Merge pull request #708 from WenmuZhou/angle_class

添加分类模型

configs/cls/cls_mv3.yml

+Global:
+  algorithm: CLS
+  use_gpu: False
+  epoch_num: 100
+  log_smooth_window: 20
+  print_batch_step: 100
+  save_model_dir: output/cls_mv3
+  save_epoch_step: 3
+  eval_batch_step: 500
+  train_batch_size_per_card: 512
+  test_batch_size_per_card: 512
+  image_shape: [3, 48, 192]
+  label_list: ['0','180']
+  distort: True
+  reader_yml: ./configs/cls/cls_reader.yml
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
+  infer_img:
+  
+Architecture:
+  function: ppocr.modeling.architectures.cls_model,ClsModel
+
+Backbone:
+  function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
+  scale: 0.35
+  model_name: small
+ 
+Head:
+  function: ppocr.modeling.heads.cls_head,ClsHead
+  class_dim: 2
+
+Loss:
+  function: ppocr.modeling.losses.cls_loss,ClsLoss
+
+Optimizer:
+  function: ppocr.optimizer,AdamDecay
+  base_lr: 0.001
+  beta1: 0.9
+  beta2: 0.999
+  decay:
+    function: cosine_decay
+    step_each_epoch: 1169
+    total_epoch: 100
\ No newline at end of file

configs/cls/cls_reader.yml

+TrainReader:
+  reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
+  num_workers: 8
+  img_set_dir: ./train_data/cls
+  label_file_path: ./train_data/cls/train.txt
+  
+EvalReader:
+  reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
+  img_set_dir: ./train_data/cls
+  label_file_path: ./train_data/cls/test.txt
+
+TestReader:
+  reader_function: ppocr.data.cls.dataset_traversal,SimpleReader

deploy/android_demo/app/src/main/cpp/native.cpp

@@ -4,29 +4,29 @@
 
 #include "native.h"
 #include "ocr_ppredictor.h"
-#include <string>
 #include <algorithm>
 #include <paddle_api.h>
+#include <string>
 
 static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
 
-extern "C"
-JNIEXPORT jlong JNICALL
-Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(JNIEnv *env, jobject thiz,
-                                                            jstring j_det_model_path,
-                                                            jstring j_rec_model_path,
-                                                            jint j_thread_num,
-                                                            jstring j_cpu_mode) {
-    std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
-    std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
-    int thread_num = j_thread_num;
-    std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
-    ppredictor::OCR_Config conf;
-    conf.thread_num = thread_num;
-    conf.mode = str_to_cpu_mode(cpu_mode);
-    ppredictor::OCR_PPredictor *orc_predictor = new ppredictor::OCR_PPredictor{conf};
-    orc_predictor->init_from_file(det_model_path, rec_model_path);
-    return reinterpret_cast<jlong>(orc_predictor);
+extern "C" JNIEXPORT jlong JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
+    JNIEnv *env, jobject thiz, jstring j_det_model_path,
+    jstring j_rec_model_path, jstring j_cls_model_path, jint j_thread_num,
+    jstring j_cpu_mode) {
+  std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
+  std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
+  std::string cls_model_path = jstring_to_cpp_string(env, j_cls_model_path);
+  int thread_num = j_thread_num;
+  std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
+  ppredictor::OCR_Config conf;
+  conf.thread_num = thread_num;
+  conf.mode = str_to_cpu_mode(cpu_mode);
+  ppredictor::OCR_PPredictor *orc_predictor =
+      new ppredictor::OCR_PPredictor{conf};
+  orc_predictor->init_from_file(det_model_path, rec_model_path, cls_model_path);
+  return reinterpret_cast<jlong>(orc_predictor);
 }
 
 /**
@@ -34,82 +34,81 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(JNIEnv *env, jobject
  * @param cpu_mode
  * @return
  */
-static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode) {
-    static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
-        {"LITE_POWER_HIGH",      paddle::lite_api::LITE_POWER_HIGH},
-        {"LITE_POWER_LOW",       paddle::lite_api::LITE_POWER_HIGH},
-        {"LITE_POWER_FULL",      paddle::lite_api::LITE_POWER_FULL},
-        {"LITE_POWER_NO_BIND",   paddle::lite_api::LITE_POWER_NO_BIND},
-        {"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
-        {"LITE_POWER_RAND_LOW",  paddle::lite_api::LITE_POWER_RAND_LOW}
-    };
-    std::string upper_key;
-    std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(), ::toupper);
-    auto index = cpu_mode_map.find(upper_key);
-    if (index == cpu_mode_map.end()) {
-        LOGE("cpu_mode not found %s", upper_key.c_str());
-        return paddle::lite_api::LITE_POWER_HIGH;
-    } else {
-        return index->second;
-    }
-
+static paddle::lite_api::PowerMode
+str_to_cpu_mode(const std::string &cpu_mode) {
+  static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
+      {"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH},
+      {"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH},
+      {"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL},
+      {"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND},
+      {"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
+      {"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}};
+  std::string upper_key;
+  std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(),
+                 ::toupper);
+  auto index = cpu_mode_map.find(upper_key);
+  if (index == cpu_mode_map.end()) {
+    LOGE("cpu_mode not found %s", upper_key.c_str());
+    return paddle::lite_api::LITE_POWER_HIGH;
+  } else {
+    return index->second;
+  }
 }
 
-extern "C"
-JNIEXPORT jfloatArray JNICALL
-Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(JNIEnv *env, jobject thiz,
-                                                               jlong java_pointer, jfloatArray buf,
-                                                               jfloatArray ddims,
-                                                               jobject original_image) {
-    LOGI("begin to run native forward");
-    if (java_pointer == 0) {
-        LOGE("JAVA pointer is NULL");
-        return cpp_array_to_jfloatarray(env, nullptr, 0);
-    }
-    cv::Mat origin = bitmap_to_cv_mat(env, original_image);
-    if (origin.size == 0) {
-        LOGE("origin bitmap cannot convert to CV Mat");
-        return cpp_array_to_jfloatarray(env, nullptr, 0);
-    }
-    ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *) java_pointer;
-    std::vector<float> dims_float_arr = jfloatarray_to_float_vector(env, ddims);
-    std::vector<int64_t> dims_arr;
-    dims_arr.resize(dims_float_arr.size());
-    std::copy(dims_float_arr.cbegin(), dims_float_arr.cend(), dims_arr.begin());
+extern "C" JNIEXPORT jfloatArray JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
+    JNIEnv *env, jobject thiz, jlong java_pointer, jfloatArray buf,
+    jfloatArray ddims, jobject original_image) {
+  LOGI("begin to run native forward");
+  if (java_pointer == 0) {
+    LOGE("JAVA pointer is NULL");
+    return cpp_array_to_jfloatarray(env, nullptr, 0);
+  }
+  cv::Mat origin = bitmap_to_cv_mat(env, original_image);
+  if (origin.size == 0) {
+    LOGE("origin bitmap cannot convert to CV Mat");
+    return cpp_array_to_jfloatarray(env, nullptr, 0);
+  }
+  ppredictor::OCR_PPredictor *ppredictor =
+      (ppredictor::OCR_PPredictor *)java_pointer;
+  std::vector<float> dims_float_arr = jfloatarray_to_float_vector(env, ddims);
+  std::vector<int64_t> dims_arr;
+  dims_arr.resize(dims_float_arr.size());
+  std::copy(dims_float_arr.cbegin(), dims_float_arr.cend(), dims_arr.begin());
 
-    // 这里值有点大，就不调用jfloatarray_to_float_vector了
-    int64_t buf_len = (int64_t) env->GetArrayLength(buf);
-    jfloat *buf_data = env->GetFloatArrayElements(buf, JNI_FALSE);
-    float *data = (jfloat *) buf_data;
-    std::vector<ppredictor::OCRPredictResult> results = ppredictor->infer_ocr(dims_arr, data,
-                                                                              buf_len,
-                                                                              NET_OCR, origin);
-    LOGI("infer_ocr finished with boxes %ld", results.size());
-    // 这里将std::vector<ppredictor::OCRPredictResult> 序列化成 float数组，传输到java层再反序列化
-    std::vector<float> float_arr;
-    for (const ppredictor::OCRPredictResult &r :results) {
-        float_arr.push_back(r.points.size());
-        float_arr.push_back(r.word_index.size());
-        float_arr.push_back(r.score);
-        for (const std::vector<int> &point : r.points) {
-            float_arr.push_back(point.at(0));
-            float_arr.push_back(point.at(1));
-        }
-        for (int index: r.word_index) {
-            float_arr.push_back(index);
-        }
+  // 这里值有点大，就不调用jfloatarray_to_float_vector了
+  int64_t buf_len = (int64_t)env->GetArrayLength(buf);
+  jfloat *buf_data = env->GetFloatArrayElements(buf, JNI_FALSE);
+  float *data = (jfloat *)buf_data;
+  std::vector<ppredictor::OCRPredictResult> results =
+      ppredictor->infer_ocr(dims_arr, data, buf_len, NET_OCR, origin);
+  LOGI("infer_ocr finished with boxes %ld", results.size());
+  // 这里将std::vector<ppredictor::OCRPredictResult> 序列化成
+  // float数组，传输到java层再反序列化
+  std::vector<float> float_arr;
+  for (const ppredictor::OCRPredictResult &r : results) {
+    float_arr.push_back(r.points.size());
+    float_arr.push_back(r.word_index.size());
+    float_arr.push_back(r.score);
+    for (const std::vector<int> &point : r.points) {
+      float_arr.push_back(point.at(0));
+      float_arr.push_back(point.at(1));
     }
-    return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
+    for (int index : r.word_index) {
+      float_arr.push_back(index);
+    }
+  }
+  return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
 }
 
-extern "C"
-JNIEXPORT void JNICALL
-Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(JNIEnv *env, jobject thiz,
-                                                               jlong java_pointer){
-    if (java_pointer == 0) {
-        LOGE("JAVA pointer is NULL");
-        return;
-    }
-    ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *) java_pointer;
-    delete ppredictor;
+extern "C" JNIEXPORT void JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(
+    JNIEnv *env, jobject thiz, jlong java_pointer) {
+  if (java_pointer == 0) {
+    LOGE("JAVA pointer is NULL");
+    return;
+  }
+  ppredictor::OCR_PPredictor *ppredictor =
+      (ppredictor::OCR_PPredictor *)java_pointer;
+  delete ppredictor;
 }
\ No newline at end of file

deploy/android_demo/app/src/main/cpp/ocr_cls_process.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 "ocr_cls_process.h"
+#include <cmath>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <iostream>
+#include <vector>
+
+const std::vector<int> CLS_IMAGE_SHAPE = {3, 32, 100};
+
+cv::Mat cls_resize_img(const cv::Mat &img) {
+  int imgC = CLS_IMAGE_SHAPE[0];
+  int imgW = CLS_IMAGE_SHAPE[2];
+  int imgH = CLS_IMAGE_SHAPE[1];
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_CUBIC);
+
+  if (resize_w < imgW) {
+    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(imgW - resize_w),
+                       cv::BORDER_CONSTANT, {0, 0, 0});
+  }
+  return resize_img;
+}
\ No newline at end of file

deploy/android_demo/app/src/main/cpp/ocr_cls_process.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "common.h"
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+extern const std::vector<int> CLS_IMAGE_SHAPE;
+
+cv::Mat cls_resize_img(const cv::Mat &img);
\ No newline at end of file

