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PaddleDetection

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未验证 提交 5e19955b 编写于 作者: C cnn 提交者： GitHub
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[dev] inference support bs > 1 (#3003) 

* bs>1 for YOLO
上级 fd494657
隐藏空白更改
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Showing with 346 addition and 190 deletion
deploy/README.md
浏览文件 @ 5e19955b
......@@ -28,6 +28,8 @@ python tools/export_model.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml
* C++部署 支持`CPU`、`GPU`和`XPU`环境，支持，windows、linux系统，支持NV Jetson嵌入式设备上部署。参考文档[C++部署](cpp/README.md)
* PaddleDetection支持TensorRT加速,相关文档请参考[TensorRT预测部署教程](TENSOR_RT.md)
**注意:** Paddle预测库版本需要>=2.1，batch_size>1仅支持YOLOv3和PP-YOLO。
## 2.PaddleServing部署
### 2.1 导出模型
......
deploy/cpp/include/object_detector.h
浏览文件 @ 5e19955b
......@@ -50,7 +50,7 @@ std::vector<int> GenerateColorMap(int num_class);
// Visualiztion Detection Result
cv::Mat VisualizeResult(const cv::Mat& img,
const std::vector<ObjectResult>& results,
const std::vector<std::string>& lable_list,
const std::vector<std::string>& lables,
const std::vector<int>& colormap,
const bool is_rbox);
......@@ -93,11 +93,12 @@ class ObjectDetector {
const std::string& run_mode = "fluid");
// Run predictor
void Predict(const cv::Mat& im,
void Predict(const std::vector<cv::Mat> imgs,
const double threshold = 0.5,
const int warmup = 0,
const int repeats = 1,
std::vector<ObjectResult>* result = nullptr,
std::vector<int>* bbox_num = nullptr,
std::vector<double>* times = nullptr);
// Get Model Label list
......@@ -120,14 +121,16 @@ class ObjectDetector {
void Preprocess(const cv::Mat& image_mat);
// Postprocess result
void Postprocess(
const cv::Mat& raw_mat,
const std::vector<cv::Mat> mats,
std::vector<ObjectResult>* result,
std::vector<int> bbox_num,
bool is_rbox);
std::shared_ptr<Predictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int> out_bbox_num_data_;
float threshold_;
ConfigPaser config_;
std::vector<int> image_shape_;
......
deploy/cpp/src/main.cc
浏览文件 @ 5e19955b
......@@ -21,6 +21,7 @@
#include <numeric>
#include <sys/types.h>
#include <sys/stat.h>
#include <math.h>
#ifdef _WIN32
#include <direct.h>
......@@ -37,6 +38,7 @@
DEFINE_string(model_dir, "", "Path of inference model");
DEFINE_string(image_file, "", "Path of input image");
DEFINE_string(image_dir, "", "Dir of input image, `image_file` has a higher priority.");
DEFINE_int32(batch_size, 1, "batch_size");
DEFINE_string(video_file, "", "Path of input video, `video_file` or `camera_id` has a highest priority.");
DEFINE_int32(camera_id, -1, "Device id of camera to predict");
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
......@@ -189,6 +191,7 @@ void PredictVideo(const std::string& video_path,
}
std::vector<PaddleDetection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
auto labels = det->GetLabelList();
auto colormap = PaddleDetection::GenerateColorMap(labels.size());
......@@ -200,8 +203,9 @@ void PredictVideo(const std::string& video_path,
if (frame.empty()) {
break;
}
det->Predict(frame, 0.5, 0, 1, &result, &det_times);
std::vector<cv::Mat> imgs;
imgs.push_back(frame);
det->Predict(imgs, 0.5, 0, 1, &result, &bbox_num, &det_times);
for (const auto& item : result) {
if (item.rect.size() > 6){
is_rbox = true;
......@@ -238,70 +242,107 @@ void PredictVideo(const std::string& video_path,
video_out.release();
}
void PredictImage(const std::vector<std::string> all_img_list,
void PredictImage(const std::vector<std::string> all_img_paths,
const int batch_size,
const double threshold,
const bool run_benchmark,
PaddleDetection::ObjectDetector* det,
const std::string& output_dir = "output") {
std::vector<double> det_t = {0, 0, 0};
for (auto image_file : all_img_list) {
// Open input image as an opencv cv::Mat object
cv::Mat im = cv::imread(image_file, 1);
int steps = ceil(float(all_img_paths.size()) / batch_size);
