generate_random_inputs.py

def is_dynamic(shape: Tuple[int]):
    return any(dim is None or dim < 0 for dim in shape)

def get_random_inputs(
    engine: trt.ICudaEngine,
    context: trt.IExecutionContext,
    input_binding_idxs: List[int],
):
    # Input data for inference
    host_inputs = []
    for binding_index in input_binding_idxs:
        # If input shape is fixed, we'll just use it
        input_shape = context.get_binding_shape(binding_index)
        # If input shape is dynamic, we'll arbitrarily select one of the
        # the min/opt/max shapes from our optimization profile
        if is_dynamic(input_shape):
            profile_index = context.active_optimization_profile
            profile_shapes = engine.get_profile_shape(profile_index, binding_index)
            # 0=min, 1=opt, 2=max, or choose any shape, (min <= shape <= max)
            input_shape = profile_shapes[1]

        host_inputs.append(np.random.random(input_shape).astype(np.float32))

    return host_inputs