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PaddleX
提交 151ae40b 编写于 作者: S syyxsxx
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deploy/cpp/src/paddlex.cpp
浏览文件 @ 151ae40b
......@@ -40,7 +40,7 @@ void Model::create_predictor(const std::string& model_dir,
}
#endif
if (yaml_input == "") {
// 读取配置文件
// read yaml file
std::ifstream yaml_fin(yaml_file);
yaml_fin.seekg(0, std::ios::end);
size_t yaml_file_size = yaml_fin.tellg();
......@@ -48,7 +48,7 @@ void Model::create_predictor(const std::string& model_dir,
yaml_fin.seekg(0);
yaml_fin.read(&yaml_input[0], yaml_file_size);
}
// 读取配置文件内容
// load yaml file
if (!load_config(yaml_input)) {
std::cerr << "Parse file 'model.yml' failed!" << std::endl;
exit(-1);
......@@ -64,13 +64,13 @@ void Model::create_predictor(const std::string& model_dir,
}
config.SwitchUseFeedFetchOps(false);
config.SwitchSpecifyInputNames(true);
// 开启图优化
// enable graph Optim
#if defined(__arm__) || defined(__aarch64__)
config.SwitchIrOptim(false);
#else
config.SwitchIrOptim(use_ir_optim);
#endif
// 开启内存优化
// enable Memory Optim
config.EnableMemoryOptim();
if (use_trt) {
config.EnableTensorRtEngine(
......@@ -108,9 +108,9 @@ bool Model::load_config(const std::string& yaml_input) {
return false;
}
}
// 构建数据处理流
// build data preprocess stream
transforms_.Init(config["Transforms"], to_rgb);
// 读入label list
// read label list
labels.clear();
for (const auto& item : config["_Attributes"]["labels"]) {
int index = labels.size();
......@@ -152,19 +152,19 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
"to function predict()!" << std::endl;
return false;
}
// 处理输入图像
// im preprocess
if (!preprocess(im, &inputs_)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
}
// 使用加载的模型进行预测
// predict
auto in_tensor = predictor_->GetInputTensor("image");
int h = inputs_.new_im_size_[0];
int w = inputs_.new_im_size_[1];
in_tensor->Reshape({1, 3, h, w});
in_tensor->copy_from_cpu(inputs_.im_data_.data());
predictor_->ZeroCopyRun();
// 取出模型的输出结果
// get result
auto output_names = predictor_->GetOutputNames();
auto output_tensor = predictor_->GetOutputTensor(output_names[0]);
std::vector<int> output_shape = output_tensor->shape();
......@@ -174,7 +174,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
}
outputs_.resize(size);
output_tensor->copy_to_cpu(outputs_.data());
// 对模型输出结果进行后处理
// postprocess
auto ptr = std::max_element(std::begin(outputs_), std::end(outputs_));
result->category_id = std::distance(std::begin(outputs_), ptr);
result->score = *ptr;
......@@ -198,12 +198,12 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
return false;
}
inputs_batch_.assign(im_batch.size(), ImageBlob());
// 处理输入图像
// preprocess
if (!preprocess(im_batch, &inputs_batch_, thread_num)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
}
// 使用加载的模型进行预测
// predict
int batch_size = im_batch.size();
auto in_tensor = predictor_->GetInputTensor("image");
int h = inputs_batch_[0].new_im_size_[0];
......@@ -218,7 +218,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
in_tensor->copy_from_cpu(inputs_data.data());
// in_tensor->copy_from_cpu(inputs_.im_data_.data());
predictor_->ZeroCopyRun();
// 取出模型的输出结果
// get result
auto output_names = predictor_->GetOutputNames();
auto output_tensor = predictor_->GetOutputTensor(output_names[0]);
std::vector<int> output_shape = output_tensor->shape();
......@@ -228,7 +228,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
}
outputs_.resize(size);
output_tensor->copy_to_cpu(outputs_.data());
// 对模型输出结果进行后处理
// postprocess
(*results).clear();
(*results).resize(batch_size);
int single_batch_size = size / batch_size;
......@@ -258,7 +258,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
return false;
}
// 处理输入图像
// preprocess
if (!preprocess(im, &inputs_)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
......@@ -288,7 +288,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
im_info_tensor->copy_from_cpu(im_info);
im_shape_tensor->copy_from_cpu(im_shape);
}
// 使用加载的模型进行预测
// predict
predictor_->ZeroCopyRun();
std::vector<float> output_box;
......@@ -306,7 +306,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
return true;
}
int num_boxes = size / 6;
// 解析预测框box
// box postprocess