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp

@@ -3,38 +3,48 @@
 //
 
 #include "ocr_ppredictor.h"
-#include "preprocess.h"
 #include "common.h"
-#include "ocr_db_post_process.h"
+#include "ocr_cls_process.h"
 #include "ocr_crnn_process.h"
+#include "ocr_db_post_process.h"
+#include "preprocess.h"
 
 namespace ppredictor {
 
-OCR_PPredictor::OCR_PPredictor(const OCR_Config &config) : _config(config) {
+OCR_PPredictor::OCR_PPredictor(const OCR_Config &config) : _config(config) {}
 
-}
+int OCR_PPredictor::init(const std::string &det_model_content,
+                         const std::string &rec_model_content,
+                         const std::string &cls_model_content) {
+  _det_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_config.thread_num, NET_OCR, _config.mode});
+  _det_predictor->init_nb(det_model_content);
 
-int
-OCR_PPredictor::init(const std::string &det_model_content, const std::string &rec_model_content) {
-    _det_predictor = std::unique_ptr<PPredictor>(
-        new PPredictor{_config.thread_num, NET_OCR, _config.mode});
-    _det_predictor->init_nb(det_model_content);
+  _rec_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _rec_predictor->init_nb(rec_model_content);
 
-    _rec_predictor = std::unique_ptr<PPredictor>(
-        new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
-    _rec_predictor->init_nb(rec_model_content);
-    return RETURN_OK;
+  _cls_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _cls_predictor->init_nb(cls_model_content);
+  return RETURN_OK;
 }
 
-int OCR_PPredictor::init_from_file(const std::string &det_model_path, const std::string &rec_model_path){
-    _det_predictor = std::unique_ptr<PPredictor>(
-        new PPredictor{_config.thread_num, NET_OCR, _config.mode});
-    _det_predictor->init_from_file(det_model_path);
-
-    _rec_predictor = std::unique_ptr<PPredictor>(
-        new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
-    _rec_predictor->init_from_file(rec_model_path);
-    return RETURN_OK;
+int OCR_PPredictor::init_from_file(const std::string &det_model_path,
+                                   const std::string &rec_model_path,
+                                   const std::string &cls_model_path) {
+  _det_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_config.thread_num, NET_OCR, _config.mode});
+  _det_predictor->init_from_file(det_model_path);
+
+  _rec_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _rec_predictor->init_from_file(rec_model_path);
+
+  _cls_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+  _cls_predictor->init_from_file(cls_model_path);
+  return RETURN_OK;
 }
 /**
  * for debug use, show result of First Step
@@ -42,145 +52,188 @@ int OCR_PPredictor::init_from_file(const std::string &det_model_path, const std:
  * @param boxes
  * @param srcimg
  */
-static void visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
-                       const std::vector<std::vector<std::vector<int>>> &boxes,
-                       const cv::Mat &srcimg) {
-    // visualization
-    cv::Point rook_points[filter_boxes.size()][4];
-    for (int n = 0; n < filter_boxes.size(); n++) {
-        for (int m = 0; m < filter_boxes[0].size(); m++) {
-            rook_points[n][m] = cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
-        }
+static void
+visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
+           const std::vector<std::vector<std::vector<int>>> &boxes,
+           const cv::Mat &srcimg) {
+  // visualization
+  cv::Point rook_points[filter_boxes.size()][4];
+  for (int n = 0; n < filter_boxes.size(); n++) {
+    for (int m = 0; m < filter_boxes[0].size(); m++) {
+      rook_points[n][m] =
+          cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
     }
-
-    cv::Mat img_vis;
-    srcimg.copyTo(img_vis);
-    for (int n = 0; n < boxes.size(); n++) {
-        const cv::Point *ppt[1] = {rook_points[n]};
-        int npt[] = {4};
-        cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
-    }
-    // 调试用，自行替换需要修改的路径
-    cv::imwrite("/sdcard/1/vis.png", img_vis);
+  }
+
+  cv::Mat img_vis;
+  srcimg.copyTo(img_vis);
+  for (int n = 0; n < boxes.size(); n++) {
+    const cv::Point *ppt[1] = {rook_points[n]};
+    int npt[] = {4};
+    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
+  }
+  // 调试用，自行替换需要修改的路径
+  cv::imwrite("/sdcard/1/vis.png", img_vis);
 }
 
 std::vector<OCRPredictResult>
-OCR_PPredictor::infer_ocr(const std::vector<int64_t> &dims, const float *input_data, int input_len,
-                          int net_flag, cv::Mat &origin) {
+OCR_PPredictor::infer_ocr(const std::vector<int64_t> &dims,
+                          const float *input_data, int input_len, int net_flag,
+                          cv::Mat &origin) {
+  PredictorInput input = _det_predictor->get_first_input();
+  input.set_dims(dims);
+  input.set_data(input_data, input_len);
+  std::vector<PredictorOutput> results = _det_predictor->infer();
+  PredictorOutput &res = results.at(0);
+  std::vector<std::vector<std::vector<int>>> filtered_box = calc_filtered_boxes(
+      res.get_float_data(), res.get_size(), (int)dims[2], (int)dims[3], origin);
+  LOGI("Filter_box size %ld", filtered_box.size());
+  return infer_rec(filtered_box, origin);
+}
 
-    PredictorInput input = _det_predictor->get_first_input();
+std::vector<OCRPredictResult> OCR_PPredictor::infer_rec(
+    const std::vector<std::vector<std::vector<int>>> &boxes,
+    const cv::Mat &origin_img) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+  std::vector<int64_t> dims = {1, 3, 0, 0};
+  std::vector<OCRPredictResult> ocr_results;
+
+  PredictorInput input = _rec_predictor->get_first_input();
+  for (auto bp = boxes.crbegin(); bp != boxes.crend(); ++bp) {
+    const std::vector<std::vector<int>> &box = *bp;
+    cv::Mat crop_img = get_rotate_crop_image(origin_img, box);
+    crop_img = infer_cls(crop_img);
+
+    float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
+    cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
+    input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+    const float *dimg = reinterpret_cast<const float *>(input_image.data);
+    int input_size = input_image.rows * input_image.cols;
+
+    dims[2] = input_image.rows;
+    dims[3] = input_image.cols;
     input.set_dims(dims);
-    input.set_data(input_data, input_len);
-    std::vector<PredictorOutput> results = _det_predictor->infer();
-    PredictorOutput &res = results.at(0);
-    std::vector<std::vector<std::vector<int>>> filtered_box
-        = calc_filtered_boxes(res.get_float_data(), res.get_size(), (int) dims[2], (int) dims[3],
-                              origin);
-    LOGI("Filter_box size %ld", filtered_box.size());
-    return infer_rec(filtered_box, origin);
-}
 
-std::vector<OCRPredictResult>
-OCR_PPredictor::infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
-                          const cv::Mat &origin_img) {
-    std::vector<float> mean = {0.5f, 0.5f, 0.5f};
-    std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
-    std::vector<int64_t> dims = {1, 3, 0, 0};
-    std::vector<OCRPredictResult> ocr_results;
-
-    PredictorInput input = _rec_predictor->get_first_input();
-    for (auto bp = boxes.crbegin(); bp != boxes.crend(); ++bp) {
-        const std::vector<std::vector<int>> &box = *bp;
-        cv::Mat crop_img = get_rotate_crop_image(origin_img, box);
-        float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
-        cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
-        input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
-        const float *dimg = reinterpret_cast<const float *>(input_image.data);
-        int input_size = input_image.rows * input_image.cols;
-
-        dims[2] = input_image.rows;
-        dims[3] = input_image.cols;
-        input.set_dims(dims);
-
-        neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean, scale);
-
-        std::vector<PredictorOutput> results = _rec_predictor->infer();
-
-        OCRPredictResult res;
-        res.word_index = postprocess_rec_word_index(results.at(0));
-        if (res.word_index.empty()) {
-            continue;
-        }
-        res.score = postprocess_rec_score(results.at(1));
-        res.points = box;
-        ocr_results.emplace_back(std::move(res));
+    neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                    scale);
+
+    std::vector<PredictorOutput> results = _rec_predictor->infer();
+
+    OCRPredictResult res;
+    res.word_index = postprocess_rec_word_index(results.at(0));
+    if (res.word_index.empty()) {
+      continue;
     }
-    LOGI("ocr_results finished %lu", ocr_results.size());
-    return ocr_results;
+    res.score = postprocess_rec_score(results.at(1));
+    res.points = box;
+    ocr_results.emplace_back(std::move(res));
+  }
+  LOGI("ocr_results finished %lu", ocr_results.size());
+  return ocr_results;
+}
+
+cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+  std::vector<int64_t> dims = {1, 3, 0, 0};
+  std::vector<OCRPredictResult> ocr_results;
+
+  PredictorInput input = _cls_predictor->get_first_input();
+
+  cv::Mat input_image = cls_resize_img(img);
+  input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+  const float *dimg = reinterpret_cast<const float *>(input_image.data);
+  int input_size = input_image.rows * input_image.cols;
+
+  dims[2] = input_image.rows;
+  dims[3] = input_image.cols;
+  input.set_dims(dims);
+
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+
+  std::vector<PredictorOutput> results = _cls_predictor->infer();
+
+  const float *scores = results.at(0).get_float_data();
+  const int *labels = results.at(1).get_int_data();
+  for (int64_t i = 0; i < results.at(0).get_size(); i++) {
+    LOGI("output scores [%f]", scores[i]);
+  }
+  for (int64_t i = 0; i < results.at(1).get_size(); i++) {
+    LOGI("output label [%d]", labels[i]);
+  }
+  int label_idx = labels[0];
+  float score = scores[label_idx];
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  if (label_idx % 2 == 1 && score > thresh) {
+    cv::rotate(srcimg, srcimg, 1);
+  }
+  return srcimg;
 }
 
 std::vector<std::vector<std::vector<int>>>
-OCR_PPredictor::calc_filtered_boxes(const float *pred, int pred_size, int output_height,
-                                    int output_width, const cv::Mat &origin) {
-    const double threshold = 0.3;
-    const double maxvalue = 1;
-
-    cv::Mat pred_map = cv::Mat::zeros(output_height, output_width, CV_32F);
-    memcpy(pred_map.data, pred, pred_size * sizeof(float));
-    cv::Mat cbuf_map;
-    pred_map.convertTo(cbuf_map, CV_8UC1);
-
-    cv::Mat bit_map;
-    cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
-
-    std::vector<std::vector<std::vector<int>>> boxes = boxes_from_bitmap(pred_map, bit_map);
-    float ratio_h = output_height * 1.0f / origin.rows;
-    float ratio_w = output_width * 1.0f / origin.cols;
-    std::vector<std::vector<std::vector<int>>> filter_boxes = filter_tag_det_res(boxes, ratio_h,
-                                                                                 ratio_w, origin);
-    return filter_boxes;
+OCR_PPredictor::calc_filtered_boxes(const float *pred, int pred_size,
+                                    int output_height, int output_width,
+                                    const cv::Mat &origin) {
+  const double threshold = 0.3;
+  const double maxvalue = 1;
+
+  cv::Mat pred_map = cv::Mat::zeros(output_height, output_width, CV_32F);
+  memcpy(pred_map.data, pred, pred_size * sizeof(float));
+  cv::Mat cbuf_map;
+  pred_map.convertTo(cbuf_map, CV_8UC1);
+
+  cv::Mat bit_map;
+  cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
+
+  std::vector<std::vector<std::vector<int>>> boxes =
+      boxes_from_bitmap(pred_map, bit_map);
+  float ratio_h = output_height * 1.0f / origin.rows;
+  float ratio_w = output_width * 1.0f / origin.cols;
+  std::vector<std::vector<std::vector<int>>> filter_boxes =
+      filter_tag_det_res(boxes, ratio_h, ratio_w, origin);
+  return filter_boxes;
 }
 