printf("total images = %d, batch_size = %d, total steps = %d\n",
all_img_paths.size(), batch_size, steps);
for (int idx = 0; idx < steps; idx++) {
std::vector<cv::Mat> batch_imgs;
int left_image_cnt = all_img_paths.size() - idx * batch_size;
if (left_image_cnt > batch_size) {
left_image_cnt = batch_size;
}
for (int bs = 0; bs < left_image_cnt; bs++) {
std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
cv::Mat im = cv::imread(image_file_path, 1);
batch_imgs.insert(batch_imgs.end(), im);
}
// Store all detected result
std::vector<PaddleDetection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
bool is_rbox = false;
if (run_benchmark) {
det->Predict(im, threshold, 10, 10, &result, &det_times);
det->Predict(batch_imgs, threshold, 10, 10, &result, &bbox_num, &det_times);
} else {
det->Predict(im, 0.5, 0, 1, &result, &det_times);
for (const auto& item : result) {
if (item.rect.size() > 6){
is_rbox = true;
printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3],
item.rect[4],
item.rect[5],
item.rect[6],
item.rect[7]);
det->Predict(batch_imgs, 0.5, 0, 1, &result, &bbox_num, &det_times);
// get labels and colormap
auto labels = det->GetLabelList();
auto colormap = PaddleDetection::GenerateColorMap(labels.size());
int item_start_idx = 0;
for (int i = 0; i < left_image_cnt; i++) {
std::cout << all_img_paths.at(idx * batch_size + i) << "result" << std::endl;
if (bbox_num[i] <= 1) {
continue;
}
else{
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3]);
for (int j = 0; j < bbox_num[i]; j++) {
PaddleDetection::ObjectResult item = result[item_start_idx + j];
if (item.rect.size() > 6){
is_rbox = true;
printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3],
item.rect[4],
item.rect[5],
item.rect[6],
item.rect[7]);
}
else{
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id,
item.confidence,
item.rect[0],
item.rect[1],
item.rect[2],
item.rect[3]);
}
}
item_start_idx = item_start_idx + bbox_num[i];
}
// Visualization result
auto labels = det->GetLabelList();
auto colormap = PaddleDetection::GenerateColorMap(labels.size());
cv::Mat vis_img = PaddleDetection::VisualizeResult(
im, result, labels, colormap, is_rbox);
std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95);
std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP;
int bbox_idx = 0;
for (int bs = 0; bs < batch_imgs.size(); bs++) {
if (bbox_num[bs] <= 1) {
continue;
}
cv::Mat im = batch_imgs[bs];
std::vector<PaddleDetection::ObjectResult> im_result;
for (int k = 0; k < bbox_num[bs]; k++) {
im_result.push_back(result[bbox_idx+k]);
}
bbox_idx += bbox_num[bs];
cv::Mat vis_img = PaddleDetection::VisualizeResult(
im, im_result, labels, colormap, is_rbox);
std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95);
std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP;
}
std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
output_path += image_file_path.substr(image_file_path.find_last_of('/') + 1);
cv::imwrite(output_path, vis_img, compression_params);
printf("Visualized output saved as %s\n", output_path.c_str());
}
;
output_path += image_file.substr(image_file.find_last_of('/') + 1);
cv::imwrite(output_path, vis_img, compression_params);
printf("Visualized output saved as %s\n", output_path.c_str());
}
det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
}
PrintBenchmarkLog(det_t, all_img_list.size());
PrintBenchmarkLog(det_t, all_img_paths.size());
}
int main(int argc, char** argv) {
......@@ -329,13 +370,17 @@ int main(int argc, char** argv) {
if (!PathExists(FLAGS_output_dir)) {
MkDirs(FLAGS_output_dir);
}
std::vector<std::string> all_img_list;
std::vector<std::string> all_imgs;
if (!FLAGS_image_file.empty()) {
all_img_list.push_back(FLAGS_image_file);
all_imgs.push_back(FLAGS_image_file);
if (FLAGS_batch_size > 1) {
std::cout << "batch_size should be 1, when image_file is not None" << std::endl;
FLAGS_batch_size = 1;
}
} else {
GetAllFiles((char *)FLAGS_image_dir.c_str(), all_img_list);
