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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// 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
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <sstream>
// for setprecision
#include <chrono>
#include <iomanip>
#include "include/jde_predictor.h"
using namespace paddle_infer; // NOLINT
namespace PaddleDetection {
// Load Model and create model predictor
void JDEPredictor::LoadModel(const std::string& model_dir,
const std::string& run_mode) {
paddle_infer::Config config;
std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
config.SetModel(prog_file, params_file);
if (this->device_ == "GPU") {
config.EnableUseGpu(200, this->gpu_id_);
config.SwitchIrOptim(true);
// use tensorrt
if (run_mode != "paddle") {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (run_mode == "trt_fp32") {
precision = paddle_infer::Config::Precision::kFloat32;
} else if (run_mode == "trt_fp16") {
precision = paddle_infer::Config::Precision::kHalf;
} else if (run_mode == "trt_int8") {
precision = paddle_infer::Config::Precision::kInt8;
} else {
printf(
"run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or "
"'trt_int8'");
}
// set tensorrt
config.EnableTensorRtEngine(1 << 30,
1,
this->min_subgraph_size_,
precision,
false,
this->trt_calib_mode_);
}
} else if (this->device_ == "XPU") {
config.EnableXpu(10 * 1024 * 1024);
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
config.SwitchIrOptim(true);
config.DisableGlogInfo();
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
}
void FilterDets(const float conf_thresh,
const cv::Mat dets,
std::vector<int>* index) {
for (int i = 0; i < dets.rows; ++i) {
float score = *dets.ptr<float>(i, 4);
if (score > conf_thresh) {
index->push_back(i);
}
}
}
void JDEPredictor::Preprocess(const cv::Mat& ori_im) {
// Clone the image : keep the original mat for postprocess
cv::Mat im = ori_im.clone();
preprocessor_.Run(&im, &inputs_);
}
void JDEPredictor::Postprocess(const cv::Mat dets,
const cv::Mat emb,
MOTResult* result) {
result->clear();
std::vector<Track> tracks;
std::vector<int> valid;
FilterDets(conf_thresh_, dets, &valid);
cv::Mat new_dets, new_emb;
for (int i = 0; i < valid.size(); ++i) {
new_dets.push_back(dets.row(valid[i]));
new_emb.push_back(emb.row(valid[i]));
}
JDETracker::instance()->update(new_dets, new_emb, &tracks);
if (tracks.size() == 0) {
MOTTrack mot_track;
Rect ret = {*dets.ptr<float>(0, 0),
*dets.ptr<float>(0, 1),
*dets.ptr<float>(0, 2),
*dets.ptr<float>(0, 3)};
mot_track.ids = 1;
mot_track.score = *dets.ptr<float>(0, 4);
mot_track.rects = ret;
result->push_back(mot_track);
} else {
std::vector<Track>::iterator titer;
for (titer = tracks.begin(); titer != tracks.end(); ++titer) {
if (titer->score < threshold_) {
continue;
} else {
float w = titer->ltrb[2] - titer->ltrb[0];
float h = titer->ltrb[3] - titer->ltrb[1];
bool vertical = w / h > 1.6;
float area = w * h;
if (area > min_box_area_ && !vertical) {
MOTTrack mot_track;
Rect ret = {
titer->ltrb[0], titer->ltrb[1], titer->ltrb[2], titer->ltrb[3]};
mot_track.rects = ret;
mot_track.score = titer->score;
mot_track.ids = titer->id;
result->push_back(mot_track);
}
}
}
}
}
void JDEPredictor::Predict(const std::vector<cv::Mat> imgs,
const double threshold,
MOTResult* result,
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
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];
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) {
auto in_tensor = predictor_->GetInputHandle(tensor_name);
if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({batch_size, 3, rh, rw});
in_tensor->CopyFromCpu(in_data_all.data());
} else if (tensor_name == "im_shape") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(im_shape_all.data());
} else if (tensor_name == "scale_factor") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(scale_factor_all.data());
}
}
auto preprocess_end = std::chrono::steady_clock::now();
std::vector<int> bbox_shape;
std::vector<int> emb_shape;
// Run predictor
auto inference_start = std::chrono::steady_clock::now();
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto bbox_tensor = predictor_->GetOutputHandle(output_names[0]);
bbox_shape = bbox_tensor->shape();
auto emb_tensor = predictor_->GetOutputHandle(output_names[1]);
emb_shape = emb_tensor->shape();
// Calculate bbox length
int bbox_size = 1;
for (int j = 0; j < bbox_shape.size(); ++j) {
bbox_size *= bbox_shape[j];
}
// Calculate emb length
int emb_size = 1;
for (int j = 0; j < emb_shape.size(); ++j) {
emb_size *= emb_shape[j];
}
bbox_data_.resize(bbox_size);
bbox_tensor->CopyToCpu(bbox_data_.data());
emb_data_.resize(emb_size);
emb_tensor->CopyToCpu(emb_data_.data());
auto inference_end = std::chrono::steady_clock::now();
// Postprocessing result
auto postprocess_start = std::chrono::steady_clock::now();
result->clear();
cv::Mat dets(bbox_shape[0], 6, CV_32FC1, bbox_data_.data());
cv::Mat emb(bbox_shape[0], emb_shape[1], CV_32FC1, emb_data_.data());
Postprocess(dets, emb, result);
auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
(*times)[0] += static_cast<double>(preprocess_diff.count() * 1000);
std::chrono::duration<float> inference_diff = inference_end - inference_start;
(*times)[1] += static_cast<double>(inference_diff.count() * 1000);
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
(*times)[2] += static_cast<double>(postprocess_diff.count() * 1000);
}
} // namespace PaddleDetection
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