-std::vector<int> OCR_PPredictor::postprocess_rec_word_index(const PredictorOutput &res) {
-    const int *rec_idx = res.get_int_data();
-    const std::vector<std::vector<uint64_t>> rec_idx_lod = res.get_lod();
+std::vector<int>
+OCR_PPredictor::postprocess_rec_word_index(const PredictorOutput &res) {
+  const int *rec_idx = res.get_int_data();
+  const std::vector<std::vector<uint64_t>> rec_idx_lod = res.get_lod();
 
-    std::vector<int> pred_idx;
-    for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
-        pred_idx.emplace_back(rec_idx[n]);
-    }
-    return pred_idx;
+  std::vector<int> pred_idx;
+  for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
+    pred_idx.emplace_back(rec_idx[n]);
+  }
+  return pred_idx;
 }
 
 float OCR_PPredictor::postprocess_rec_score(const PredictorOutput &res) {
-    const float *predict_batch = res.get_float_data();
-    const std::vector<int64_t> predict_shape = res.get_shape();
-    const std::vector<std::vector<uint64_t>> predict_lod = res.get_lod();
-    int blank = predict_shape[1];
-    float score = 0.f;
-    int count = 0;
-    for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
-        int argmax_idx = argmax(predict_batch + n * predict_shape[1],
-                                predict_batch + (n + 1) * predict_shape[1]);
-        float max_value = predict_batch[n * predict_shape[1] + argmax_idx];
-        if (blank - 1 - argmax_idx > 1e-5) {
-            score += max_value;
-            count += 1;
-        }
-
-    }
-    if (count == 0) {
-        LOGE("calc score count 0");
-    } else {
-        score /= count;
+  const float *predict_batch = res.get_float_data();
+  const std::vector<int64_t> predict_shape = res.get_shape();
+  const std::vector<std::vector<uint64_t>> predict_lod = res.get_lod();
+  int blank = predict_shape[1];
+  float score = 0.f;
+  int count = 0;
+  for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
+    int argmax_idx = argmax(predict_batch + n * predict_shape[1],
+                            predict_batch + (n + 1) * predict_shape[1]);
+    float max_value = predict_batch[n * predict_shape[1] + argmax_idx];
+    if (blank - 1 - argmax_idx > 1e-5) {
+      score += max_value;
+      count += 1;
     }
-    LOGI("calc score: %f", score);
-    return score;
-
+  }
+  if (count == 0) {
+    LOGE("calc score count 0");
+  } else {
+    score /= count;
+  }
+  LOGI("calc score: %f", score);
+  return score;
 }
 
-
-NET_TYPE OCR_PPredictor::get_net_flag() const {
-    return NET_OCR;
-}
+NET_TYPE OCR_PPredictor::get_net_flag() const { return NET_OCR; }
 }
\ No newline at end of file

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h

@@ -4,10 +4,10 @@
 
 #pragma once
 
-#include <string>
+#include "ppredictor.h"
 #include <opencv2/opencv.hpp>
 #include <paddle_api.h>
-#include "ppredictor.h"
+#include <string>
 
 namespace ppredictor {
 
@@ -15,17 +15,18 @@ namespace ppredictor {
  * Config
  */
 struct OCR_Config {
-    int thread_num = 4; // Thread num
-    paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
+  int thread_num = 4; // Thread num
+  paddle::lite_api::PowerMode mode =
+      paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
 };
 
 /**
  * PolyGone Result
  */
 struct OCRPredictResult {
-    std::vector<int> word_index;
-    std::vector<std::vector<int>> points;
-    float score;
+  std::vector<int> word_index;
+  std::vector<std::vector<int>> points;
+  float score;
 };
 
 /**
@@ -35,78 +36,87 @@ struct OCRPredictResult {
  */
 class OCR_PPredictor : public PPredictor_Interface {
 public:
-    OCR_PPredictor(const OCR_Config &config);
-
-    virtual ~OCR_PPredictor() {
-
-    }
-
-    /**
-     * 初始化二个模型的Predictor
-     * @param det_model_content
-     * @param rec_model_content
-     * @return
-     */
-    int init(const std::string &det_model_content, const std::string &rec_model_content);
-    int init_from_file(const std::string &det_model_path, const std::string &rec_model_path);
-    /**
-     * Return OCR result
-     * @param dims
-     * @param input_data
-     * @param input_len
-     * @param net_flag
-     * @param origin
-     * @return
-     */
-    virtual std::vector<OCRPredictResult>
-    infer_ocr(const std::vector<int64_t> &dims, const float *input_data, int input_len,
-              int net_flag, cv::Mat &origin);
-
-
-    virtual NET_TYPE get_net_flag() const;
-
+  OCR_PPredictor(const OCR_Config &config);
+
+  virtual ~OCR_PPredictor() {}
+
+  /**
+   * 初始化二个模型的Predictor
+   * @param det_model_content
+   * @param rec_model_content
+   * @return
+   */
+  int init(const std::string &det_model_content,
+           const std::string &rec_model_content,
+           const std::string &cls_model_content);
+  int init_from_file(const std::string &det_model_path,
+                     const std::string &rec_model_path,
+                     const std::string &cls_model_path);
+  /**
+   * Return OCR result
+   * @param dims
+   * @param input_data
+   * @param input_len
+   * @param net_flag
+   * @param origin
+   * @return
+   */
+  virtual std::vector<OCRPredictResult>
+  infer_ocr(const std::vector<int64_t> &dims, const float *input_data,
+            int input_len, int net_flag, cv::Mat &origin);
+
+  virtual NET_TYPE get_net_flag() const;
 
 private:
-
-    /**
-     * calcul Polygone from the result image of first model
-     * @param pred
-     * @param output_height
-     * @param output_width
-     * @param origin
-     * @return
-     */
-    std::vector<std::vector<std::vector<int>>>
-    calc_filtered_boxes(const float *pred, int pred_size, int output_height, int output_width,
-                        const cv::Mat &origin);
-
-    /**
-     * infer for second model
-     *
-     * @param boxes
-     * @param origin
-     * @return
-     */
-    std::vector<OCRPredictResult>
-    infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes, const cv::Mat &origin);
-
-    /**
-     * Postprocess or sencod model to extract text
-     * @param res
-     * @return
-     */
-    std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
-
-    /**
-     * calculate confidence of second model text result
-     * @param res
-     * @return
-     */
-    float postprocess_rec_score(const PredictorOutput &res);
-
-    std::unique_ptr<PPredictor> _det_predictor;
-    std::unique_ptr<PPredictor> _rec_predictor;
-    OCR_Config _config;
-
+  /**
+   * calcul Polygone from the result image of first model
+   * @param pred
+   * @param output_height
+   * @param output_width
+   * @param origin
+   * @return
+   */
+  std::vector<std::vector<std::vector<int>>>
+  calc_filtered_boxes(const float *pred, int pred_size, int output_height,
+                      int output_width, const cv::Mat &origin);
+
+  /**
+   * infer for second model
+   *
+   * @param boxes
+   * @param origin
+   * @return
+   */
+  std::vector<OCRPredictResult>
+  infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
+            const cv::Mat &origin);
+
+  /**
+  * infer for cls model
+  *
+  * @param boxes
+  * @param origin
+  * @return
+  */
+  cv::Mat infer_cls(const cv::Mat &origin, float thresh = 0.5);
+
+  /**
+   * Postprocess or sencod model to extract text
+   * @param res
+   * @return
+   */
+  std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
+
+  /**
+   * calculate confidence of second model text result
+   * @param res
+   * @return
+   */
+  float postprocess_rec_score(const PredictorOutput &res);
+
+  std::unique_ptr<PPredictor> _det_predictor;
+  std::unique_ptr<PPredictor> _rec_predictor;
+  std::unique_ptr<PPredictor> _cls_predictor;
+  OCR_Config _config;
 };
 }

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java

@@ -29,7 +29,7 @@ public class OCRPredictorNative {
     public OCRPredictorNative(Config config) {
         this.config = config;
         loadLibrary();
-        nativePointer = init(config.detModelFilename, config.recModelFilename,
+        nativePointer = init(config.detModelFilename, config.recModelFilename,config.clsModelFilename,
                 config.cpuThreadNum, config.cpuPower);
         Log.i("OCRPredictorNative", "load success " + nativePointer);
 
@@ -38,7 +38,7 @@ public class OCRPredictorNative {
     public void release() {
         if (nativePointer != 0) {
             nativePointer = 0;
-            destory(nativePointer);
+//            destory(nativePointer);
         }
     }
 
@@ -55,10 +55,11 @@ public class OCRPredictorNative {
         public String cpuPower;
         public String detModelFilename;
         public String recModelFilename;
+        public String clsModelFilename;
 
     }
 
-    protected native long init(String detModelPath, String recModelPath, int threadNum, String cpuMode);
+    protected native long init(String detModelPath, String recModelPath,String clsModelPath, int threadNum, String cpuMode);
 
     protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
 

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java

@@ -121,7 +121,8 @@ public class Predictor {
         config.cpuThreadNum = cpuThreadNum;
         config.detModelFilename = realPath + File.separator + "ch_det_mv3_db_opt.nb";
         config.recModelFilename = realPath + File.separator + "ch_rec_mv3_crnn_opt.nb";
-        Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename);
+        config.clsModelFilename = realPath + File.separator + "cls_opt_arm.nb";
+        Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
         config.cpuPower = cpuPowerMode;
         paddlePredictor = new OCRPredictorNative(config);
 

deploy/cpp_infer/include/config.h

@@ -57,6 +57,12 @@ public:
 
     this->char_list_file.assign(config_map_["char_list_file"]);
 
