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PaddleSeg
提交 adac158d 编写于 作者: J joey12300
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change utils.h, seg_conf_parser.h to google code style.

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inference/utils/seg_conf_parser.h
浏览文件 @ adac158d
...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
// you may not use this file except in compliance with the License. // you may not use this file except in compliance with the License.
// You may obtain a copy of the License at // 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 // Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, // distributed under the License is distributed on an "AS IS" BASIS,
...@@ -12,168 +12,167 @@ ...@@ -12,168 +12,167 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
#pragma once #pragma once
#include <iostream> #include <yaml-cpp/yaml.h>
#include <vector> #include <iostream>
#include <string> #include <vector>
#include <string>
#include <yaml-cpp/yaml.h>
namespace PaddleSolution { namespace PaddleSolution {
class PaddleSegModelConfigPaser {
class PaddleSegModelConfigPaser { public:
public: PaddleSegModelConfigPaser()
PaddleSegModelConfigPaser() :_class_num(0),
:_class_num(0), _channels(0),
_channels(0), _use_gpu(0),
_use_gpu(0), _batch_size(1),
_batch_size(1), _model_file_name("__model__"),
_model_file_name("__model__"), _param_file_name("__params__") {
_param_file_name("__params__") { }
} ~PaddleSegModelConfigPaser() {
~PaddleSegModelConfigPaser() { }
}
void reset() {
void reset() { _resize.clear();
_resize.clear(); _mean.clear();
_mean.clear(); _std.clear();
_std.clear(); _img_type.clear();
_img_type.clear(); _class_num = 0;
_class_num = 0; _channels = 0;
_channels = 0; _use_gpu = 0;
_use_gpu = 0; _batch_size = 1;
_batch_size = 1; _model_file_name.clear();
_model_file_name.clear(); _model_path.clear();
_model_path.clear(); _param_file_name.clear();
_param_file_name.clear(); }
}
std::string process_parenthesis(const std::string& str) {
std::string process_parenthesis(const std::string& str) { if (str.size() < 2) {
if (str.size() < 2) { return str;
return str; }
} std::string nstr(str);
std::string nstr(str); if (str[0] == '(' && str.back() == ')') {
if (str[0] == '(' && str.back() == ')') { nstr[0] = '[';
nstr[0] = '['; nstr[str.size() - 1] = ']';
nstr[str.size() - 1] = ']'; }
} return nstr;
return nstr; }
}
template <typename T>
template <typename T> std::vector<T> parse_str_to_vec(const std::string& str) {
std::vector<T> parse_str_to_vec(const std::string& str) { std::vector<T> data;
std::vector<T> data; auto node = YAML::Load(str);
auto node = YAML::Load(str); for (const auto& item : node) {
for (const auto& item : node) { data.push_back(item.as<T>());
data.push_back(item.as<T>()); }
} return data;
return data; }
}
bool load_config(const std::string& conf_file) {
bool load_config(const std::string& conf_file) { reset();
reset(); YAML::Node config = YAML::LoadFile(conf_file);
// 1. get resize
YAML::Node config = YAML::LoadFile(conf_file); auto str = config["DEPLOY"]["EVAL_CROP_SIZE"].as<std::string>();
// 1. get resize _resize = parse_str_to_vec<int>(process_parenthesis(str));
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"]) {
// 2. get mean _mean.push_back(item.as<float>());
for (const auto& item : config["DEPLOY"]["MEAN"]) { }
_mean.push_back(item.as<float>());
} // 3. get std
for (const auto& item : config["DEPLOY"]["STD"]) {
// 3. get std _std.push_back(item.as<float>());
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>();
// 4. get image type // 5. get class number
_img_type = config["DEPLOY"]["IMAGE_TYPE"].as<std::string>(); _class_num = config["DEPLOY"]["NUM_CLASSES"].as<int>();
// 5. get class number // 7. set model path
_class_num = config["DEPLOY"]["NUM_CLASSES"].as<int>(); _model_path = config["DEPLOY"]["MODEL_PATH"].as<std::string>();
// 7. set model path // 8. get model file_name
_model_path = config["DEPLOY"]["MODEL_PATH"].as<std::string>(); _model_file_name = config["DEPLOY"]["MODEL_FILENAME"].as<std::string>();
// 8. get model file_name // 9. get model param file name
_model_file_name = config["DEPLOY"]["MODEL_FILENAME"].as<std::string>(); _param_file_name =
// 9. get model param file name config["DEPLOY"]["PARAMS_FILENAME"].as<std::string>();
