ci: Add script to test raft, failing

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ jobs:
 
     strategy:
       matrix:
-        ci_script: [pr_compile]
+        ci_script: [pr_compile, pr_run_tests]
 
     steps:
       - name: Checkout

CMakeLists.txt

@@ -103,22 +103,19 @@ target_include_directories(${PROJECT_NAME}_node PUBLIC
 
 # Testing
 if(BUILD_TESTING)
-  if($ENV{ROS_VERSION} EQUAL 2)
-    find_package(ament_lint_auto REQUIRED)
-    ament_lint_auto_find_test_dependencies()
-    find_package(ament_cmake_gtest)
-
-    # Unit tests
-    ament_add_gtest(test_usb_cam_utils test/test_usb_cam_utils.cpp)
-    target_link_libraries(test_usb_cam_utils ${PROJECT_NAME})
-
-    ament_add_gtest(test_pixel_formats test/test_pixel_formats.cpp)
-    target_link_libraries(test_pixel_formats ${PROJECT_NAME})
-
-    # Integration tests (commented out as per TODO)
-    # ament_add_gtest(test_usb_cam_lib test/test_usb_cam_lib.cpp)
-    # target_link_libraries(test_usb_cam_lib ${PROJECT_NAME})
-  endif()
+  # Find GTest package
+  find_package(GTest REQUIRED)
+  include_directories(${GTEST_INCLUDE_DIRS})
+
+  catkin_add_gtest(test_learning_interface test/test_learning_interface.cpp)
+  target_link_libraries(test_learning_interface
+      ${PROJECT_NAME}
+      ${catkin_LIBRARIES}
+      GTest::gtest_main
+      ${NVINFER}
+      ${NVINFER_PLUGIN}
+      ${CUDA_LIBRARIES}
+  )
 endif()
 
 # Installation rules

ci/pr_run_tests.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+source /opt/ros/noetic/setup.bash
+
+rm -rf build
+mkdir -p build
+cd build
+if cmake .. -DBUILD_TESTING=ON
+then
+    echo "CMake successfull"
+    if make test_learning_interface
+    then
+        echo "Make successfull"
+    else
+        echo "Make failed"
+        exit 1
+    fi
+else
+    echo "CMake failed"
+    exit 1
+fi
+
+if ./devel/lib/usb_cam/test_learning_interface
+then
+    echo "Tests successful"
+else
+    echo "Tests failed"
+    exit 1
+fi
+
+

include/usb_cam/learning/interface.hpp

@@ -22,7 +22,6 @@ class LearningInterface {
 
     void load_model();
     bool run_inference(size_t batch_size);
-
 
     virtual ~LearningInterface() {
         // if (_context) _context->destroy();
@@ -36,19 +35,24 @@ class LearningInterface {
         delete[] _output_buffer;
     }
 
+    float* get_input_buffer() { return _input_buffer; }
+    nvinfer1::ICudaEngine* get_engine() { return _engine; }
+    nvinfer1::IExecutionContext* get_context() { return _context; }
+    nvinfer1::IRuntime* get_runtime() { return _runtime; }
+
 protected:
     float* _input_buffer = nullptr;
     float* _output_buffer = nullptr;
+    nvinfer1::ICudaEngine* _engine = nullptr;
+    nvinfer1::IExecutionContext* _context = nullptr;
+    nvinfer1::IRuntime* _runtime = nullptr;
     size_t input_height;
     size_t input_width;
     size_t output_height;
     size_t output_width;
     std::string _model_path;
 
 private:
-    nvinfer1::ICudaEngine* _engine = nullptr;
-    nvinfer1::IExecutionContext* _context = nullptr;
-    nvinfer1::IRuntime* _runtime = nullptr;
     void* _buffers[2] = { nullptr, nullptr };
 };
 

src/interface.cpp

@@ -30,20 +30,22 @@ void LearningInterface::load_model() {
                     // Allocate buffers for input and output
                     size_t input_size;
                     size_t output_size;
-                    for (int io = 0; io < _engine->getNbIOTensors(); io++) {
-                        const char* name = _engine->getIOTensorName(io);
+                    for (int io = 0; io < _engine->getNbBindings(); io++) {
+                        const char* name = _engine->getBindingName(io);
                         std::cout << io << ": " << name;
-                        const nvinfer1::Dims dims = _engine->getTensorShape(name);
+                        const nvinfer1::Dims dims = _engine->getBindingDimensions(io);
 
                         size_t total_dims = 1;
                         for (int d = 0; d < dims.nbDims; d++) {
                             total_dims *= dims.d[d];
                         }
 
                         std::cout << " size: " << total_dims << std::endl;
-                        if (io == 0) {
+
+                        // Check if it's an input or output binding
+                        if (_engine->bindingIsInput(io)) {
                             input_size = total_dims * sizeof(float);
-                        } else if (io == 1) {
+                        } else {
                             output_size = total_dims * sizeof(float);
                         }
                     }

test/test_learning_interface.cpp

@@ -0,0 +1,61 @@
+#include <gtest/gtest.h>
+#include "usb_cam/learning/raft.hpp"
+#include <opencv2/opencv.hpp>
+#include <cuda_runtime.h>
+#include <fstream>
+
+// Define a fixture for Raft tests
+class RaftTest : public ::testing::Test {
+protected:
+    // Initialize variables for the test
+    std::string model_path = "path/to/model.onnx"; // Change to a valid model path
+    size_t input_height = 224;
+    size_t input_width = 224;
+
+    Raft* raft;
+
+    void SetUp() override {
+        // Instantiate the Raft model with the test parameters
+        raft = new Raft(model_path, input_height, input_width);
+        raft->load_model();
+    }
+
+    void TearDown() override {
+        delete raft;
+    }
+};
+
+TEST_F(RaftTest, TestModelLoad) {
+    // Test that the model loads successfully
+    ASSERT_NE(raft->get_engine(), nullptr);
+    ASSERT_NE(raft->get_context(), nullptr);
+    ASSERT_NE(raft->get_runtime(), nullptr);
+}
+
+TEST_F(RaftTest, TestSetInput) {
+    // Create a dummy input buffer
+    std::vector<uint8_t> input_data(input_height * input_width, 128);
+
+    // Set input and check if it is copied to the device
+    raft->set_input(input_data.data(), input_height, input_width);
+
+    // Allocate host memory to copy back the data from GPU for verification
+    std::vector<float> host_input(input_height * input_width);
+    cudaMemcpy(host_input.data(), raft->get_input_buffer(), input_height * input_width * sizeof(float), cudaMemcpyDeviceToHost);
+
+    // Verify the data (simple check to see if values are scaled correctly)
+    for (size_t i = 0; i < host_input.size(); ++i) {
+        ASSERT_NEAR(host_input[i], 128.0f / 255.0f, 1e-5);
+    }
+}
+
+TEST_F(RaftTest, TestRunInference) {
+    // Dummy batch size
+    size_t batch_size = 1;
+
+    // Run inference
+    bool success = raft->run_inference(batch_size);
+
+    // Check if inference ran successfully
+    ASSERT_TRUE(success);
+}