Source code for torch.onnx

import torch._C as _C

TensorProtoDataType = _C._onnx.TensorProtoDataType
OperatorExportTypes = _C._onnx.OperatorExportTypes


# TODO: Update these variables when there
# is a new ir_version and producer_version
# and use these values in the exporter
ir_version = _C._onnx.IR_VERSION
producer_name = "pytorch"
producer_version = _C._onnx.PRODUCER_VERSION

class ExportTypes:

def _export(*args, **kwargs):
    from torch.onnx import utils
    result = utils._export(*args, **kwargs)
    return result

[docs]def export(model, args, f, export_params=True, verbose=False, training=False, input_names=None, output_names=None, aten=False, export_raw_ir=False, operator_export_type=None, opset_version=None, _retain_param_name=True, do_constant_folding=True, example_outputs=None, strip_doc_string=True, dynamic_axes=None, keep_initializers_as_inputs=None, custom_opsets=None, enable_onnx_checker=True, use_external_data_format=False): r""" Export a model into ONNX format. This exporter runs your model once in order to get a trace of its execution to be exported; at the moment, it supports a limited set of dynamic models (e.g., RNNs.) Arguments: model (torch.nn.Module): the model to be exported. args (tuple of arguments): the inputs to the model, e.g., such that ``model(*args)`` is a valid invocation of the model. Any non-Tensor arguments will be hard-coded into the exported model; any Tensor arguments will become inputs of the exported model, in the order they occur in args. If args is a Tensor, this is equivalent to having called it with a 1-ary tuple of that Tensor. (Note: passing keyword arguments to the model is not currently supported. Give us a shout if you need it.) f: a file-like object (has to implement fileno that returns a file descriptor) or a string containing a file name. A binary Protobuf will be written to this file. export_params (bool, default True): if specified, all parameters will be exported. Set this to False if you want to export an untrained model. In this case, the exported model will first take all of its parameters as arguments, the ordering as specified by ``model.state_dict().values()`` verbose (bool, default False): if specified, we will print out a debug description of the trace being exported. training (bool, default False): export the model in training mode. At the moment, ONNX is oriented towards exporting models for inference only, so you will generally not need to set this to True. input_names(list of strings, default empty list): names to assign to the input nodes of the graph, in order output_names(list of strings, default empty list): names to assign to the output nodes of the graph, in order aten (bool, default False): [DEPRECATED. use operator_export_type] export the model in aten mode. If using aten mode, all the ops original exported by the functions in symbolic_opset<version>.py are exported as ATen ops. export_raw_ir (bool, default False): [DEPRECATED. use operator_export_type] export the internal IR directly instead of converting it to ONNX ops. operator_export_type (enum, default OperatorExportTypes.ONNX): OperatorExportTypes.ONNX: all ops are exported as regular ONNX ops. OperatorExportTypes.ONNX_ATEN: all ops are exported as ATen ops. OperatorExportTypes.ONNX_ATEN_FALLBACK: if symbolic is missing, fall back on ATen op. OperatorExportTypes.RAW: export raw ir. opset_version (int, default is 9): by default we export the model to the opset version of the onnx submodule. Since ONNX's latest opset may evolve before next stable release, by default we export to one stable opset version. Right now, supported stable opset version is 9. The opset_version must be _onnx_master_opset or in _onnx_stable_opsets which are defined in torch/onnx/ do_constant_folding (bool, default False): If True, the constant-folding optimization is applied to the model during export. Constant-folding optimization will replace some of the ops that have all constant inputs, with pre-computed constant nodes. example_outputs (tuple of Tensors, default None): Model's example outputs being exported. example_outputs must be provided when exporting a ScriptModule or TorchScript Function. strip_doc_string (bool, default True): if True, strips the field "doc_string" from the exported model, which information about the stack trace. dynamic_axes (dict<string, dict<int, string>> or dict<string, list(int)>, default empty dict): a dictionary to specify dynamic axes of input/output, such that: - KEY: input and/or output names - VALUE: index of dynamic axes for given key and potentially the name to be used for exported dynamic axes. In general the value is defined according to one of the following ways or a combination of both: (1). A list of integers specifying the dynamic axes of provided input. In this scenario automated names will