_param_file_name = config["DEPLOY"]["PARAMS_FILENAME"].as<std::string>(); // 10. get pre_processor
// 10. get pre_processor _pre_processor = config["DEPLOY"]["PRE_PROCESSOR"].as<std::string>();
_pre_processor = config["DEPLOY"]["PRE_PROCESSOR"].as<std::string>(); // 11. use_gpu
// 11. use_gpu _use_gpu = config["DEPLOY"]["USE_GPU"].as<int>();
_use_gpu = config["DEPLOY"]["USE_GPU"].as<int>(); // 12. predictor_mode
// 12. predictor_mode _predictor_mode = config["DEPLOY"]["PREDICTOR_MODE"].as<std::string>();
_predictor_mode = config["DEPLOY"]["PREDICTOR_MODE"].as<std::string>(); // 13. batch_size
// 13. batch_size _batch_size = config["DEPLOY"]["BATCH_SIZE"].as<int>();
_batch_size = config["DEPLOY"]["BATCH_SIZE"].as<int>(); // 14. channels
// 14. channels _channels = config["DEPLOY"]["CHANNELS"].as<int>();
_channels = config["DEPLOY"]["CHANNELS"].as<int>(); return true;
return true; }
}
void debug() const {
void debug() const { std::cout << "EVAL_CROP_SIZE: ("
<< _resize[0] << ", " << _resize[1]
std::cout << "EVAL_CROP_SIZE: (" << _resize[0] << ", " << _resize[1] << ")" << std::endl; << ")" << std::endl;
std::cout << "MEAN: [";
std::cout << "MEAN: ["; for (int i = 0; i < _mean.size(); ++i) {
for (int i = 0; i < _mean.size(); ++i) { if (i != _mean.size() - 1) {
if (i != _mean.size() - 1) { std::cout << _mean[i] << ", ";
std::cout << _mean[i] << ", "; } else {
} else { std::cout << _mean[i];
std::cout << _mean[i]; }
} }
} std::cout << "]" << std::endl;
std::cout << "]" << std::endl;
std::cout << "STD: [";
std::cout << "STD: ["; for (int i = 0; i < _std.size(); ++i) {
for (int i = 0; i < _std.size(); ++i) { if (i != _std.size() - 1) {
if (i != _std.size() - 1) { std::cout << _std[i] << ", ";
std::cout << _std[i] << ", "; } else {
} std::cout << _std[i];
else { }
std::cout << _std[i]; }
} std::cout << "]" << std::endl;
}
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.IMAGE_TYPE: " << _img_type << std::endl; std::cout << "DEPLOY.CHANNELS: " << _channels << std::endl;
std::cout << "DEPLOY.NUM_CLASSES: " << _class_num << std::endl; std::cout << "DEPLOY.MODEL_PATH: " << _model_path << std::endl;
std::cout << "DEPLOY.CHANNELS: " << _channels << std::endl; std::cout << "DEPLOY.MODEL_FILENAME: " << _model_file_name << std::endl;
std::cout << "DEPLOY.MODEL_PATH: " << _model_path << std::endl; std::cout << "DEPLOY.PARAMS_FILENAME: "
std::cout << "DEPLOY.MODEL_FILENAME: " << _model_file_name << std::endl; << _param_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.PRE_PROCESSOR: " << _pre_processor << std::endl; std::cout << "DEPLOY.USE_GPU: " << _use_gpu << std::endl;
std::cout << "DEPLOY.USE_GPU: " << _use_gpu << std::endl; std::cout << "DEPLOY.PREDICTOR_MODE: " << _predictor_mode << std::endl;
std::cout << "DEPLOY.PREDICTOR_MODE: " << _predictor_mode << std::endl; std::cout << "DEPLOY.BATCH_SIZE: " << _batch_size << std::endl;
std::cout << "DEPLOY.BATCH_SIZE: " << _batch_size << std::endl; }
}
// DEPLOY.EVAL_CROP_SIZE
// DEPLOY.EVAL_CROP_SIZE std::vector<int> _resize;
std::vector<int> _resize; // DEPLOY.MEAN
// DEPLOY.MEAN std::vector<float> _mean;
std::vector<float> _mean; // DEPLOY.STD
// DEPLOY.STD std::vector<float> _std;
std::vector<float> _std; // DEPLOY.IMAGE_TYPE
// DEPLOY.IMAGE_TYPE std::string _img_type;
std::string _img_type; // DEPLOY.NUM_CLASSES
// DEPLOY.NUM_CLASSES int _class_num;
int _class_num; // DEPLOY.CHANNELS
// DEPLOY.CHANNELS int _channels;
int _channels; // DEPLOY.MODEL_PATH
// DEPLOY.MODEL_PATH std::string _model_path;
std::string _model_path; // DEPLOY.MODEL_FILENAME
// DEPLOY.MODEL_FILENAME std::string _model_file_name;
std::string _model_file_name; // DEPLOY.PARAMS_FILENAME
// DEPLOY.PARAMS_FILENAME std::string _param_file_name;
std::string _param_file_name; // DEPLOY.PRE_PROCESSOR
// DEPLOY.PRE_PROCESSOR std::string _pre_processor;
std::string _pre_processor; // DEPLOY.USE_GPU
// DEPLOY.USE_GPU int _use_gpu;
int _use_gpu; // DEPLOY.PREDICTOR_MODE
// DEPLOY.PREDICTOR_MODE std::string _predictor_mode;
std::string _predictor_mode; // DEPLOY.BATCH_SIZE
// DEPLOY.BATCH_SIZE int _batch_size;
int _batch_size; };
};
} // namespace PaddleSolution
}
inference/utils/utils.h
浏览文件 @ adac158d
...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
// you may not use this file except in compliance with the License. // you may not use this file except in compliance with the License.
