Fix CI and license badge. (#356)

* feat: enable pifpaf inference support * refact: rm comments * feat: openpifpaf decoder finalized * fix: ci * fix: license badge * fix: gcc7 compilation

Author: ganler

README.md

@@ -10,7 +10,7 @@
  <a href="https://drive.google.com/drive/folders/1w9EjMkrjxOmMw3Rf6fXXkiv_ge7M99jR?usp=sharing" title="PreTrainedModels"><img src="https://img.shields.io/badge/trained%20models-GoogleDrive-brightgreen.svg"></a>
  <a href="https://en.cppreference.com/w/cpp/17" title="CppStandard"><img src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B"></a>
  <a href="https://github.com/tensorlayer/hyperpose/graphs/commit-activity" title="Maintenance"><img src="https://img.shields.io/badge/maintained%3F-YES-brightgreen.svg"></a>
- <a href="https://github.com/tensorlayer/tensorlayer/blob/master/LICENSE.rst" title="TensorLayer"><img src="https://img.shields.io/github/license/tensorlayer/tensorlayer">
+ <a href="https://github.com/tensorlayer/tensorlayer/blob/master/LICENSE.rst" title="TensorLayer"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg">
 </p>
 
 ---
@@ -85,6 +85,8 @@ We compare the prediction performance of HyperPose with [OpenPose 1.6](https://g
 | OpenPose (MobileNet) | 17.9 MB | 432 x 368 | **84.32 FPS** | 8.5 FPS (TF-Pose) |
 | OpenPose (ResNet18) | 45.0 MB | 432 x 368 | **62.52 FPS** | N/A |
 | OpenPifPaf (ResNet50) | 97.6 MB | 97 x 129 | **178.6 FPS** | 35.3 |
+<<<<<<< HEAD
+=======
 
 ## Accuracy
 We evaluate accuracy of pose estimation models developed by hyperpose (mainly over Mscoco2017 dataset). the development environment is Ubuntu16.04, with 4 V100-DGXs and 24 Intel Xeon CPU. The training procedure takes 1~2 weeks using 1 V100-DGX for each model. (If you want to train from strach, loading the pretrained backbone weight is recommended.)
@@ -97,6 +99,7 @@ We evaluate accuracy of pose estimation models developed by hyperpose (mainly ov
 | LightweightOpenpose (tinyvgg) | 23.6 MB | 432 x 368 | Mscoco2017(all 5000 images) | 47.3 map | - |
 | LightweightOpenpose (resnet50) | 42.7 MB | 432 x 368 | Mscoco2017(all 5000 images) | 48.2 map | - |
 | PoseProposal (resnet18) | 45.2 MB | 384 x 384 | MPII(all 2729 images) | 54.9 map (Pckh) | 72.8 map (Pckh)|
+>>>>>>> master
 
 </a>
 <p align="center">

src/pifpaf_decoder/openpifpaf_postprocessor.cpp

@@ -439,9 +439,8 @@ namespace aiapp_impl {
  using xysv = std::optional<std::tuple<float, float, float, float>>;
 
  struct queue_item { // -score, xyv, start_i, end_i
- template <typename... Args>
- queue_item(Args&&... args)
- : data(std::make_tuple(std::forward<Args>(args)...))
+ explicit queue_item(float f, xysv xysv_, int s, int e)
+ : data(std::make_tuple(f, std::move(xysv_), s, e))
  {
  }
  std::tuple<float, xysv, int, int> data;
@@ -462,7 +461,11 @@ namespace aiapp_impl {
  // frontierActive = true;
  // blockFrontier.clear();
  std::set<std::pair<int, int>> in_frontier{};
+<<<<<<< HEAD
+ std::priority_queue<queue_item, std::deque<queue_item>, std::greater<>> frontier;
+=======
  std::priority_queue<queue_item, std::deque<queue_item>, std::greater<queue_item>> frontier;
+>>>>>>> master
 
  const auto add_to_frontier = [&](size_t start_i) {
  for (const auto& [end_i, to_p] : BY_SOURCE_MAP[start_i]) {