifier.cpp

@@ -22,7 +22,7 @@
 #include "include/paddlex/paddlex.h"
 
 DEFINE_string(model_dir, "", "Path of inference model");
-DEFINE_string(cfg_file, "", "Path of PaddelX model yml file");
+DEFINE_string(cfg_dir, "", "Path of inference model");
 DEFINE_string(device, "CPU", "Device name");
 DEFINE_string(image, "", "Path of test image file");
 DEFINE_string(image_list, "", "Path of test image list file");
@@ -35,8 +35,8 @@ int main(int argc, char** argv) {
     std::cerr << "--model_dir need to be defined" << std::endl;
     return -1;
   }
-  if (FLAGS_cfg_file == "") {
-    std::cerr << "--cfg_file need to be defined" << std::endl;
+  if (FLAGS_cfg_dir == "") {
+    std::cerr << "--cfg_dir need to be defined" << std::endl;
     return -1;
   }
   if (FLAGS_image == "" & FLAGS_image_list == "") {
@@ -46,7 +46,7 @@ int main(int argc, char** argv) {
 
   // 加载模型
   PaddleX::Model model;
-  model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_device);
+  model.Init(FLAGS_model_dir, FLAGS_cfg_dir, FLAGS_device);
 
   // 进行预测
   if (FLAGS_image_list != "") {
@@ -62,7 +62,7 @@ int main(int argc, char** argv) {
       model.predict(im, &result);
       std::cout << "Predict label: " << result.category
                 << ", label_id:" << result.category_id
-                << ", score: " << result.score <<  std::endl;
+                << ", score: " << result.score << std::endl;
     }
   } else {
     PaddleX::ClsResult result;

deploy/openvino/src/paddlex.cpp

@@ -13,8 +13,6 @@
 // limitations under the License.
 
 #include "include/paddlex/paddlex.h"
-#include <iostream>
-#include <fstream>
 
 using namespace InferenceEngine;
 
@@ -52,24 +50,20 @@ bool Model::load_config(const std::string& cfg_dir) {
   }
   // 构建数据处理流
   transforms_.Init(config["Transforms"], to_rgb);
-  // 读入label lis
-  for (const auto& item : config["_Attributes"]["labels"]) {
-    int index = labels.size();
-    labels[index] = item.as<std::string>();
-  }
-
+  // 读入label list
+  labels.clear();
+  labels = config["_Attributes"]["labels"].as<std::vector<std::string>>();
   return true;
 }
 
-bool Model::preprocess(cv::Mat* input_im, ImageBlob* inputs) {
-  if (!transforms_.Run(input_im, inputs)) {
+bool Model::preprocess(cv::Mat* input_im) {
+  if (!transforms_.Run(input_im, inputs_)) {
     return false;
   }
   return true;
 }
 
 bool Model::predict(const cv::Mat& im, ClsResult* result) {
-  inputs_.clear();
   if (type == "detector") {
     std::cerr << "Loading model is a 'detector', DetResult should be passed to "
                  "function predict()!"
@@ -84,17 +78,17 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
   // 处理输入图像
   InferRequest infer_request = executable_network_.CreateInferRequest();
   std::string input_name = network_.getInputsInfo().begin()->first;
-  inputs_.blob = infer_request.GetBlob(input_name);
-  cv::Mat im_clone = im.clone();
-  if (!preprocess(&im_clone, &inputs_)) {
+  inputs_ = infer_request.GetBlob(input_name);
+
+  auto im_clone = im.clone();
+  if (!preprocess(&im_clone)) {
     std::cerr << "Preprocess failed!" << std::endl;
     return false;
   }
-  
+
   infer_request.Infer();
 
   std::string output_name = network_.getOutputsInfo().begin()->first;
-  std::cout << "ouput node name" << output_name << std::endl;
   output_ = infer_request.GetBlob(output_name);
   MemoryBlob::CPtr moutput = as<MemoryBlob>(output_);
   auto moutputHolder = moutput->rmap();
@@ -105,122 +99,10 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
   result->category_id = std::distance(outputs_data, ptr);
   result->score = *ptr;
   result->category = labels[result->category_id];
+  //for (int i=0;i<sizeof(outputs_data);i++){
+  //    std::cout <<  labels[i] << std::endl;
+  //    std::cout <<  outputs_[i] << std::endl;
+  //    }
 }
 
-
-bool Model::predict(const cv::Mat& im, SegResult* result) {
-  result->clear();
-  inputs_.clear();
-  if (type == "classifier") {
-    std::cerr << "Loading model is a 'classifier', ClsResult should be passed "
-                 "to function predict()!" << std::endl;
-    return false;
-  } else if (type == "detector") {
-    std::cerr << "Loading model is a 'detector', DetResult should be passed to "
-                 "function predict()!" << std::endl;
-    return false;
-  }
-  //
-  InferRequest infer_request = executable_network_.CreateInferRequest();
-  std::string input_name = network_.getInputsInfo().begin()->first;
-  inputs_.blob = infer_request.GetBlob(input_name);
-  
-  //
-  cv::Mat im_clone = im.clone();
-  if (!preprocess(&im_clone, &inputs_)) {
-    std::cerr << "Preprocess failed!" << std::endl;
-    return false;
-  }
-
-  //
-  infer_request.Infer();
- 
-  OutputsDataMap out_map = network_.getOutputsInfo();
-  auto iter = out_map.begin();
-  iter++;
-  std::string output_name_score = iter->first;
-  Blob::Ptr output_score = infer_request.GetBlob(output_name_score);
-  MemoryBlob::CPtr moutput_score = as<MemoryBlob>(output_score);
-  TensorDesc blob_score = moutput_score->getTensorDesc();
-  std::vector<size_t> output_score_shape = blob_score.getDims();
-  int size = 1;
-  for (auto& i : output_score_shape) {
-    size *= static_cast<int>(i);
-    result->score_map.shape.push_back(static_cast<int>(i));
-  }
-  result->score_map.data.resize(size);
-  auto moutputHolder_score = moutput_score->rmap();
-  float* score_data = moutputHolder_score.as<float *>();
-  memcpy(result->score_map.data.data(),score_data,moutput_score->byteSize());
-
-  iter++;
-  std::string output_name_label = iter->first;
-  Blob::Ptr output_label = infer_request.GetBlob(output_name_label);
-  MemoryBlob::CPtr moutput_label = as<MemoryBlob>(output_label);
-  TensorDesc blob_label = moutput_label->getTensorDesc();
-  std::vector<size_t> output_label_shape = blob_label.getDims();
-  size = 1;
-  for (auto& i : output_label_shape) {
-    size *= static_cast<int>(i);
-    result->label_map.shape.push_back(static_cast<int>(i));
-  }
-  result->label_map.data.resize(size);
-  auto moutputHolder_label = moutput_label->rmap();
-  int* label_data = moutputHolder_label.as<int *>();
-  memcpy(result->label_map.data.data(),label_data,moutput_label->byteSize());
-
-
-  std::vector<uint8_t> label_map(result->label_map.data.begin(),
-                                 result->label_map.data.end());
-  cv::Mat mask_label(result->label_map.shape[1],
-                     result->label_map.shape[2],
-                     CV_8UC1,
-                     label_map.data());
-
-  cv::Mat mask_score(result->score_map.shape[2],
-                     result->score_map.shape[3],
-                     CV_32FC1,
-                     result->score_map.data.data());
-  int idx = 1;
-  int len_postprocess = inputs_.im_size_before_resize_.size();
-  for (std::vector<std::string>::reverse_iterator iter =
-           inputs_.reshape_order_.rbegin();
-       iter != inputs_.reshape_order_.rend();
-       ++iter) {
-    if (*iter == "padding") {
-      auto before_shape = inputs_.im_size_before_resize_[len_postprocess - idx];
-      inputs_.im_size_before_resize_.pop_back();
-      auto padding_w = before_shape[0];
-      auto padding_h = before_shape[1];
-      mask_label = mask_label(cv::Rect(0, 0, padding_h, padding_w));
-      mask_score = mask_score(cv::Rect(0, 0, padding_h, padding_w));
-    } else if (*iter == "resize") {
-      auto before_shape = inputs_.im_size_before_resize_[len_postprocess - idx];
-      inputs_.im_size_before_resize_.pop_back();
-      auto resize_w = before_shape[0];
-      auto resize_h = before_shape[1];
-      cv::resize(mask_label,
-                 mask_label,
-                 cv::Size(resize_h, resize_w),
-                 0,
-                 0,
-                 cv::INTER_NEAREST);
-      cv::resize(mask_score,
-                 mask_score,
-                 cv::Size(resize_h, resize_w),
-                 0,
-                 0,
-                 cv::INTER_LINEAR);
-    }
-    ++idx;
-  }
-  result->label_map.data.assign(mask_label.begin<uint8_t>(),
-                                mask_label.end<uint8_t>());
-  result->label_map.shape = {mask_label.rows, mask_label.cols};
-  result->score_map.data.assign(mask_score.begin<float>(),
-                                mask_score.end<float>());
-  result->score_map.shape = {mask_score.rows, mask_score.cols};
-  return true;
-}
-  
 }  // namespce of PaddleX

deploy/openvino/src/transforms.cpp

@@ -13,7 +13,6 @@
 // limitations under the License.
 