+    this->use_angle_cls = bool(stoi(config_map_["use_angle_cls"]));
+
+    this->cls_model_dir.assign(config_map_["cls_model_dir"]);
+
+    this->cls_thresh = stod(config_map_["cls_thresh"]);
+
     this->visualize = bool(stoi(config_map_["visualize"]));
   }
 
@@ -84,8 +90,14 @@ public:
 
   std::string rec_model_dir;
 
+  bool use_angle_cls;
+
   std::string char_list_file;
 
+  std::string cls_model_dir;
+
+  double cls_thresh;
+
   bool visualize = true;
 
   void PrintConfigInfo();

deploy/cpp_infer/include/ocr_cls.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/preprocess_op.h>
+#include <include/utility.h>
+
+namespace PaddleOCR {
+
+class Classifier {
+public:
+  explicit Classifier(const std::string &model_dir, const bool &use_gpu,
+                      const int &gpu_id, const int &gpu_mem,
+                      const int &cpu_math_library_num_threads,
+                      const bool &use_mkldnn, const bool &use_zero_copy_run,
+                      const double &cls_thresh) {
+    this->use_gpu_ = use_gpu;
+    this->gpu_id_ = gpu_id;
+    this->gpu_mem_ = gpu_mem;
+    this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
+    this->use_mkldnn_ = use_mkldnn;
+    this->use_zero_copy_run_ = use_zero_copy_run;
+
+    this->cls_thresh = cls_thresh;
+
+    LoadModel(model_dir);
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_dir);
+
+  cv::Mat Run(cv::Mat &img);
+
+private:
+  std::shared_ptr<PaddlePredictor> predictor_;
+
+  bool use_gpu_ = false;
+  int gpu_id_ = 0;
+  int gpu_mem_ = 4000;
+  int cpu_math_library_num_threads_ = 4;
+  bool use_mkldnn_ = false;
+  bool use_zero_copy_run_ = false;
+  double cls_thresh = 0.5;
+
+  std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+  bool is_scale_ = true;
+
+  // pre-process
+  ClsResizeImg resize_op_;
+  Normalize normalize_op_;
+  Permute permute_op_;
+
+}; // class Classifier
+
+} // namespace PaddleOCR

deploy/cpp_infer/include/ocr_rec.h

@@ -27,6 +27,7 @@
 #include <fstream>
 #include <numeric>
 
+#include <include/ocr_cls.h>
 #include <include/postprocess_op.h>
 #include <include/preprocess_op.h>
 #include <include/utility.h>
@@ -56,7 +57,8 @@ public:
   // Load Paddle inference model
   void LoadModel(const std::string &model_dir);
 
-  void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img);
+  void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img,
+           Classifier *cls);
 
 private:
   std::shared_ptr<PaddlePredictor> predictor_;

deploy/cpp_infer/include/preprocess_op.h

@@ -56,4 +56,10 @@ public:
                    const std::vector<int> &rec_image_shape = {3, 32, 320});
 };
 
+class ClsResizeImg {
+public:
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img,
+                   const std::vector<int> &rec_image_shape = {3, 32, 320});
+};
+
 } // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/src/main.cpp

@@ -53,6 +53,15 @@ int main(int argc, char **argv) {
       config.cpu_math_library_num_threads, config.use_mkldnn,
       config.use_zero_copy_run, config.max_side_len, config.det_db_thresh,
       config.det_db_box_thresh, config.det_db_unclip_ratio, config.visualize);
+
+  Classifier *cls = nullptr;
+  if (config.use_angle_cls == true) {
+    cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
+                         config.gpu_mem, config.cpu_math_library_num_threads,
+                         config.use_mkldnn, config.use_zero_copy_run,
+                         config.cls_thresh);
+  }
+
   CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
                      config.gpu_mem, config.cpu_math_library_num_threads,
                      config.use_mkldnn, config.use_zero_copy_run,
@@ -62,7 +71,7 @@ int main(int argc, char **argv) {
   std::vector<std::vector<std::vector<int>>> boxes;
   det.Run(srcimg, boxes);
 
-  rec.Run(boxes, srcimg);
+  rec.Run(boxes, srcimg, cls);
 
   auto end = std::chrono::system_clock::now();
   auto duration =

deploy/cpp_infer/src/ocr_cls.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 <include/ocr_cls.h>
+
+namespace PaddleOCR {
+
+cv::Mat Classifier::Run(cv::Mat &img) {
+  cv::Mat src_img;
+  img.copyTo(src_img);
+  cv::Mat resize_img;
+
+  std::vector<int> rec_image_shape = {3, 32, 100};
+  int index = 0;
+  float wh_ratio = float(img.cols) / float(img.rows);
+
+  this->resize_op_.Run(img, resize_img, rec_image_shape);
+
+  this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                          this->is_scale_);
+
+  std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
+
+  this->permute_op_.Run(&resize_img, input.data());
+
+  // Inference.
+  if (this->use_zero_copy_run_) {
+    auto input_names = this->predictor_->GetInputNames();
+    auto input_t = this->predictor_->GetInputTensor(input_names[0]);
+    input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
+    input_t->copy_from_cpu(input.data());
+    this->predictor_->ZeroCopyRun();
+  } else {
+    paddle::PaddleTensor input_t;
+    input_t.shape = {1, 3, resize_img.rows, resize_img.cols};
+    input_t.data =
+        paddle::PaddleBuf(input.data(), input.size() * sizeof(float));
+    input_t.dtype = PaddleDType::FLOAT32;
+    std::vector<paddle::PaddleTensor> outputs;
+    this->predictor_->Run({input_t}, &outputs, 1);
+  }
+
+  std::vector<float> softmax_out;
+  std::vector<int64_t> label_out;
+  auto output_names = this->predictor_->GetOutputNames();
+  auto softmax_out_t = this->predictor_->GetOutputTensor(output_names[0]);
+  auto label_out_t = this->predictor_->GetOutputTensor(output_names[1]);
+  auto softmax_shape_out = softmax_out_t->shape();
+  auto label_shape_out = label_out_t->shape();
+
+  int softmax_out_num =
+      std::accumulate(softmax_shape_out.begin(), softmax_shape_out.end(), 1,
+                      std::multiplies<int>());
+
+  int label_out_num =
+      std::accumulate(label_shape_out.begin(), label_shape_out.end(), 1,
+                      std::multiplies<int>());
+  softmax_out.resize(softmax_out_num);
+  label_out.resize(label_out_num);
+
+  softmax_out_t->copy_to_cpu(softmax_out.data());
+  label_out_t->copy_to_cpu(label_out.data());
+
+  int label = label_out[0];
+  float score = softmax_out[label];
+  //    std::cout << "\nlabel "<<label<<" score: "<<score;
+  if (label % 2 == 1 && score > this->cls_thresh) {
+    cv::rotate(src_img, src_img, 1);
+  }
+  return src_img;
+}
+
+void Classifier::LoadModel(const std::string &model_dir) {
+  AnalysisConfig config;
+  config.SetModel(model_dir + "/model", model_dir + "/params");
+
+  if (this->use_gpu_) {
+    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+
+  // false for zero copy tensor
+  config.SwitchUseFeedFetchOps(!this->use_zero_copy_run_);
+  // true for multiple input
+  config.SwitchSpecifyInputNames(true);
+
+  config.SwitchIrOptim(true);
+
+  config.EnableMemoryOptim();
+  config.DisableGlogInfo();
+
+  this->predictor_ = CreatePaddlePredictor(config);
+}
+} // namespace PaddleOCR

deploy/cpp_infer/src/ocr_rec.cpp

@@ -17,7 +17,7 @@
 namespace PaddleOCR {
 
 void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
-                         cv::Mat &img) {
+                         cv::Mat &img, Classifier *cls) {
   cv::Mat srcimg;
   img.copyTo(srcimg);
   cv::Mat crop_img;
@@ -27,6 +27,9 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
   int index = 0;
   for (int i = boxes.size() - 1; i >= 0; i--) {
     crop_img = GetRotateCropImage(srcimg, boxes[i]);
+    if (cls != nullptr) {
+      crop_img = cls->Run(crop_img);
+    }
 
     float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
 

deploy/cpp_infer/src/preprocess_op.cpp

@@ -116,4 +116,26 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
              cv::INTER_LINEAR);
 }
 
+void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
+                       const std::vector<int> &rec_image_shape) {
+  int imgC, imgH, imgW;
+  imgC = rec_image_shape[0];
+  imgH = rec_image_shape[1];
+  imgW = rec_image_shape[2];
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w, resize_h;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_LINEAR);
+  if (resize_w < imgW) {
+    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w,
+                       cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
+  }
+}
+
 } // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/tools/config.txt

@@ -13,6 +13,11 @@ det_db_box_thresh  0.5
 det_db_unclip_ratio  2.0
 det_model_dir  ./inference/det_db
 
+# cls config
+use_angle_cls 0
+cls_model_dir ../inference/cls
+cls_thresh  0.9
+
 # rec config
 rec_model_dir  ./inference/rec_crnn
 char_list_file ../../ppocr/utils/ppocr_keys_v1.txt

deploy/lite/Makefile

@@ -40,8 +40,8 @@ CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SY
 
 #CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
 
-ocr_db_crnn: fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o
-	$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o  -o ocr_db_crnn  $(CXX_LIBS) $(LDFLAGS)
+ocr_db_crnn: fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o
+	$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o -o ocr_db_crnn  $(CXX_LIBS) $(LDFLAGS)
 
 ocr_db_crnn.o: ocr_db_crnn.cc
 	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ocr_db_crnn.o -c ocr_db_crnn.cc
@@ -49,6 +49,9 @@ ocr_db_crnn.o: ocr_db_crnn.cc
 crnn_process.o: fetch_opencv crnn_process.cc
 	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o crnn_process.o -c crnn_process.cc
 
+cls_process.o: fetch_opencv cls_process.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o cls_process.o -c cls_process.cc
+
 db_post_process.o: fetch_clipper fetch_opencv db_post_process.cc
 	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o db_post_process.o -c db_post_process.cc
 
@@ -73,5 +76,5 @@ fetch_opencv:
 
 .PHONY: clean
 clean:
-	rm -f ocr_db_crnn.o clipper.o db_post_process.o crnn_process.o
+	rm -f ocr_db_crnn.o clipper.o db_post_process.o crnn_process.o cls_process.o
 	rm -f ocr_db_crnn

deploy/lite/cls_process.cc

+// 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 "cls_process.h" //NOLINT
+#include <algorithm>
+#include <memory>
+#include <string>
+
+const std::vector<int> rec_image_shape{3, 32, 100};
+
+cv::Mat ClsResizeImg(cv::Mat img) {
+  int imgC, imgH, imgW;
+  imgC = rec_image_shape[0];
+  imgH = rec_image_shape[1];
+  imgW = rec_image_shape[2];
+
+  float ratio = static_cast<float>(img.cols) / static_cast<float>(img.rows);
+
+  int resize_w, resize_h;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_LINEAR);
+  if (resize_w < imgW) {
+    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w,
+                       cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
+  }
+  return resize_img;
+}
\ No newline at end of file

deploy/lite/cls_process.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "math.h" //NOLINT
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+
+cv::Mat ClsResizeImg(cv::Mat img);
\ No newline at end of file

deploy/lite/ocr_db_crnn.cc

@@ -15,6 +15,7 @@
 #include "paddle_api.h" // NOLINT
 #include <chrono>
 
+#include "cls_process.h"
 #include "crnn_process.h"
 #include "db_post_process.h"
 