GetAllFiles((char *)FLAGS_image_dir.c_str(), all_imgs);
}
PredictImage(all_img_list, FLAGS_threshold, FLAGS_run_benchmark, &det, FLAGS_output_dir);
PredictImage(all_imgs, FLAGS_batch_size, FLAGS_threshold, FLAGS_run_benchmark, &det, FLAGS_output_dir);
}
return 0;
}
deploy/cpp/src/object_detector.cc
浏览文件 @ 5e19955b
......@@ -93,7 +93,7 @@ void ObjectDetector::LoadModel(const std::string& model_dir,
// Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat& img,
const std::vector<ObjectResult>& results,
const std::vector<std::string>& lable_list,
const std::vector<std::string>& lables,
const std::vector<int>& colormap,
const bool is_rbox=false) {
cv::Mat vis_img = img.clone();
......@@ -101,7 +101,7 @@ cv::Mat VisualizeResult(const cv::Mat& img,
// Configure color and text size
std::ostringstream oss;
oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
oss << lable_list[results[i].class_id] << " ";
oss << lables[results[i].class_id] << " ";
oss << results[i].confidence;
std::string text = oss.str();
int c1 = colormap[3 * results[i].class_id + 0];
......@@ -121,20 +121,20 @@ cv::Mat VisualizeResult(const cv::Mat& img,
if (is_rbox)
{
// Draw object, text, and background
for (int k=0; k<4; k++)
for (int k = 0; k < 4; k++)
{
cv::Point pt1 = cv::Point(results[i].rect[(k*2)%8],
results[i].rect[(k*2+1)%8]);
cv::Point pt2 = cv::Point(results[i].rect[(k*2+2)%8],
results[i].rect[(k*2+3)%8]);
cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
results[i].rect[(k * 2 + 1) % 8]);
cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
results[i].rect[(k * 2 + 3) % 8]);
cv::line(vis_img, pt1, pt2, roi_color, 2);
}
}
else
{
int w = results[i].rect[1] - results[i].rect[0];
int h = results[i].rect[3] - results[i].rect[2];
cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[2], w, h);
int w = results[i].rect[2] - results[i].rect[0];
int h = results[i].rect[3] - results[i].rect[1];
cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
// Draw roi object, text, and background
cv::rectangle(vis_img, roi, roi_color, 2);
}
......@@ -144,7 +144,7 @@ cv::Mat VisualizeResult(const cv::Mat& img,
// Configure text background
cv::Rect text_back = cv::Rect(results[i].rect[0],
results[i].rect[2] - text_size.height,
results[i].rect[1] - text_size.height,
text_size.width,
text_size.height);
// Draw text, and background
......@@ -168,76 +168,100 @@ void ObjectDetector::Preprocess(const cv::Mat& ori_im) {
}
void ObjectDetector::Postprocess(
const cv::Mat& raw_mat,
const std::vector<cv::Mat> mats,
std::vector<ObjectResult>* result,
std::vector<int> bbox_num,
bool is_rbox=false) {
result->clear();
int rh = 1;
int rw = 1;
if (config_.arch_ == "Face") {
rh = raw_mat.rows;
rw = raw_mat.cols;
}
int start_idx = 0;
for (int im_id = 0; im_id < bbox_num.size(); im_id++) {
cv::Mat raw_mat = mats[im_id];
for (int j = start_idx; j < start_idx+bbox_num[im_id]; j++) {
int rh = 1;
int rw = 1;
if (config_.arch_ == "Face") {
rh = raw_mat.rows;
rw = raw_mat.cols;
}
if (is_rbox)
{
int total_size = output_data_.size() / 10;
for (int j = 0; j < total_size; ++j) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
// Confidence score
float score = output_data_[1 + j * 10];
int x1 = (output_data_[2 + j * 10] * rw);
int y1 = (output_data_[3 + j * 10] * rh);
int x2 = (output_data_[4 + j * 10] * rw);
int y2 = (output_data_[5 + j * 10] * rh);
int x3 = (output_data_[6 + j * 10] * rw);
int y3 = (output_data_[7 + j * 10] * rh);
int x4 = (output_data_[8 + j * 10] * rw);
int y4 = (output_data_[9 + j * 10] * rh);
if (score > threshold_ && class_id > -1) {
ObjectResult result_item;
result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
if (is_rbox) {
for (int j = 0; j < bbox_num[im_id]; ++j) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
// Confidence score
float score = output_data_[1 + j * 10];
int x1 = (output_data_[2 + j * 10] * rw);
int y1 = (output_data_[3 + j * 10] * rh);
