1jddlmZddlmZddlmZmZmZddlZ ddl m cm Z ddl mZmZmZer ddlmZddlZGddZGd d ZddZGddZGddZdS)) annotations) namedtuple) TYPE_CHECKINGAnyNewTypeN) IR_VERSION ModelProto NodeProto)SequencecleZdZUdZedeZedZded<edZ ded<dS) DeviceTypezDescribes device type._TyperCPUCUDAN) __name__ __module__ __qualname____doc__rintrr__annotations__rBC:\Projects\Historical\venv\Lib\site-packages\onnx/backend/base.pyr r s[  GGS ! !EqC%((Drr ceZdZdZddZdS) DevicezyDescribes device type and device id syntax: device_type:device_id(optional) example: 'CPU', 'CUDA', 'CUDA:1' devicestrreturnNonec|d}tt|d|_d|_t |dkrt |d|_dSdS)N:rr)splitgetattrr type device_idlenr)selfroptionss r__init__zDevice.__init__!s],,s##J 33  w<z"namedtupledict..,s???1q!???rrenameTr(rkeyrct|tr|}tt|||SN) isinstancersuperr% __getitem__)r(r8field_names_maps rgetitemznamedtupledict..getitem1sC c3   '!#&CT$ZZ&&223777r)r(rr8rrr) enumerate setdefaultrr=)r+r,r.r/datar?r>s @rnamedtupledictrC)s@? +(>(>???O h%%% h =d = = =f = =D888888 D KrceZdZdZd dZdS) BackendRepzBackendRep is the handle that a Backend returns after preparing to execute a model repeatedly. Users will then pass inputs to the run function of BackendRep to retrieve the corresponding results. inputsrr/rtuple[Any, ...]c dS)zAbstract function.r:r)r(rFr/s rrunzBackendRep.run@swrN)rFrr/rrrG)rrrrrIrrrrErE:s2 rrEceZdZdZe ddd Ze ddd Ze dddZe dddZed dZ dS)!BackendaBackend is the entity that will take an ONNX model with inputs, perform a computation, and then return the output. For one-off execution, users can use run_node and run_model to obtain results quickly. For repeated execution, users should use prepare, in which the Backend does all of the preparation work for executing the model repeatedly (e.g., loading initializers), and returns a BackendRep handle. rmodelr rrr/rrboolc dS)NTrclsrLrr/s r is_compatiblezBackend.is_compatiblePs trBackendRep | Nonec Dtj|dSr:)onnxchecker check_modelrOs rpreparezBackend.prepareZs   '''trrFrGc R|j||fi|}|J||Sr:)rWrI)rPrLrFrr/backends r run_modelzBackend.run_modeles=#+eV66v66"""{{6"""rNnoder outputs_info4Sequence[tuple[numpy.dtype, tuple[int, ...]]] | Nonedict[str, Any]tuple[Any, ...] | Nonec d|vrOtj}t|_d|di|_t j||nt j|dS)a6Simple run one operator and return the results. Args: node: The node proto. inputs: Inputs to the node. device: The device to run on. outputs_info: a list of tuples, which contains the element type and shape of each output. First element of the tuple is the dtype, and the second element is the shape. More use case can be found in https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py kwargs: Other keyword arguments. opset_versionN) c_checkerCheckerContextr ir_version opset_importsrTrU check_node)rPr[rFrr\r/special_contexts rrun_nodezBackend.run_nodemsm0 f $ $'688O)3O &-/1H,IO ) L # #D/ : : : : L # #D ) ) )trcdS)zChecks whether the backend is compiled with particular device support. In particular it's used in the testing suite. Tr)rPrs rsupports_devicezBackend.supports_devices tr)r)rLr rrr/rrrM)rLr rrr/rrrR) rLr rFrrrr/rrrG)rN) r[r rFrrrr\r]r/r^rr_)rrrrM) rrrr classmethodrQrWrZrirkrrrrKrKEs[[;@####[#  [B[rrK) r+rr,r-r.rr/rrr0) __future__r collectionsrtypingrrr onnx.checkerrTonnx.onnx_cpp2py_export.checkeronnx_cpp2py_exportrUrcrr r collections.abcr numpyr rrCrErKrrrrusy#"""""""""""..........3333333332222222222((((((LLL - - - - - - - -"OOOOOOOOOOr