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=False, example_outputs=None, strip_doc_string=True, dynamic_axes=None):
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.)
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]}
"""
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)
def export(*args, **kwargs):
from torch.onnx import utils
return utils.export(*args, **kwargs)
def export(model,
args,
f,
export_params=True,
verbose=False,
training=TrainingMode.EVAL,
input_names=None,
output_names=None,
operator_export_type=OperatorExportTypes.ONNX,
opset_version=None,
do_constant_folding=True,
dynamic_axes=None,
keep_initializers_as_inputs=None,
custom_opsets=None,
export_modules_as_functions=False):
r"""
Exports a model into ONNX format. If ``model`` is not a
:class:`torch.jit.ScriptModule` nor a :class:`torch.jit.ScriptFunction`, this runs
``model`` once in order to convert it to a TorchScript graph to be exported
(the equivalent of :func:`torch.jit.trace`). Thus this has the same limited support
for dynamic control flow as :func:`torch.jit.trace`.
Args:
model (torch.nn.Module, torch.jit.ScriptModule or torch.jit.ScriptFunction):
the model to be exported.
args (tuple or torch.Tensor):
args can be structured either as:
1. ONLY A TUPLE OF ARGUMENTS::
args = (x, y, z)
The tuple should contain model inputs 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 the tuple.
2. A TENSOR::
args = torch.Tensor([1])
This is equivalent to a 1-ary tuple of that Tensor.
3. A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS::
args = (x,
{'y': input_y,
'z': input_z})
All but the last element of the tuple will be passed as non-keyword arguments,
and named arguments will be set from the last element. If a named argument is
not present in the dictionary, it is assigned the default value, or None if a
default value is not provided.
.. note::
If a dictionary is the last element of the args tuple, it will be
interpreted as containing named arguments. In order to pass a dict as the
last non-keyword arg, provide an empty dict as the last element of the args
tuple. For example, instead of::
torch.onnx.export(
model,
(x,
# WRONG: will be interpreted as named arguments
{y: z}),
"test.onnx.pb")
Write::
torch.onnx.export(
model,
(x,
{y: z},
{}),
"test.onnx.pb")
f: a file-like object (such that ``f.fileno()`` returns a file descriptor)
or a string containing a file name. A binary protocol buffer will be written
to this file.
export_params (bool, default True): if True, 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, with the ordering as specified by ``model.state_dict().values()``
verbose (bool, default False): if True, prints a description of the
model being exported to stdout. In addition, the final ONNX graph will include the
field ``doc_string``` from the exported model which mentions the source code locations
for ``model``.
training (enum, default TrainingMode.EVAL):
* ``TrainingMode.EVAL``: export the model in inference mode.
* ``TrainingMode.PRESERVE``: export the model in inference mode if model.training is
False and in training mode if model.training is True.
* ``TrainingMode.TRAINING``: export the model in training mode. Disables optimizations
which might interfere with training.
input_names (list of str, default empty list): names to assign to the
input nodes of the graph, in order.
output_names (list of str, default empty list): names to assign to the
output nodes of the graph, in order.
operator_export_type (enum, default OperatorExportTypes.ONNX):
* ``OperatorExportTypes.ONNX``: Export all ops as regular ONNX ops
(in the default opset domain).
* ``OperatorExportTypes.ONNX_FALLTHROUGH``: Try to convert all ops
to standard ONNX ops in the default opset domain. If unable to do so
(e.g. because support has not been added to convert a particular torch op to ONNX),
fall back to exporting the op into a custom opset domain without conversion. Applies
to `custom ops <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_
as well as ATen ops. For the exported model to be usable, the runtime must support
these non-standard ops.
* ``OperatorExportTypes.ONNX_ATEN``: All ATen ops (in the TorchScript namespace "aten")
are exported as ATen ops (in opset domain "org.pytorch.aten").
`ATen <https://pytorch.org/cppdocs/#aten>`_ is PyTorch's built-in tensor library, so
this instructs the runtime to use PyTorch's implementation of these ops.
.. warning::
Models exported this way are probably runnable only by Caffe2.
This may be useful if the numeric differences in implementations of operators are
causing large differences in behavior between PyTorch and Caffe2 (which is more
common on untrained models).