for (int i = 0; i < num_boxes; ++i) {
Box box;
box.category_id = static_cast<int>(round(output_box[i * 6]));
......@@ -321,7 +321,7 @@ bool Model::predict(const cv::Mat& im, DetResult* result) {
box.coordinate = {xmin, ymin, w, h};
result->boxes.push_back(std::move(box));
}
// 实例分割需解析mask
// mask postprocess
if (name == "MaskRCNN") {
std::vector<float> output_mask;
auto output_mask_tensor = predictor_->GetOutputTensor(output_names[1]);
......@@ -366,12 +366,12 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
inputs_batch_.assign(im_batch.size(), ImageBlob());
int batch_size = im_batch.size();
// 处理输入图像
// preprocess
if (!preprocess(im_batch, &inputs_batch_, thread_num)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
}
// 对RCNN类模型做批量padding
// RCNN model padding
if (batch_size > 1) {
if (name == "FasterRCNN" || name == "MaskRCNN") {
int max_h = -1;
......@@ -452,10 +452,10 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
im_info_tensor->copy_from_cpu(im_info.data());
im_shape_tensor->copy_from_cpu(im_shape.data());
}
// 使用加载的模型进行预测
// predict
predictor_->ZeroCopyRun();
// 读取所有box
// get all box
std::vector<float> output_box;
auto output_names = predictor_->GetOutputNames();
auto output_box_tensor = predictor_->GetOutputTensor(output_names[0]);
......@@ -472,7 +472,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
}
auto lod_vector = output_box_tensor->lod();
int num_boxes = size / 6;
// 解析预测框box
// box postprocess
(*results).clear();
(*results).resize(batch_size);
for (int i = 0; i < lod_vector[0].size() - 1; ++i) {
......@@ -492,7 +492,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
}
}
// 实例分割需解析mask
// mask postprocess
if (name == "MaskRCNN") {
std::vector<float> output_mask;
auto output_mask_tensor = predictor_->GetOutputTensor(output_names[1]);
......@@ -537,7 +537,7 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
return false;
}
// 处理输入图像
// preprocess
if (!preprocess(im, &inputs_)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
......@@ -549,10 +549,10 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
im_tensor->Reshape({1, 3, h, w});
im_tensor->copy_from_cpu(inputs_.im_data_.data());
// 使用加载的模型进行预测
// predict
predictor_->ZeroCopyRun();
// 获取预测置信度，经过argmax后的labelmap
// get 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();
......@@ -565,7 +565,7 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
result->label_map.data.resize(size);
output_label_tensor->copy_to_cpu(result->label_map.data.data());
// 获取预测置信度scoremap
// get scoremap
auto output_score_tensor = predictor_->GetOutputTensor(output_names[1]);
std::vector<int> output_score_shape = output_score_tensor->shape();
size = 1;
......@@ -577,7 +577,7 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
result->score_map.data.resize(size);
output_score_tensor->copy_to_cpu(result->score_map.data.data());
// 解析输出结果到原图大小
// get origin image result
std::vector<uint8_t> label_map(result->label_map.data.begin(),
result->label_map.data.end());
cv::Mat mask_label(result->label_map.shape[1],
......@@ -647,7 +647,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
return false;
}
// 处理输入图像
// preprocess
inputs_batch_.assign(im_batch.size(), ImageBlob());
if (!preprocess(im_batch, &inputs_batch_, thread_num)) {
std::cerr << "Preprocess failed!" << std::endl;
......@@ -670,10 +670,10 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
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
// predict
predictor_->ZeroCopyRun();
// 获取预测置信度，经过argmax后的labelmap
// get 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();
......@@ -698,7 +698,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
(*results)[i].label_map.data.data());
}
// 获取预测置信度scoremap
// get scoremap
auto output_score_tensor = predictor_->GetOutputTensor(output_names[1]);
std::vector<int> output_score_shape = output_score_tensor->shape();
size = 1;
......@@ -722,7 +722,7 @@ bool Model::predict(const std::vector<cv::Mat>& im_batch,
(*results)[i].score_map.data.data());
}
// 解析输出结果到原图大小
// get origin image result
for (int i = 0; i < batch_size; ++i) {
std::vector<uint8_t> label_map((*results)[i].label_map.data.begin(),
(*results)[i].label_map.data.end());
......