@@ -105,11 +106,55 @@ cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
   return resize_img;
 }
 
+cv::Mat RunClsModel(cv::Mat img, std::shared_ptr<PaddlePredictor> predictor_cls,
+                    const float thresh = 0.5) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat crop_img;
+  cv::Mat resize_img;
+
+  int index = 0;
+  float wh_ratio =
+      static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);
+
+  resize_img = ClsResizeImg(crop_img);
+  resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
+
+  const float *dimg = reinterpret_cast<const float *>(resize_img.data);
+
+  std::unique_ptr<Tensor> input_tensor0(std::move(predictor_cls->GetInput(0)));
+  input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
+  auto *data0 = input_tensor0->mutable_data<float>();
+
+  NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
+  // Run CLS predictor
+  predictor_cls->Run();
+
+  // Get output and run postprocess
+  std::unique_ptr<const Tensor> softmax_out(
+      std::move(predictor_cls->GetOutput(0)));
+  std::unique_ptr<const Tensor> label_out(
+      std::move(predictor_cls->GetOutput(1)));
+  auto *softmax_scores = softmax_out->mutable_data<float>();
+  auto *label_idxs = label_out->data<int64>();
+  int label_idx = label_idxs[0];
+  float score = softmax_scores[label_idx];
+
+  if (label_idx % 2 == 1 && score > thresh) {
+    cv::rotate(srcimg, srcimg, 1);
+  }
+  return srcimg;
+}
+
 void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
                  std::shared_ptr<PaddlePredictor> predictor_crnn,
                  std::vector<std::string> &rec_text,
                  std::vector<float> &rec_text_score,
-                 std::vector<std::string> charactor_dict) {
+                 std::vector<std::string> charactor_dict,
+                 std::shared_ptr<PaddlePredictor> predictor_cls) {
   std::vector<float> mean = {0.5f, 0.5f, 0.5f};
   std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
 
@@ -121,6 +166,7 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
   int index = 0;
   for (int i = boxes.size() - 1; i >= 0; i--) {
     crop_img = GetRotateCropImage(srcimg, boxes[i]);
+    crop_img = RunClsModel(crop_img, predictor_cls);
     float wh_ratio =
         static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);
 
@@ -323,8 +369,9 @@ int main(int argc, char **argv) {
   }
   std::string det_model_file = argv[1];
   std::string rec_model_file = argv[2];
-  std::string img_path = argv[3];
-  std::string dict_path = argv[4];
+  std::string cls_model_file = argv[3];
+  std::string img_path = argv[4];
+  std::string dict_path = argv[5];
 
   //// load config from txt file
   auto Config = LoadConfigTxt("./config.txt");
@@ -333,6 +380,7 @@ int main(int argc, char **argv) {
 
   auto det_predictor = loadModel(det_model_file);
   auto rec_predictor = loadModel(rec_model_file);
+  auto cls_predictor = loadModel(cls_model_file);
 
   auto charactor_dict = ReadDict(dict_path);
   charactor_dict.push_back(" ");
@@ -343,7 +391,7 @@ int main(int argc, char **argv) {
   std::vector<std::string> rec_text;
   std::vector<float> rec_text_score;
   RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
-              charactor_dict);
+              charactor_dict, cls_predictor);
 
   auto end = std::chrono::system_clock::now();
   auto duration =

doc/doc_ch/angle_class.md

+## 文字角度分类
+
+### 数据准备
+
+请按如下步骤设置数据集：
+
+训练数据的默认存储路径是 `PaddleOCR/train_data/cls`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：
+
+```
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
+```
+
+请参考下文组织您的数据。
+- 训练集
+
+首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（cls_gt_train.txt）记录图片路径和标签。
+
+**注意：** 默认请将图片路径和图片标签用 `\t` 分割，如用其他方式分割将造成训练报错
+
+0和180分别表示图片的角度为0度和180度
+
+```
+" 图像文件名                 图像标注信息 "
+
+train_data/cls/word_001.jpg   0
+train_data/cls/word_002.jpg   180
+```
+
+最终训练集应有如下文件结构：
+```
+|-train_data
+    |-cls
+        |- cls_gt_train.txt
+        |- train
+            |- word_001.png
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+- 测试集
+
+同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个cls_gt_test.txt，测试集的结构如下所示：
+
+```
+|-train_data
+    |-cls
+        |- 和一个cls_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+### 启动训练
+
+PaddleOCR提供了训练脚本、评估脚本和预测脚本。
+
+开始训练:
+
+*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
+
+```
+# 设置PYTHONPATH路径
+export PYTHONPATH=$PYTHONPATH:.
+# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+# 启动训练
+python3 tools/train.py -c configs/cls/cls_mv3.yml
+```
+
+- 数据增强
+
+PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
+
+默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。
+
+训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考：
+[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
+[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+
+*由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
+
+### 训练
+
+PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/cls_mv3/best_accuracy` 。
+
+如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
+
+**注意，预测/评估时的配置文件请务必与训练一致。**
+
+### 评估
+
+评估数据集可以通过`configs/cls/cls_reader.yml`  修改EvalReader中的 `label_file_path` 设置。
+
+*注意* 评估时必须确保配置文件中 infer_img 字段为空
+```
+export CUDA_VISIBLE_DEVICES=0
+# GPU 评估， Global.checkpoints 为待测权重
+python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+```
+
+### 预测
+
+* 训练引擎的预测
+
+使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
+
+默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重：
+
+```
+# 预测分类结果
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+```
+
+预测图片：
+
+![](../imgs_words/en/word_1.png)
+
+得到输入图像的预测结果：
+
+```
+infer_img: doc/imgs_words/en/word_1.png
+    scores: [[0.93161047 0.06838956]]
+    label: [0]
+```

doc/doc_ch/inference.md

@@ -11,24 +11,28 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
 - [一、训练模型转inference模型](#训练模型转inference模型)
     - [检测模型转inference模型](#检测模型转inference模型)
     - [识别模型转inference模型](#识别模型转inference模型)  
-    
+    - [方向分类模型转inference模型](#方向模型转inference模型)  
+
 - [二、文本检测模型推理](#文本检测模型推理)
     - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
     - [2. DB文本检测模型推理](#DB文本检测模型推理)
     - [3. EAST文本检测模型推理](#EAST文本检测模型推理)
     - [4. SAST文本检测模型推理](#SAST文本检测模型推理)  
-    
+
 - [三、文本识别模型推理](#文本识别模型推理)
     - [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
     - [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
     - [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
-    - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)  
-    
-- [四、文本检测、识别串联推理](#文本检测、识别串联推理)
+    - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
+
+- [四、方向分类模型推理](#方向识别模型推理)
+    - [1. 方向分类模型推理](#方向分类模型推理)
+
+- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
     - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
     - [2. 其他模型推理](#其他模型推理)
-    
-    
+
+
 <a name="训练模型转inference模型"></a>
 ## 一、训练模型转inference模型
 <a name="检测模型转inference模型"></a>
@@ -84,6 +88,32 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
   └─  params    识别inference模型的参数文件
 ```
 
+<a name="方向分类模型转inference模型"></a>
+### 方向分类模型转inference模型
+
+下载方向分类模型：
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/
+```
+
+方向分类模型转inference模型与检测的方式相同，如下：
+```
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
+        Global.save_inference_dir=./inference/cls/
+```
+
+转换成功后，在目录下有两个文件：
+```
+/inference/cls/
+  └─  model     识别inference模型的program文件
+  └─  params    识别inference模型的参数文件
+```
+
 <a name="文本检测模型推理"></a>
 ## 二、文本检测模型推理
 
@@ -275,15 +305,36 @@ dict_character = list(self.character_str)
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
 ```
 
-<a name="文本检测、识别串联推理"></a>
-## 四、文本检测、识别串联推理
+
+<a name="方向分类模型推理"></a>
+## 四、方向分类模型推理
+
+下面将介绍方向分类模型推理。
+
+<a name="方向分类模型推理"></a>
+### 1. 方向分类模型推理
+
+方向分类模型推理，可以执行如下命令：
+
+```
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+```
+
+![](../imgs_words/ch/word_4.jpg)
+
+执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
+
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
+
+<a name="文本检测、方向分类和文字识别串联推理"></a>
+## 五、文本检测、方向分类和文字识别串联推理
 <a name="超轻量中文OCR模型推理"></a>
 ### 1. 超轻量中文OCR模型推理
 
-在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
+在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。可视化识别结果默认保存到 ./inference_results 文件夹里面。
 
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls true
 ```
 
 执行命令后，识别结果图像如下：

doc/doc_en/angle_class_en.md

+## TEXT ANGLE CLASSIFICATION
+
+### DATA PREPARATION
+
+Please organize the dataset as follows:
+
+The default storage path for training data is `PaddleOCR/train_data/cls`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
+
+```
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
+```
+
+please refer to the following to organize your data.
+
+- Training set
+
+First put the training images in the same folder (train_images), and use a txt file (cls_gt_train.txt) to store the image path and label.
+
+* Note: by default, the image path and image label are split with `\t`, if you use other methods to split, it will cause training error
+
+0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
+
+```
+" Image file name           Image annotation "
+
+train_data/word_001.jpg   0
+train_data/word_002.jpg   180
+```
+
+The final training set should have the following file structure:
+
+```
+|-train_data
+    |-cls
+        |- cls_gt_train.txt
+        |- train
+            |- word_001.png
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+- Test set
+
+Similar to the training set, the test set also needs to be provided a folder
+containing all images (test) and a cls_gt_test.txt. The structure of the test set is as follows:
+
+```
+|-train_data
+    |-cls
+        |- cls_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+### TRAINING
+
+PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
+
+Start training:
+
+```
+# Set PYTHONPATH path
+export PYTHONPATH=$PYTHONPATH:.
+# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+# Training icdar15 English data
+python3 tools/train.py -c configs/cls/cls_mv3.yml
+```
+
+- Data Augmentation
+
+PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
+
+The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
+
+Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
+[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
+[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+
+
+- Training
+
+PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/cls_mv3/best_accuracy` during the evaluation process.
+
+If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
+
+**Note that the configuration file for prediction/evaluation must be consistent with the training.**
+
+### EVALUATION
+
+The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader.
+
+```
+export CUDA_VISIBLE_DEVICES=0
+# GPU evaluation, Global.checkpoints is the weight to be tested
+python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+```
+
+### PREDICTION
+
+* Training engine prediction
+
+Using the model trained by paddleocr, you can quickly get prediction through the following script.
+
+The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
+
+```
+# Predict English results
+python3 tools/infer_rec.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+```
+
+Input image:
+
+![](../imgs_words/en/word_1.png)
+
+Get the prediction result of the input image:
+
+```
+infer_img: doc/imgs_words/en/word_1.png
+    scores: [[0.93161047 0.06838956]]
+    label: [0]
+```

doc/doc_en/inference_en.md

@@ -12,25 +12,28 @@ Next, we first introduce how to convert a trained model into an inference model,
 - [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
     - [Convert detection model to inference model](#Convert_detection_model)
     - [Convert recognition model to inference model](#Convert_recognition_model)
-    
-    
+    - [Convert angle classification model to inference model](#Convert_angle_class_model)
+
+
 - [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
     - [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
     - [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
     - [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
     - [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
-    
+
 - [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
     - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
     - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
     - [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
     - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
-    
-    
-- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
+
+- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
+    - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
+
+- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
     - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
     - [2. OTHER MODELS](#OTHER_MODELS)
-    
+
 <a name="CONVERT"></a>
 ## CONVERT TRAINING MODEL TO INFERENCE MODEL
 <a name="Convert_detection_model"></a>
@@ -87,6 +90,33 @@ After the conversion is successful, there are two files in the directory:
   └─  params    Identify the parameter files of the inference model
 ```
 