* ``OperatorExportTypes.ONNX_ATEN_FALLBACK``: Try to export each ATen op
(in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
(e.g. because support has not been added to convert a particular torch op to ONNX),
fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
context.
For example::
graph(%0 : Float):
%3 : int = prim::Constant[value=0]()
# conversion unsupported
%4 : Float = aten::triu(%0, %3)
# conversion supported
%5 : Float = aten::mul(%4, %0)
return (%5)
Assuming ``aten::triu`` is not supported in ONNX, this will be exported as::
graph(%0 : Float):
%1 : Long() = onnx::Constant[value={0}]()
# not converted
%2 : Float = aten::ATen[operator="triu"](%0, %1)
# converted
%3 : Float = onnx::Mul(%2, %0)
return (%3)
If PyTorch was built with Caffe2 (i.e. with ``BUILD_CAFFE2=1``), then
Caffe2-specific behavior will be enabled, including special support
for ops are produced by the modules described in
`Quantization <https://pytorch.org/docs/stable/quantization.html>`_.
.. warning::
Models exported this way are probably runnable only by Caffe2.
opset_version (int, default 9): The version of the
`default (ai.onnx) opset <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_
to target. Must be >= 7 and <= 15.
do_constant_folding (bool, default True): Apply the constant-folding optimization.
Constant-folding will replace some of the ops that have all constant inputs
with pre-computed constant nodes.
dynamic_axes (dict<string, dict<int, string>> or dict<string, list(int)>, default empty dict):
By default the exported model will have the shapes of all input and output tensors
set to exactly match those given in ``args``. To specify axes of tensors as
dynamic (i.e. known only at run-time), set ``dynamic_axes`` to a dict with schema:
* KEY (str): an input or output name. Each name must also be provided in ``input_names`` or
``output_names``.
* VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a
list, each element is an axis index.
For example::
class SumModule(torch.nn.Module):
def forward(self, x):
return torch.sum(x, dim=1)
torch.onnx.export(SumModule(), (torch.ones(2, 2),), "onnx.pb",
input_names=["x"], output_names=["sum"])
Produces::
input {
name: "x"
...
shape {
dim {
dim_value: 2 # axis 0
}
dim {
dim_value: 2 # axis 1
...
output {
name: "sum"
...
shape {
dim {
dim_value: 2 # axis 0
...
While::
torch.onnx.export(SumModule(), (torch.ones(2, 2),), "onnx.pb",
input_names=["x"], output_names=["sum"],
dynamic_axes={
# dict value: manually named axes
"x": {0: "my_custom_axis_name"},
# list value: automatic names
"sum": [0],
})
Produces::
input {
name: "x"
...
shape {
dim {
dim_param: "my_custom_axis_name" # axis 0
}
dim {
dim_value: 2 # axis 1
...
output {
name: "sum"
...
shape {
dim {
dim_param: "sum_dynamic_axes_1" # axis 0
...
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 (e.g. constant folding) by
backends/runtimes.
If ``opset_version < 9``, initializers MUST be part of graph
inputs and this argument will be ignored and the behavior will be
equivalent to setting this argument to True.
If None, then the behavior is chosen automatically as follows:
* If ``operator_export_type=OperatorExportTypes.ONNX``, the behavior is equivalent
to setting this argument to False.
* Else, the behavior is equivalent to setting this argument to True.
custom_opsets (dict<str, int>, default empty dict): A dict with schema:
* KEY (str): opset domain name
* VALUE (int): opset version
If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
the opset version is set to 1. Only custom opset domain name and version should be
indicated through this argument.
export_modules_as_functions (bool or set of type of nn.Module, default False): Flag to enable
exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
particular types of modules to export as local functions in ONNX.
This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
``opset_version`` < 15 implies IR version < 8, which means no local function support.
* ``False``(default): export ``nn.Module`` forward calls as fine grained nodes.
* ``True``: export all ``nn.Module`` forward calls as local function nodes.
* Set of type of nn.Module: export ``nn.Module`` forward calls as local function nodes,
only if the type of the ``nn.Module`` is found in the set.
Raises:
CheckerError: If the ONNX checker detects an invalid ONNX graph. Will still export the
model to the file ``f`` even if this is raised.