int x2 = (output_data_[4 + j * 10] * rw);
int y2 = (output_data_[5 + j * 10] * rh);
int x3 = (output_data_[6 + j * 10] * rw);
int y3 = (output_data_[7 + j * 10] * rh);
int x4 = (output_data_[8 + j * 10] * rw);
int y4 = (output_data_[9 + j * 10] * rh);
if (score > threshold_ && class_id > -1) {
ObjectResult result_item;
result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
}
}
}
}
}
else
{
int total_size = output_data_.size() / 6;
for (int j = 0; j < total_size; ++j) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
// Confidence score
float score = output_data_[1 + j * 6];
int xmin = (output_data_[2 + j * 6] * rw);
int ymin = (output_data_[3 + j * 6] * rh);
int xmax = (output_data_[4 + j * 6] * rw);
int ymax = (output_data_[5 + j * 6] * rh);
int wd = xmax - xmin;
int hd = ymax - ymin;
if (score > threshold_ && class_id > -1) {
ObjectResult result_item;
result_item.rect = {xmin, xmax, ymin, ymax};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
else {
for (int j = 0; j < bbox_num[im_id]; ++j) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
// Confidence score
float score = output_data_[1 + j * 6];
int xmin = (output_data_[2 + j * 6] * rw);
int ymin = (output_data_[3 + j * 6] * rh);
int xmax = (output_data_[4 + j * 6] * rw);
int ymax = (output_data_[5 + j * 6] * rh);
int wd = xmax - xmin;
int hd = ymax - ymin;
if (score > threshold_ && class_id > -1) {
ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
}
}
}
}
start_idx += bbox_num[im_id];
}
}
void ObjectDetector::Predict(const cv::Mat& im,
void ObjectDetector::Predict(const std::vector<cv::Mat> imgs,
const double threshold,
const int warmup,
const int repeats,
std::vector<ObjectResult>* result,
std::vector<int>* bbox_num,
std::vector<double>* times) {
auto preprocess_start = std::chrono::steady_clock::now();
int batch_size = imgs.size();
// in_data_batch
std::vector<float> in_data_all;
std::vector<float> im_shape_all(batch_size * 2);
std::vector<float> scale_factor_all(batch_size * 2);
// Preprocess image
Preprocess(im);
for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
cv::Mat im = imgs.at(bs_idx);
Preprocess(im);
im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
// TODO: reduce cost time
in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
}
// Prepare input tensor
auto input_names = predictor_->GetInputNames();
for (const auto& tensor_name : input_names) {
......@@ -245,14 +269,14 @@ void ObjectDetector::Predict(const cv::Mat& im,
if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({1, 3, rh, rw});
in_tensor->CopyFromCpu(inputs_.im_data_.data());
in_tensor->Reshape({batch_size, 3, rh, rw});
in_tensor->CopyFromCpu(in_data_all.data());
} else if (tensor_name == "im_shape") {
in_tensor->Reshape({1, 2});
in_tensor->CopyFromCpu(inputs_.im_shape_.data());
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(im_shape_all.data());
} else if (tensor_name == "scale_factor") {
in_tensor->Reshape({1, 2});
in_tensor->CopyFromCpu(inputs_.scale_factor_.data());
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(scale_factor_all.data());
}
}
auto preprocess_end = std::chrono::steady_clock::now();
......@@ -266,10 +290,6 @@ void ObjectDetector::Predict(const cv::Mat& im,
std::vector<int> output_shape = out_tensor->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
}
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
}
......@@ -286,6 +306,8 @@ void ObjectDetector::Predict(const cv::Mat& im,
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
......@@ -298,11 +320,23 @@ void ObjectDetector::Predict(const cv::Mat& im,
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
// Postprocessing result
Postprocess(im, result, is_rbox);
Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
bbox_num->clear();
for (int k=0; k<out_bbox_num_data_.size(); k++) {
int tmp = out_bbox_num_data_[k];
bbox_num->push_back(tmp);
}
auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
......
