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提交 26cefa3d 编写于 作者: J jack
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add seg batch predict

上级 cb6edab6
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Showing with 220 addition and 26 deletion
deploy/cpp/demo/classifier.cpp
浏览文件 @ 26cefa3d
......@@ -32,7 +32,7 @@ DEFINE_int32(gpu_id, 0, "GPU card id");
DEFINE_string(key, "", "key of encryption");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
DEFINE_int32(batch_size, 1, "Batch size when infering");
DEFINE_int32(batch_size, 1, "Batch size of infering");
int main(int argc, char** argv) {
// Parsing command-line
......@@ -53,8 +53,8 @@ int main(int argc, char** argv) {
// 进行预测
double total_running_time_s = 0.0;
double total_imreaad_time_s = 0.0;
double total_imread_time_s = 0.0;
int imgs = 1;
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
if (!inf) {
......@@ -63,31 +63,32 @@ int main(int argc, char** argv) {
}
// 多batch预测
std::string image_path;
std::vector<std::string> image_path_vec;
std::vector<std::string> image_paths;
while (getline(inf, image_path)) {
image_path_vec.push_back(image_path);
image_paths.push_back(image_path);
}
for(int i = 0; i < image_path_vec.size(); i += FLAGS_batch_size) {
imgs = image_paths.size();
for(int i = 0; i < image_paths.size(); i += FLAGS_batch_size) {
auto start = system_clock::now();
// 读图像
int im_vec_size = std::min((int)image_path_vec.size(), i + FLAGS_batch_size);
int im_vec_size = std::min((int)image_paths.size(), i + FLAGS_batch_size);
std::vector<cv::Mat> im_vec(im_vec_size - i);
std::vector<PaddleX::ClsResult> results(im_vec_size - i, PaddleX::ClsResult());
#pragma omp parallel for num_threads(im_vec_size - i)
for(int j = i; j < im_vec_size; ++j){
im_vec[j - i] = std::move(cv::imread(image_path_vec[j], 1));
im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
}
auto imread_end = system_clock::now();
model.predict(im_vec, results);
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imreaad_time_s += double(imread_duration.count()) * microseconds::period::num / microseconds::period::den;
total_imread_time_s += double(imread_duration.count()) * microseconds::period::num / microseconds::period::den;
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += double(duration.count()) * microseconds::period::num / microseconds::period::den;
for(int j = i; j < im_vec_size; ++j) {
std::cout << "Path:" << image_path_vec[j]
std::cout << "Path:" << image_paths[j]
<< ", predict label: " << results[j - i].category
<< ", label_id:" << results[j - i].category_id
<< ", score: " << results[j - i].score << std::endl;
......@@ -105,11 +106,15 @@ int main(int argc, char** argv) {
<< ", label_id:" << result.category_id
<< ", score: " << result.score << std::endl;
}
std::cout << "Total average running time: "
std::cout << "Total running time: "
<< total_running_time_s
<< " s, total average read img time: "
<< total_imreaad_time_s
<< " s, batch_size = "
<< " s, average running time: "
<< total_running_time_s / imgs
<< " s/img, total read img time: "
<< total_imread_time_s
<< " s, average read time: "
<< total_imread_time_s / imgs
<< " s/img, batch_size = "
<< FLAGS_batch_size
<< std::endl;
return 0;
......