+<a name="Convert_angle_class_model"></a>
+### Convert angle classification model to inference model
+
+Download the angle classification model:
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/
+```
+
+The angle classification model is converted to the inference model in the same way as the detection, as follows:
+```
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+#  Global.save_inference_dir Set the address where the converted model will be saved.
+
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
+        Global.save_inference_dir=./inference/cls/
+```
+
+After the conversion is successful, there are two files in the directory:
+```
+/inference/cls/
+  └─  model     Identify the saved model files
+  └─  params    Identify the parameter files of the inference model
+```
+
+
 <a name="DETECTION_MODEL_INFERENCE"></a>
 ## TEXT DETECTION MODEL INFERENCE
 
@@ -276,16 +306,39 @@ If the chars dictionary is modified during training, you need to specify the new
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
 ```
 
+
+<a name="ANGLE_CLASSIFICATION_MODEL_INFERENCE"></a>
+## ANGLE CLASSIFICATION MODEL INFERENCE
+
+The following will introduce the angle classification model inference.
+
+
+<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
+### 1.ANGLE CLASSIFICATION MODEL INFERENCE
+
+For angle classification model inference, you can execute the following commands:
+
+```
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+```
+
+![](../imgs_words/ch/word_4.jpg)
+
+After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
+
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
+
+
 <a name="CONCATENATION"></a>
-## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
+## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
 
 <a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
 ### 1. LIGHTWEIGHT CHINESE MODEL
 
-When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visualized recognition results are saved to the `./inference_results` folder by default.
+When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model.The visualized recognition results are saved to the `./inference_results` folder by default.
 
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls true
 ```
 
 After executing the command, the recognition result image is as follows:

ppocr/data/cls/__init__.py

+# 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.

ppocr/data/cls/dataset_traversal.py

+# 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
+import sys
+import math
+import random
+import numpy as np
+import cv2
+
+from ppocr.utils.utility import initial_logger
+from ppocr.utils.utility import get_image_file_list
+
+logger = initial_logger()
+
+from ppocr.data.rec.img_tools import resize_norm_img, warp
+from ppocr.data.cls.randaugment import RandAugment
+
+
+def random_crop(img):
+    img_h, img_w = img.shape[:2]
+    if img_w > img_h * 4:
+        w = random.randint(img_h * 2, img_w)
+        i = random.randint(0, img_w - w)
+
+        img = img[:, i:i + w, :]
+    return img
+
+
+class SimpleReader(object):
+    def __init__(self, params):
+        if params['mode'] != 'train':
+            self.num_workers = 1
+        else:
+            self.num_workers = params['num_workers']
+        if params['mode'] != 'test':
+            self.img_set_dir = params['img_set_dir']
+            self.label_file_path = params['label_file_path']
+        self.use_gpu = params['use_gpu']
+        self.image_shape = params['image_shape']
+        self.mode = params['mode']
+        self.infer_img = params['infer_img']
+        self.use_distort = params['mode'] == 'train' and params['distort']
+        self.randaug = RandAugment()
+        self.label_list = params['label_list']
+        if "distort" in params:
+            self.use_distort = params['distort'] and params['use_gpu']
+            if not params['use_gpu']:
+                logger.info(
+                    "Distort operation can only support in GPU.Distort will be set to False."
+                )
+        if params['mode'] == 'train':
+            self.batch_size = params['train_batch_size_per_card']
+            self.drop_last = True
+        else:
+            self.batch_size = params['test_batch_size_per_card']
+            self.drop_last = False
+            self.use_distort = False
+
+    def __call__(self, process_id):
+        if self.mode != 'train':
+            process_id = 0
+
+        def get_device_num():
+            if self.use_gpu:
+                gpus = os.environ.get("CUDA_VISIBLE_DEVICES", 1)
+                gpu_num = len(gpus.split(','))
+                return gpu_num
+            else:
+                cpu_num = os.environ.get("CPU_NUM", 1)
+                return int(cpu_num)
+
+        def sample_iter_reader():
+            if self.mode != 'train' and self.infer_img is not None:
+                image_file_list = get_image_file_list(self.infer_img)
+                for single_img in image_file_list:
+                    img = cv2.imread(single_img)
+                    if img.shape[-1] == 1 or len(list(img.shape)) == 2:
+                        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+                    norm_img = resize_norm_img(img, self.image_shape)
+
+                    norm_img = norm_img[np.newaxis, :]
+                    yield norm_img
+            else:
+                with open(self.label_file_path, "rb") as fin:
+                    label_infor_list = fin.readlines()
+                img_num = len(label_infor_list)
+                img_id_list = list(range(img_num))
+                random.shuffle(img_id_list)
+                if sys.platform == "win32" and self.num_workers != 1:
+                    print("multiprocess is not fully compatible with Windows."
+                          "num_workers will be 1.")
+                    self.num_workers = 1
+                if self.batch_size * get_device_num(
+                ) * self.num_workers > img_num:
+                    raise Exception(
+                        "The number of the whole data ({}) is smaller than the batch_size * devices_num * num_workers ({})".
+                        format(img_num, self.batch_size * get_device_num() *
+                               self.num_workers))
+                for img_id in range(process_id, img_num, self.num_workers):
+                    label_infor = label_infor_list[img_id_list[img_id]]
+                    substr = label_infor.decode('utf-8').strip("\n").split("\t")
+                    label = self.label_list.index(substr[1])
+
+                    img_path = self.img_set_dir + "/" + substr[0]
+                    img = cv2.imread(img_path)
+                    if img is None:
+                        logger.info("{} does not exist!".format(img_path))
+                        continue
+                    if img.shape[-1] == 1 or len(list(img.shape)) == 2:
+                        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+                    if self.use_distort:
+                        img = warp(img, 10)
+                        img = self.randaug(img)
+                    norm_img = resize_norm_img(img, self.image_shape)
+                    norm_img = norm_img[np.newaxis, :]
+                    yield (norm_img, label)
+
+        def batch_iter_reader():
+            batch_outs = []
+            for outs in sample_iter_reader():
+                batch_outs.append(outs)
+                if len(batch_outs) == self.batch_size:
+                    yield batch_outs
+                    batch_outs = []
+            if not self.drop_last:
+                if len(batch_outs) != 0:
+                    yield batch_outs
+
+        if self.infer_img is None:
+            return batch_iter_reader
+        return sample_iter_reader

ppocr/data/cls/randaugment.py

+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+import random
+import six
+
+
+class RawRandAugment(object):
+    def __init__(self, num_layers=2, magnitude=5, fillcolor=(128, 128, 128)):
+        self.num_layers = num_layers
+        self.magnitude = magnitude
+        self.max_level = 10
+
+        abso_level = self.magnitude / self.max_level
+        self.level_map = {
+            "shearX": 0.3 * abso_level,
+            "shearY": 0.3 * abso_level,
+            "translateX": 150.0 / 331 * abso_level,
+            "translateY": 150.0 / 331 * abso_level,
+            "rotate": 30 * abso_level,
+            "color": 0.9 * abso_level,
+            "posterize": int(4.0 * abso_level),
+            "solarize": 256.0 * abso_level,
+            "contrast": 0.9 * abso_level,
+            "sharpness": 0.9 * abso_level,
+            "brightness": 0.9 * abso_level,
+            "autocontrast": 0,
+            "equalize": 0,
+            "invert": 0
+        }
+
+        # from https://stackoverflow.com/questions/5252170/
+        # specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot,
+                                   Image.new("RGBA", rot.size, (128, ) * 4),
+                                   rot).convert(img.mode)
+
+        rnd_ch_op = random.choice
+
+        self.func = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, magnitude * rnd_ch_op([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, magnitude * rnd_ch_op([-1, 1]), 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, magnitude * img.size[0] * rnd_ch_op([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, 0, 1, magnitude * img.size[1] * rnd_ch_op([-1, 1])),
+                fillcolor=fillcolor),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "posterize": lambda img, magnitude:
+            ImageOps.posterize(img, magnitude),
+            "solarize": lambda img, magnitude:
+            ImageOps.solarize(img, magnitude),
+            "contrast": lambda img, magnitude:
+            ImageEnhance.Contrast(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "sharpness": lambda img, magnitude:
+            ImageEnhance.Sharpness(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "brightness": lambda img, magnitude:
+            ImageEnhance.Brightness(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "autocontrast": lambda img, magnitude:
+            ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img)
+        }
+
+    def __call__(self, img):
+        avaiable_op_names = list(self.level_map.keys())
+        for layer_num in range(self.num_layers):
+            op_name = np.random.choice(avaiable_op_names)
+            img = self.func[op_name](img, self.level_map[op_name])
+        return img
+
+
+class RandAugment(RawRandAugment):
+    """ RandAugment wrapper to auto fit different img types """
+
+    def __init__(self, *args, **kwargs):
+        if six.PY2:
+            super(RandAugment, self).__init__(*args, **kwargs)
+        else:
+            super().__init__(*args, **kwargs)
+
+    def __call__(self, img):
+        if not isinstance(img, Image.Image):
+            img = np.ascontiguousarray(img)
+            img = Image.fromarray(img)
+
+        if six.PY2:
+            img = super(RandAugment, self).__call__(img)
+        else:
+            img = super().__call__(img)
+
+        if isinstance(img, Image.Image):
+            img = np.asarray(img)
+
+        return img

