PhI;6dZddlmZddlZddlZddlZddlmZmZmZm Z m Z m Z m Z ddl Z ddl mZddlmZddlmZddlmZmZej.d Zd d hZej4Gd d Zej8ddd ddZej8dd d dZej8d!dZ d" d#dZ ej8dZ!ej8d$dZ"ej8d%dZ#ej8d&dZ$ej8d'dZ%ej8d(dZ&ej8d)dZ'ej8d*dZ(ej8d*dZ)ej8d*dZ*y)+zFUtilities for manipulating the torch.Graph object and the torchscript.) annotationsN)AnyDictIterableOptionalSequenceTupleUnion)_C)_onnx)GLOBALS) _beartype registrationz^(.+)_(([ifstgz])|(ty))$inplaceatenceZdZUdZded<ded<ded<ded <d ed <d ed <ddZejdd ddZejddddZ e Z ejdd ddZ y) GraphContextaIExtra context for symbolic functions with all methods from torch.Graph. NOTE: This class is not meant for external consumption. Please do not depend on it outside of torch.onnx as the interface may evolve. Attributes: graph: The _C.Graph being constructed. block: The current _C.Block being constructed. opset: The opset version. original_node: Current node that is being converted from. params_dict: Mapping from graph initializer name to IValue. env: Mapping from Torch domain graph Value to ONNX domain graph Value. z_C.Graphgraphz_C.Blockblockintopset_C.Node original_nodezDict[str, '_C.IValue'] params_dictzDict[_C.Value, _C.Value]envc.t|j|SN)getattrr)selfnames iC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torch/onnx/_internal/jit_utils.py __getattr__zGraphContext.__getattr__0stzz4((outputsc&t||g|d|i|S)aCreates an ONNX operator "opname", taking "raw_args" as inputs and "kwargs" as attributes. The set of operators and the inputs/attributes they take is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md Args: opname: The ONNX operator name, e.g., `Abs` or `Add`, or an operator qualified with a namespace, e.g., `aten::add`. raw_args: The inputs to the operator; usually provided as arguments to the `symbolic` definition. outputs: The number of outputs this operator returns. By default an operator is assumed to return a single output. If `outputs` is greater than one, this functions returns a tuple of output `Value`, representing each output of the ONNX operator in order. kwargs: The attributes of the ONNX operator, whose keys are named according to the following convention: `alpha_f` indicates the `alpha` attribute with type `f`. The valid type specifiers are `f` (float), `i` (int), `s` (string) or `t` (Tensor). An attribute specified with type float accepts either a single float, or a list of floats (e.g., you would say `dims_i` for a `dims` attribute that takes a list of integers). Returns: The value representing the single output of this operator (see the `outputs` keyword argument for multi-return nodes). r&)_add_op)ropnamer&raw_argskwargss r!opzGraphContext.op3s!HtVJhJJ6JJr#) overload_namec4|jdg|||d|S)z~Generates an ONNX ATen op node. This function is for backward compatibility with the old symbolic functions. z aten::ATen) operator_soverload_name_s)r,)roperatorr.argsr+s r!aten_opzGraphContext.aten_opYs9 tww    )     r#c|jjd|j}|jj}t j |||t ||g|d|i|S)aCreates an ONNX operator from onnx-script function, taking "raw_args" as inputs and "kwargs" as attributes. onnx-script repository: https://github.com/microsoft/onnx-script Args: onnx_fn: ONNXFunction from onnx-script; An example can be found at https://github.com/microsoft/onnx-script#example raw_args: The inputs to the