Changed to prepare onnx

src/engines/tensorrt_engine.pyx

@@ -114,13 +114,21 @@ cdef class TensorRTEngine(InferenceEngine):
             return None
 
     @staticmethod
-    def convert_from_source(bytes onnx_model, str calib_cache_path=None):
+    def convert_from_source(bytes onnx_model, str calib_cache_path=None, bint force_static_input=False):
         gpu_mem = TensorRTEngine.get_gpu_memory_bytes(0)
         workspace_bytes = int(gpu_mem * 0.9)
 
         explicit_batch_flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
         trt_logger = trt.Logger(trt.Logger.WARNING)
 
+        if force_static_input:
+            try:
+                from engines.onnx_tensorrt_compat import prepare_for_tensorrt
+                onnx_model = prepare_for_tensorrt(onnx_model)
+                constants_inf.log(<str>'Prepared ONNX model for TensorRT static Jetson build')
+            except Exception as e:
+                constants_inf.logerror(<str>f'ONNX TensorRT compatibility preparation failed: {str(e)}')
+
         with trt.Builder(trt_logger) as builder, \
                 builder.create_network(explicit_batch_flag) as network, \
                 trt.OnnxParser(network, trt_logger) as parser, \
@@ -129,6 +137,8 @@ cdef class TensorRTEngine(InferenceEngine):
             config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace_bytes)
 
             if not parser.parse(onnx_model):
+                for i in range(parser.num_errors):
+                    constants_inf.logerror(<str>f'TensorRT ONNX parser error: {parser.get_error(i)}')
                 return None
 
             input_tensor = network.get_input(0)
@@ -137,7 +147,9 @@ cdef class TensorRTEngine(InferenceEngine):
             H = max(shape[2], 1280) if shape[2] != -1 else 1280
             W = max(shape[3], 1280) if shape[3] != -1 else 1280
 
-            if shape[0] == -1:
+            if force_static_input:
+                input_tensor.shape = (1, C, H, W)
+            elif shape[0] == -1 or shape[2] == -1 or shape[3] == -1:
                 max_batch = TensorRTEngine.calculate_max_batch_size(gpu_mem, H, W)
                 profile = builder.create_optimization_profile()
                 profile.set_shape(