"""
from torch.onnx import utils
return utils.export(model, args, f, export_params, verbose, training,
input_names, output_names, operator_export_type,
opset_version, do_constant_folding, dynamic_axes,
keep_initializers_as_inputs, custom_opsets,
export_modules_as_functions)
def export(model,
args,
f,
export_params=True,
verbose=False,
training=TrainingMode.EVAL,
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 (enum, default TrainingMode.EVAL):
TrainingMode.EVAL: export the model in inference mode.
TrainingMode.PRESERVE: export the model in inference mode if model.training is
False and to a training friendly mode if model.training is True.
TrainingMode.TRAINING: export the model in a training friendly mode.
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): 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:
https://github.com/onnx/onnx/blob/8b3f7e2e7a0f2aba0e629e23d89f07c7fc0e6a5e/onnx/onnx.proto#L423
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(model, args, f, export_params=True, verbose=False, training=TrainingMode.EVAL,
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.)
Args:
model (torch.nn.Module): the model to be exported.
args (tuple of arguments or torch.Tensor, a dictionary consisting of named arguments (optional)):
a dictionary to specify the input to the corresponding named parameter:
- KEY: str, named parameter
- VALUE: corresponding input
args can be structured either as:
1. ONLY A TUPLE OF ARGUMENTS or torch.Tensor::
"args = (x, y, z)"
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.
2. A TUPLE OF ARGUEMENTS WITH A DICTIONARY OF NAMED PARAMETERS::
"args = (x,
{
'y': input_y,
'z': input_z
})"
The inputs to the model are structured as a tuple consisting of
non-keyword arguments and the last value of this tuple being a dictionary
consisting of named parameters and the corresponding inputs as key-value pairs.
If certain named argument is not present in the dictionary, it is assigned
the default value, or None if default value is not provided.
Cases in which an dictionary input is the last input of the args tuple
would cause a conflict when a dictionary of named parameters is used.
The model below provides such an example.
class Model(torch.nn.Module):
def forward(self, k, x):
...
return x
m = Model()
k = torch.randn(2, 3)
x = {torch.tensor(1.): torch.randn(2, 3)}
In the previous iteration, the call to export API would look like
torch.onnx.export(model, (k, x), 'test.onnx')
This would work as intended. However, the export function
would now assume that the `x` input is intended to represent the optional
dictionary consisting of named arguments. In order to prevent this from being
an issue a constraint is placed to provide an empty dictionary as the last
input in the tuple args in such cases. The new call would look like this.
torch.onnx.export(model, (k, x, {}), 'test.onnx')
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 (enum, default TrainingMode.EVAL):
TrainingMode.EVAL: export the model in inference mode.
TrainingMode.PRESERVE: export the model in inference mode if model.training is
False and to a training friendly mode if model.training is True.
TrainingMode.TRAINING: export the model in a training friendly mode.
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
(with ONNX namespace).
OperatorExportTypes.ONNX_ATEN: All ops are exported as ATen ops
(with aten namespace).
OperatorExportTypes.ONNX_ATEN_FALLBACK: If an ATen op is not supported
in ONNX or its symbolic is missing, fall back on ATen op. Registered ops
are exported to ONNX regularly.
Example graph::
graph(%0 : Float)::
%3 : int = prim::Constant[value=0]()
%4 : Float = aten::triu(%0, %3) # missing op
%5 : Float = aten::mul(%4, %0) # registered op
return (%5)
is exported as::
graph(%0 : Float)::
%1 : Long() = onnx::Constant[value={0}]()
%2 : Float = aten::ATen[operator="triu"](%0, %1) # missing op
%3 : Float = onnx::Mul(%2, %0) # registered op
return (%3)
In the above example, aten::triu is not supported in ONNX, hence
exporter falls back on this op.
OperatorExportTypes.RAW: Export raw ir.
OperatorExportTypes.ONNX_FALLTHROUGH: If an op is not supported
in ONNX, fall through and export the operator as is, as a custom
ONNX op. Using this mode, the op can be exported and implemented by
the user for their runtime backend.