deploy/cpp/src/transforms.cpp
浏览文件 @ 151ae40b
......@@ -12,12 +12,14 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "include/paddlex/transforms.h"
#include <math.h>
#include <iostream>
#include <string>
#include <vector>
#include <math.h>
#include "include/paddlex/transforms.h"
namespace PaddleX {
......@@ -195,7 +197,7 @@ std::shared_ptr<Transform> Transforms::CreateTransform(
}
bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
// 按照transforms中预处理算子顺序处理图像
// do all preprocess ops by order
if (to_rgb_) {
cv::cvtColor(*im, *im, cv::COLOR_BGR2RGB);
}
......@@ -211,8 +213,8 @@ bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
}
}
// 将图像由NHWC转为NCHW格式
// 同时转为连续的内存块存储到ImageBlob
// data format NHWC to NCHW
// img data save to ImageBlob
int h = im->rows;
int w = im->cols;
int c = im->channels();
......
deploy/cpp/src/visualize.cpp
浏览文件 @ 151ae40b
......@@ -47,7 +47,7 @@ cv::Mat Visualize(const cv::Mat& img,
boxes[i].coordinate[2],
boxes[i].coordinate[3]);
// 生成预测框和标题
// draw box and title
std::string text = boxes[i].category;
int c1 = colormap[3 * boxes[i].category_id + 0];
int c2 = colormap[3 * boxes[i].category_id + 1];
......@@ -63,13 +63,13 @@ cv::Mat Visualize(const cv::Mat& img,
origin.x = roi.x;
origin.y = roi.y;
// 生成预测框标题的背景
// background
cv::Rect text_back = cv::Rect(boxes[i].coordinate[0],
boxes[i].coordinate[1] - text_size.height,
text_size.width,
text_size.height);
// 绘图和文字
// draw
cv::rectangle(vis_img, roi, roi_color, 2);
cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img,
......@@ -80,7 +80,7 @@ cv::Mat Visualize(const cv::Mat& img,
cv::Scalar(255, 255, 255),
thickness);
// 生成实例分割mask
// mask
if (boxes[i].mask.data.size() == 0) {
continue;
}
......
deploy/openvino/demo/classifier.cpp
浏览文件 @ 151ae40b
......@@ -44,11 +44,11 @@ int main(int argc, char** argv) {
return -1;
}
// 加载模型
// load model
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_device);
// 进行预测
// predict
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
if (!inf) {
......
deploy/openvino/demo/detector.cpp
浏览文件 @ 151ae40b
......@@ -54,13 +54,13 @@ int main(int argc, char** argv) {
return -1;
}
//
// load model
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_device);
int imgs = 1;
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
// 进行预测
// predict
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
if (!inf) {
......@@ -96,7 +96,7 @@ int main(int argc, char** argv) {
<< result.boxes[i].coordinate[3] << ")" << std::endl;
}
if (FLAGS_save_dir != "") {
// 可视化
// visualize
cv::Mat vis_img = PaddleX::Visualize(
im, result, model.labels, colormap, FLAGS_threshold);
std::string save_path =
......
