add lite demo code

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 ./readable_label.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/readable_label.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"