 #include <iostream>
-#include <fstream>
 #include <string>
 #include <vector>
 
@@ -27,7 +26,7 @@ std::map<std::string, int> interpolations = {{"LINEAR", cv::INTER_LINEAR},
                                              {"CUBIC", cv::INTER_CUBIC},
                                              {"LANCZOS4", cv::INTER_LANCZOS4}};
 
-bool Normalize::Run(cv::Mat* im, ImageBlob* data){
+bool Normalize::Run(cv::Mat* im){
   for (int h = 0; h < im->rows; h++) {
     for (int w = 0; w < im->cols; w++) {
       im->at<cv::Vec3f>(h, w)[0] =
@@ -41,6 +40,19 @@ bool Normalize::Run(cv::Mat* im, ImageBlob* data){
   return true;
 }
 
+bool CenterCrop::Run(cv::Mat* im) {
+  int height = static_cast<int>(im->rows);
+  int width = static_cast<int>(im->cols);
+  if (height < height_ || width < width_) {
+    std::cerr << "[CenterCrop] Image size less than crop size" << std::endl;
+    return false;
+  }
+  int offset_x = static_cast<int>((width - width_) / 2);
+  int offset_y = static_cast<int>((height - height_) / 2);
+  cv::Rect crop_roi(offset_x, offset_y, width_, height_);
+  *im = (*im)(crop_roi);
+  return true;
+}
 
 
 float ResizeByShort::GenerateScale(const cv::Mat& im) {
@@ -58,109 +70,11 @@ float ResizeByShort::GenerateScale(const cv::Mat& im) {
   return scale;
 }
 
-bool ResizeByShort::Run(cv::Mat* im, ImageBlob* data) {
-  data->im_size_before_resize_.push_back({im->rows, im->cols});
-  data->reshape_order_.push_back("resize");
-
+bool ResizeByShort::Run(cv::Mat* im) {
   float scale = GenerateScale(*im);
   int width = static_cast<int>(scale * im->cols);
   int height = static_cast<int>(scale * im->rows);
   cv::resize(*im, *im, cv::Size(width, height), 0, 0, cv::INTER_LINEAR);
-  
-  data->new_im_size_[0] = im->rows;
-  data->new_im_size_[1] = im->cols;
-  data->scale = scale;
-  
-  return true;
-}
-
-bool CenterCrop::Run(cv::Mat* im, ImageBlob* data) {
-  int height = static_cast<int>(im->rows);
-  int width = static_cast<int>(im->cols);
-  if (height < height_ || width < width_) {
-    std::cerr << "[CenterCrop] Image size less than crop size" << std::endl;
-    return false;
-  }
-  int offset_x = static_cast<int>((width - width_) / 2);
-  int offset_y = static_cast<int>((height - height_) / 2);
-  cv::Rect crop_roi(offset_x, offset_y, width_, height_);
-  *im = (*im)(crop_roi);
-  data->new_im_size_[0] = im->rows;
-  data->new_im_size_[1] = im->cols;
-  return true;
-}
-
-
-bool Padding::Run(cv::Mat* im, ImageBlob* data) {
-  data->im_size_before_resize_.push_back({im->rows, im->cols});
-  data->reshape_order_.push_back("padding");
-
-  int padding_w = 0;
-  int padding_h = 0;
-  if (width_ > 1 & height_ > 1) {
-    padding_w = width_ - im->cols;
-    padding_h = height_ - im->rows;
-  } else if (coarsest_stride_ >= 1) {
-    int h = im->rows;
-    int w = im->cols;
-    padding_h =
-        ceil(h * 1.0 / coarsest_stride_) * coarsest_stride_ - im->rows;
-    padding_w =
-        ceil(w * 1.0 / coarsest_stride_) * coarsest_stride_ - im->cols;
-  }
-
-  if (padding_h < 0 || padding_w < 0) {
-    std::cerr << "[Padding] Computed padding_h=" << padding_h
-              << ", padding_w=" << padding_w
-              << ", but they should be greater than 0." << std::endl;
-    return false;
-  }
-  cv::copyMakeBorder(
-      *im, *im, 0, padding_h, 0, padding_w, cv::BORDER_CONSTANT, cv::Scalar(0));
-  data->new_im_size_[0] = im->rows;
-  data->new_im_size_[1] = im->cols;
-  return true;
-}
-
-bool ResizeByLong::Run(cv::Mat* im, ImageBlob* data) {
-  if (long_size_ <= 0) {
-    std::cerr << "[ResizeByLong] long_size should be greater than 0"
-              << std::endl;
-    return false;
-  }
-  data->im_size_before_resize_.push_back({im->rows, im->cols});
-  data->reshape_order_.push_back("resize");
-  int origin_w = im->cols;
-  int origin_h = im->rows;
-
-  int im_size_max = std::max(origin_w, origin_h);
-  float scale =
-      static_cast<float>(long_size_) / static_cast<float>(im_size_max);
-  cv::resize(*im, *im, cv::Size(), scale, scale, cv::INTER_NEAREST);
-  data->new_im_size_[0] = im->rows;
-  data->new_im_size_[1] = im->cols;
-  data->scale = scale;
-  return true;
-}
-
-bool Resize::Run(cv::Mat* im, ImageBlob* data) {
-  if (width_ <= 0 || height_ <= 0) {
-    std::cerr << "[Resize] width and height should be greater than 0"
-              << std::endl;
-    return false;
-  }
-  if (interpolations.count(interp_) <= 0) {
-    std::cerr << "[Resize] Invalid interpolation method: '" << interp_ << "'"
-              << std::endl;
-    return false;
-  }
-  data->im_size_before_resize_.push_back({im->rows, im->cols});
-  data->reshape_order_.push_back("resize");
-
-  cv::resize(
-      *im, *im, cv::Size(width_, height_), 0, 0, interpolations[interp_]);
-  data->new_im_size_[0] = im->rows;
-  data->new_im_size_[1] = im->cols;
   return true;
 }
 
@@ -180,16 +94,10 @@ std::shared_ptr<Transform> Transforms::CreateTransform(
     const std::string& transform_name) {
   if (transform_name == "Normalize") {
     return std::make_shared<Normalize>();
-  } else if (transform_name == "ResizeByShort") {
-    return std::make_shared<ResizeByShort>();
   } else if (transform_name == "CenterCrop") {
     return std::make_shared<CenterCrop>();
-  } else if (transform_name == "Resize") {
-    return std::make_shared<Resize>();
-  } else if (transform_name == "Padding") {
-    return std::make_shared<Padding>();
-  } else if (transform_name == "ResizeByLong") {
-    return std::make_shared<ResizeByLong>();
+  } else if (transform_name == "ResizeByShort") {
+    return std::make_shared<ResizeByShort>();
   } else {
     std::cerr << "There's unexpected transform(name='" << transform_name
               << "')." << std::endl;
@@ -197,20 +105,15 @@ std::shared_ptr<Transform> Transforms::CreateTransform(
   }
 }
 
-bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
+bool Transforms::Run(cv::Mat* im, Blob::Ptr blob) {
   // 按照transforms中预处理算子顺序处理图像
   if (to_rgb_) {
     cv::cvtColor(*im, *im, cv::COLOR_BGR2RGB);
   }
   (*im).convertTo(*im, CV_32FC3);
-  
-  data->ori_im_size_[0] = im->rows;
-  data->ori_im_size_[1] = im->cols;
-  data->new_im_size_[0] = im->rows;
-  data->new_im_size_[1] = im->cols;
 
   for (int i = 0; i < transforms_.size(); ++i) {
-    if (!transforms_[i]->Run(im,data)) {
+    if (!transforms_[i]->Run(im)) {
       std::cerr << "Apply transforms to image failed!" << std::endl;
       return false;
     }
@@ -218,15 +121,13 @@ bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
 
   // 将图像由NHWC转为NCHW格式
   // 同时转为连续的内存块存储到Blob
-  
-  SizeVector blobSize = data->blob->getTensorDesc().getDims();
+  SizeVector blobSize = blob->getTensorDesc().getDims();
   const size_t width = blobSize[3];
   const size_t height = blobSize[2];
   const size_t channels = blobSize[1];
-  MemoryBlob::Ptr mblob = InferenceEngine::as<MemoryBlob>(data->blob);
+  MemoryBlob::Ptr mblob = InferenceEngine::as<MemoryBlob>(blob);
   auto mblobHolder = mblob->wmap();
   float *blob_data = mblobHolder.as<float *>();
-    
   for (size_t c = 0; c < channels; c++) {
       for (size_t  h = 0; h < height; h++) {
           for (size_t w = 0; w < width; w++) {

