Skip to content

P
PaddleSeg

PaddlePaddle / PaddleSeg

通知 285
Star 8
Fork 1
P
PaddleSeg
提交 adac158d 编写于 作者: J joey12300
浏览文件
操作

change utils.h, seg_conf_parser.h to google code style.

上级 0fc9b582
隐藏空白更改
内联 并排
Showing with 258 addition and 254 deletion
inference/utils/seg_conf_parser.h
浏览文件 @ adac158d
......@@ -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
inference/utils/utils.h
浏览文件 @ adac158d
......@@ -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
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或