deploy/cpp/src/preprocess_op.cc
浏览文件 @ 5e19955b
......@@ -129,7 +129,6 @@ void PadStride::Run(cv::Mat* im, ImageBlob* data) {
static_cast<float>(im->rows),
static_cast<float>(im->cols),
};
}
......
deploy/python/infer.py
浏览文件 @ 5e19955b
......@@ -21,6 +21,7 @@ from functools import reduce
from PIL import Image
import cv2
import numpy as np
import math
import paddle
from paddle.inference import Config
from paddle.inference import create_predictor
......@@ -85,18 +86,29 @@ class Detector(object):
self.det_times = Timer()
self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
def preprocess(self, im):
def preprocess(self, image_list):
preprocess_ops = []
for op_info in self.pred_config.preprocess_infos:
new_op_info = op_info.copy()
op_type = new_op_info.pop('type')
preprocess_ops.append(eval(op_type)(**new_op_info))
im, im_info = preprocess(im, preprocess_ops,
self.pred_config.input_shape)
inputs = create_inputs(im, im_info)
input_im_lst = []
input_im_info_lst = []
for im_path in image_list:
im, im_info = preprocess(im_path, preprocess_ops,
self.pred_config.input_shape)
input_im_lst.append(im)
input_im_info_lst.append(im_info)
inputs = create_inputs(input_im_lst, input_im_info_lst)
return inputs
def postprocess(self, np_boxes, np_masks, inputs, threshold=0.5):
def postprocess(self,
np_boxes,
np_masks,
inputs,
np_boxes_num,
threshold=0.5):
# postprocess output of predictor
results = {}
if self.pred_config.arch in ['Face']:
......@@ -108,14 +120,15 @@ class Detector(object):
np_boxes[:, 4] *= h
np_boxes[:, 5] *= w
results['boxes'] = np_boxes
results['boxes_num'] = np_boxes_num
if np_masks is not None:
results['masks'] = np_masks
return results
def predict(self, image, threshold=0.5, warmup=0, repeats=1):
def predict(self, image_list, threshold=0.5, warmup=0, repeats=1):
'''
Args:
image (str/np.ndarray): path of image/ np.ndarray read by cv2
image_list (list): ,list of image
threshold (float): threshold of predicted box' score
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
......@@ -124,7 +137,7 @@ class Detector(object):
shape: [N, im_h, im_w]
'''
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(image)
inputs = self.preprocess(image_list)
np_boxes, np_masks = None, None
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
......@@ -146,6 +159,8 @@ class Detector(object):
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu()
boxes_num = self.predictor.get_output_handle(output_names[1])
np_boxes_num = boxes_num.copy_to_cpu()
if self.pred_config.mask:
masks_tensor = self.predictor.get_output_handle(output_names[2])
np_masks = masks_tensor.copy_to_cpu()
......@@ -155,12 +170,12 @@ class Detector(object):
results = []
if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
print('[WARNNING] No object detected.')
