ci: Fix GPU usage

.github/workflows/ci.yml

@@ -6,6 +6,8 @@ jobs:
     runs-on: self-hosted
     container:
       image: omavteam/v4l2_camera:latest
+      options: |
+          --gpus all
 
     strategy:
       matrix:
@@ -31,4 +33,6 @@ jobs:
 
       - name: Run ${{ matrix.ci_script }}
         run: |
+          export ONNX_VERBOSE=1
+          export TRT_LOGGER=VERBOSE
           bash -x ./ci/${{ matrix.ci_script }}.sh

src/interface.cpp

@@ -98,17 +98,21 @@ void LearningInterface::_build(std::string onnx_path) {
     // TODO: What about different hardware?
     config->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, JETSON_MEM_LIMIT_B);
     nvonnxparser::IParser* parser = nvonnxparser::createParser(*network, _logger);
-    bool parsed = parser->parseFromFile(onnx_path.c_str(), static_cast<int>(nvinfer1::ILogger::Severity::kINFO));
-    IHostMemory* plan{ builder->buildSerializedNetwork(*network, *config) };
-
-    _runtime = createInferRuntime(_logger);
-    _engine = _runtime->deserializeCudaEngine(plan->data(), plan->size());
-    _context = _engine->createExecutionContext();
+    const bool parsed = parser->parseFromFile(onnx_path.c_str(), static_cast<int>(nvinfer1::ILogger::Severity::kVERBOSE));
+    if (parsed) {
+        IHostMemory* plan = builder->buildSerializedNetwork(*network, *config);
+
+        _runtime = createInferRuntime(_logger);
+        _engine = _runtime->deserializeCudaEngine(plan->data(), plan->size());
+        _context = _engine->createExecutionContext();
+        delete plan;
+    } else {
+        std::cerr << "Could not parse onnx file: " << onnx_path.c_str() << std::endl;
+    }
 
     delete network;
     delete config;
     delete parser;
-    delete plan;
 }
 
 bool LearningInterface::_save_engine(const std::string& engine_path) {

test.py

@@ -0,0 +1,15 @@
+import onnx
+
+model_path = "test/resources/depth_anything_v2_vits.onnx"
+onnx_model = onnx.load(model_path)
+
+# Print all model outputs
+print("Model outputs:")
+for output in onnx_model.graph.output:
+    print(output.name)
+
+# Print all model inputs
+print("Model inputs:")
+for input in onnx_model.graph.input:
+    print(input.name)
+