deploy/openvino/demo/segmenter.cpp
浏览文件 @ 151ae40b
......@@ -48,11 +48,9 @@ int main(int argc, char** argv) {
return -1;
}
//
std::cout << "init start" << std::endl;
// load model
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_device);
std::cout << "init done" << std::endl;
int imgs = 1;
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
......
deploy/openvino/src/paddlex.cpp
浏览文件 @ 151ae40b
......@@ -67,9 +67,9 @@ bool Model::load_config(const std::string& cfg_file) {
return false;
}
}
// 构建数据处理流
// init preprocess ops
transforms_.Init(config["Transforms"], type, to_rgb);
// 读入label lis
// read label list
for (const auto& item : config["_Attributes"]["labels"]) {
int index = labels.size();
labels[index] = item.as<std::string>();
......@@ -98,7 +98,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
<< std::endl;
return false;
}
// 处理输入图像
// preprocess
InferenceEngine::InferRequest infer_request =
executable_network_.CreateInferRequest();
std::string input_name = network_.getInputsInfo().begin()->first;
......@@ -109,6 +109,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
return false;
}
// predict
infer_request.Infer();
std::string output_name = network_.getOutputsInfo().begin()->first;
......@@ -118,7 +119,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
auto moutputHolder = moutput->rmap();
float* outputs_data = moutputHolder.as<float *>();
// 对模型输出结果进行后处理
// post process
auto ptr = std::max_element(outputs_data, outputs_data+sizeof(outputs_data));
result->category_id = std::distance(outputs_data, ptr);
result->score = *ptr;
......@@ -206,20 +207,20 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
"function predict()!" << std::endl;
return false;
}
//
// init infer
InferenceEngine::InferRequest infer_request =
executable_network_.CreateInferRequest();
std::string input_name = network_.getInputsInfo().begin()->first;
inputs_.blob = infer_request.GetBlob(input_name);
//
// preprocess
cv::Mat im_clone = im.clone();
if (!preprocess(&im_clone, &inputs_)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
}
//
// predict
infer_request.Infer();
InferenceEngine::OutputsDataMap out_map = network_.getOutputsInfo();
......
deploy/openvino/src/transforms.cpp
浏览文件 @ 151ae40b
......@@ -201,7 +201,7 @@ std::shared_ptr<Transform> Transforms::CreateTransform(
}
bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
// 按照transforms中预处理算子顺序处理图像
// preprocess by order
if (to_rgb_) {
cv::cvtColor(*im, *im, cv::COLOR_BGR2RGB);
}
......@@ -224,8 +224,8 @@ bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
}
}
// 将图像由NHWC转为NCHW格式
// 同时转为连续的内存块存储到Blob
// image format NHWC to NCHW
// img data save to ImageBlob
InferenceEngine::SizeVector blobSize = data->blob->getTensorDesc().getDims();
const size_t width = blobSize[3];
const size_t height = blobSize[2];
......
deploy/openvino/src/visualize.cpp
浏览文件 @ 151ae40b
......@@ -47,7 +47,7 @@ cv::Mat Visualize(const cv::Mat& img,
boxes[i].coordinate[2],
boxes[i].coordinate[3]);
// 生成预测框和标题
// draw box and title
std::string text = boxes[i].category;
int c1 = colormap[3 * boxes[i].category_id + 0];
int c2 = colormap[3 * boxes[i].category_id + 1];
......@@ -63,13 +63,13 @@ cv::Mat Visualize(const cv::Mat& img,
origin.x = roi.x;
origin.y = roi.y;
// 生成预测框标题的背景
// background
cv::Rect text_back = cv::Rect(boxes[i].coordinate[0],
boxes[i].coordinate[1] - text_size.height,
text_size.width,
text_size.height);
// 绘图和文字
// draw
cv::rectangle(vis_img, roi, roi_color, 2);
cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img,
......@@ -80,7 +80,7 @@ cv::Mat Visualize(const cv::Mat& img,
cv::Scalar(255, 255, 255),
thickness);
// 生成实例分割mask
// mask
if (boxes[i].mask.data.size() == 0) {
continue;
}
......