deploy/cpp/demo/segmenter.cpp
浏览文件 @ 26cefa3d
......@@ -14,14 +14,18 @@
#include <glog/logging.h>
#include <algorithm>
#include <chrono>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#include <utility>
#include "include/paddlex/paddlex.h"
#include "include/paddlex/visualize.h"
using namespace std::chrono;
DEFINE_string(model_dir, "", "Path of inference model");
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
DEFINE_bool(use_trt, false, "Infering with TensorRT");
......@@ -30,6 +34,7 @@ DEFINE_string(key, "", "key of encryption");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
DEFINE_string(save_dir, "output", "Path to save visualized image");
DEFINE_int32(batch_size, 1, "Batch size of infering");
int main(int argc, char** argv) {
// 解析命令行参数
......@@ -46,8 +51,11 @@ int main(int argc, char** argv) {
// 加载模型
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_trt, FLAGS_gpu_id, FLAGS_key);
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_trt, FLAGS_gpu_id, FLAGS_key, FLAGS_batch_size);
double total_running_time_s = 0.0;
double total_imread_time_s = 0.0;
int imgs = 1;
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
// 进行预测
if (FLAGS_image_list != "") {
......@@ -57,23 +65,46 @@ int main(int argc, char** argv) {
return -1;
}
std::string image_path;
std::vector<std::string> image_paths;
while (getline(inf, image_path)) {
PaddleX::SegResult result;
cv::Mat im = cv::imread(image_path, 1);
model.predict(im, &result);
image_paths.push_back(image_path);
}
imgs = image_paths.size();
for(int i = 0; i < image_paths.size(); i += FLAGS_batch_size){
auto start = system_clock::now();
int im_vec_size = std::min((int)image_paths.size(), i + FLAGS_batch_size);
std::vector<cv::Mat> im_vec(im_vec_size - i);
std::vector<PaddleX::SegResult> results(im_vec_size - i, PaddleX::SegResult());
#pragma omp parallel for num_threads(im_vec_size - i)
for(int j = i; j < im_vec_size; ++j){
im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
}
auto imread_end = system_clock::now();
model.predict(im_vec, results);
auto imread_duration = duration_cast<microseconds>(imread_end - start);
total_imread_time_s += double(imread_duration.count()) * microseconds::period::num / microseconds::period::den;
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += double(duration.count()) * microseconds::period::num / microseconds::period::den;
// 可视化
cv::Mat vis_img =
PaddleX::Visualize(im, result, model.labels, colormap);
std::string save_path =
PaddleX::generate_save_path(FLAGS_save_dir, image_path);
cv::imwrite(save_path, vis_img);
result.clear();
std::cout << "Visualized output saved as " << save_path << std::endl;
for(int j = 0; j < im_vec_size - i; ++j) {
cv::Mat vis_img =
PaddleX::Visualize(im_vec[j], results[j], model.labels, colormap);
std::string save_path =
PaddleX::generate_save_path(FLAGS_save_dir, image_paths[i + j]);
cv::imwrite(save_path, vis_img);
std::cout << "Visualized output saved as " << save_path << std::endl;
}
}
} else {
auto start = system_clock::now();
PaddleX::SegResult result;
cv::Mat im = cv::imread(FLAGS_image, 1);
model.predict(im, &result);
auto end = system_clock::now();
auto duration = duration_cast<microseconds>(end - start);
total_running_time_s += double(duration.count()) * microseconds::period::num / microseconds::period::den;
// 可视化
cv::Mat vis_img = PaddleX::Visualize(im, result, model.labels, colormap);
std::string save_path =
......@@ -82,6 +113,17 @@ int main(int argc, char** argv) {
result.clear();
std::cout << "Visualized output saved as " << save_path << std::endl;
}
std::cout << "Total running time: "
<< total_running_time_s
<< " s, average running time: "
<< total_running_time_s / imgs
<< " s/img, total read img time: "
<< total_imread_time_s
<< " s, average read img time: "
<< total_imread_time_s / imgs
<< " s, batch_size = "
<< FLAGS_batch_size
<< std::endl;
return 0;
}
deploy/cpp/include/paddlex/paddlex.h
浏览文件 @ 26cefa3d
......@@ -69,8 +69,12 @@ class Model {
bool predict(const cv::Mat& im, DetResult* result);
bool predict(const std::vector<cv::Mat> &im_batch, std::vector<DetResult> &result);
bool predict(const cv::Mat& im, SegResult* result);
bool predict(const std::vector<cv::Mat> &im_batch, std::vector<SegResult> &result);
bool postprocess(SegResult* result);
bool postprocess(DetResult* result);
......
deploy/cpp/src/paddlex.cpp
浏览文件 @ 26cefa3d
......@@ -161,6 +161,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
result->category_id = std::distance(std::begin(outputs_), ptr);
result->score = *ptr;
result->category = labels[result->category_id];
return true;
}
bool Model::predict(const std::vector<cv::Mat> &im_batch, std::vector<ClsResult> &results) {
......@@ -322,6 +323,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
static_cast<int>(box->coordinate[3])};
}
}
return true;
}
bool Model::predict(const cv::Mat& im, SegResult* result) {
......@@ -430,6 +432,147 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
result->score_map.data.assign(mask_score.begin<float>(),
mask_score.end<float>());
result->score_map.shape = {mask_score.rows, mask_score.cols};
return true;
}
bool Model::predict(const std::vector<cv::Mat> &im_batch, std::vector<SegResult> &result) {
for(auto &inputs: inputs_batch_) {
inputs.clear();
}
if (type == "classifier") {
std::cerr << "Loading model is a 'classifier', ClsResult should be passed "
"to function predict()!"
<< std::endl;
return false;
} else if (type == "detector") {
std::cerr << "Loading model is a 'detector', DetResult should be passed to "
"function predict()!"
