diff --git a/inference/utils/seg_conf_parser.h b/inference/utils/seg_conf_parser.h
index 5af7cd9debe66de4cc84627ba2917ca977c575d7..b1e40ff84bf0ac9e4e932875b341ee9741efb4ff 100644
--- a/inference/utils/seg_conf_parser.h
+++ b/inference/utils/seg_conf_parser.h
@@ -4,7 +4,7 @@
 // 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
+// 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,
@@ -12,168 +12,167 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#pragma once
-#include <iostream>
-#include <vector>
-#include <string>
-
-#include <yaml-cpp/yaml.h>
-namespace PaddleSolution {
-
-    class PaddleSegModelConfigPaser {
-    public:
-        PaddleSegModelConfigPaser()
-            :_class_num(0),
-            _channels(0),
-            _use_gpu(0),
-            _batch_size(1),
-            _model_file_name("__model__"),
-            _param_file_name("__params__") {
-        }
-        ~PaddleSegModelConfigPaser() {
-        }
-
-        void reset() {
-            _resize.clear();
-            _mean.clear();
-            _std.clear();
-            _img_type.clear();
-            _class_num = 0;
-            _channels = 0;
-            _use_gpu = 0;
-            _batch_size = 1;
-            _model_file_name.clear();
-            _model_path.clear();
-            _param_file_name.clear();
-        }
-
-        std::string process_parenthesis(const std::string& str) {
-            if (str.size() < 2) {
-                return str;
-            }
-            std::string nstr(str);
-            if (str[0] == '(' && str.back() == ')') {
-                nstr[0] = '[';
-                nstr[str.size() - 1] = ']';
-            }
-            return nstr;
-        }
-
-        template <typename T>
-        std::vector<T> parse_str_to_vec(const std::string& str) {
-            std::vector<T> data;
-            auto node = YAML::Load(str);
-            for (const auto& item : node) {
-                data.push_back(item.as<T>());
-            }
-            return data;
-        }
-
-        bool load_config(const std::string& conf_file) {
-
-            reset();
-
-            YAML::Node config = YAML::LoadFile(conf_file);
-            // 1. get resize
-            auto str = config["DEPLOY"]["EVAL_CROP_SIZE"].as<std::string>();
-            _resize = parse_str_to_vec<int>(process_parenthesis(str));
-
-            // 2. get mean
-            for (const auto& item : config["DEPLOY"]["MEAN"]) {
-                _mean.push_back(item.as<float>());
-            }
-
-            // 3. get std
-            for (const auto& item : config["DEPLOY"]["STD"]) {
-                _std.push_back(item.as<float>());
-            }
-
-            // 4. get image type
-            _img_type = config["DEPLOY"]["IMAGE_TYPE"].as<std::string>();
-            // 5. get class number
-            _class_num = config["DEPLOY"]["NUM_CLASSES"].as<int>();
-            // 7. set model path
-            _model_path = config["DEPLOY"]["MODEL_PATH"].as<std::string>();
-            // 8. get model file_name
-            _model_file_name = config["DEPLOY"]["MODEL_FILENAME"].as<std::string>();
-            // 9. get model param file name
-            _param_file_name = config["DEPLOY"]["PARAMS_FILENAME"].as<std::string>();
-            // 10. get pre_processor
-            _pre_processor = config["DEPLOY"]["PRE_PROCESSOR"].as<std::string>();
-            // 11. use_gpu
-            _use_gpu = config["DEPLOY"]["USE_GPU"].as<int>();
-            // 12. predictor_mode
-            _predictor_mode = config["DEPLOY"]["PREDICTOR_MODE"].as<std::string>();
-            // 13. batch_size
-            _batch_size = config["DEPLOY"]["BATCH_SIZE"].as<int>();
-            // 14. channels
-            _channels = config["DEPLOY"]["CHANNELS"].as<int>();
-            return true;
-        }
-
-        void debug() const {
-
-            std::cout << "EVAL_CROP_SIZE: (" << _resize[0] << ", " << _resize[1] << ")" << std::endl;
-
-            std::cout << "MEAN: [";
-            for (int i = 0; i < _mean.size(); ++i) {