deploy/raspberry/CMakeLists.txt

@@ -0,0 +1,116 @@
+cmake_minimum_required(VERSION 3.0)
+project(PaddleX CXX C)
+
+
+option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static."   OFF)
+
+SET(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH})
+SET(LITE_DIR "" CACHE PATH "Location of libraries")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(NGRAPH_LIB "" CACHE PATH "Location of libraries")
+
+
+include(cmake/yaml-cpp.cmake)
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}")
+include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
+
+macro(safe_set_static_flag)
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+endmacro()
+
+if (NOT DEFINED LITE_DIR OR ${LITE_DIR} STREQUAL "")
+	message(FATAL_ERROR "please set LITE_DIR with -LITE_DIR=/path/influence_engine")
+endif()
+
+if (NOT DEFINED OPENCV_DIR OR ${OPENCV_DIR} STREQUAL "")
+    message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
+endif()
+
+if (NOT DEFINED GFLAGS_DIR OR ${GFLAGS_DIR} STREQUAL "")
+    message(FATAL_ERROR "please set GFLAGS_DIR with -DGFLAGS_DIR=/path/gflags")
+endif()
+
+
+
+
+
+link_directories("${LITE_DIR}/lib")
+include_directories("${LITE_DIR}/include")
+
+
+
+link_directories("${GFLAGS_DIR}/lib")
+include_directories("${GFLAGS_DIR}/include")
+
+
+
+
+if (WIN32)
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
+  unset(OpenCV_DIR CACHE)
+else ()
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/cmake NO_DEFAULT_PATH)
+endif ()
+
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    set(CMAKE_C_FLAGS_DEBUG   "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_C_FLAGS_RELEASE  "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
+    set(CMAKE_CXX_FLAGS_DEBUG  "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_CXX_FLAGS_RELEASE   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+    if (WITH_STATIC_LIB)
+        safe_set_static_flag()
+        add_definitions(-DSTATIC_LIB)
+    endif()
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfloat-abi=hard -mfpu=neon-vfpv4 -g -o2 -fopenmp -std=c++11")
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+endif()
+
+
+if(WITH_STATIC_LIB)
+	set(DEPS ${LITE_DIR}/lib/libpaddle_full_api_shared${CMAKE_STATIC_LIBRARY_SUFFIX})
+else()
+	set(DEPS ${LITE_DIR}/lib/libpaddle_full_api_shared${CMAKE_SHARED_LIBRARY_SUFFIX})
+endif()
+
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        glog gflags  z  yaml-cpp
+        )
+else()
+    set(DEPS ${DEPS}
+        glog gflags_static libprotobuf zlibstatic xxhash libyaml-cppmt)
+    set(DEPS ${DEPS} libcmt shlwapi)
+endif(NOT WIN32)
+
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+set(DEPS ${DEPS} ${OpenCV_LIBS})
+add_executable(classifier demo/classifier.cpp src/transforms.cpp src/paddlex.cpp)
+ADD_DEPENDENCIES(classifier ext-yaml-cpp)
+target_link_libraries(classifier ${DEPS})
+
+
+add_executable(segmenter demo/segmenter.cpp src/transforms.cpp src/paddlex.cpp src/visualize.cpp)
+ADD_DEPENDENCIES(segmenter ext-yaml-cpp)
+target_link_libraries(segmenter ${DEPS})
+
+add_executable(detector demo/detector.cpp src/transforms.cpp src/paddlex.cpp src/visualize.cpp)
+ADD_DEPENDENCIES(detector ext-yaml-cpp)
+target_link_libraries(detector ${DEPS})

deploy/raspberry/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+   6. Trademarks. This License does not grant permission to use the trade
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+   7. Disclaimer of Warranty. Unless required by applicable law or
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+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

deploy/raspberry/cmake/yaml-cpp.cmake

@@ -0,0 +1,30 @@
+find_package(Git REQUIRED)
+
+include(ExternalProject)
+
+message("${CMAKE_BUILD_TYPE}")
+
+ExternalProject_Add(
+        ext-yaml-cpp
+        URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
+        URL_MD5 9542d6de397d1fbd649ed468cb5850e6
+        CMAKE_ARGS
+        -DYAML_CPP_BUILD_TESTS=OFF
+        -DYAML_CPP_BUILD_TOOLS=OFF
+        -DYAML_CPP_INSTALL=OFF
+        -DYAML_CPP_BUILD_CONTRIB=OFF
+        -DMSVC_SHARED_RT=OFF
+        -DBUILD_SHARED_LIBS=OFF
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+        -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+        -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
+        -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+        -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        -DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
+        # Disable install step
+        INSTALL_COMMAND ""
+        LOG_DOWNLOAD ON
+        LOG_BUILD 1
+)
+

deploy/raspberry/demo/classifier.cpp

@@ -0,0 +1,79 @@
+//   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 <glog/logging.h>
+
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+
+#include "include/paddlex/paddlex.h"
+
+DEFINE_string(model_dir, "", "Path of inference model");
+DEFINE_string(cfg_dir, "", "Path of PaddelX model yml file");
+DEFINE_string(image, "", "Path of test image file");
+DEFINE_string(image_list, "", "Path of test image list file");
+DEFINE_int32(thread_num, 1, "num of thread to infer");
+
+int main(int argc, char** argv) {
+  // Parsing command-line
+  google::ParseCommandLineFlags(&argc, &argv, true);
+
+  if (FLAGS_model_dir == "") {
+    std::cerr << "--model_dir need to be defined" << std::endl;
+    return -1;
+  }
+  if (FLAGS_cfg_dir == "") {
+    std::cerr << "--cfg_dir need to be defined" << std::endl;
+    return -1;
+  }
+  if (FLAGS_image == "" & FLAGS_image_list == "") {
+    std::cerr << "--image or --image_list need to be defined" << std::endl;
+    return -1;
+  }
+
+  // 加载模型
+  PaddleX::Model model;
+  model.Init(FLAGS_model_dir, FLAGS_cfg_dir, FLAGS_thread_num);
+  std::cout << "init is done" << std::endl;
+  // 进行预测
+  if (FLAGS_image_list != "") {
+    std::ifstream inf(FLAGS_image_list);
+    if (!inf) {
+      std::cerr << "Fail to open file " << FLAGS_image_list << std::endl;
+      return -1;
+    }
+    std::string image_path;
+
+    while (getline(inf, image_path)) {
+      PaddleX::ClsResult result;
+      cv::Mat im = cv::imread(image_path, 1);
+      model.predict(im, &result);
+      std::cout << "Predict label: " << result.category
+                << ", label_id:" << result.category_id
+                << ", score: " << result.score 
+                << ", num_img: " << model.count_num_ << std::endl;
+    }
+  } else {
+    PaddleX::ClsResult result;
+    cv::Mat im = cv::imread(FLAGS_image, 1);
+    model.predict(im, &result);
+    std::cout << "Predict label: " << result.category
+              << ", label_id:" << result.category_id
+              << ", score: " << result.score << std::endl;
+  }
+
+  return 0;
+}

deploy/raspberry/demo/detector.cpp

@@ -0,0 +1,113 @@
+//   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 <glog/logging.h>
+#include <omp.h>
+
+#include <algorithm>
+#include <chrono>  // NOLINT
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+#include <utility>
+
+#include "include/paddlex/paddlex.h"
+#include "include/paddlex/visualize.h"
+
+using namespace std::chrono;  // NOLINT
+
+DEFINE_string(model_dir, "", "Path of openvino model xml file");
+DEFINE_string(cfg_dir, "", "Path of PaddleX model yaml file");
+DEFINE_string(image, "", "Path of test image file");
+DEFINE_string(image_list, "", "Path of test image list file");
+DEFINE_int32(thread_num, 1, "num of thread to infer");
+DEFINE_string(save_dir, "", "Path to save visualized image");
+DEFINE_int32(batch_size, 1, "Batch size of infering");
+DEFINE_double(threshold,
+              0.5,
+              "The minimum scores of target boxes which are shown");
+
+int main(int argc, char** argv) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  if (FLAGS_model_dir == "") {
+    std::cerr << "--model_dir need to be defined" << std::endl;
+    return -1;
+  }
+  if (FLAGS_cfg_dir == "") {
+    std::cerr << "--cfg_dir need to be defined" << std::endl;
+    return -1;
+  }
+  if (FLAGS_image == "" & FLAGS_image_list == "") {
+    std::cerr << "--image or --image_list need to be defined" << std::endl;
+    return -1;
+  }
+
+  //
+  PaddleX::Model model; 
+  model.Init(FLAGS_model_dir, FLAGS_cfg_dir, FLAGS_thread_num);
+
+  int imgs = 1;
+  auto colormap = PaddleX::GenerateColorMap(model.labels.size());
+  // 进行预测
+  if (FLAGS_image_list != "") {
+    std::ifstream inf(FLAGS_image_list);
+    if(!inf){
+      std::cerr << "Fail to open file " << FLAGS_image_list << std::endl;
+      return -1;
+    }
+    std::string image_path;
+
+    while (getline(inf, image_path)) {
+      PaddleX::DetResult result;
+      cv::Mat im = cv::imread(image_path, 1);
+      model.predict(im, &result);
+      if(FLAGS_save_dir != ""){
+        cv::Mat vis_img =
+          PaddleX::Visualize(im, result, model.labels, colormap, FLAGS_threshold);  
+        std::string save_path =
+          PaddleX::generate_save_path(FLAGS_save_dir, FLAGS_image);      
+        cv::imwrite(save_path, vis_img);
+        std::cout << "Visualized output saved as " << save_path << std::endl;
+      }
+    }
+  }else {
+  PaddleX::DetResult result;
+  cv::Mat im = cv::imread(FLAGS_image, 1);
+  model.predict(im, &result);
+  for (int i = 0; i < result.boxes.size(); ++i) {
+      std::cout << "image file: " << FLAGS_image << std::endl;
+      std::cout << ", predict label: " << result.boxes[i].category
+                << ", label_id:" << result.boxes[i].category_id
+                << ", score: " << result.boxes[i].score
+                << ", box(xmin, ymin, w, h):(" << result.boxes[i].coordinate[0]
+                << ", " << result.boxes[i].coordinate[1] << ", "
+                << result.boxes[i].coordinate[2] << ", "
+                << result.boxes[i].coordinate[3] << ")" << std::endl;
+    }
+    if(FLAGS_save_dir != ""){
+    // 可视化
+      cv::Mat vis_img =
+          PaddleX::Visualize(im, result, model.labels, colormap, FLAGS_threshold);
+      std::string save_path =
+          PaddleX::generate_save_path(FLAGS_save_dir, FLAGS_image);
+      cv::imwrite(save_path, vis_img);
+      result.clear();
+      std::cout << "Visualized output saved as " << save_path << std::endl;
+    }
+  }
+  return 0;
+}
+
+

deploy/openvino/demo/segmenter.cpp → deploy/raspberry/demo/segmenter.cpp

@@ -25,12 +25,12 @@
 
 
 DEFINE_string(model_dir, "", "Path of openvino model xml file");
-DEFINE_string(cfg_file, "", "Path of PaddleX model yaml file");
+DEFINE_string(cfg_dir, "", "Path of PaddleX model yaml file");
 DEFINE_string(image, "", "Path of test image file");
 DEFINE_string(image_list, "", "Path of test image list file");
-DEFINE_string(device, "CPU", "Device name");
 DEFINE_string(save_dir, "", "Path to save visualized image");
-
+DEFINE_int32(batch_size, 1, "Batch size of infering");
+DEFINE_int32(thread_num, 1, "num of thread to infer");
 
 int main(int argc, char** argv) {
   google::ParseCommandLineFlags(&argc, &argv, true);
@@ -38,8 +38,8 @@ int main(int argc, char** argv) {
     std::cerr << "--model_dir need to be defined" << std::endl;
     return -1;
   }
-  if (FLAGS_cfg_file == "") {
-    std::cerr << "--cfg_file need to be defined" << std::endl;
+  if (FLAGS_cfg_dir == "") {
+    std::cerr << "--cfg_dir need to be defined" << std::endl;
     return -1;
   }
   if (FLAGS_image == "" & FLAGS_image_list == "") {
@@ -48,8 +48,10 @@ int main(int argc, char** argv) {
   }
 