Example graph::
graph(%x.1 : Long(1, strides=[1]))::
%1 : None = prim::Constant()
%2 : Tensor = aten::sum(%x.1, %1)
%y.1 : Tensor[] = prim::ListConstruct(%2)
return (%y.1)
is exported as::
graph(%x.1 : Long(1, strides=[1]))::
%1 : Tensor = onnx::ReduceSum[keepdims=0](%x.1)
%y.1 : Long() = prim::ListConstruct(%1)
return (%y.1)
In the above example, prim::ListConstruct is not supported, hence
exporter falls through.
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_main_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): 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:
1. 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
2. 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
3. MIXED MODE OF (1) and (2)::
``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.
use_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:
https://github.com/onnx/onnx/blob/8b3f7e2e7a0f2aba0e629e23d89f07c7fc0e6a5e/onnx/onnx.proto#L423
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(*args, **kwargs):
from torch.onnx import utils
return utils.export(*args, **kwargs)
model = net.get_pose_net(num_layers=34,
heads={
'hm': 1,
'wh': 2,
'id': 512,
'reg': 2
})
model = load_model(model, "../weights/all_dla34.pth")
model.eval()
model.cuda()
# # https://github.com/xingyizhou/CenterNet/blob/master/readme/MODEL_ZOO.md 这里下载的
# # 如果下载不了，可以尝试我提供的连接：http://zifuture.com:1000/fs/public_models/ctdet_coco_dla_2x.pth
# checkpoint = torch.load(r"ctdet_coco_dla_2x.pth", map_location="cpu")
# checkpoint = checkpoint["state_dict"]
# change = OrderedDict()
# for key, op in checkpoint.items():
# change[key.replace("module.", "", 1)] = op
#
# model.load_state_dict(change)
# model.eval()
# model.cuda()
input = torch.zeros((1, 3, 608, 1088)).cuda()
#
# # 有个已经导出好的模型：http://zifuture.com:1000/fs/public_models/dladcnv2.onnx
onnx.export(model, (input),
"../weights/all_dla34.onnx",
output_names=["hm", "wh", "reg", "id", "hm_pool"],
verbose=True)
#onnx.export(model, (input), "../models/all_dla34.onnx", output_names=["hm", "wh", "reg", "id"], verbose=True)
# 请下载官方的代码，然后执行这个就可以生成了
import numpy as np
import torch
import torch.onnx.utils as onnx
import models.networks.pose_dla_dcn as net
from collections import OrderedDict
import cv2
model = net.get_pose_net(num_layers=34, heads={'hm': 80, 'wh': 2, 'reg': 2})
# https://github.com/xingyizhou/CenterNet/blob/master/readme/MODEL_ZOO.md 这里下载的
# 如果下载不了，可以尝试我提供的连接：http://zifuture.com:1000/fs/public_models/ctdet_coco_dla_2x.pth
checkpoint = torch.load(r"ctdet_coco_dla_2x.pth", map_location="cpu")
checkpoint = checkpoint["state_dict"]
change = OrderedDict()
for key, op in checkpoint.items():
change[key.replace("module.", "", 1)] = op
model.load_state_dict(change)
model.eval()
model.cuda()
input = torch.zeros((1, 3, 32, 32)).cuda()
# 有个已经导出好的模型：http://zifuture.com:1000/fs/public_models/dladcnv2.onnx
onnx.export(model, (input),
"dladcnv2.onnx",
output_names=["hm", "wh", "reg", "hm_pool"],
verbose=True)
import numpy as np
import torch
import torch.onnx.utils as onnx
import models.networks.pose_dla_conv as net
from collections import OrderedDict
model = net.get_pose_net(num_layers=34,
heads={
'hm': 1,
'wh': 4,
'reg': 2,
'id': 128
})
checkpoint = torch.load(r"models/model_54_crowdhuman.pth", map_location="cpu")
checkpoint = checkpoint["state_dict"]
change = OrderedDict()
for key, op in checkpoint.items():
change[key.replace("module.", "", 1)] = op
model.load_state_dict(change)
model.eval()
model.cuda()
input = torch.zeros((1, 3, 608, 1088)).cuda()
[hm, wh, reg, hm_pool, id_feature] = model(input)
onnx.export(model, (input),
"model_54_crowdhuman_1088x608.onnx",
output_names=["hm", "wh", "reg", "hm_pool", "id"],
verbose=True)
