PhddlmZddlZddlZddlmZmZmZddlZddl m Z ddl m Z erddlZe j ddZe j d d dZy) ) annotationsN)Tuple TYPE_CHECKINGUnion) _type_utils) _beartypecddl}|j}||_tjj |j j|_|jj|j|jj|_ |d|jjd}|j!D]6\}}|j"j%} || _t)|| _8t,j.j1||} t,j.j3| rt-j4| t,j.j7| } t,j.j3| st-j8| t;| d5} | j=|j?djAddd|S#1swY|SxYw)aCreate a TensorProto with external data from a PyTorch tensor. The external data is saved to os.path.join(basepath, location). Args: tensor: Tensor to be saved. name: Name of the tensor (i.e., initializer name in ONNX graph). location: Relative location of the external data file (e.g., "/tmp/initializers/weight_0" when model is "/tmp/model_name.onnx"). basepath: Base path of the external data file (e.g., "/tmp/external_data" while model must be in "/tmp"). Reference for ONNX's external data format: How to load? https://github.com/onnx/onnx/blob/5dac81ac0707bdf88f56c35c0a5e8855d3534673/onnx/external_data_helper.py#L187 How to save? https://github.com/onnx/onnx/blob/5dac81ac0707bdf88f56c35c0a5e8855d3534673/onnx/external_data_helper.py#L43 How to set ONNX fields? https://github.com/onnx/onnx/blob/5dac81ac0707bdf88f56c35c0a5e8855d3534673/onnx/external_data_helper.py#L88 rN)locationoffsetlengthxbT)force)!onnx TensorProtonamejit_type_utils JitScalarType from_dtypedtype onnx_type data_typedimsextendshapeEXTERNAL data_locationuntyped_storagenbytesitems external_dataaddkeystrvalueospathjoinexistsremovedirnamemakedirsopenwritenumpytobytes) tensorrr basepathr tensor_protokey_value_pairskventryexternal_data_file_pathexternal_data_dir_path data_files pC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torch/onnx/_internal/fx/serialization.py'_create_tensor_proto_with_external_datar;s0#4##%LL+99DD ikV\\*!%!1!1!:!:L ((*113O  %%'1**..0 !f ( !ggll8X> ww~~-. )* WW__-DE 77>>0 1 *+ %t ,   4 088:; -  - s 70G11G;cddl}|j}|j||jjDchc]}|j } }|D]} t j| } | jD]\} } |r| jdd} | | vr| j| n>| D]9}|j| s| j|s&|} | j|ntjj|| }t| | ||}|jj j#||j$|tjj||ycc}w)amLoad PyTorch tensors from files and add to "onnx_model" as external initializers. Output files: ONNX model file path: ONNX initializer folder: os.path.join(basepath, initializer_location) After running this function, you can do ort_sess = onnxruntime.InferenceSession(os.path.join(basepath, model_location)) to execute the model. Arguments: basepath: Base path of the external data file (e.g., "/tmp/large-onnx-model"). model_location: Relative location of the ONNX model file. E.g., "model.onnx" so that the model file is saved to "/tmp/large-onnx-model/model.onnx". initializer_location: Relative location of the ONNX initializer folder. E.g., "initializers" so that the initializers are saved to "/tmp/large-onnx-model/initializers". torch_load_paths: Files which containing serialized PyTorch tensors to be saved as ONNX initializers. They are loaded by torch.load. onnx_model: ONNX model to be saved with external initializers. If an input name matches a tensor loaded from "torch_load_paths", the tensor will be saved as that input's external initializer. rename_initializer: Replaces "." by "_" for all ONNX initializer names. Not needed by the official torch.onnx.dynamo_export. This is a hack for supporting `FXSymbolicTracer` tracer with fake tensor mode. In short, `FXSymbolicTracer` lifts FX parameters (self.linear_weight) as inputs (`def forward(self, linear_weight)`) and therefore, `.` cannot be used. rN._)r ModelProtoCopyFromgraphinputrtorchloadrreplacer)endswithr%r&r'r; initializerappendsave)r1model_locationinitializer_locationtorch_load_paths onnx_modelrename_initializerronnx_model_with_initializersrBonnx_input_namesr& state_dictrr0onnx_input_namerelative_tensor_file_pathr2s r:save_model_with_external_datarTSsZN#24??#4  ))*50:0@0@0F0FG0Fu 0FG ZZ% &,,.LD&! ||C-'' ''-'7O&//59W /(//@ (8)+ 5I4(P %C7L ) . . : : A A, OK/!TDII*BGGLL>,RSYHsE5) r0z torch.Tensorrr#r r#r1r#returnzonnx.TensorProto)F)r1r#rJr#rKr#rLz"Tuple[Union[str, io.BytesIO], ...]rMzonnx.ModelProtorNboolrUNone) __future__rior%typingrrrrC torch.onnxrrtorch.onnx._internalrrbeartyper;rTr:r`s" .. 4* @ @ #@/2@>A@@@F  % VTVTVTVT9 VT  VT  VT VTVTr_