   //
+  std::cout << "init start" << std::endl;
   PaddleX::Model model; 
-  model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_device);
+  model.Init(FLAGS_model_dir, FLAGS_cfg_dir, FLAGS_thread_num);
+  std::cout << "init done" << std::endl;
   int imgs = 1;
   auto colormap = PaddleX::GenerateColorMap(model.labels.size());
   
@@ -60,6 +62,7 @@ int main(int argc, char** argv) {
     return -1;
     }
     std::string image_path;
+
     while (getline(inf, image_path)) {
       PaddleX::SegResult result;
       cv::Mat im = cv::imread(image_path, 1);
@@ -71,7 +74,9 @@ int main(int argc, char** argv) {
         cv::imwrite(save_path, vis_img);
         std::cout << "Visualized output saved as " << save_path << std::endl;
       }
+
     }
+
   }else{
     PaddleX::SegResult result;
     cv::Mat im = cv::imread(FLAGS_image, 1);

deploy/raspberry/include/paddlex/config_parser.h

@@ -0,0 +1,57 @@
+//   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 <iostream>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "yaml-cpp/yaml.h"
+
+#ifdef _WIN32
+#define OS_PATH_SEP "\\"
+#else
+#define OS_PATH_SEP "/"
+#endif
+
+namespace PaddleX {
+
+// Inference model configuration parser
+class ConfigPaser {
+ public:
+  ConfigPaser() {}
+
+  ~ConfigPaser() {}
+
+  bool load_config(const std::string& model_dir,
+                   const std::string& cfg = "model.yml") {
+    // Load as a YAML::Node
+    YAML::Node config;
+    config = YAML::LoadFile(model_dir + OS_PATH_SEP + cfg);
+
+    if (config["Transforms"].IsDefined()) {
+      YAML::Node transforms_ = config["Transforms"];
+    } else {
+      std::cerr << "There's no field 'Transforms' in model.yml" << std::endl;
+      return false;
+    }
+    return true;
+  }
+
+  YAML::Node Transforms_;
+};
+
+}  // namespace PaddleDetection

deploy/raspberry/include/paddlex/paddlex.h

@@ -0,0 +1,71 @@
+//   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 <functional>
+#include <iostream>
+#include <numeric>
+#include <chrono> 
+
+#include "yaml-cpp/yaml.h"
+
+#ifdef _WIN32
+#define OS_PATH_SEP "\\"
+#else
+#define OS_PATH_SEP "/"
+#endif
+
+#include "paddle_api.h"
+#include <arm_neon.h>
+#include "include/paddlex/config_parser.h"
+#include "include/paddlex/results.h"
+#include "include/paddlex/transforms.h"
+
+using namespace paddle::lite_api;
+
+namespace PaddleX {
+
+class Model {
+ public:
+  void Init(const std::string& model_dir,
+            const std::string& cfg_dir,
+            int thread_num) {
+    create_predictor(model_dir, cfg_dir, thread_num);
+  }
+
+  void create_predictor(const std::string& model_dir,
+                        const std::string& cfg_dir,
+                        int thread_num);
+
+  bool load_config(const std::string& model_dir);
+
+  bool preprocess(cv::Mat* input_im, ImageBlob* inputs);
+
+  bool predict(const cv::Mat& im, ClsResult* result);
+  
+  bool predict(const cv::Mat& im, DetResult* result);  
+
+  bool predict(const cv::Mat& im, SegResult* result);
+
+
+  std::string type;
+  std::string name;
+  std::map<int, std::string> labels;
+  Transforms transforms_;
+  ImageBlob inputs_;
+  std::shared_ptr<PaddlePredictor> predictor_;
+
+};
+}  // namespce of PaddleX

deploy/raspberry/include/paddlex/results.h

@@ -0,0 +1,71 @@
+//   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 <iostream>
+#include <string>
+#include <vector>
+
+namespace PaddleX {
+
+template <class T>
+struct Mask {
+  std::vector<T> data;
+  std::vector<int> shape;
+  void clear() {
+    data.clear();
+    shape.clear();
+  }
+};
+
+struct Box {
+  int category_id;
+  std::string category;
+  float score;
+  std::vector<float> coordinate;
+  Mask<float> mask;
+};
+
+class BaseResult {
+ public:
+  std::string type = "base";
+};
+
+class ClsResult : public BaseResult {
+ public:
+  int category_id;
+  std::string category;
+  float score;
+  std::string type = "cls";
+};
+
+class DetResult : public BaseResult {
+ public:
+  std::vector<Box> boxes;
+  int mask_resolution;
+  std::string type = "det";
+  void clear() { boxes.clear(); }
+};
+
+class SegResult : public BaseResult {
+ public:
+  Mask<int64_t> label_map;
+  Mask<float> score_map;
+  void clear() {
+    label_map.clear();
+    score_map.clear();
+  }
+};
+}  // namespce of PaddleX

deploy/raspberry/include/paddlex/transforms.h

@@ -0,0 +1,225 @@
+//   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 <yaml-cpp/yaml.h>
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "paddle_api.h"
+
+using namespace paddle::lite_api;
+
+namespace PaddleX {
+
+/*
+ * @brief
+ * This class represents object for storing all preprocessed data
+ * */
+class ImageBlob {
+ public:
+  // Original image height and width
+  std::vector<int> ori_im_size_ = std::vector<int>(2);
+
+  // Newest image height and width after process
+  std::vector<int> new_im_size_ = std::vector<int>(2);
+  // Image height and width before resize
+  std::vector<std::vector<int>> im_size_before_resize_;
+  // Reshape order
+  std::vector<std::string> reshape_order_;
+  // Resize scale
+  float scale = 1.0;
+  // Buffer for image data after preprocessing
+  std::unique_ptr<Tensor> input_tensor_;
+
+  void clear() {
+    im_size_before_resize_.clear();
+    reshape_order_.clear();
+  }
+};
+
+
+
+// Abstraction of preprocessing opration class
+class Transform {
+ public:
+  virtual void Init(const YAML::Node& item) = 0;
+  virtual bool Run(cv::Mat* im, ImageBlob* data) = 0;
+};
+
+class Normalize : public Transform {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    mean_ = item["mean"].as<std::vector<float>>();
+    std_ = item["std"].as<std::vector<float>>();
+  }
+
+  virtual bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<float> mean_;
+  std::vector<float> std_;
+};
+
+class ResizeByShort : public Transform {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    short_size_ = item["short_size"].as<int>();
+    if (item["max_size"].IsDefined()) {
+      max_size_ = item["max_size"].as<int>();
+    } else {
+      max_size_ = -1;
+    }
+  };
+  virtual bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  float GenerateScale(const cv::Mat& im);
+  int short_size_;
+  int max_size_;
+};
+
+/*
+ * @brief
+ * This class execute resize by long operation on image matrix. At first, it resizes
+ * the long side of image matrix to specified length. Accordingly, the short side
+ * will be resized in the same proportion.
+ * */
+class ResizeByLong : public Transform {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    long_size_ = item["long_size"].as<int>();
+  }
+  virtual bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int long_size_;
+};
+
+/*
+ * @brief
+ * This class execute resize operation on image matrix. It resizes width and height
+ * to specified length.
+ * */
+class Resize : public Transform {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    if (item["interp"].IsDefined()) {
+      interp_ = item["interp"].as<std::string>();
+    }
+    if (item["target_size"].IsScalar()) {
+      height_ = item["target_size"].as<int>();
+      width_ = item["target_size"].as<int>();
+    } else if (item["target_size"].IsSequence()) {
+      std::vector<int> target_size = item["target_size"].as<std::vector<int>>();
+      width_ = target_size[0];
+      height_ = target_size[1];
+    }
+    if (height_ <= 0 || width_ <= 0) {
+      std::cerr << "[Resize] target_size should greater than 0" << std::endl;
+      exit(-1);
+    }
+  }
+  virtual bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int height_;
+  int width_;
+  std::string interp_;
+};
+
+
+class CenterCrop : public Transform {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    if (item["crop_size"].IsScalar()) {
+      height_ = item["crop_size"].as<int>();
+      width_ = item["crop_size"].as<int>();
+    } else if (item["crop_size"].IsSequence()) {
+      std::vector<int> crop_size = item["crop_size"].as<std::vector<int>>();
+      width_ = crop_size[0];
+      height_ = crop_size[1];
+    }
+  }
+  virtual bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int height_;
+  int width_;
+};
+
+
+/*
+ * @brief
+ * This class execute padding operation on image matrix. It makes border on edge
+ * of image matrix.
+ * */
+class Padding : public Transform {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    if (item["coarsest_stride"].IsDefined()) {
+      coarsest_stride_ = item["coarsest_stride"].as<int>();
+      if (coarsest_stride_ < 1) {
+        std::cerr << "[Padding] coarest_stride should greater than 0"
+                  << std::endl;
+        exit(-1);
+      }
+    }
+    if (item["target_size"].IsDefined()) {
+      if (item["target_size"].IsScalar()) {
+        width_ = item["target_size"].as<int>();
+        height_ = item["target_size"].as<int>();
+      } else if (item["target_size"].IsSequence()) {
+        width_ = item["target_size"].as<std::vector<int>>()[0];
+        height_ = item["target_size"].as<std::vector<int>>()[1];
+      }
+    }
+    if (item["im_padding_value"].IsDefined()) {
+      im_value_ = item["im_padding_value"].as<std::vector<float>>();
+    }
+    else {
+      im_value_ = {0, 0, 0};
+    }
+  }
+  
+  virtual bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int coarsest_stride_ = -1;
+  int width_ = 0;
+  int height_ = 0;
+  std::vector<float> im_value_;
+};
+
+class Transforms {
+ public:
+  void Init(const YAML::Node& node, bool to_rgb = true);
+  std::shared_ptr<Transform> CreateTransform(const std::string& name);
+  bool Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<std::shared_ptr<Transform>> transforms_;
+  bool to_rgb_ = true;
+};
+
+}  // namespace PaddleX