be generated and applied to dynamic axes of provided input/output during export. OR (2). An inner dictionary that specifies a mapping FROM the index of dynamic axis in corresponding input/output TO the name that is desired to be applied on such axis of such input/output during export. Example. if we have the following shape for inputs and outputs: .. code-block:: none shape(input_1) = ('b', 3, 'w', 'h') and shape(input_2) = ('b', 4) and shape(output) = ('b', 'd', 5) Then dynamic axes can be defined either as: (a). ONLY INDICES: dynamic_axes = {'input_1':[0, 2, 3], 'input_2':[0], 'output':[0, 1]} where automatic names will be generated for exported dynamic axes (b). INDICES WITH CORRESPONDING NAMES: dynamic_axes = {'input_1':{0:'batch', 1:'width', 2:'height'}, 'input_2':{0:'batch'}, 'output':{0:'batch', 1:'detections'} where provided names will be applied to exported dynamic axes (c). MIXED MODE OF (a) and (b) dynamic_axes = {'input_1':[0, 2, 3], 'input_2':{0:'batch'}, 'output':[0,1]} keep_initializers_as_inputs (bool, default None): If True, all the initializers (typically corresponding to parameters) in the exported graph will also be added as inputs to the graph. If False, then initializers are not added as inputs to the graph, and only the non-parameter inputs are added as inputs. This may allow for better optimizations (such as constant folding etc.) by backends/runtimes that execute these graphs. If unspecified (default None), then the behavior is chosen automatically as follows. If operator_export_type is OperatorExportTypes.ONNX, the behavior is equivalent to setting this argument to False. For other values of operator_export_type, the behavior is equivalent to setting this argument to True. Note that for ONNX opset version < 9, initializers MUST be part of graph inputs. Therefore, if opset_version argument is set to a 8 or lower, this argument will be ignored. custom_opsets (dict<string, int>, default empty dict): A dictionary to indicate custom opset domain and version at export. If model contains a custom opset, it is optional to specify the domain and opset version in the dictionary: - KEY: opset domain name - VALUE: opset version If the custom opset is not provided in this dictionary, opset version is set to 1 by default. enable_onnx_checker (bool, default True): If True the onnx model checker will be run as part of the export, to ensure the exported model is a valid ONNX model. external_data_format (bool, default False): If True, then the model is exported in ONNX external data format, in which case some of the model parameters are stored in external binary files and not in the ONNX model file itself. See link for format details: Also, in this case, argument 'f' must be a string specifying the location of the model. The external binary files will be stored in the same location specified by the model location 'f'. If False, then the model is stored in regular format, i.e. model and parameters are all in one file. This argument is ignored for all export types other than ONNX. """ from torch.onnx import utils return utils.export(model, args, f, export_params, verbose, training, input_names, output_names, aten, export_raw_ir, operator_export_type, opset_version, _retain_param_name, do_constant_folding, example_outputs, strip_doc_string, dynamic_axes, keep_initializers_as_inputs, custom_opsets, enable_onnx_checker, use_external_data_format)
def export_to_pretty_string(*args, **kwargs): from torch.onnx import utils return utils.export_to_pretty_string(*args, **kwargs) def _export_to_pretty_string(*args, **kwargs): from torch.onnx import utils return utils._export_to_pretty_string(*args, **kwargs) def _optimize_trace(graph, operator_export_type): from torch.onnx import utils return utils._optimize_graph(graph, operator_export_type)
[docs]def set_training(model, mode): r""" A context manager to temporarily set the training mode of 'model' to 'mode', resetting it when we exit the with-block. A no-op if mode is None. """ from torch.onnx import utils return utils.set_training(model, mode)
def _run_symbolic_function(*args, **kwargs): from torch.onnx import utils return utils._run_symbolic_function(*args, **kwargs) def _run_symbolic_method(*args, **kwargs): from torch.onnx import utils return utils._run_symbolic_method(*args, **kwargs)
[docs]def is_in_onnx_export(): r""" Check whether it's in the middle of the ONNX export. This function returns True in the middle of torch.onnx.export(). torch.onnx.export should be executed with single thread. """ from torch.onnx import utils return utils.is_in_onnx_export()
[docs]def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version): from torch.onnx import utils return utils.register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version)


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