pytorch guarda el modelo onnx

1. Pytorch puede guardar directamente el modelo como onnx y puede convertirse a otros formatos de modelos (pb) a través de onnx;
2. Pytorch también puede guardar directamente el modelo como caffemodel, pero requiere una cierta cantidad de código para implementar;

Configuración del entorno :

sistema operativo	ubuntu18.04
Modelo de GPU	GTX960
versión cuda	10.0
versión de pytorch	1.2.0

Código de implementación :

import torch
import torch.onnx
from torch.autograd import Variable

x = Variable(torch.randn(1, 3, 32, 32)).cuda()
torch_out = torch.onnx.export(model, x, 
                              "test.onnx",
                               export_params=True,
                               verbose=True)

Descripción de exportación de API :

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=False, example_outputs=None, strip_doc_string=True, dynamic_axes=None)

Descripción de parámetros:

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.)
    See also: :ref:`onnx-export`
    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/symbolic_helper.py
        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): 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.
        example_outputs: example outputs of the model that is being exported.
        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 specifiying 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:
                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]}