deploy/openvino/python/convertor.py → deploy/raspberry/python/convertor.py

@@ -1,17 +1,3 @@
-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
 import os
 from six import text_type as _text_type
 import argparse

deploy/openvino/python/demo.py → deploy/raspberry/python/demo.py

@@ -27,13 +27,6 @@ def arg_parser():
         default=None,
         help="path to openvino model .xml file")
     parser.add_argument(
-        "--device",
-        "-d",
-        type=str,
-        default='CPU',
-        help="Specify the target device to infer on:[CPU, GPU, FPGA, HDDL, MYRIAD,HETERO]"
-             "Default value is CPU")
-    parser.add_argument(
         "--img",
         "-i",
         type=str,
@@ -49,27 +42,43 @@ def arg_parser():
 
 
     parser.add_argument(
-        "--cfg_file",
+        "--cfg_dir",
         "-c",
         type=str,
         default=None,
         help="Path to PaddelX model yml file")
 
 
+    parser.add_argument(
+        "--thread_num",
+        "-t",
+        type=int,
+        default=1,
+        help="Path to PaddelX model yml file")
+
+    parser.add_argument(
+        "--input_shape",
+        "-ip",
+        type=str,
+        default=None,
+        help=" image input shape of model [NCHW] like [1,3,224,244] ")
+
+
+
     return parser
 
 
 def main():
     parser = arg_parser()
     args = parser.parse_args()
-    model_xml = args.model_dir
-    model_yaml = args.cfg_file
-
+    model_nb = args.model_dir
+    model_yaml = args.cfg_dir
+    thread_num = args.thread_num
+    input_shape = args.input_shape
+    input_shape = input_shape[1:-1].split(",",3)
+    shape = list(map(int,input_shape))
     #model init
-    if("CPU" not in args.device):
-        predictor = deploy.Predictor(model_xml,model_yaml,args.device)
-    else:
-        predictor = deploy.Predictor(model_xml,model_yaml)
+    predictor = deploy.Predictor(model_nb,model_yaml,thread_num,shape)
     
     #predict
     if(args.img_list != None):

deploy/raspberry/scripts/build.sh

@@ -0,0 +1,22 @@
+# Paddle-Lite预编译库的路径
+LITE_DIR=/path/to/Paddle-Lite/inference/lib
+
+# gflags预编译库的路径
+GFLAGS_DIR=$(pwd)/deps/gflags
+# glog预编译库的路径
+GLOG_DIR=$(pwd)/deps/glog
+
+# opencv预编译库的路径, 如果使用自带预编译版本可不修改
+OPENCV_DIR=$(pwd)/deps/opencv
+# 下载自带预编译版本
+exec $(pwd)/scripts/install_third-party.sh
+
+rm -rf build
+mkdir -p build
+cd build
+cmake .. \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DGFLAGS_DIR=${GFLAGS_DIR} \
+    -DLITE_DIR=${LITE_DIR} \
+    -DCMAKE_CXX_FLAGS="-march=armv7-a"  
+make

deploy/raspberry/scripts/install_third-party.sh

@@ -0,0 +1,32 @@
+# download third-part lib
+if [ ! -d "./deps" ]; then
+    mkdir deps
+fi
+if [ ! -d "./deps/gflag" ]; then
+    cd deps
+    git clone https://github.com/gflags/gflags
+    cd gflags
+    cmake .
+    make -j 4
+    cd ..
+    cd ..
+fi
+if [ ! -d "./deps/glog" ]; then
+    cd deps
+    git clone https://github.com/google/glog
+    sudo apt-get install autoconf automake libtool
+    cd glog
+    ./autogen.sh
+    ./configure
+    make -j 4
+    cd ..
+    cd ..
+fi
+OPENCV_URL=https://bj.bcebos.com/paddlex/deploy/armopencv/opencv.tar.bz2
+if [ ! -d "./deps/opencv" ]; then
+    cd deps
+    wget -c ${OPENCV_URL}
+    tar xvfj opencv.tar.bz2
+    rm -rf opencv.tar.bz2
+    cd ..
+fi