-                if (i != _mean.size() - 1) {
-                    std::cout << _mean[i] << ", ";
-                } else {
-                    std::cout << _mean[i];
-                }
-            }
-            std::cout << "]" << std::endl;
-
-            std::cout << "STD: [";
-            for (int i = 0; i < _std.size(); ++i) {
-                if (i != _std.size() - 1) {
-                    std::cout << _std[i] << ", ";
-                }
-                else {
-                    std::cout << _std[i];
-                }
-            }
-            std::cout << "]" << std::endl;
-
-            std::cout << "DEPLOY.IMAGE_TYPE: " << _img_type << std::endl;
-            std::cout << "DEPLOY.NUM_CLASSES: " << _class_num << std::endl;
-            std::cout << "DEPLOY.CHANNELS: " << _channels << std::endl;
-            std::cout << "DEPLOY.MODEL_PATH: " << _model_path << std::endl;
-            std::cout << "DEPLOY.MODEL_FILENAME: " << _model_file_name << std::endl;
-            std::cout << "DEPLOY.PARAMS_FILENAME: " << _param_file_name << std::endl;
-            std::cout << "DEPLOY.PRE_PROCESSOR: " << _pre_processor << std::endl;
-            std::cout << "DEPLOY.USE_GPU: " << _use_gpu << std::endl;
-            std::cout << "DEPLOY.PREDICTOR_MODE: " << _predictor_mode << std::endl;
-            std::cout << "DEPLOY.BATCH_SIZE: " << _batch_size << std::endl;
-        }
-
-        // DEPLOY.EVAL_CROP_SIZE
-        std::vector<int> _resize;
-        // DEPLOY.MEAN
-        std::vector<float> _mean;
-        // DEPLOY.STD
-        std::vector<float> _std;
-        // DEPLOY.IMAGE_TYPE
-        std::string _img_type;
-        // DEPLOY.NUM_CLASSES
-        int _class_num;
-        // DEPLOY.CHANNELS
-        int _channels;
-        // DEPLOY.MODEL_PATH
-        std::string _model_path;
-        // DEPLOY.MODEL_FILENAME
-        std::string _model_file_name;
-        // DEPLOY.PARAMS_FILENAME
-        std::string _param_file_name;
-        // DEPLOY.PRE_PROCESSOR
-        std::string _pre_processor;
-        // DEPLOY.USE_GPU
-        int _use_gpu;
-        // DEPLOY.PREDICTOR_MODE
-        std::string _predictor_mode;
-        // DEPLOY.BATCH_SIZE
-        int _batch_size;
-    };
-
-}
+#pragma once
+#include <yaml-cpp/yaml.h>
+#include <iostream>
+#include <vector>
+#include <string>
+
+namespace PaddleSolution {
+class PaddleSegModelConfigPaser {
+ public:
+    PaddleSegModelConfigPaser()
+        :_class_num(0),
+        _channels(0),
+        _use_gpu(0),
+        _batch_size(1),
+        _model_file_name("__model__"),
+        _param_file_name("__params__") {
+    }
+    ~PaddleSegModelConfigPaser() {
+    }
+
+    void reset() {
+        _resize.clear();
+        _mean.clear();
+        _std.clear();
+        _img_type.clear();
+        _class_num = 0;
+        _channels = 0;
+        _use_gpu = 0;
+        _batch_size = 1;
+        _model_file_name.clear();
+        _model_path.clear();
+        _param_file_name.clear();
+    }
+
+    std::string process_parenthesis(const std::string& str) {
+        if (str.size() < 2) {
+            return str;
+        }
+        std::string nstr(str);
+        if (str[0] == '(' && str.back() == ')') {
+            nstr[0] = '[';
+            nstr[str.size() - 1] = ']';
+        }
+        return nstr;
+    }
+
+    template <typename T>
+    std::vector<T> parse_str_to_vec(const std::string& str) {
+        std::vector<T> data;
+        auto node = YAML::Load(str);
+        for (const auto& item : node) {
+            data.push_back(item.as<T>());
+        }
+        return data;
+    }
+
+    bool load_config(const std::string& conf_file) {
+        reset();
+
+        YAML::Node config = YAML::LoadFile(conf_file);
+        // 1. get resize
+        auto str = config["DEPLOY"]["EVAL_CROP_SIZE"].as<std::string>();
+        _resize = parse_str_to_vec<int>(process_parenthesis(str));
+
+        // 2. get mean
+        for (const auto& item : config["DEPLOY"]["MEAN"]) {
+            _mean.push_back(item.as<float>());
