Method prepare

caffe2/python/onnx/backend.py:678–737

For Onnx Caffe2Backend, we require that init_graph don't initialize the actual input of the predict_graph, for example, if "img" is the input blob for the predict_net, we require that in init_graph and in initializer of the predict_graph, "img" is not initalized. We don't h

(cls, model, device='CPU', raw_values_dict=None, **kwargs)

    @classmethod
    def prepare(cls, model, device='CPU', raw_values_dict=None, **kwargs):
        '''
        For Onnx Caffe2Backend, we require that init_graph don't initialize the actual input of the predict_graph,

        for example, if "img" is the input blob for the predict_net, we require that in init_graph and in
        initializer of the predict_graph, "img" is not initalized. We don't have a check for this, since
        there is no way we can know which blob is the input of the predict_graph.
        '''
        if not kwargs.pop('no_check_UNSAFE', False):
            super(Caffe2Backend, cls).prepare(model, device, **kwargs)
        opset_version = None
        for imp in model.opset_import:
            if not imp.HasField("domain") or imp.domain == "":
                opset_version = imp.version
                if imp.version > cls._known_opset_version:
                    warnings.warn("This version of onnx-caffe2 targets ONNX operator set version {}, but the model we are trying to import uses version {}.  We will try to import it anyway, but if the model uses operators which had BC-breaking changes in the intervening versions, import will fail.".format(cls._known_opset_version, imp.version))
            else:
                warnings.warn("Unrecognized operator set {}".format(imp.domain))
        if opset_version is None:
            if model.ir_version >= 0x00000003:
                raise RuntimeError("Model with IR version >= 3 did not specify ONNX operator set version (onnx-caffe2 requires it)")
            else:
                opset_version = 1

        # Prior to onnx version update to onnx-1.8.0, errors caused by failures in
        # in the onnx shape inference call were being supressed. Hence a try-catch block
        # is added around the infer_shapes call to avoid these failures and preserve status
        try:
            model = onnx.shape_inference.infer_shapes(model)
        except RuntimeError:
            warnings.warn("ShapeInferenceWarning: Inferred shape and existing shape differ in rank")

        ws = Workspace()
        device_option = get_device_option(Device(device))

        init_net, predict_net = cls._onnx_model_to_caffe2_net(model, device, opset_version, False)

        if raw_values_dict:
            cls._external_value_resolution_pass(model, raw_values_dict)

        # Directly load initializer data into blobs in workspace
        cls._direct_initialize_parameters(
            model.graph.initializer,
            ws,
            device_option,
        )

        initialized = {init.name for init in model.graph.initializer}

        cls._direct_initialize_inputs(
            model.graph.input,
            initialized,
            ws,
            device_option,
        )

        uninitialized = [value_info.name for value_info in model.graph.input if value_info.name not in initialized]