deploy/raspberry/src/paddlex.cpp

@@ -0,0 +1,276 @@
+//   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 "include/paddlex/paddlex.h"
+#include <iostream>
+#include <fstream>
+
+using namespace paddle::lite_api;
+
+namespace PaddleX {
+
+void Model::create_predictor(const std::string& model_dir,
+                            const std::string& cfg_dir,
+                            int thread_num) {
+  MobileConfig config;
+  config.set_model_from_file(model_dir);
+  config.set_threads(thread_num);  
+  load_config(cfg_dir);
+  predictor_ = CreatePaddlePredictor<MobileConfig>(config);
+}
+
+bool Model::load_config(const std::string& cfg_dir) {
+  YAML::Node config = YAML::LoadFile(cfg_dir);
+  type = config["_Attributes"]["model_type"].as<std::string>();
+  name = config["Model"].as<std::string>();
+  bool to_rgb = true;
+  if (config["TransformsMode"].IsDefined()) {
+    std::string mode = config["TransformsMode"].as<std::string>();
+    if (mode == "BGR") {
+      to_rgb = false;
+    } else if (mode != "RGB") {
+      std::cerr << "[Init] Only 'RGB' or 'BGR' is supported for TransformsMode"
+                << std::endl;
+      return false;
+    }
+  }
+  // 构建数据处理流
+  transforms_.Init(config["Transforms"], to_rgb);
+  // 读入label lis
+  for (const auto& item : config["_Attributes"]["labels"]) {
+    int index = labels.size();
+    labels[index] = item.as<std::string>();
+  }
+
+  return true;
+}
+
+bool Model::preprocess(cv::Mat* input_im, ImageBlob* inputs) {
+  if (!transforms_.Run(input_im, inputs)) {
+    return false;
+  }
+  return true;
+}
+
+bool Model::predict(const cv::Mat& im, ClsResult* result) {
+  inputs_.clear();
+  if (type == "detector") {
+    std::cerr << "Loading model is a 'detector', DetResult should be passed to "
+                 "function predict()!"
+              << std::endl;
+    return false;
+  } else if (type == "segmenter") {
+    std::cerr << "Loading model is a 'segmenter', SegResult should be passed "
+                 "to function predict()!"
+              << std::endl;
+    return false;
+  }
+  // 处理输入图像
+  inputs_.input_tensor_ = std::move(predictor_->GetInput(0));
+  cv::Mat im_clone = im.clone();
+  if (!preprocess(&im_clone, &inputs_)) {
+    std::cerr << "Preprocess failed!" << std::endl;
+    return false;
+  }
+  
+  ;
+  predictor_->Run();
+
+
+  std::unique_ptr<const Tensor> output_tensor(std::move(predictor_->GetOutput(0)));
+  const float *outputs_data = output_tensor->mutable_data<float>();
+
+
+  // 对模型输出结果进行后处理
+  auto ptr = std::max_element(outputs_data, outputs_data+sizeof(outputs_data));
+  result->category_id = std::distance(outputs_data, ptr);
+  result->score = *ptr;
+  result->category = labels[result->category_id];
+  //for (int i=0;i<sizeof(outputs_data);i++){
+  //    std::cout <<  labels[i] << std::endl;
+  //    std::cout <<  outputs_[i] << std::endl;
+  //    }
+}
+
+bool Model::predict(const cv::Mat& im, DetResult* result) {
+  inputs_.clear();
+  result->clear();
+  if (type == "classifier") {
+    std::cerr << "Loading model is a 'classifier', ClsResult should be passed "
+                 "to function predict()!" << std::endl;
+    return false;
+  } else if (type == "segmenter") {
+    std::cerr << "Loading model is a 'segmenter', SegResult should be passed "
+                 "to function predict()!" << std::endl;
+    return false;
+  }
+  
+  inputs_.input_tensor_ = std::move(predictor_->GetInput(0));
+
+  cv::Mat im_clone = im.clone();
+  if (!preprocess(&im_clone, &inputs_)) {
+    std::cerr << "Preprocess failed!" << std::endl;
+    return false;
+  }
+  int h = inputs_.new_im_size_[0];
+  int w = inputs_.new_im_size_[1];
+  if (name == "YOLOv3") {
+    std::unique_ptr<Tensor> im_size_tensor(std::move(predictor_->GetInput(1)));
+    const std::vector<int64_t> IM_SIZE_SHAPE = {1,2};
+    im_size_tensor->Resize(IM_SIZE_SHAPE);
+    auto *im_size_data = im_size_tensor->mutable_data<int>();
+    memcpy(im_size_data, inputs_.ori_im_size_.data(), 1*2*sizeof(int));
+  }
+  
+  
+  predictor_->Run();
+ 
+  
+
+  auto output_names = predictor_->GetOutputNames();
+  auto output_box_tensor = predictor_->GetTensor(output_names[0]);
+  const float *output_box = output_box_tensor->mutable_data<float>();
+  std::vector<int64_t> output_box_shape = output_box_tensor->shape();
+  int size = 1;
+  for (const auto& i : output_box_shape) {
+    size *= i;
+  }
+  int num_boxes = size / 6;
+  for (int i = 0; i < num_boxes; ++i) {
+    Box box;
+    box.category_id = static_cast<int>(round(output_box[i * 6]));
+    box.category = labels[box.category_id];
+    box.score = output_box[i * 6 + 1];
+    float xmin = output_box[i * 6 + 2];
+    float ymin = output_box[i * 6 + 3];
+    float xmax = output_box[i * 6 + 4];
+    float ymax = output_box[i * 6 + 5];
+    float w = xmax - xmin + 1;
+    float h = ymax - ymin + 1;
+    box.coordinate = {xmin, ymin, w, h};
+    result->boxes.push_back(std::move(box));
+  }
+  return true;
+}
+
+
+bool Model::predict(const cv::Mat& im, SegResult* result) {
+  result->clear();
+  inputs_.clear();
+  if (type == "classifier") {
+    std::cerr << "Loading model is a 'classifier', ClsResult should be passed "
+                 "to function predict()!" << std::endl;
+    return false;
+  } else if (type == "detector") {
+    std::cerr << "Loading model is a 'detector', DetResult should be passed to "
+                 "function predict()!" << std::endl;
+    return false;
+  }
+ 
+  inputs_.input_tensor_ = std::move(predictor_->GetInput(0));
+
+  
+  cv::Mat im_clone = im.clone();
+  if (!preprocess(&im_clone, &inputs_)) {
+    std::cerr << "Preprocess failed!" << std::endl;
+    return false;
+  }
+  std::cout << "Preprocess is done" << std::endl;
+ 
+
+  predictor_->Run();
+
+ 
+
+  auto output_names = predictor_->GetOutputNames();
+
+  auto output_label_tensor = predictor_->GetTensor(output_names[0]);
+  std::cout << "output0" << output_names[0] << std::endl; 
+  std::cout << "output1" << output_names[1] << std::endl; 
+  const int64_t *label_data = output_label_tensor->mutable_data<int64_t>();
+  std::vector<int64_t> output_label_shape = output_label_tensor->shape();
+  int size = 1;
+  for (const auto& i : output_label_shape) {
+    size *= i;
+    result->label_map.shape.push_back(i);
+  }
+  result->label_map.data.resize(size);
+  memcpy(result->label_map.data.data(), label_data, size*sizeof(int64_t));
+
+  auto output_score_tensor = predictor_->GetTensor(output_names[1]);
+  const float *score_data = output_score_tensor->mutable_data<float>();
+  std::vector<int64_t> output_score_shape = output_score_tensor->shape();
+  size = 1;
+  for (const auto& i : output_score_shape) {
+    size *= i;
+    result->score_map.shape.push_back(i);
+  }
+  result->score_map.data.resize(size);
+  memcpy(result->score_map.data.data(), score_data, size*sizeof(float));
+
+
+  std::vector<uint8_t> label_map(result->label_map.data.begin(),
+                                 result->label_map.data.end());
+  cv::Mat mask_label(result->label_map.shape[1],
+                     result->label_map.shape[2],
+                     CV_8UC1,
+                     label_map.data());
+
+  cv::Mat mask_score(result->score_map.shape[2],
+                     result->score_map.shape[3],
+                     CV_32FC1,
+                     result->score_map.data.data());
+  int idx = 1;
+  int len_postprocess = inputs_.im_size_before_resize_.size();
+  for (std::vector<std::string>::reverse_iterator iter =
+           inputs_.reshape_order_.rbegin();
+       iter != inputs_.reshape_order_.rend();
+       ++iter) {
+    if (*iter == "padding") {
+      auto before_shape = inputs_.im_size_before_resize_[len_postprocess - idx];
+      inputs_.im_size_before_resize_.pop_back();
+      auto padding_w = before_shape[0];
+      auto padding_h = before_shape[1];
+      mask_label = mask_label(cv::Rect(0, 0, padding_h, padding_w));
+      mask_score = mask_score(cv::Rect(0, 0, padding_h, padding_w));
+    } else if (*iter == "resize") {
+      auto before_shape = inputs_.im_size_before_resize_[len_postprocess - idx];
+      inputs_.im_size_before_resize_.pop_back();
+      auto resize_w = before_shape[0];
+      auto resize_h = before_shape[1];
+      cv::resize(mask_label,
+                 mask_label,
+                 cv::Size(resize_h, resize_w),
+                 0,
+                 0,
+                 cv::INTER_NEAREST);
+      cv::resize(mask_score,
+                 mask_score,
+                 cv::Size(resize_h, resize_w),
+                 0,
+                 0,
+                 cv::INTER_LINEAR);
+    }
+    ++idx;
+  }
+  result->label_map.data.assign(mask_label.begin<uint8_t>(),
+                                mask_label.end<uint8_t>());
+  result->label_map.shape = {mask_label.rows, mask_label.cols};
+  result->score_map.data.assign(mask_score.begin<float>(),
+                                mask_score.end<float>());
+  result->score_map.shape = {mask_score.rows, mask_score.cols};
+  return true;
+}
+ 
+}  // namespce of PaddleX