<< std::endl;
return false;
}
// 处理输入图像
if (!preprocess(im_batch, inputs_batch_)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
}
int batch_size = im_batch.size();
result.clear();
result.resize(batch_size);
int h = inputs_batch_[0].new_im_size_[0];
int w = inputs_batch_[0].new_im_size_[1];
auto im_tensor = predictor_->GetInputTensor("image");
im_tensor->Reshape({batch_size, 3, h, w});
std::vector<float> inputs_data(batch_size * 3 * h * w);
for(int i = 0; i <inputs_batch_.size(); ++i) {
std::copy(inputs_batch_[i].im_data_.begin(), inputs_batch_[i].im_data_.end(), inputs_data.begin() + i * 3 * h * w);
}
im_tensor->copy_from_cpu(inputs_data.data());
//im_tensor->copy_from_cpu(inputs_.im_data_.data());
// 使用加载的模型进行预测
predictor_->ZeroCopyRun();
// 获取预测置信度，经过argmax后的labelmap
auto output_names = predictor_->GetOutputNames();
auto output_label_tensor = predictor_->GetOutputTensor(output_names[0]);
std::vector<int> output_label_shape = output_label_tensor->shape();
int size = 1;
for (const auto& i : output_label_shape) {
size *= i;
}
std::vector<int64_t> output_labels(size, 0);
output_label_tensor->copy_to_cpu(output_labels.data());
auto output_labels_iter = output_labels.begin();
int single_batch_size = size / batch_size;
for(int i = 0; i < batch_size; ++i) {
result[i].label_map.data.resize(single_batch_size);
result[i].label_map.shape.push_back(1);
for(int j = 1; j < output_label_shape.size(); ++j) {
result[i].label_map.shape.push_back(output_label_shape[j]);
}
std::copy(output_labels_iter + i * single_batch_size, output_labels_iter + (i + 1) * single_batch_size, result[i].label_map.data.data());
}
// 获取预测置信度scoremap
auto output_score_tensor = predictor_->GetOutputTensor(output_names[1]);
std::vector<int> output_score_shape = output_score_tensor->shape();
size = 1;
for (const auto& i : output_score_shape) {
size *= i;
}
std::vector<float> output_scores(size, 0);
output_score_tensor->copy_to_cpu(output_scores.data());
auto output_scores_iter = output_scores.begin();
int single_batch_score_size = size / batch_size;
for(int i = 0; i < batch_size; ++i) {
result[i].score_map.data.resize(single_batch_score_size);
result[i].score_map.shape.push_back(1);
for(int j = 1; j < output_score_shape.size(); ++j) {
result[i].score_map.shape.push_back(output_score_shape[j]);
}
std::copy(output_scores_iter + i * single_batch_score_size, output_scores_iter + (i + 1) * single_batch_score_size, result[i].score_map.data.data());
}
// 解析输出结果到原图大小
for(int i = 0; i < batch_size; ++i) {
std::vector<uint8_t> label_map(result[i].label_map.data.begin(),
result[i].label_map.data.end());
cv::Mat mask_label(result[i].label_map.shape[1],
result[i].label_map.shape[2],
CV_8UC1,
label_map.data());
cv::Mat mask_score(result[i].score_map.shape[2],
result[i].score_map.shape[3],
CV_32FC1,
result[i].score_map.data.data());
int idx = 1;
int len_postprocess = inputs_batch_[i].im_size_before_resize_.size();
for (std::vector<std::string>::reverse_iterator iter =
inputs_batch_[i].reshape_order_.rbegin();
iter != inputs_batch_[i].reshape_order_.rend();
++iter) {
if (*iter == "padding") {
auto before_shape = inputs_batch_[i].im_size_before_resize_[len_postprocess - idx];
inputs_batch_[i].im_size_before_resize_.pop_back();
auto padding_w = before_shape[0];
auto padding_h = before_shape[1];
mask_label = mask_label(cv::Rect(0, 0, padding_h, padding_w));
mask_score = mask_score(cv::Rect(0, 0, padding_h, padding_w));
} else if (*iter == "resize") {
auto before_shape = inputs_batch_[i].im_size_before_resize_[len_postprocess - idx];
inputs_batch_[i].im_size_before_resize_.pop_back();
auto resize_w = before_shape[0];
auto resize_h = before_shape[1];
cv::resize(mask_label,
mask_label,
cv::Size(resize_h, resize_w),
0,
0,
cv::INTER_NEAREST);
cv::resize(mask_score,
mask_score,
cv::Size(resize_h, resize_w),
0,
0,
cv::INTER_LINEAR);
}
++idx;
}
result[i].label_map.data.assign(mask_label.begin<uint8_t>(),
mask_label.end<uint8_t>());
result[i].label_map.shape = {mask_label.rows, mask_label.cols};
result[i].score_map.data.assign(mask_score.begin<float>(),
mask_score.end<float>());
result[i].score_map.shape = {mask_score.rows, mask_score.cols};
}
return true;
}
} // namespce of PaddleX
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