ppocr/modeling/architectures/cls_model.py

+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import fluid
+
+from ppocr.utils.utility import create_module
+from ppocr.utils.utility import initial_logger
+
+logger = initial_logger()
+from copy import deepcopy
+
+
+class ClsModel(object):
+    def __init__(self, params):
+        super(ClsModel, self).__init__()
+        global_params = params['Global']
+        self.infer_img = global_params['infer_img']
+
+        backbone_params = deepcopy(params["Backbone"])
+        backbone_params.update(global_params)
+        self.backbone = create_module(backbone_params['function']) \
+            (params=backbone_params)
+
+        head_params = deepcopy(params["Head"])
+        head_params.update(global_params)
+        self.head = create_module(head_params['function']) \
+            (params=head_params)
+
+        loss_params = deepcopy(params["Loss"])
+        loss_params.update(global_params)
+        self.loss = create_module(loss_params['function']) \
+            (params=loss_params)
+
+        self.image_shape = global_params['image_shape']
+
+    def create_feed(self, mode):
+        image_shape = deepcopy(self.image_shape)
+        image_shape.insert(0, -1)
+        if mode == "train":
+            image = fluid.data(name='image', shape=image_shape, dtype='float32')
+            label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+            feed_list = [image, label]
+            labels = {'label': label}
+            loader = fluid.io.DataLoader.from_generator(
+                feed_list=feed_list,
+                capacity=64,
+                use_double_buffer=True,
+                iterable=False)
+        else:
+            labels = None
+            loader = None
+            image = fluid.data(name='image', shape=image_shape, dtype='float32')
+        return image, labels, loader
+
+    def __call__(self, mode):
+        image, labels, loader = self.create_feed(mode)
+        inputs = image
+        conv_feas = self.backbone(inputs)
+        predicts = self.head(conv_feas, labels, mode)
+        if mode == "train":
+            loss = self.loss(predicts, labels)
+            label = labels['label']
+            acc = fluid.layers.accuracy(predicts['predict'], label, k=1)
+            outputs = {'total_loss': loss, 'decoded_out': \
+                predicts['decoded_out'], 'label': label, 'acc': acc}
+            return loader, outputs
+        elif mode == "export":
+            return [image, predicts]
+        else:
+            return loader, predicts

ppocr/modeling/heads/cls_head.py

+#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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+
+
+class ClsHead(object):
+    def __init__(self, params):
+        super(ClsHead, self).__init__()
+        self.class_dim = params['class_dim']
+
+    def __call__(self, inputs, labels=None, mode=None):
+        pool = fluid.layers.pool2d(
+            input=inputs, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
+
+        out = fluid.layers.fc(
+            input=pool,
+            size=self.class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                name="fc_0.w_0",
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            bias_attr=fluid.param_attr.ParamAttr(name="fc_0.b_0"))
+
+        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+        out_label = fluid.layers.argmax(out, axis=1)
+        predicts = {'predict': softmax_out, 'decoded_out': out_label}
+        return predicts

ppocr/modeling/losses/cls_loss.py

+# copyright (c) 2019 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid as fluid
+
+
+class ClsLoss(object):
+    def __init__(self, params):
+        super(ClsLoss, self).__init__()
+        self.loss_func = fluid.layers.cross_entropy
+
+    def __call__(self, predicts, labels):
+        predict = predicts['predict']
+        label = labels['label']
+        # softmax_out = fluid.layers.softmax(predict, use_cudnn=False)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        sum_cost = fluid.layers.mean(cost)
+        return sum_cost

tools/eval.py

@@ -45,10 +45,12 @@ from ppocr.utils.save_load import init_model
 from eval_utils.eval_det_utils import eval_det_run
 from eval_utils.eval_rec_utils import test_rec_benchmark
 from eval_utils.eval_rec_utils import eval_rec_run
+from eval_utils.eval_cls_utils import eval_cls_run
 
 
 def main():
-    startup_prog, eval_program, place, config, train_alg_type = program.preprocess()
+    startup_prog, eval_program, place, config, train_alg_type = program.preprocess(
+    )
     eval_build_outputs = program.build(
         config, eval_program, startup_prog, mode='test')
     eval_fetch_name_list = eval_build_outputs[1]
@@ -67,6 +69,14 @@ def main():
             'fetch_varname_list':eval_fetch_varname_list}
         metrics = eval_det_run(exe, config, eval_info_dict, "eval")
         logger.info("Eval result: {}".format(metrics))
+    elif train_alg_type == 'cls':
+        eval_reader = reader_main(config=config, mode="eval")
+        eval_info_dict = {'program': eval_program, \
+                          'reader': eval_reader, \
+                          'fetch_name_list': eval_fetch_name_list, \
+                          'fetch_varname_list': eval_fetch_varname_list}
+        metrics = eval_cls_run(exe, eval_info_dict)
+        logger.info("Eval result: {}".format(metrics))
     else:
         reader_type = config['Global']['reader_yml']
         if "benchmark" not in reader_type:

tools/eval_utils/eval_cls_utils.py

+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+__all__ = ['eval_cls_run']
+
+import logging
+
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
+
+def eval_cls_run(exe, eval_info_dict):
+    """
+    Run evaluation program, return program outputs.
+    """
+    total_sample_num = 0
+    total_acc_num = 0
+    total_batch_num = 0
+
+    for data in eval_info_dict['reader']():
+        img_num = len(data)
+        img_list = []
+        label_list = []
+        for ino in range(img_num):
+            img_list.append(data[ino][0])
+            label_list.append(data[ino][1])
+
+        img_list = np.concatenate(img_list, axis=0)
+        outs = exe.run(eval_info_dict['program'], \
+                       feed={'image': img_list}, \
+                       fetch_list=eval_info_dict['fetch_varname_list'], \
+                       return_numpy=False)
+        softmax_outs = np.array(outs[1])
+        if len(softmax_outs.shape) != 1:
+            softmax_outs = np.array(outs[0])
+        acc, acc_num = cal_cls_acc(softmax_outs, label_list)
+        total_acc_num += acc_num
+        total_sample_num += len(label_list)
+        # logger.info("eval batch id: {}, acc: {}".format(total_batch_num, acc))
+        total_batch_num += 1
+    avg_acc = total_acc_num * 1.0 / total_sample_num
+    metrics = {'avg_acc': avg_acc, "total_acc_num": total_acc_num, \
+               "total_sample_num": total_sample_num}
+    return metrics
+
+
+def cal_cls_acc(preds, labels):
+    acc_num = 0
+    for pred, label in zip(preds, labels):
+        if pred == label:
+            acc_num += 1
+    return acc_num / len(preds), acc_num

tools/infer/predict_cls.py

+# 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.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+import tools.infer.utility as utility
+from ppocr.utils.utility import initial_logger
+
+logger = initial_logger()
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+import cv2
+import copy
+import numpy as np
+import math
+import time
+from paddle import fluid
+
+
+class TextClassifier(object):
+    def __init__(self, args):
+        self.predictor, self.input_tensor, self.output_tensors = \
+            utility.create_predictor(args, mode="cls")
+        self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
+        self.cls_batch_num = args.rec_batch_num
+        self.label_list = args.label_list
+        self.use_zero_copy_run = args.use_zero_copy_run
+
+    def resize_norm_img(self, img):
+        imgC, imgH, imgW = self.cls_image_shape
+        h = img.shape[0]
+        w = img.shape[1]
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+        resized_image = resized_image.astype('float32')
+        if self.cls_image_shape[0] == 1:
+            resized_image = resized_image / 255
+            resized_image = resized_image[np.newaxis, :]
+        else:
+            resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+        padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im
+
+    def __call__(self, img_list):
+        img_list = copy.deepcopy(img_list)
+        img_num = len(img_list)
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        for img in img_list:
+            width_list.append(img.shape[1] / float(img.shape[0]))
+        # Sorting can speed up the cls process
+        indices = np.argsort(np.array(width_list))
+
+        cls_res = [['', 0.0]] * img_num
+        batch_num = self.cls_batch_num
+        predict_time = 0
+        for beg_img_no in range(0, img_num, batch_num):
+            end_img_no = min(img_num, beg_img_no + batch_num)
+            norm_img_batch = []
+            max_wh_ratio = 0
+            for ino in range(beg_img_no, end_img_no):
+                h, w = img_list[indices[ino]].shape[0:2]
+                wh_ratio = w * 1.0 / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(img_list[indices[ino]])
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+            norm_img_batch = np.concatenate(norm_img_batch)
+            norm_img_batch = norm_img_batch.copy()
+            starttime = time.time()
+
+            if self.use_zero_copy_run:
+                self.input_tensor.copy_from_cpu(norm_img_batch)
+                self.predictor.zero_copy_run()
+            else:
+                norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
+                self.predictor.run([norm_img_batch])
+
+            prob_out = self.output_tensors[0].copy_to_cpu()
+            label_out = self.output_tensors[1].copy_to_cpu()
+            if len(label_out.shape) != 1:
+                prob_out, label_out = label_out, prob_out
+
+            elapse = time.time() - starttime
+            predict_time += elapse
+            for rno in range(len(label_out)):
+                label_idx = label_out[rno]
+                score = prob_out[rno][label_idx]
+                label = self.label_list[label_idx]
+                cls_res[indices[beg_img_no + rno]] = [label, score]
+                if '180' in label and score > 0.9999:
+                    img_list[indices[beg_img_no + rno]] = cv2.rotate(
+                        img_list[indices[beg_img_no + rno]], 1)
+        return img_list, cls_res, predict_time
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    text_classifier = TextClassifier(args)
+    valid_image_file_list = []
+    img_list = []
+    for image_file in image_file_list[:10]:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        valid_image_file_list.append(image_file)
+        img_list.append(img)
+    try:
+        img_list, cls_res, predict_time = text_classifier(img_list)
+    except Exception as e:
+        print(e)
+        exit()
+    for ino in range(len(img_list)):
+        print("Predicts of %s:%s" % (valid_image_file_list[ino], cls_res[ino]))
+    print("Total predict time for %d images:%.3f" %
+          (len(img_list), predict_time))
+
+
+if __name__ == "__main__":
+    main(utility.parse_args())

tools/infer/predict_system.py

@@ -13,16 +13,19 @@
 # limitations under the License.
 import os
 import sys
+
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 
 import tools.infer.utility as utility
 from ppocr.utils.utility import initial_logger
+
 logger = initial_logger()
 import cv2
 import tools.infer.predict_det as predict_det
 import tools.infer.predict_rec as predict_rec
+import tools.infer.predict_cls as predict_cls
 import copy
 import numpy as np
 import math
@@ -37,6 +40,9 @@ class TextSystem(object):
     def __init__(self, args):
         self.text_detector = predict_det.TextDetector(args)
         self.text_recognizer = predict_rec.TextRecognizer(args)
+        self.use_angle_cls = args.use_angle_cls
+        if self.use_angle_cls:
+            self.text_classifier = predict_cls.TextClassifier(args)
 
     def get_rotate_crop_image(self, img, points):
         '''
@@ -91,6 +97,11 @@ class TextSystem(object):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
+        if self.use_angle_cls:
+            img_crop_list, angle_list, elapse = self.text_classifier(
+                img_crop_list)
+            print("cls num  : {}, elapse : {}".format(
+                len(img_crop_list), elapse))
         rec_res, elapse = self.text_recognizer(img_crop_list)
         print("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
@@ -110,8 +121,8 @@ def sorted_boxes(dt_boxes):
     _boxes = list(sorted_boxes)
 
     for i in range(num_boxes - 1):
-        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
-            (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
+                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
             tmp = _boxes[i]
             _boxes[i] = _boxes[i + 1]
             _boxes[i + 1] = tmp

tools/infer/utility.py

@@ -15,6 +15,7 @@
 import argparse
 import os, sys
 from ppocr.utils.utility import initial_logger
+
 logger = initial_logger()
 from paddle.fluid.core import PaddleTensor
 from paddle.fluid.core import AnalysisConfig
@@ -31,34 +32,34 @@ def parse_args():
         return v.lower() in ("true", "t", "1")
 
     parser = argparse.ArgumentParser()
-    #params for prediction engine
+    # params for prediction engine
     parser.add_argument("--use_gpu", type=str2bool, default=True)
     parser.add_argument("--ir_optim", type=str2bool, default=True)
     parser.add_argument("--use_tensorrt", type=str2bool, default=False)
     parser.add_argument("--gpu_mem", type=int, default=8000)
 