results = {'boxes': np.array([])}
results = {'boxes': np.array([]), 'boxes_num': [0]}
else:
results = self.postprocess(
np_boxes, np_masks, inputs, threshold=threshold)
np_boxes, np_masks, inputs, np_boxes_num, threshold=threshold)
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
self.det_times.img_num += len(image_list)
return results
......@@ -249,21 +264,45 @@ class DetectorSOLOv2(Detector):
return dict(segm=np_segms, label=np_label, score=np_score)
def create_inputs(im, im_info):
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
model_arch (str): model type
Returns:
inputs (dict): input of model
"""
inputs = {}
inputs['image'] = np.array((im, )).astype('float32')
inputs['im_shape'] = np.array((im_info['im_shape'], )).astype('float32')
inputs['scale_factor'] = np.array(
(im_info['scale_factor'], )).astype('float32')
im_shape = []
scale_factor = []
for e in im_info:
im_shape.append(np.array((e['im_shape'], )).astype('float32'))
scale_factor.append(np.array((e['scale_factor'], )).astype('float32'))
origin_scale_factor = np.concatenate(scale_factor, axis=0)
imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
max_shape_h = max([e[0] for e in imgs_shape])
max_shape_w = max([e[1] for e in imgs_shape])
padding_imgs = []
padding_imgs_shape = []
padding_imgs_scale = []
for img in imgs:
im_c, im_h, im_w = img.shape[:]
padding_im = np.zeros(
(im_c, max_shape_h, max_shape_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = img
padding_imgs.append(padding_im)
padding_imgs_shape.append(
np.array([max_shape_h, max_shape_w]).astype('float32'))
rescale = [
float(max_shape_h) / float(im_h), float(max_shape_w) / float(im_w)
]
padding_imgs_scale.append(np.array(rescale).astype('float32'))
inputs['image'] = np.stack(padding_imgs, axis=0)
inputs['im_shape'] = np.stack(padding_imgs_shape, axis=0)
inputs['scale_factor'] = origin_scale_factor
return inputs
......@@ -426,15 +465,30 @@ def get_test_images(infer_dir, infer_img):
return images
def visualize(image_file, results, labels, output_dir='output/', threshold=0.5):
def visualize(image_list, results, labels, output_dir='output/', threshold=0.5):
# visualize the predict result
im = visualize_box_mask(image_file, results, labels, threshold=threshold)
img_name = os.path.split(image_file)[-1]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
out_path = os.path.join(output_dir, img_name)
im.save(out_path, quality=95)
print("save result to: " + out_path)
start_idx = 0
for idx, image_file in enumerate(image_list):
im_bboxes_num = results['boxes_num'][idx]
im_results = {}
if 'boxes' in results:
im_results['boxes'] = results['boxes'][start_idx:start_idx +
im_bboxes_num, :]
if 'masks' in results:
im_results['masks'] = results['masks'][start_idx:start_idx +
im_bboxes_num, :]
if 'segm' in results:
im_results['segm'] = results['segm'][start_idx:start_idx +
im_bboxes_num, :]
start_idx += im_bboxes_num
im = visualize_box_mask(
image_file, im_results, labels, threshold=threshold)
img_name = os.path.split(image_file)[-1]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
out_path = os.path.join(output_dir, img_name)
im.save(out_path, quality=95)
print("save result to: " + out_path)
def print_arguments(args):
......@@ -444,19 +498,24 @@ def print_arguments(args):
print('------------------------------------------')
def predict_image(detector, image_list):
for i, img_file in enumerate(image_list):
def predict_image(detector, image_list, batch_size=1):
batch_loop_cnt = math.ceil(float(len(image_list)) / batch_size)
for i in range(batch_loop_cnt):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, len(image_list))
batch_image_list = image_list[start_index:end_index]
if FLAGS.run_benchmark:
detector.predict(img_file, FLAGS.threshold, warmup=10, repeats=10)
detector.predict(
batch_image_list, FLAGS.threshold, warmup=10, repeats=10)
cm, gm, gu = get_current_memory_mb()
detector.cpu_mem += cm
detector.gpu_mem += gm
detector.gpu_util += gu
print('Test iter {}, file name:{}'.format(i, img_file))
print('Test iter {}'.format(i))
else:
results = detector.predict(img_file, FLAGS.threshold)
results = detector.predict(batch_image_list, FLAGS.threshold)
visualize(
img_file,
batch_image_list,
results,
detector.pred_config.labels,
output_dir=FLAGS.output_dir,
......@@ -535,8 +594,10 @@ def main():
predict_video(detector, FLAGS.camera_id)
else:
# predict from image
if FLAGS.image_dir is None and FLAGS.image_file is not None:
assert FLAGS.batch_size == 1, "batch_size should be 1, when image_file is not None"
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
predict_image(detector, img_list)
predict_image(detector, img_list, FLAGS.batch_size)
if not FLAGS.run_benchmark:
detector.det_times.info(average=True)
else:
......