deploy/raspberry/src/transforms.cpp

@@ -0,0 +1,240 @@
+//   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 <iostream>
+#include <string>
+#include <vector>
+#include <math.h>
+
+#include "include/paddlex/transforms.h"
+
+namespace PaddleX {
+
+std::map<std::string, int> interpolations = {{"LINEAR", cv::INTER_LINEAR},
+                                             {"NEAREST", cv::INTER_NEAREST},
+                                             {"AREA", cv::INTER_AREA},
+                                             {"CUBIC", cv::INTER_CUBIC},
+                                             {"LANCZOS4", cv::INTER_LANCZOS4}};
+
+bool Normalize::Run(cv::Mat* im, ImageBlob* data){
+  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] / 255.0 - mean_[0]) / std_[0];
+      im->at<cv::Vec3f>(h, w)[1] =
+          (im->at<cv::Vec3f>(h, w)[1] / 255.0 - mean_[1]) / std_[1];
+      im->at<cv::Vec3f>(h, w)[2] =
+          (im->at<cv::Vec3f>(h, w)[2] / 255.0 - mean_[2]) / std_[2];
+    }
+  }
+  return true;
+}
+
+
+
+float ResizeByShort::GenerateScale(const cv::Mat& im) {
+  int origin_w = im.cols;
+  int origin_h = im.rows;
+  int im_size_max = std::max(origin_w, origin_h);
+  int im_size_min = std::min(origin_w, origin_h);
+  float scale =
+      static_cast<float>(short_size_) / static_cast<float>(im_size_min);
+  if (max_size_ > 0) {
+    if (round(scale * im_size_max) > max_size_) {
+      scale = static_cast<float>(max_size_) / static_cast<float>(im_size_max);
+    }
+  }
+  return scale;
+}
+
+bool ResizeByShort::Run(cv::Mat* im, ImageBlob* data) {
+  data->im_size_before_resize_.push_back({im->rows, im->cols});
+  data->reshape_order_.push_back("resize");
+
+  float scale = GenerateScale(*im);
+  int width = static_cast<int>(round(scale * im->cols));
+  int height = static_cast<int>(round(scale * im->rows));
+  cv::resize(*im, *im, cv::Size(width, height), 0, 0, cv::INTER_LINEAR);
+  
+  data->new_im_size_[0] = im->rows;
+  data->new_im_size_[1] = im->cols;
+  data->scale = scale;
+  
+  return true;
+}
+
+bool CenterCrop::Run(cv::Mat* im, ImageBlob* data) {
+  int height = static_cast<int>(im->rows);
+  int width = static_cast<int>(im->cols);
+  if (height < height_ || width < width_) {
+    std::cerr << "[CenterCrop] Image size less than crop size" << std::endl;
+    return false;
+  }
+  int offset_x = static_cast<int>((width - width_) / 2);
+  int offset_y = static_cast<int>((height - height_) / 2);
+  cv::Rect crop_roi(offset_x, offset_y, width_, height_);
+  *im = (*im)(crop_roi);
+  data->new_im_size_[0] = im->rows;
+  data->new_im_size_[1] = im->cols;
+  return true;
+}
+
+
+bool Padding::Run(cv::Mat* im, ImageBlob* data) {
+  data->im_size_before_resize_.push_back({im->rows, im->cols});
+  data->reshape_order_.push_back("padding");
+
+  int padding_w = 0;
+  int padding_h = 0;
+  if (width_ > 1 & height_ > 1) {
+    padding_w = width_ - im->cols;
+    padding_h = height_ - im->rows;
+  } else if (coarsest_stride_ >= 1) {
+    int h = im->rows;
+    int w = im->cols;
+    padding_h =
+        ceil(h * 1.0 / coarsest_stride_) * coarsest_stride_ - im->rows;
+    padding_w =
+        ceil(w * 1.0 / coarsest_stride_) * coarsest_stride_ - im->cols;
+  }
+
+  if (padding_h < 0 || padding_w < 0) {
+    std::cerr << "[Padding] Computed padding_h=" << padding_h
+              << ", padding_w=" << padding_w
+              << ", but they should be greater than 0." << std::endl;
+    return false;
+  }
+  cv::Scalar value = cv::Scalar(im_value_[0], im_value_[1], im_value_[2]);
+  cv::copyMakeBorder(
+      *im, *im, 0, padding_h, 0, padding_w, cv::BORDER_CONSTANT, value);
+  data->new_im_size_[0] = im->rows;
+  data->new_im_size_[1] = im->cols;
+  return true;
+}
+
+bool ResizeByLong::Run(cv::Mat* im, ImageBlob* data) {
+  if (long_size_ <= 0) {
+    std::cerr << "[ResizeByLong] long_size should be greater than 0"
+              << std::endl;
+    return false;
+  }
+  data->im_size_before_resize_.push_back({im->rows, im->cols});
+  data->reshape_order_.push_back("resize");
+  int origin_w = im->cols;
+  int origin_h = im->rows;
+
+  int im_size_max = std::max(origin_w, origin_h);
+  float scale =
+      static_cast<float>(long_size_) / static_cast<float>(im_size_max);
+  cv::resize(*im, *im, cv::Size(), scale, scale, cv::INTER_NEAREST);
+  data->new_im_size_[0] = im->rows;
+  data->new_im_size_[1] = im->cols;
+  data->scale = scale;
+  return true;
+}
+
+bool Resize::Run(cv::Mat* im, ImageBlob* data) {
+  if (width_ <= 0 || height_ <= 0) {
+    std::cerr << "[Resize] width and height should be greater than 0"
+              << std::endl;
+    return false;
+  }
+  if (interpolations.count(interp_) <= 0) {
+    std::cerr << "[Resize] Invalid interpolation method: '" << interp_ << "'"
+              << std::endl;
+    return false;
+  }
+  data->im_size_before_resize_.push_back({im->rows, im->cols});
+  data->reshape_order_.push_back("resize");
+
+  cv::resize(
+      *im, *im, cv::Size(width_, height_), 0, 0, interpolations[interp_]);
+  data->new_im_size_[0] = im->rows;
+  data->new_im_size_[1] = im->cols;
+  return true;
+}
+
+void Transforms::Init(const YAML::Node& transforms_node, bool to_rgb) {
+  transforms_.clear();
+  to_rgb_ = to_rgb;
+  for (const auto& item : transforms_node) {
+    std::string name = item.begin()->first.as<std::string>();
+    std::cout << "trans name: " << name << std::endl;
+    std::shared_ptr<Transform> transform = CreateTransform(name);
+    transform->Init(item.begin()->second);
+    transforms_.push_back(transform);
+  }
+}
+
+std::shared_ptr<Transform> Transforms::CreateTransform(
+    const std::string& transform_name) {
+  if (transform_name == "Normalize") {
+    return std::make_shared<Normalize>();
+  } else if (transform_name == "ResizeByShort") {
+    return std::make_shared<ResizeByShort>();
+  } else if (transform_name == "CenterCrop") {
+    return std::make_shared<CenterCrop>();
+  } else if (transform_name == "Resize") {
+    return std::make_shared<Resize>();
+  } else if (transform_name == "Padding") {
+    return std::make_shared<Padding>();
+  } else if (transform_name == "ResizeByLong") {
+    return std::make_shared<ResizeByLong>();
+  } else {
+    std::cerr << "There's unexpected transform(name='" << transform_name
+              << "')." << std::endl;
+    exit(-1);
+  }
+}
+
+bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
+  // 按照transforms中预处理算子顺序处理图像
+  if (to_rgb_) {
+    cv::cvtColor(*im, *im, cv::COLOR_BGR2RGB);
+  }
+  (*im).convertTo(*im, CV_32FC3);
+  
+  data->ori_im_size_[0] = im->rows;
+  data->ori_im_size_[1] = im->cols;
+  data->new_im_size_[0] = im->rows;
+  data->new_im_size_[1] = im->cols;
+
+  for (int i = 0; i < transforms_.size(); ++i) {
+    if (!transforms_[i]->Run(im, data)) {
+      std::cerr << "Apply transforms to image failed!" << std::endl;
+      return false;
+    }
+  }
+
+  // 将图像由NHWC转为NCHW格式
+  // 同时转为连续的内存块存储到Blob
+  
+  int height = im->rows;
+  int width = im->cols;
+  int channels = im->channels();  
+  const std::vector<int64_t> INPUT_SHAPE = {1, channels, height, width};
+  data->input_tensor_->Resize(INPUT_SHAPE);
+  auto *input_data = data->input_tensor_->mutable_data<float>();
+
+  for (size_t c = 0; c < channels; c++) {
+      for (size_t  h = 0; h < height; h++) {
+          for (size_t w = 0; w < width; w++) {
+              input_data[c * width * height + h * width + w] =
+                      im->at<cv::Vec3f>(h, w)[c];
+          }
+      }
+  }
+  return true;
+}
+}  // namespace PaddleX