+        }
+
+        // 3. get std
+        for (const auto& item : config["DEPLOY"]["STD"]) {
+            _std.push_back(item.as<float>());
+        }
+
+        // 4. get image type
+        _img_type = config["DEPLOY"]["IMAGE_TYPE"].as<std::string>();
+        // 5. get class number
+        _class_num = config["DEPLOY"]["NUM_CLASSES"].as<int>();
+        // 7. set model path
+        _model_path = config["DEPLOY"]["MODEL_PATH"].as<std::string>();
+        // 8. get model file_name
+        _model_file_name = config["DEPLOY"]["MODEL_FILENAME"].as<std::string>();
+        // 9. get model param file name
+        _param_file_name =
+                        config["DEPLOY"]["PARAMS_FILENAME"].as<std::string>();
+        // 10. get pre_processor
+        _pre_processor = config["DEPLOY"]["PRE_PROCESSOR"].as<std::string>();
+        // 11. use_gpu
+        _use_gpu = config["DEPLOY"]["USE_GPU"].as<int>();
+        // 12. predictor_mode
+        _predictor_mode = config["DEPLOY"]["PREDICTOR_MODE"].as<std::string>();
+        // 13. batch_size
+        _batch_size = config["DEPLOY"]["BATCH_SIZE"].as<int>();
+        // 14. channels
+        _channels = config["DEPLOY"]["CHANNELS"].as<int>();
+        return true;
+    }
+
+    void debug() const {
+        std::cout << "EVAL_CROP_SIZE: ("
+                  << _resize[0] << ", " << _resize[1]
+                  << ")" << std::endl;
+        std::cout << "MEAN: [";
+        for (int i = 0; i < _mean.size(); ++i) {
+            if (i != _mean.size() - 1) {
+                std::cout << _mean[i] << ", ";
+            } else {
+                std::cout << _mean[i];
+            }
+        }
+        std::cout << "]" << std::endl;
+
+        std::cout << "STD: [";
+        for (int i = 0; i < _std.size(); ++i) {
+            if (i != _std.size() - 1) {
+                std::cout << _std[i] << ", ";
+            } else {
+                std::cout << _std[i];
+            }
+        }
+        std::cout << "]" << std::endl;
+
+        std::cout << "DEPLOY.IMAGE_TYPE: " << _img_type << std::endl;
+        std::cout << "DEPLOY.NUM_CLASSES: " << _class_num << std::endl;
+        std::cout << "DEPLOY.CHANNELS: " << _channels << std::endl;
+        std::cout << "DEPLOY.MODEL_PATH: " << _model_path << std::endl;
+        std::cout << "DEPLOY.MODEL_FILENAME: " << _model_file_name << std::endl;
+        std::cout << "DEPLOY.PARAMS_FILENAME: "
+                  << _param_file_name << std::endl;
+        std::cout << "DEPLOY.PRE_PROCESSOR: " << _pre_processor << std::endl;
+        std::cout << "DEPLOY.USE_GPU: " << _use_gpu << std::endl;
+        std::cout << "DEPLOY.PREDICTOR_MODE: " << _predictor_mode << std::endl;
+        std::cout << "DEPLOY.BATCH_SIZE: " << _batch_size << std::endl;
+    }
+
+    // DEPLOY.EVAL_CROP_SIZE
+    std::vector<int> _resize;
+    // DEPLOY.MEAN
+    std::vector<float> _mean;
+    // DEPLOY.STD
+    std::vector<float> _std;
+    // DEPLOY.IMAGE_TYPE
+    std::string _img_type;
+    // DEPLOY.NUM_CLASSES
+    int _class_num;
+    // DEPLOY.CHANNELS
+    int _channels;
+    // DEPLOY.MODEL_PATH
+    std::string _model_path;
+    // DEPLOY.MODEL_FILENAME
+    std::string _model_file_name;
+    // DEPLOY.PARAMS_FILENAME
+    std::string _param_file_name;
+    // DEPLOY.PRE_PROCESSOR
+    std::string _pre_processor;
+    // DEPLOY.USE_GPU
+    int _use_gpu;
+    // DEPLOY.PREDICTOR_MODE
+    std::string _predictor_mode;
+    // DEPLOY.BATCH_SIZE
+    int _batch_size;
+};
+
+}  // namespace PaddleSolution
diff --git a/inference/utils/utils.h b/inference/utils/utils.h
index b730f23827ccb76110c866189bf20a2d5aa4873c..7e322daa03c02e704509f032d5709684a341060f 100644
--- a/inference/utils/utils.h
+++ b/inference/utils/utils.h
@@ -4,7 +4,7 @@
 // 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
+// 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,
@@ -30,105 +30,110 @@
 #endif
 
 namespace PaddleSolution {
-    namespace utils {
-        inline std::string path_join(const std::string& dir, const std::string& path) {
