pytorch保存onnx模型
因为一些原因，需要用pytorch去创建、训练和保存模型。pytorch保存的模型通常为pth、pt、pkl的格式，但这种类型的模型不能在其他框架（tensorflow）下直接加载，因此需要将模型保存为其他格式的。在网上进行相应的学习后，总结出一下两点：
- pytorch可以直接将模型保存为onnx的，并且可以通过onnx转换为其他格式的模型（pb）；
- pytorch也可以直接将模型保存为caffemodel，但是需要一定的代码量去实现；
环境配置：
| 操作系统 | ubuntu18.04 |
|---|---|
| GPU型号 | GTX960 |
| cuda版本 | 10.0 |
| pytorch版本 | 1.2.0 |
实现代码：
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)
API export说明：
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)
参数说明：
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]}