docs/deploy/raspberry/Raspberry.md

@@ -0,0 +1,154 @@
+# 树莓派
+PaddleX支持通过Paddle-Lite和基于OpenVINO的神经计算棒(NCS2)这两种方式在树莓派上完成预测部署。
+
+
+## 硬件环境配置  
+
+对于尚未安装系统的树莓派首先需要进行系统安装、环境配置等步奏来初始化硬件环境，过程中需要的软硬件如下： 
+
+- 硬件：micro SD，显示器，键盘，鼠标
+- 软件：Raspbian OS
+### Step1：系统安装
+- 格式化micro SD卡为FAT格式，Windows和Mac下建议使用[SD Memory Card Formatter](https://www.sdcard.org/downloads/formatter/)工具，Linux下请参考[NOOBS For Raspberry Pi](http://qdosmsq.dunbar-it.co.uk/blog/2013/06/noobs-for-raspberry-pi/)  
+- 下载NOOBS版本的Raspbian OS [下载地址](https://www.raspberrypi.org/downloads/)并将解压后的文件复制到SD中，插入SD到树莓派上通电，然后将自动安装系统
+### Step2：环境配置
+- 启用VNC和SSH服务：打开LX终端输入，输入如下命令，选择Interfacing Option然后选择P2 SSH 和 P3 VNC分别打开SSH与VNC。打开后就可以通过SSH或者VNC的方式连接树莓派
+```
+sudo raspi-config
+```
+- 更换源：由于树莓派官方源速度很慢，建议在官网查询国内源 [树莓派软件源](https://www.jianshu.com/p/67b9e6ebf8a0)。更换后执行
+```
+sudo apt-get update
+sudo apt-get upgrade
+```
+
+## Paddle-Lite部署
+基于Paddle-Lite的部署目前可以支持PaddleX的分类、分割与检测模型。部署的流程包括：PaddleX模型转换与转换后的模型部署  
+
+**说明**：PaddleX安装请参考[PaddleX](https://paddlex.readthedocs.io/zh_CN/latest/install.html)，Paddle-Lite详细资料请参考[Paddle-Lite](https://paddle-lite.readthedocs.io/zh/latest/index.html)
+
+请确保系统已经安装好上述基本软件，并配置好相应环境，**下面所有示例以工作目录 `/root/projects/`演示**。
+
+## Paddle-Lite模型转换
+将PaddleX模型转换为Paddle-Lite模型，具体请参考[Paddle-Lite模型转换](./export_nb_model.md)
+
+## Paddle-Lite 预测
+### Step1 下载PaddleX预测代码
+```
+mkdir -p /root/projects
+cd /root/projects
+git clone https://github.com/PaddlePaddle/PaddleX.git
+```
+**说明**：其中C++预测代码在PaddleX/deploy/raspberry 目录，该目录不依赖任何PaddleX下其他目录，如果需要在python下预测部署请参考[python预测部署](./python.md)。  
+
+### Step2：Paddle-Lite预编译库下载
+提供了下载的opt工具对应的Paddle-Lite在ArmLinux下面的预编译库:[Paddle-Lite(ArmLinux)预编译库](https://bj.bcebos.com/paddlex/deploy/lite/inference_lite_2.6.1_armlinux.tar.bz2)。建议用户使用预编译库，若需要自行编译，在树莓派上LX终端输入
+```
+git clone https://github.com/PaddlePaddle/Paddle-Lite.git
+cd Paddle-Lite
+sudo ./lite/tools/build.sh  --arm_os=armlinux --arm_abi=armv7hf --arm_lang=gcc  --build_extra=ON full_publish
+```  
+
+预编库位置：`./build.lite.armlinux.armv7hf.gcc/inference_lite_lib.armlinux.armv7hf/cxx`  
+
+**注意**：预测库版本需要跟opt版本一致，更多Paddle-Lite编译内容请参考[Paddle-Lite编译](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html)；更多预编译Paddle-Lite预测库请参考[Paddle-Lite Release Note](https://github.com/PaddlePaddle/Paddle-Lite/releases)
+
+### Step3 软件依赖
+提供了依赖软件的预编包或者一键编译，用户不需要单独下载或编译第三方依赖软件。若需要自行编译第三方依赖软件请参考：
+
+- gflags：编译请参考 [编译文档](https://gflags.github.io/gflags/#download)  
+
+- glog：编译请参考[编译文档](https://github.com/google/glog)
+
+- opencv: 编译请参考 
+[编译文档](https://docs.opencv.org/master/d7/d9f/tutorial_linux_install.html)
+### Step4: 编译
+编译`cmake`的命令在`scripts/build.sh`中，修改LITE_DIR为Paddle-Lite预测库目录，若自行编译第三方依赖软件请根据Step1中编译软件的实际情况修改主要参数，其主要内容说明如下： 
+``` 
+# Paddle-Lite预编译库的路径
+LITE_DIR=/path/to/Paddle-Lite/inference/lib
+# gflags预编译库的路径
+GFLAGS_DIR=$(pwd)/deps/gflags
+# glog预编译库的路径
+GLOG_DIR=$(pwd)/deps/glog
+# opencv预编译库的路径
+OPENCV_DIR=$(pwd)/deps/opencv/
+```
+执行`build`脚本：
+ ```shell
+ sh ./scripts/build.sh
+ ```  
+
+
+### Step3: 预测
+
+编译成功后，分类任务的预测可执行程序为`classifier`,分割任务的预测可执行程序为`segmenter`，检测任务的预测可执行程序为`detector`，其主要命令参数说明如下：  
+|  参数   | 说明  |
+|  ----  | ----  |
+| --model_dir  | 模型转换生成的.xml文件路径，请保证模型转换生成的三个文件在同一路径下|
+| --image  | 要预测的图片文件路径 |
+| --image_list  | 按行存储图片路径的.txt文件 |
+| --thread_num | 预测的线程数，默认值为1 |
+| --cfg_dir | PaddleX model 的.yml配置文件 |
+| --save_dir | 可视化结果图片保存地址，仅适用于检测和分割任务，默认值为" "既不保存可视化结果 |
+
+### 样例
+`样例一`：
+单张图片分类任务  
+测试图片 `/path/to/test_img.jpeg`  
+
+```shell
+./build/classifier --model_dir=/path/to/nb_model 
+--image=/path/to/test_img.jpeg --cfg_dir=/path/to/PadlleX_model.yml  --thread_num=4
+```
+
+
+`样例二`:
+多张图片分割任务
+预测多个图片`/path/to/image_list.txt`，image_list.txt内容的格式如下：
+```
+/path/to/images/test_img1.jpeg
+/path/to/images/test_img2.jpeg
+...
+/path/to/images/test_imgn.jpeg
+```
+
+```shell
+./build/segmenter --model_dir=/path/to/models/nb_model --image_list=/root/projects/images_list.txt --cfg_dir=/path/to/PadlleX_model.yml --save_dir ./output --thread_num=4  
+```  
+
+## 性能测试
+### 测试环境：
+硬件：Raspberry Pi 3 Model B
+系统：raspbian OS
+软件：paddle-lite 2.6.1
+### 测试结果
+单位ms，num表示paddle-lite下使用的线程数
+|模型|lite(num=4)|输入图片大小|
+| ----|  ---- | ----|
+|mobilenet-v2|136.19|224*224|
+|resnet-50|1131.42|224*224|
+|deeplabv3|2162.03|512*512|
+|hrnet|6118.23|512*512|
+|yolov3-darknet53|4741.15|320*320|
+|yolov3-mobilenet|1424.01|320*320|
+|densenet121|1144.92|224*224|
+|densenet161|2751.57|224*224|
+|densenet201|1847.06|224*224|
+|HRNet_W18|1753.06|224*224|
+|MobileNetV1|177.63|224*224|
+|MobileNetV3_large_ssld|133.99|224*224|
+|MobileNetV3_small_ssld|53.99|224*224|
+|ResNet101|2290.56|224*224|
+|ResNet101_vd|2337.51|224*224|
+|ResNet101_vd_ssld|3124.49|224*224|
+|ShuffleNetV2|115.97|224*224|
+|Xception41|1418.29|224*224|
+|Xception65|2094.7|224*224|  
+
+从测试结果看建议用户在树莓派上使用MobileNetV1-V3,ShuffleNetV2这类型的小型网络
+
+## NCS2部署
+树莓派支持通过OpenVINO在NCS2上跑PaddleX模型预测，目前仅支持PaddleX的分类网络，基于NCS2的方式包含Paddle模型转OpenVINO IR以及部署IR在NCS2上进行预测两个步骤。
+- 模型转换请参考：[PaddleX模型转换为OpenVINO IR]('./openvino/export_openvino_model.md')，raspbian OS上的OpenVINO不支持模型转换，需要先在host侧转换FP16的IR。
+- 预测部署请参考[OpenVINO部署](./openvino/linux.md)中VPU在raspbian OS部署的部分

docs/deploy/raspberry/export_nb_model.md

@@ -0,0 +1,31 @@
+# Paddle-Lite模型转换
+将Paddle模型转换为Paddle-Lite的nb模型，模型转换主要包括PaddleX转inference model和inference model转Paddle-Lite nb模型
+### Step1：导出inference模型
+PaddleX模型转Paddle-Lite模型之前需要先把PaddleX模型导出为inference格式模型，导出的模型将包括__model__、__params__和model.yml三个文件名。具体方法请参考[Inference模型导出](../export_model.md)。
+### Step2：导出Paddle-Lite模型
+Paddle-Lite模型需要通过Paddle-Lite的opt工具转出模型，下载并解压: [模型优化工具opt（2.6.1-linux）](https://bj.bcebos.com/paddlex/deploy/Rasoberry/opt.zip)，在Linux系统下运行：
+``` bash
+./opt --model_file=<model_path> \
+      --param_file=<param_path> \
+      --valid_targets=arm \
+      --optimize_out_type=naive_buffer \
+      --optimize_out=model_output_name
+```
+|  参数   | 说明  |
+|  ----  | ----  |
+| --model_file  | 导出inference模型中包含的网络结构文件：`__model__`所在的路径|
+| --param_file  | 导出inference模型中包含的参数文件：`__params__`所在的路径|
+| --valid_targets  |指定模型可执行的backend，这里请指定为`arm`|
+| --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，这里请指定为`naive_buffer`|  
+若安装了python版本的Paddle-Lite也可以通过如下方式转换
+``` 
+./paddle_lite_opt --model_file=<model_path> \
+      --param_file=<param_path> \
+      --valid_targets=arm \
+      --optimize_out_type=naive_buffer \
+      --optimize_out=model_output_name
+```
+
+详细的使用方法和参数含义请参考: [使用opt转化模型](https://paddle-lite.readthedocs.io/zh/latest/user_guides/opt/opt_bin.html)，更多opt预编译版本请参考[Paddle-Lite Release Note](https://github.com/PaddlePaddle/Paddle-Lite/releases)
+
+**注意**：opt版本需要跟预测库版本保持一致，如使2.6.0的python版预测库，请从上面Release Note中下载2.6.0版本的opt转换模型

docs/deploy/raspberry/python.md

@@ -0,0 +1,53 @@
+# Python预测部署
+文档说明了在树莓派上使用python版本的Paddle-Lite进行PaddleX模型好的预测部署，Paddle-Lite python版本的预测库下载，用户也可以下载whl文件进行安装[Paddle-Lite_2.6.0_python](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.0/armlinux_python_installer.zip)，更多版本请参考[Paddle-Lite Release Note](https://github.com/PaddlePaddle/Paddle-Lite/releases)
+```
+python -m pip install paddlelite
+```
+部署前需要先将PaddleX模型转换为Paddle-Lite的nb模型，具体请参考[Paddle-Lite模型转换](./export_nb_model.md)
+
+
+
+## 前置条件
+* Python 3.6+
+* Paddle-Lite_python 2.6.0+
+
+请确保系统已经安装好上述基本软件，**下面所有示例以工作目录 `/root/projects/`演示**。
+
+## 预测部署
+运行/root/projects/PaddleX/deploy/raspberry/python目录下demo.py文件可以进行预测，其命令参数说明如下：
+
+|  参数   | 说明  |
+|  ----  | ----  |
+| --model_dir  | 模型转换生成的.xml文件路径，请保证模型转换生成的三个文件在同一路径下|
+| --img  | 要预测的图片文件路径 |
+| --image_list  | 按行存储图片路径的.txt文件 |
+| --cfg_dir | PaddleX model 的.yml配置文件 |
+| --thread_num  | 预测的线程数, 默认值为1 |
+| --input_shape  | 模型输入中图片输入的大小[N,C,H.W] |
+  
+### 样例
+`样例一`：  
+测试图片 `/path/to/test_img.jpeg`  
+
+```
+cd /root/projects/python  
+
+python demo.py --model_dir /path/to/openvino_model --img /path/to/test_img.jpeg --cfg_dir /path/to/PadlleX_model.yml --thread_num 4 --input_shape [1,3,224,224]
+```  
+
+样例二`:
+
+预测多个图片`/path/to/image_list.txt`，image_list.txt内容的格式如下：
+
+```
+/path/to/images/test_img1.jpeg
+/path/to/images/test_img2.jpeg
+...
+/path/to/images/test_imgn.jpeg
+```
+
+```
+cd /root/projects/python  
+
+python demo.py --model_dir /path/to/models/openvino_model --image_list /root/projects/images_list.txt --cfg_dir=/path/to/PadlleX_model.yml --thread_num 4 --input_shape [1,3,224,224]
+```