// You may obtain a copy of the License at // 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 // Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, // distributed under the License is distributed on an "AS IS" BASIS,
...@@ -30,105 +30,110 @@ ...@@ -30,105 +30,110 @@
#endif #endif
namespace PaddleSolution { namespace PaddleSolution {
namespace utils { namespace utils {
inline std::string path_join(const std::string& dir, const std::string& path) { inline std::string path_join(const std::string& dir,
std::string seperator = "/"; const std::string& path) {
#ifdef _WIN32 std::string seperator = "/";
seperator = "\\"; #ifdef _WIN32
#endif seperator = "\\";
return dir + seperator + path; #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) { while ((entry = readdir(dir)) != NULL) {
std::string item = entry->d_name; std::string item = entry->d_name;
auto ext = strrchr(entry->d_name, '.'); auto ext = strrchr(entry->d_name, '.');
if (!ext || std::string(ext) == "." || std::string(ext) == "..") { if (!ext || std::string(ext) == "." || std::string(ext) == "..") {
continue; continue;
} }
if (exts.find(ext) != std::string::npos) { if (exts.find(ext) != std::string::npos) {
imgs.push_back(path_join(path, entry->d_name)); imgs.push_back(path_join(path, entry->d_name));
}
} }
return imgs;
} }
#else return imgs;
// 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) #else
{ // scan a directory and get all files with input extensions
std::vector<std::string> imgs; inline std::vector<std::string> get_directory_images(
for (const auto& item : std::experimental::filesystem::directory_iterator(path)) { const std::string& path, const std::string& exts) {
auto suffix = item.path().extension().string(); std::vector<std::string> imgs;
if (exts.find(suffix) != std::string::npos && suffix.size() > 0) { for (const auto& item :
auto fullname = path_join(path, item.path().filename().string()); std::experimental::filesystem::directory_iterator(path)) {
imgs.push_back(item.path().string()); 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 // normalize and HWC_BGR -> CHW_RGB
inline void normalize(cv::Mat& im, float* data, std::vector<float>& fmean, std::vector<float>& fstd) { inline void normalize(cv::Mat& im, float* data, std::vector<float>& fmean,
int rh = im.rows; std::vector<float>& fstd) {
int rw = im.cols; int rh = im.rows;
int rc = im.channels(); int rw = im.cols;
double normf = (double)1.0 / 255.0; int rc = im.channels();
#pragma omp parallel for double normf = static_cast<double>(1.0) / 255.0;
for (int h = 0; h < rh; ++h) { #pragma omp parallel for
const uchar* ptr = im.ptr<uchar>(h); for (int h = 0; h < rh; ++h) {
int im_index = 0; const uchar* ptr = im.ptr<uchar>(h);
for (int w = 0; w < rw; ++w) { int im_index = 0;
for (int c = 0; c < rc; ++c) { for (int w = 0; w < rw; ++w) {
int top_index = (c * rh + h) * rw + w; for (int c = 0; c < rc; ++c) {
float pixel = static_cast<float>(ptr[im_index++]); int top_index = (c * rh + h) * rw + w;
pixel = (pixel * normf - fmean[c]) / fstd[c]; float pixel = static_cast<float>(ptr[im_index++]);
data[top_index] = pixel; pixel = (pixel * normf - fmean[c]) / fstd[c];
} data[top_index] = pixel;
} }
} }
} }
}
// argmax // argmax
inline void argmax(float* out, std::vector<int>& shape, std::vector<uchar>& mask, std::vector<uchar>& scoremap) { inline void argmax(float* out, std::vector<int>& shape,
int out_img_len = shape[1] * shape[2]; std::vector<uchar>& mask, std::vector<uchar>& scoremap) {
int blob_out_len = out_img_len * shape[0]; 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); 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; float max_value = -1;
#pragma omp parallel private(label) int label = 0;
for (int i = 0; i < out_img_len; ++i) { #pragma omp parallel private(label)
max_value = -1; for (int i = 0; i < out_img_len; ++i) {
label = 0; max_value = -1;
#pragma omp for reduction(max : max_value) label = 0;
for (int j = 0; j < shape[0]; ++j) { #pragma omp for reduction(max : max_value)
int index = i + j * out_img_len; for (int j = 0; j < shape[0]; ++j) {
if (index >= blob_out_len) { int index = i + j * out_img_len;
continue; if (index >= blob_out_len) {
} continue;
float value = out[index]; }
if (value > max_value) { float value = out[index];
max_value = value; if (value > max_value) {
label = j; 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
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