saliency testing (#201)

added benchmarking for saliency and matching

---------

Co-authored-by: Samir Rashid <Samir-Rashid@godsped.com>
Co-authored-by: Triton UAS <ucsdtuas@gmail.com>
Co-authored-by: Igor Aprelev <hashreds@users.noreply.github.com>

docker/Makefile

@@ -29,7 +29,6 @@ stop-jetson-pixhawk-compose:
 run-jetson-cuda-check:
  docker run -it --rm --runtime nvidia -i ghcr.io/tritonuas/obcpp:jetson /obcpp/build/bin/cuda_check
 
-
 # NOTE: Use this target for development where you want to quicly edit source code and recompile.
 # This will spawn up the jetson container, launch you into a bash shell and mount the 
 # host's obcpp directory at "/obcpp" in the container. This means you can edit the source 

docker/jetson-pixhawk-compose.yml

@@ -1,7 +1,7 @@
 version: "3"
 services:
  obcpp:
- image: tritonuas/obcpp:jetson
+ image: ghcr.io/tritonuas/obcpp:jetson
  runtime: nvidia
  network_mode: "host"
  devices:

src/cv/CMakeLists.txt

@@ -14,6 +14,7 @@ set(FILES
 set(LIB_DEPS
  obcpp_protos
  obcpp_utilities
+ obcpp_camera
 )
 
 add_library(${LIB_NAME} STATIC

src/cv/saliency.cpp

@@ -7,6 +7,7 @@
 #include <opencv2/opencv.hpp>
 #include "utilities/logging.hpp"
 
+
 #define REGULAR_TARGET 1
 #define MANNIKIN 2
 
@@ -34,7 +35,12 @@ std::vector<CroppedTarget> Saliency::salience(cv::Mat image) {
  tensor = tensor.toType(c10::kFloat).div(255);
  // swap axis
  tensor = Saliency::transpose(tensor, { (2), (0), (1) });
- auto input_to_net = ToInput(tensor);
+
+ // eventually add device as member of Saliency
+ c10::Device device = torch::cuda::is_available() ? torch::kCUDA : torch::kCPU;
+ auto tensor_cuda = tensor.to(device);
+
+ auto input_to_net = ToInput(tensor_cuda);
 
  /* 
  * forward() runs an inference on the input image using the provided model

tests/integration/cv_matching.cpp

@@ -7,6 +7,7 @@
 
 #include "cv/matching.hpp"
 #include "utilities/constants.hpp"
+#include "utilities/common.hpp"
 
 // Download these test images by running "make pull_matching_test_images" or from the test.zip here https://drive.google.com/drive/u/1/folders/1opXBWx6bBF7J3-JSFtrhfFIkYis9qhGR
 // Or, any cropped not-stolen images will work
@@ -79,7 +80,10 @@ int main(int argc, char* argv[]) {
  cv::Mat ref4 = cv::imread(refImagePath4);
  referenceImages.push_back(std::make_pair(ref4, BottleDropIndex(1)));
 
+ auto startLoad = getUnixTime_ms().count();
  Matching matcher(bottlesToDrop, referenceImages, modelPath);
+ auto endLoad = getUnixTime_ms().count();
+ LOG_F(INFO, "time to load: %lu milliseconds", endLoad - startLoad);
  cv::Mat image = cv::imread(imageMatchPath);
  Bbox dummyBox(0, 0, 0, 0);
  CroppedTarget cropped = {
@@ -95,15 +99,21 @@ int main(int argc, char* argv[]) {
  false
  };
 
+ auto startMatch = getUnixTime_ms().count();
  MatchResult result = matcher.match(cropped);
+ auto endMatch = getUnixTime_ms().count();
  LOG_F(INFO, "\nTRUE MATCH TEST:\nClosest is bottle at index %d\nThe similarity is %.3f\n",
  int(result.bottleDropIndex),
  result.distance);
 
+ auto startMatchFalse = getUnixTime_ms().count();
  MatchResult resultFalse = matcher.match(croppedFalse);
+ auto endMatchFalse = getUnixTime_ms().count();
  LOG_F(INFO, "\nFALSE MATCH TEST:\nClosest is bottle at index %d\nThe similarity is %.3f\n",
  int(resultFalse.bottleDropIndex),
  resultFalse.distance);
+ LOG_F(INFO, "time to match: %lu milliseconds",
+ (endMatch - startMatch + endMatchFalse - startMatchFalse)/2);
 
  return 0;
 }

tests/integration/cv_saliency.cpp

@@ -5,22 +5,32 @@
 #include <string>
 
 #include "cv/saliency.hpp"
+#include "utilities/common.hpp"
 #include "loguru.hpp"
 
 // expected arguments: <path-to-model> <path-to-image> 
 int main(int argc, const char* argv[]) {
+
  if (argc != 3) {
  std::cerr << "usage: example-app <path-to-model> <path-to-image>\n";
  return -1;
  }
 
  // convert image to tensor
+
  const char* modelPath = argv[1];
+ auto startLoad = getUnixTime_ms().count();
  Saliency sal(modelPath);
+ auto endLoad = getUnixTime_ms().count();
+ LOG_F(INFO, "time to load: %u milliseconds", endLoad - startLoad);
+
  const char* imgPath = argv[2];
  cv::Mat img = cv::imread(imgPath, cv::IMREAD_COLOR);
 
+ auto startSalience = getUnixTime_ms().count();
  std::vector<CroppedTarget> predictions = sal.salience(img); 
+ auto endSalience = getUnixTime_ms().count();
+ LOG_F(INFO, "time to saliency: %u milliseconds", endSalience - startSalience);
 
  img = cv::imread(imgPath, cv::IMREAD_COLOR);
 
@@ -36,6 +46,7 @@ int main(int argc, const char* argv[]) {
  // cv::namedWindow("cropped targets", cv::WINDOW_FULLSCREEN);
  // cv::imshow("cropped targets", img);
  // cv::waitKey(0); 
+
  cv::imwrite("croppedTargets.jpg", img);
  LOG_F(INFO, "saved croppedTargets.jpg to build/");
  // testing: save input image to file path (cv::imsave?) with bounding boxes overlayed