devcontainer: Downgrade tensorrt

.devcontainer/Dockerfile

@@ -30,9 +30,10 @@ RUN echo 'export PATH=$PATH:/usr/local/cuda/bin' >> /home/asl/.bash_aliases && \
     echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64' >> /home/asl/.bash_aliases
 
 # TensorRT: Install from .deb file
-COPY resources/nv-tensorrt-local-repo-ubuntu2004-10.6.0-cuda-12.6_1.0-1_amd64.deb /tmp/tensorrt.deb
-RUN sudo dpkg -i /tmp/tensorrt.deb || sudo apt-get install -f -y
-RUN sudo cp /var/nv-tensorrt-local-repo-ubuntu2004-10.6.0-cuda-12.6/*-keyring.gpg /usr/share/keyrings/ && sudo apt update && sudo apt install -y tensorrt
+COPY resources/nv-tensorrt-repo-ubuntu2004-cuda11.6-trt8.4.3.1-ga-20220813_1-1_amd64.deb /tmp/
+RUN sudo dpkg -i /tmp/resources/nv-tensorrt-repo-ubuntu2004-cuda11.6-trt8.4.3.1-ga-20220813_1-1_amd64.deb || sudo apt-get install -f -y
+# RUN sudo cp /var/nv-tensorrt-repo-ubuntu2004-cuda11.6-trt8.4.3.1-ga-20220813/*-keyring.gpg /usr/share/keyrings/ && sudo apt update && sudo apt install -y tensorrt
+RUN sudo apt update && sudo apt install -y tensorrt
 
 # OpenCV Build Stage
 FROM base AS opencv_build
@@ -63,5 +64,4 @@ USER root
 
 # Clean up
 RUN rm -rf /tmp/* /var/lib/apt/lists/* /var/tmp/* /var/cache/apt/archives/*
-
-ENTRYPOINT ["/bin/bash"]
+ENTRYPOINT ["/bin/bash"]

.devcontainer/build_and_push_images.sh

@@ -1 +1 @@
-docker buildx build -t omavteam/v4l2_camera:latest -f .devcontainer/Dockerfile --push .
+docker buildx build --tag omavteam/v4l2_camera:latest -f .devcontainer/Dockerfile --push .

src/interface.cpp

@@ -30,23 +30,23 @@ 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);
-                        std::cout << io << ": " << name;
-                        const nvinfer1::Dims dims = _engine->getTensorShape(name);
+                    // for (int io = 0; io < _engine->getNbIOTensors(); io++) {
+                    //     const char* name = _engine->getIOTensorName(io);
+                    //     std::cout << io << ": " << name;
+                    //     const nvinfer1::Dims dims = _engine->getTensorShape(name);
 
-                        size_t total_dims = 1;
-                        for (int d = 0; d < dims.nbDims; d++) {
-                            total_dims *= dims.d[d];
-                        }
+                    //     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) {
-                            input_size = total_dims * sizeof(float);
-                        } else if (io == 1) {
-                            output_size = total_dims * sizeof(float);
-                        }
-                    }
+                    //     std::cout << " size: " << total_dims << std::endl;
+                    //     if (io == 0) {
+                    //         input_size = total_dims * sizeof(float);
+                    //     } else if (io == 1) {
+                    //         output_size = total_dims * sizeof(float);
+                    //     }
+                    // }
 
                     // Allocate device buffers
                     cudaMalloc(&_buffers[0], input_size);

src/usb_cam_node.cpp

@@ -72,7 +72,7 @@ class UsbCamNode {
 
     UsbCamNode() : m_node("~") {
         // Setup the network that outputs derivates of the image captured
-        networks.push_back(std::make_unique<Raft>("resources/raft.onnx", 240, 320));
+        // networks.push_back(std::make_unique<Raft>("resources/raft.onnx", 240, 320));
 
         // Advertise the main image topic
         image_transport::ImageTransport it(m_node);
@@ -177,12 +177,12 @@ class UsbCamNode {
         m_image_pub.publish(m_image, *ci);
 
         // Run all the networks
-        for (const auto& net : networks) {
-            net->set_input(m_image.data.data(), 1920, 1200);
-            if (net->run_inference(1)) {
-                net->publish();
-            }
-        }
+        // for (const auto& net : networks) {
+        //     net->set_input(m_image.data.data(), 1920, 1200);
+        //     if (net->run_inference(1)) {
+        //         net->publish();
+        //     }
+        // }
 
         return true;
     }