-            std::string seperator = "/";
-            #ifdef _WIN32
-            seperator = "\\";
-            #endif
-            return dir + seperator + path;
+namespace utils {
+    inline std::string path_join(const std::string& dir,
+                                 const std::string& path) {
+        std::string seperator = "/";
+        #ifdef _WIN32
+        seperator = "\\";
+        #endif
+        return dir + seperator + path;
+    }
+    #ifndef _WIN32
+    // scan a directory and get all files with input extensions
+    inline std::vector<std::string> get_directory_images(
+                        const std::string& path, const std::string& exts) {
+        std::vector<std::string> imgs;
+        struct dirent *entry;
+        DIR *dir = opendir(path.c_str());
+        if (dir == NULL) {
+            closedir(dir);
+            return imgs;
         }
-        #ifndef _WIN32
-        // scan a directory and get all files with input extensions
-        inline std::vector<std::string> get_directory_images(const std::string& path, const std::string& exts)
-        {
-            std::vector<std::string> imgs;
-            struct dirent *entry;
-            DIR *dir = opendir(path.c_str());
-            if (dir == NULL) {
-                closedir(dir);
-                return imgs;
-            }
 
-            while ((entry = readdir(dir)) != NULL) {
-                std::string item = entry->d_name;
-                auto ext = strrchr(entry->d_name, '.');
-                if (!ext || std::string(ext) == "." || std::string(ext) == "..") {
-                    continue;
-                }
-                if (exts.find(ext) != std::string::npos) {
-                    imgs.push_back(path_join(path, entry->d_name));
-                }
+        while ((entry = readdir(dir)) != NULL) {
+            std::string item = entry->d_name;
+            auto ext = strrchr(entry->d_name, '.');
+            if (!ext || std::string(ext) == "." || std::string(ext) == "..") {
+                continue;
+            }
+            if (exts.find(ext) != std::string::npos) {
+                imgs.push_back(path_join(path, entry->d_name));
             }
-            return imgs;
         }
-        #else
-        // scan a directory and get all files with input extensions
-        inline std::vector<std::string> get_directory_images(const std::string& path, const std::string& exts)
-        {
-            std::vector<std::string> imgs;
-            for (const auto& item : std::experimental::filesystem::directory_iterator(path)) {
-                auto suffix = item.path().extension().string();
-                if (exts.find(suffix) != std::string::npos && suffix.size() > 0) {
-                    auto fullname = path_join(path, item.path().filename().string());
-                    imgs.push_back(item.path().string());
-                }
+        return imgs;
+    }
+    #else
+    // scan a directory and get all files with input extensions
+    inline std::vector<std::string> get_directory_images(
+                    const std::string& path, const std::string& exts) {
+        std::vector<std::string> imgs;
+        for (const auto& item :
+                    std::experimental::filesystem::directory_iterator(path)) {
+            auto suffix = item.path().extension().string();
+            if (exts.find(suffix) != std::string::npos && suffix.size() > 0) {
+                auto fullname = path_join(path,
+                                          item.path().filename().string());
+                imgs.push_back(item.path().string());
             }
-            return imgs;
         }
-        #endif
+        return imgs;
+    }
+    #endif
 
-        // normalize and HWC_BGR -> CHW_RGB
-        inline void normalize(cv::Mat& im, float* data, std::vector<float>& fmean, std::vector<float>& fstd) {
-            int rh = im.rows;
-            int rw = im.cols;
-            int rc = im.channels();
-            double normf = (double)1.0 / 255.0;