operator; usually provided as arguments to the `symbolic` definition. outputs: The number of outputs this operator returns. By default an operator is assumed to return a single output. If `outputs` is greater than one, this functions returns a tuple of output `Value`, representing each output of the ONNX operator in order. kwargs: The attributes of the ONNX operator, whose keys are named according to the following convention: `alpha_f` indicates the `alpha` attribute with type `f`. The valid type specifiers are `f` (float), `i` (int), `s` (string) or `t` (Tensor). An attribute specified with type float accepts either a single float, or a list of floats (e.g., you would say `dims_i` for a `dims` attribute that takes a list of integers). Returns: The value representing the single output of this operator (see the `outputs` keyword argument for multi-return nodes). ::r&)rdomainr versionrcustom_onnx_symbolicr()ronnx_fnr&r*r+ symbolic_name opset_versions r! onnxscript_opzGraphContext.onnxscript_opksfH#==//07<<.A  -- G ))-GPt]QXQwQ&QQr#N)r strreturnr)r)r>r*Union[torch.Tensor, _C.Value]r&r)r2r>r.r>)r*r@r&r) __name__ __module__ __qualname____doc____annotations__r"rbeartyper,r4atr=r#r!rrs  O O J'' !!)  #K#K1#K #K#KJAC     B  (R1(R (R(Rr#rr$)r&n_blockscJ|j|g|d|i|}t|tr|dj}n|j}g}t |D]:} |j } t j|| } |j| <|t||fS)aPCreates an ONNX operator "opname", taking inputs and attributes. Args: graph_context: The context for the current graph. opname: The ONNX operator name, e.g., `Abs` or `Add`, or an operator qualified with a namespace, e.g., `aten::add`. inputs: The inputs to the operator. outputs: The number of outputs this operator returns. By default an operator is assumed to return a single output. If `outputs` is greater than one, this functions returns a tuple of output `Value`, representing each output of the ONNX operator in order. n_blocks: The number of sub-blocks to create in the node. attributes: The attributes of the ONNX operator. Returns: A tuple of (output_values, new_contexts, node) where: output_values: One or more output value of this operator (see the `outputs` keyword argument for multi-return nodes). new_contexts: A tuple of new graph contexts for each sub-block. node: The node representing the operator. r&r)r) r, isinstancernoderangeaddBlock dataclassesreplaceappendtuple) graph_contextr)r&rIinputs attributes output_valuesrL new_contexts_ new_block new_contexts r!add_op_with_blocksr[s@%M$$VTfTgTTM-*Q$$&!!#L 8_MMO !))-yI K(  % -t 33r#r%c |Dcgc]}t||}}|jDcic] \}}| || } }}d|vrd|z}t|j||| |j|j |t j} |dk(r| jSt| jScc}wcc}}w)a@Creates an ONNX operator "opname", taking "args" as inputs and attributes "kwargs". The set of operators and the inputs/attributes they take is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md This function is monkey-patched onto Graph. Args: graph_context: The Torch Graph or Block. opname: The ONNX operator name, e.g., `Abs` or `Add`, or an operator qualified with a namespace, e.g., `aten::add`. args: The inputs to the operator; usually provided as arguments to the `symbolic` definition. outputs: The number of outputs this operator returns. By default an operator is assumed to return a single output. If `outputs` is greater than one, this functions returns a tuple of