deploy/python/utils.py
浏览文件 @ 5e19955b
......@@ -34,6 +34,8 @@ def argsparser():
type=str,
default=None,
help="Dir of image file, `image_file` has a higher priority.")
parser.add_argument(
"--batch_size", type=int, default=1, help="batch_size for infer.")
parser.add_argument(
"--video_file",
type=str,
......
ppdet/engine/trainer.py
浏览文件 @ 5e19955b
......@@ -436,7 +436,7 @@ class Trainer(object):
image = visualize_results(
image, bbox_res, mask_res, segm_res, keypoint_res,
int(outs['im_id']), catid2name, draw_threshold)
int(im_id), catid2name, draw_threshold)
self.status['result_image'] = np.array(image.copy())
if self._compose_callback:
self._compose_callback.on_step_end(self.status)
......
ppdet/modeling/architectures/s2anet.py
浏览文件 @ 5e19955b
......@@ -83,11 +83,13 @@ class S2ANet(BaseArch):
nms_pre = self.s2anet_bbox_post_process.nms_pre
pred_scores, pred_bboxes = self.s2anet_head.get_prediction(nms_pre)
# post_process
pred_bboxes, bbox_num = self.s2anet_bbox_post_process(pred_scores,
pred_bboxes)
# rescale the prediction back to origin image
pred_bboxes = self.s2anet_bbox_post_process.get_pred(
pred_bboxes, bbox_num, im_shape, scale_factor)
# output
output = {'bbox': pred_bboxes, 'bbox_num': bbox_num}
return output
......
ppdet/modeling/layers.py
浏览文件 @ 5e19955b
......@@ -334,8 +334,11 @@ class RCNNBox(object):
self.num_classes = num_classes
def __call__(self, bbox_head_out, rois, im_shape, scale_factor):
bbox_pred, cls_prob = bbox_head_out
roi, rois_num = rois
bbox_pred = bbox_head_out[0]
cls_prob = bbox_head_out[1]
roi = rois[0]
rois_num = rois[1]
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
scale_list = []
origin_shape_list = []
......
ppdet/modeling/post_process.py
浏览文件 @ 5e19955b
......@@ -264,7 +264,6 @@ class S2ANetBBoxPostProcess(nn.Layer):
bbox_num = self.fake_bbox_num
pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [-1, 10])
assert pred_cls_score_bbox.shape[1] == 10
return pred_cls_score_bbox, bbox_num
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
......@@ -281,7 +280,6 @@ class S2ANetBBoxPostProcess(nn.Layer):
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image.
