tools/importers/onnx/lib/onnx_converters.py
def load_model(self, path):
        """ Return a list of ONNX nodes """
        self.model = common.importer.ImporterModel()

        graph = self._load_onnx(path)
        #self.nodes  = utils.ONNX(self.graph).parse_onnx_model()

        input_tensors = {
            t.name: numpy_helper.to_array(t) for t in graph.initializer
        }

        for id in input_tensors:
            self.add_tensor(id, input_tensors[id])
            
        # add input_node first
        for i in graph.input:
            if i.name not in input_tensors:
                inode = ONNX_OP_TYPE_TO_CONVERTER_MAP["Input"](self).convert(i)
                self.add_node(inode)        

        for onnx_node in graph.node:
            # we need to visit the nodes in order such that all "inputs" to each node are
            # processed before this node is processed... fortunately the onnx graph.node
            # list is already sorted topologically.
            node = self.get_converter(onnx_node).convert(onnx_node)
            self.add_node(node)

        return self.model
 Return a list of ONNX nodes 
Return a list of ONNX nodes
Return a list of ONNX nodes
def load_model(self, path):
        self.model = common.importer.ImporterModel()
        graph = self._load_onnx(path)
        input_tensors = {
            t.name: numpy_helper.to_array(t) for t in graph.initializer
        }
        for id in input_tensors:
            self.add_tensor(id, input_tensors[id])
        for i in graph.input:
            if i.name not in input_tensors:
                inode = ONNX_OP_TYPE_TO_CONVERTER_MAP["Input"](self).convert(i)
                self.add_node(inode)        
        for onnx_node in graph.node:
            node = self.get_converter(onnx_node).convert(onnx_node)
            self.add_node(node)
        return self.model
def load_model ( self , path ) : self . model = common . importer . ImporterModel ( ) graph = self . _load_onnx ( path ) input_tensors = { t . name : numpy_helper . to_array ( t ) for t in graph . initializer } for id in input_tensors : self . add_tensor ( id , input_tensors [ id ] ) for i in graph . input : if i . name not in input_tensors : inode = ONNX_OP_TYPE_TO_CONVERTER_MAP [ "Input" ] ( self ) . convert ( i ) self . add_node ( inode ) for onnx_node in graph . node : node = self . get_converter ( onnx_node ) . convert ( onnx_node ) self . add_node ( node ) return self . model
Return a list of ONNX nodes
Return a list of ONNX nodes
""" Return a list of ONNX nodes """ #self.nodes  = utils.ONNX(self.graph).parse_onnx_model() # add input_node first # we need to visit the nodes in order such that all "inputs" to each node are # processed before this node is processed... fortunately the onnx graph.node # list is already sorted topologically.
self
path