output `Value`, representing each output of the ONNX operator in order. kwargs: The attributes of the ONNX operator, whose keys are named according to the following convention: `alpha_f` indicates the `alpha` attribute with type `f`. The valid type specifiers are `f` (float), `i` (int), `s` (string) or `t` (Tensor). An attribute specified with type float accepts either a single float, or a list of floats (e.g., you would say `dims_i` for a `dims` attribute that takes a list of integers). Returns: (Union[_C.Value, Tuple[_C.Value, ...]]) The value representing the single output of this operator (see the `outputs` keyword argument for multi-return nodes). r6zonnx::)rr< n_outputsshape_inferencer$) _const_if_tensoritems _create_noderrrr onnx_shape_inferenceoutputrRr&) rSr)r&r3r+argrTkvrUrLs r!r(r(sN?C Cds}c2dF C$*<<>C>41aQ]!Q$>JC 6F" !--#))44 D!|{{}   +DDsB6 B;B;c^||St|tjr|St|d|S)Nzonnx::Constant)value_z)rKr Valuer()rSrds r!r_r_s/ { #rxx  ="2C @@r#cHt|tjr'|}|j|||} |j | } nTt|tj r:|} | j ||} |dkDr!td|D]} | jt j} t| |k(sJ|jd} t|jD]\}}|tvrt!| ||| |rtj"| ||| S)z:Creates an node 'domain_op', taking inputs and attributes.r$zaten::)r)rKr Graphcreate insertNodeBlockaddNoderM addOutputrRr&len startswithsortedr`_SKIP_NODE_ATTRIBUTES_add_attribute(_jit_pass_onnx_node_shape_type_inference)graph_or_block domain_oprTrUrr<r]r^rrLrrX node_ouputsrkeyvalues r!raras ."((+||Ivy9% NBHH -}}Y/ q=1i( ) 'K { y (( (    )DZ--/0 U ' ' tS%d31 33D+}U Kr#crt|txr&t|tttj f Sr)rKrr>bytestorchTensor)r{s r! _is_onnx_listr7s2 eX & z UELL)0,r#c6|jdk(sJ|dS)z,Convert a scalar tensor into a Python value.r$r)numelxs r!_scalarr>s 779>> Q4Kr#c|tjtjjk(xrtj Sr)r operator_export_type_C_onnxOperatorExportTypesONNX_ATEN_FALLBACK_CAFFE2_ATEN_FALLBACKrHr#r!_is_caffe2_aten_fallbackrEs1 $$(C(C(V(VV *  ) )r#ctj|}|td|d|jd|jd}}t |r|dz }|rbt rXt |tjr>|jdkDr tdt|}t |trd}nd}t||d ||S) z7Initializes the right attribute based on type of value.zInvalid attribute specifier 'z<' names must be suffixed with type, e.g. 'dim_i' or 'dims_i'r$sz Should not pass tensor attributefirX) _ATTR_PATTERNmatch ValueErrorgrouprrrKr~rrrfloatr)rLrzr{rmr kinds r!ruruMs C Ay+C51C C  QWWQZ$DU   (* eU\\ *{{}q  !CDDENE%' $74D6 $T5 11r#cx|jjtjj Sr)type isSubtypeOfr TensorTypegetrs r! _is_tensorrhs& 668   1 1 3 44r#ct|sytjtj|j }|j Sr)rtypingcastr rrdevice)r{ tensor_types r!get_device_from_valuerms6 e ++bmmUZZ\:K    r#cd|vrtd|d|jdd\}}d|vrtd|d||fS)z(Parse node kind into domain and Op name.r6z Node kind: z& is invalid. '::' is not in node kind.r$z) is invalid. '::' should only apear once.)rsplit)rr7r)s r!parse_node_kindrus[ 4;tf,RSTTZZa(NFF v~;tf,UVWW 6>r#c |dk(S) Check if the domain is official.rrHr7s r!is_atenr V r#c |dk(S)rprimrHrs r!is_primrrr#c |dk(S)ronnxrHrs r!is_onnxrrr#) rSrr)r>rT_C.Valuer&rrIrr?z-Tuple[Any, Tuple[GraphContext, ...], _C.Node])rSrr)r>r3r@r&r)rSr)T)rwzUnion[_C.Graph, _C.Block]rxr>rTrrUdictrrr<rr]rr^boolr?r)rz torch.Tensor)r?r)rLrrzr>r{rrr)rrr?r)r{rr?zOptional[torch.device])rr>r?zTuple[str, str])r7r>r?r)+rD __future__rrOrerrrrrrr r r~r torch._Cr rtorch.onnx._globalsr torch.onnx._internalrrcompilerrt dataclassrrFr[r(r_rarrrrurrrrrrrHr#r!rsL"  HHH %'8 56 "F+ |R|R|R~   ,4,4 ,4,4 ,4  ,43,4,4^   ;!;! ;! );! ;!;!| AA"!$-$$ $ $  $  $$$ $N      224 55         r#