"""
assert bboxes.shape[1] == 10
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
......@@ -307,6 +305,7 @@ class S2ANetBBoxPostProcess(nn.Layer):
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
pred_bbox = pred_bbox.reshape([-1, 8])
scaled_bbox = pred_bbox / scale_factor_list
origin_h = origin_shape_list[:, 0]
origin_w = origin_shape_list[:, 1]
......
ppdet/modeling/proposal_generator/rpn_head.py
浏览文件 @ 5e19955b
......@@ -62,11 +62,11 @@ class RPNHead(nn.Layer):
Args:
anchor_generator (dict): configure of anchor generation
rpn_target_assign (dict): configure of rpn targets assignment
train_proposal (dict): configure of proposals generation
train_proposal (dict): configure of proposals generation
at the stage of training
test_proposal (dict): configure of proposals generation
at the stage of prediction
in_channel (int): channel of input feature maps which can be
in_channel (int): channel of input feature maps which can be
derived by from_config
"""
......@@ -156,31 +156,35 @@ class RPNHead(nn.Layer):
"""
prop_gen = self.train_proposal if self.training else self.test_proposal
im_shape = inputs['im_shape']
rpn_rois_list = [[] for i in range(batch_size)]
rpn_prob_list = [[] for i in range(batch_size)]
rpn_rois_num_list = [[] for i in range(batch_size)]
# Collect multi-level proposals for each batch
# Get 'topk' of them as final output
bs_rois_collect = []
bs_rois_num_collect = []
# Generate proposals for each level and each batch.
# Discard batch-computing to avoid sorting bbox cross different batches.
for rpn_score, rpn_delta, anchor in zip(scores, bbox_deltas, anchors):
for i in range(batch_size):
for i in range(batch_size):
rpn_rois_list = []
rpn_prob_list = []
rpn_rois_num_list = []
for rpn_score, rpn_delta, anchor in zip(scores, bbox_deltas,
anchors):
rpn_rois, rpn_rois_prob, rpn_rois_num, post_nms_top_n = prop_gen(
scores=rpn_score[i:i + 1],
bbox_deltas=rpn_delta[i:i + 1],
anchors=anchor,
im_shape=im_shape[i:i + 1])
if rpn_rois.shape[0] > 0:
rpn_rois_list[i].append(rpn_rois)
rpn_prob_list[i].append(rpn_rois_prob)
rpn_rois_num_list[i].append(rpn_rois_num)
# Collect multi-level proposals for each batch
# Get 'topk' of them as final output
rois_collect = []
rois_num_collect = []
for i in range(batch_size):
rpn_rois_list.append(rpn_rois)
rpn_prob_list.append(rpn_rois_prob)
rpn_rois_num_list.append(rpn_rois_num)
if len(scores) > 1:
rpn_rois = paddle.concat(rpn_rois_list[i])
rpn_prob = paddle.concat(rpn_prob_list[i]).flatten()
rpn_rois = paddle.concat(rpn_rois_list)
rpn_prob = paddle.concat(rpn_prob_list).flatten()
if rpn_prob.shape[0] > post_nms_top_n:
topk_prob, topk_inds = paddle.topk(rpn_prob, post_nms_top_n)
topk_rois = paddle.gather(rpn_rois, topk_inds)
......@@ -188,17 +192,19 @@ class RPNHead(nn.Layer):
topk_rois = rpn_rois
topk_prob = rpn_prob
else:
topk_rois = rpn_rois_list[i][0]
topk_prob = rpn_prob_list[i][0].flatten()
rois_collect.append(topk_rois)
rois_num_collect.append(paddle.shape(topk_rois)[0])
rois_num_collect = paddle.concat(rois_num_collect)
topk_rois = rpn_rois_list[0]
topk_prob = rpn_prob_list[0].flatten()
bs_rois_collect.append(topk_rois)
bs_rois_num_collect.append(paddle.shape(topk_rois)[0])
bs_rois_num_collect = paddle.concat(bs_rois_num_collect)
return rois_collect, rois_num_collect
return bs_rois_collect, bs_rois_num_collect
def get_loss(self, pred_scores, pred_deltas, anchors, inputs):
"""
pred_scores (list[Tensor]): Multi-level scores prediction
pred_scores (list[Tensor]): Multi-level scores prediction
pred_deltas (list[Tensor]): Multi-level deltas prediction
anchors (list[Tensor]): Multi-level anchors
inputs (dict): ground truth info, including im, gt_bbox, gt_score
......
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