-            #pragma omp parallel for
-            for (int h = 0; h < rh; ++h) {
-                const uchar* ptr = im.ptr<uchar>(h);
-                int im_index = 0;
-                for (int w = 0; w < rw; ++w) {
-                    for (int c = 0; c < rc; ++c) {
-                        int top_index = (c * rh + h) * rw + w;
-                        float pixel = static_cast<float>(ptr[im_index++]);
-                        pixel = (pixel * normf - fmean[c]) / fstd[c];
-                        data[top_index] = pixel;
-                    }
+    // normalize and HWC_BGR -> CHW_RGB
+    inline void normalize(cv::Mat& im, float* data, std::vector<float>& fmean,
+                          std::vector<float>& fstd) {
+        int rh = im.rows;
+        int rw = im.cols;
+        int rc = im.channels();
+        double normf = static_cast<double>(1.0) / 255.0;
+        #pragma omp parallel for
+        for (int h = 0; h < rh; ++h) {
+            const uchar* ptr = im.ptr<uchar>(h);
+            int im_index = 0;
+            for (int w = 0; w < rw; ++w) {
+                for (int c = 0; c < rc; ++c) {
+                    int top_index = (c * rh + h) * rw + w;
+                    float pixel = static_cast<float>(ptr[im_index++]);
+                    pixel = (pixel * normf - fmean[c]) / fstd[c];
+                    data[top_index] = pixel;
                 }
             }
         }
+    }
 
-        // argmax
-        inline void argmax(float* out, std::vector<int>& shape, std::vector<uchar>& mask, std::vector<uchar>& scoremap) {
-            int out_img_len = shape[1] * shape[2];
-            int blob_out_len = out_img_len * shape[0];
-            /*
-            Eigen::TensorMap<Eigen::Tensor<float, 3>> out_3d(out, shape[0], shape[1], shape[2]);
-            Eigen::Tensor<Eigen::DenseIndex, 2> argmax = out_3d.argmax(0);
-            */
-            float max_value = -1;
-            int label = 0;
-            #pragma omp parallel private(label)
-            for (int i = 0; i < out_img_len; ++i) {
-                max_value = -1;
-                label = 0;
-                #pragma omp for reduction(max : max_value)
-                for (int j = 0; j < shape[0]; ++j) {
-                    int index = i + j * out_img_len;
-                    if (index >= blob_out_len) {
-                        continue;
-                    }
-                    float value = out[index];
-                    if (value > max_value) {
-                        max_value = value;
-                        label = j;
-                    }
+    // argmax
+    inline void argmax(float* out, std::vector<int>& shape,
+                       std::vector<uchar>& mask, std::vector<uchar>& scoremap) {
+        int out_img_len = shape[1] * shape[2];
+        int blob_out_len = out_img_len * shape[0];
+        /*
+        Eigen::TensorMap<Eigen::Tensor<float, 3>> out_3d(out, shape[0], shape[1], shape[2]);
+        Eigen::Tensor<Eigen::DenseIndex, 2> argmax = out_3d.argmax(0);
+        */
+        float max_value = -1;
+        int label = 0;
+        #pragma omp parallel private(label)
+        for (int i = 0; i < out_img_len; ++i) {
+            max_value = -1;
+            label = 0;
+            #pragma omp for reduction(max : max_value)
+            for (int j = 0; j < shape[0]; ++j) {
+                int index = i + j * out_img_len;
+                if (index >= blob_out_len) {
+                    continue;
+                }
+                float value = out[index];
+                if (value > max_value) {
+                    max_value = value;
+                    label = j;
                 }
-                if (label == 0) max_value = 0;
-                mask[i] = uchar(label);
-                scoremap[i] = uchar(max_value * 255);
             }
+            if (label == 0) max_value = 0;
+            mask[i] = uchar(label);
+            scoremap[i] = uchar(max_value * 255);
         }
     }
-}
+}  // namespace utils
+}  // namespace PaddleSolution