deploy/raspberry/demo/classifier.cpp
浏览文件 @ 151ae40b
......@@ -44,11 +44,11 @@ int main(int argc, char** argv) {
return -1;
}
// 加载模型
// load model
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_thread_num);
std::cout << "init is done" << std::endl;
// 进行预测
// predict
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
if (!inf) {
......
deploy/raspberry/demo/detector.cpp
浏览文件 @ 151ae40b
......@@ -54,13 +54,13 @@ int main(int argc, char** argv) {
return -1;
}
//
// load model
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_thread_num);
int imgs = 1;
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
// 进行预测
// predict
if (FLAGS_image_list != "") {
std::ifstream inf(FLAGS_image_list);
if (!inf) {
......@@ -97,7 +97,7 @@ int main(int argc, char** argv) {
<< result.boxes[i].coordinate[3] << ")" << std::endl;
}
if (FLAGS_save_dir != "") {
// 可视化
// visualize
cv::Mat vis_img = PaddleX::Visualize(
im, result, model.labels, colormap, FLAGS_threshold);
std::string save_path =
......
deploy/raspberry/demo/segmenter.cpp
浏览文件 @ 151ae40b
......@@ -47,7 +47,7 @@ int main(int argc, char** argv) {
return -1;
}
//
// load model
std::cout << "init start" << std::endl;
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_cfg_file, FLAGS_thread_num);
......
deploy/raspberry/src/paddlex.cpp
浏览文件 @ 151ae40b
......@@ -46,9 +46,9 @@ bool Model::load_config(const std::string& cfg_file) {
return false;
}
}
// 构建数据处理流
// init preprocess ops
transforms_.Init(config["Transforms"], to_rgb);
// 读入label lis
// read label list
for (const auto& item : config["_Attributes"]["labels"]) {
int index = labels.size();
labels[index] = item.as<std::string>();
......@@ -77,14 +77,14 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
<< std::endl;
return false;
}
// 处理输入图像
// preprocess
inputs_.input_tensor_ = std::move(predictor_->GetInput(0));
cv::Mat im_clone = im.clone();
if (!preprocess(&im_clone, &inputs_)) {
std::cerr << "Preprocess failed!" << std::endl;
return false;
}
// predict
predictor_->Run();
std::unique_ptr<const paddle::lite_api::Tensor> output_tensor(
......@@ -92,7 +92,7 @@ bool Model::predict(const cv::Mat& im, ClsResult* result) {
const float *outputs_data = output_tensor->mutable_data<float>();
// 对模型输出结果进行后处理
// postprocess
auto ptr = std::max_element(outputs_data, outputs_data+sizeof(outputs_data));
result->category_id = std::distance(outputs_data, ptr);
result->score = *ptr;
......
deploy/raspberry/src/transforms.cpp
浏览文件 @ 151ae40b
......@@ -201,7 +201,7 @@ std::shared_ptr<Transform> Transforms::CreateTransform(
}
bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
// 按照transforms中预处理算子顺序处理图像
// preprocess by order
if (to_rgb_) {
cv::cvtColor(*im, *im, cv::COLOR_BGR2RGB);
}
......@@ -218,8 +218,8 @@ bool Transforms::Run(cv::Mat* im, ImageBlob* data) {
}
}
// 将图像由NHWC转为NCHW格式
// 同时转为连续的内存块存储到Blob
// image format NHWC to NCHW
// img data save to ImageBlob
int height = im->rows;
int width = im->cols;
int channels = im->channels();
......
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