-    #params for text detector
+    # params for text detector
     parser.add_argument("--image_dir", type=str)
     parser.add_argument("--det_algorithm", type=str, default='DB')
     parser.add_argument("--det_model_dir", type=str)
     parser.add_argument("--det_max_side_len", type=float, default=960)
 
-    #DB parmas
+    # DB parmas
     parser.add_argument("--det_db_thresh", type=float, default=0.3)
     parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
     parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
 
-    #EAST parmas
+    # EAST parmas
     parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
     parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
     parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
 
-    #SAST parmas
+    # SAST parmas
     parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
     parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
     parser.add_argument("--det_sast_polygon", type=bool, default=False)
 
-    #params for text recognizer
+    # params for text recognizer
     parser.add_argument("--rec_algorithm", type=str, default='CRNN')
     parser.add_argument("--rec_model_dir", type=str)
     parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
@@ -70,14 +71,24 @@ def parse_args():
         type=str,
         default="./ppocr/utils/ppocr_keys_v1.txt")
     parser.add_argument("--use_space_char", type=bool, default=True)
-    parser.add_argument("--enable_mkldnn", type=bool, default=False)
-    parser.add_argument("--use_zero_copy_run", type=bool, default=False)
+
+    # params for text classifier
+    parser.add_argument("--use_angle_cls", type=str2bool, default=False)
+    parser.add_argument("--cls_model_dir", type=str)
+    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
+    parser.add_argument("--label_list", type=list, default=['0', '180'])
+    parser.add_argument("--cls_batch_num", type=int, default=30)
+
+    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
+    parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
     return parser.parse_args()
 
 
 def create_predictor(args, mode):
     if mode == "det":
         model_dir = args.det_model_dir
+    elif mode == 'cls':
+        model_dir = args.cls_model_dir
     else:
         model_dir = args.rec_model_dir
 
@@ -105,7 +116,7 @@ def create_predictor(args, mode):
             config.set_mkldnn_cache_capacity(10)
             config.enable_mkldnn()
 
-    #config.enable_memory_optim()
+    # config.enable_memory_optim()
     config.disable_glog_info()
 
     if args.use_zero_copy_run:

tools/infer_cls.py

+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect.
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+import tools.program as program
+from paddle import fluid
+from ppocr.utils.utility import initial_logger
+
+logger = initial_logger()
+from ppocr.data.reader_main import reader_main
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import create_module
+from ppocr.utils.utility import get_image_file_list
+
+
+def main():
+    config = program.load_config(FLAGS.config)
+    program.merge_config(FLAGS.opt)
+    logger.info(config)
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    #     check_gpu(use_gpu)
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    rec_model = create_module(config['Architecture']['function'])(params=config)
+    startup_prog = fluid.Program()
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            _, outputs = rec_model(mode="test")
+            fetch_name_list = list(outputs.keys())
+            fetch_varname_list = [outputs[v].name for v in fetch_name_list]
+    eval_prog = eval_prog.clone(for_test=True)
+    exe.run(startup_prog)
+
+    init_model(config, eval_prog, exe)
+
+    blobs = reader_main(config, 'test')()
+    infer_img = config['Global']['infer_img']
+    infer_list = get_image_file_list(infer_img)
+    max_img_num = len(infer_list)
+    if len(infer_list) == 0:
+        logger.info("Can not find img in infer_img dir.")
+    for i in range(max_img_num):
+        logger.info("infer_img:%s" % infer_list[i])
+        img = next(blobs)
+        predict = exe.run(program=eval_prog,
+                          feed={"image": img},
+                          fetch_list=fetch_varname_list,
+                          return_numpy=False)
+        scores = np.array(predict[0])
+        label = np.array(predict[1])
+        if len(label.shape) != 1:
+            label, scores = scores, label
+        logger.info('\t scores: {}'.format(scores))
+        logger.info('\t label: {}'.format(label))
+    # save for inference model
+    target_var = []
+    for key, values in outputs.items():
+        target_var.append(values)
+
+    fluid.io.save_inference_model(
+        "./output",
+        feeded_var_names=['image'],
+        target_vars=target_var,
+        executor=exe,
+        main_program=eval_prog,
+        model_filename="model",
+        params_filename="params")
+
+
+if __name__ == '__main__':
+    parser = program.ArgsParser()
+    FLAGS = parser.parse_args()
+    main()

tools/program.py

@@ -30,6 +30,7 @@ import time
 from ppocr.utils.stats import TrainingStats
 from eval_utils.eval_det_utils import eval_det_run
 from eval_utils.eval_rec_utils import eval_rec_run
+from eval_utils.eval_cls_utils import eval_cls_run
 from ppocr.utils.save_load import save_model
 import numpy as np
 from ppocr.utils.character import cal_predicts_accuracy, cal_predicts_accuracy_srn, CharacterOps
@@ -409,6 +410,87 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
     return
 
 
+def train_eval_cls_run(config, exe, train_info_dict, eval_info_dict):
+    train_batch_id = 0
+    log_smooth_window = config['Global']['log_smooth_window']
+    epoch_num = config['Global']['epoch_num']
+    print_batch_step = config['Global']['print_batch_step']
+    eval_batch_step = config['Global']['eval_batch_step']
+    start_eval_step = 0
+    if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
+        start_eval_step = eval_batch_step[0]
+        eval_batch_step = eval_batch_step[1]
+        logger.info(
+            "During the training process, after the {}th iteration, an evaluation is run every {} iterations".
+            format(start_eval_step, eval_batch_step))
+    save_epoch_step = config['Global']['save_epoch_step']
+    save_model_dir = config['Global']['save_model_dir']
+    if not os.path.exists(save_model_dir):
+        os.makedirs(save_model_dir)
+    train_stats = TrainingStats(log_smooth_window, ['loss', 'acc'])
+    best_eval_acc = -1
+    best_batch_id = 0
+    best_epoch = 0
+    train_loader = train_info_dict['reader']
+    for epoch in range(epoch_num):
+        train_loader.start()
+        try:
+            while True:
+                t1 = time.time()
+                train_outs = exe.run(
+                    program=train_info_dict['compile_program'],
+                    fetch_list=train_info_dict['fetch_varname_list'],
+                    return_numpy=False)
+                fetch_map = dict(
+                    zip(train_info_dict['fetch_name_list'],
+                        range(len(train_outs))))
+
+                loss = np.mean(np.array(train_outs[fetch_map['total_loss']]))
+                lr = np.mean(np.array(train_outs[fetch_map['lr']]))
+                acc = np.mean(np.array(train_outs[fetch_map['acc']]))
+
+                t2 = time.time()
+                train_batch_elapse = t2 - t1
+                stats = {'loss': loss, 'acc': acc}
+                train_stats.update(stats)
+                if train_batch_id > start_eval_step and (train_batch_id - start_eval_step) \
+                    % print_batch_step == 0:
+                    logs = train_stats.log()
+                    strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
+                        epoch, train_batch_id, lr, logs, train_batch_elapse)
+                    logger.info(strs)
+
+                if train_batch_id > 0 and\
+                    train_batch_id % eval_batch_step == 0:
+                    model_average = train_info_dict['model_average']
+                    if model_average != None:
+                        model_average.apply(exe)
+                    metrics = eval_cls_run(exe, eval_info_dict)
+                    eval_acc = metrics['avg_acc']
+                    eval_sample_num = metrics['total_sample_num']
+                    if eval_acc > best_eval_acc:
+                        best_eval_acc = eval_acc
+                        best_batch_id = train_batch_id
+                        best_epoch = epoch
+                        save_path = save_model_dir + "/best_accuracy"
+                        save_model(train_info_dict['train_program'], save_path)
+                    strs = 'Test iter: {}, acc:{:.6f}, best_acc:{:.6f}, best_epoch:{}, best_batch_id:{}, eval_sample_num:{}'.format(
+                        train_batch_id, eval_acc, best_eval_acc, best_epoch,
+                        best_batch_id, eval_sample_num)
+                    logger.info(strs)
+                train_batch_id += 1
+
+        except fluid.core.EOFException:
+            train_loader.reset()
+        if epoch == 0 and save_epoch_step == 1:
+            save_path = save_model_dir + "/iter_epoch_0"
+            save_model(train_info_dict['train_program'], save_path)
+        if epoch > 0 and epoch % save_epoch_step == 0:
+            save_path = save_model_dir + "/iter_epoch_%d" % (epoch)
+            save_model(train_info_dict['train_program'], save_path)
+    return
+
+
 def preprocess():
     FLAGS = ArgsParser().parse_args()
     config = load_config(FLAGS.config)
@@ -421,7 +503,7 @@ def preprocess():
 
     alg = config['Global']['algorithm']
     assert alg in [
-        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN'
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
     ]
     if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']:
         config['Global']['char_ops'] = CharacterOps(config['Global'])
@@ -432,7 +514,9 @@ def preprocess():
 
     if alg in ['EAST', 'DB', 'SAST']:
         train_alg_type = 'det'
-    else:
+    elif alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']:
         train_alg_type = 'rec'
+    else:
+        train_alg_type = 'cls'
 
     return startup_program, train_program, place, config, train_alg_type

tools/train.py

@@ -75,7 +75,8 @@ def main():
 
     # dump mode structure
     if config['Global']['debug']:
-        if train_alg_type == 'rec' and 'attention' in config['Global']['loss_type']:
+        if train_alg_type == 'rec' and 'attention' in config['Global'][
+                'loss_type']:
             logger.warning('Does not suport dump attention...')
         else:
             summary(train_program)
@@ -96,8 +97,10 @@ def main():
 
     if train_alg_type == 'det':
         program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
-    else:
+    elif train_alg_type == 'rec':
         program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
+    else:
+        program.train_eval_cls_run(config, exe, train_info_dict, eval_info_dict)
 
 
 def test_reader():
@@ -119,6 +122,7 @@ def test_reader():
 
 
 if __name__ == '__main__':
-    startup_program, train_program, place, config, train_alg_type = program.preprocess()
+    startup_program, train_program, place, config, train_alg_type = program.preprocess(
+    )
     main()
 #     test_reader()