/usr/local/lib64/python3.6/site-packages/torch/nn/modules/__pycache__
Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/modules/__pycache__/module.cpython-36.pyc (66530B)
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_addindent s
r5 Z _extra_state)hookreturnc C s t jt}| t|j< |S )a Registers a forward pre-hook common to all modules.
.. warning ::
This adds global state to the `nn.module` module
and it is only intended for debugging/profiling purposes.
The hook will be called every time before :func:`forward` is invoked.
It should have the following signature::
hook(module, input) -> None or modified input
The input contains only the positional arguments given to the module.
Keyword arguments won't be passed to the hooks and only to the ``forward``.
The hook can modify the input. User can either return a tuple or a
single modified value in the hook. We will wrap the value into a tuple
if a single value is returned(unless that value is already a tuple).
This hook has precedence over the specific module hooks registered with
``register_forward_pre_hook``.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
)hooksr _global_forward_pre_hooksid)r6 handler! r! r" register_module_forward_pre_hook4 s
r< c C s t jt}| t|j< |S )a Registers a global forward hook for all the modules
.. warning ::
This adds global state to the `nn.module` module
and it is only intended for debugging/profiling purposes.
The hook will be called every time after :func:`forward` has computed an output.
It should have the following signature::
hook(module, input, output) -> None or modified output
The input contains only the positional arguments given to the module.
Keyword arguments won't be passed to the hooks and only to the ``forward``.
The hook can modify the output. It can modify the input inplace but
it will not have effect on forward since this is called after
:func:`forward` is called.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
This hook will be executed before specific module hooks registered with
``register_forward_hook``.
)r8 r _global_forward_hooksr: )r6 r; r! r! r" register_module_forward_hookT s
r> c C s, t dkrtdda tjt}| t|j< |S )a Registers a backward hook common to all the modules.
This function is deprecated in favor of
:func:`torch.nn.modules.module.register_module_full_backward_hook`
and the behavior of this function will change in future versions.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
TztCannot use both regular backward hooks and full backward hooks as a global Module hook. Please use only one of them.F) _global_is_full_backward_hook RuntimeErrorr8 r _global_backward_hooksr: )r6 r; r! r! r" register_module_backward_hooks s
rB c C s, t dkrtdda tjt}| t|j< |S )a Registers a backward hook common to all the modules.
.. warning ::
This adds global state to the `nn.module` module
and it is only intended for debugging/profiling purposes.
The hook will be called every time the gradients with respect to module
inputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> Tensor or None
The :attr:`grad_input` and :attr:`grad_output` are tuples. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:`grad_input` in
subsequent computations. :attr:`grad_input` will only correspond to the inputs given
as positional arguments and all kwarg arguments will not appear in the hook. Entries
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
Global hooks are called before hooks registered with `register_backward_hook`
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
FztCannot use both regular backward hooks and full backward hooks as a global Module hook. Please use only one of them.T)r? r@ r8 r rA r: )r6 r; r! r! r" "register_module_full_backward_hook s #
rC )inputr7 c G s t dS )a Defines the computation performed at every call.
Should be overridden by all subclasses.
.. note::
Although the recipe for forward pass needs to be defined within
this function, one should call the :class:`Module` instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.
N)NotImplementedError)r rD r! r! r" _forward_unimplemented s rF c @ s e Zd ZU dZdZedZeee e
ddddZ e Z
edef
dee e edd
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ddZee d ddddZed dddZeddddZeddddZedd d Zed d d Zd!d" Ze ed gdf e d#d$d%Z de e e ee f e d&d'd(Z de e e ee f e d&d)d*Z!e e d+d,d-Z"e e e#ef e d.d/d0Z$e e d+d1d2Z%e e d+d3d4Z&e e d+d5d6Z'e e d+d7d8Z(e e ee f e d&d9d:Z)e*de e e ee f e e e#ef ee d;dd?d=Z+e*de eee d@dAd=Z+dBd= Z+ed e,e,ge def f e-dCdDdEZ.ed e,e,ge def f e-dCdFdGZ/dHdI Z0dJdK Z1ed e-dCdLdMZ2ed e-dCdNdOZ3dPdQ Z4dRdS Z5e5Z6edef 6dTdU Z7ee ed f dVdWdXZ8ee ed f ddYdZd[Z9d\d] Z:d^d_ Z;d`da Zeef dcZ?e*de?eee?dddedfZ@e*deedgdhdidfZ@ddkdfZ@ddldmZAdndo ZBddgedpdqdrZCddsdtZDdeeEe dudvdwZFdeeeEeGeef dxdydzZHdeeEe dud{d|ZIdeeeEeGeef dxd}d~ZJeEd dddZKeEeGed f dddZLeEd dddZMde eNd eedddZOde ee dddZPe e d+ddZQde ee dddZRdeddddZSe e d+ddZTdd ZUedddZVdd ZWdd ZXdd ZYdS )r a Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:`to`, etc.
:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool
Fr) N)r7 c C sd t jjd d| _t | _t | _t | _t | _ d| _
t | _ t | _ t | _
t | _t | _dS )z_
Initializes internal Module state, shared by both nn.Module and ScriptModule.
zpython.nn_moduleTN)torch_CZ_log_api_usage_oncetrainingr _parameters_buffersset_non_persistent_buffers_set_backward_hooks_is_full_backward_hook_forward_hooks_forward_pre_hooks_state_dict_hooks_load_state_dict_pre_hooks_modules)r r! r! r" __init__ s zModule.__init__.T)nametensor
persistentr7 c C s |dkr t | tjjr tdd| jkr2tdnt |tjjsVt dj
tj |nd|krht dn|dkrzt d nzt
| |r|| jkrt d
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|nV|d k rt |tj rt d j
tj ||n(|| j|< |r| jj| n | jj| d S )
a Adds a buffer to the module.
This is typically used to register a buffer that should not to be
considered a model parameter. For example, BatchNorm's ``running_mean``
is not a parameter, but is part of the module's state. Buffers, by
default, are persistent and will be saved alongside parameters. This
behavior can be changed by setting :attr:`persistent` to ``False``. The
only difference between a persistent buffer and a non-persistent buffer
is that the latter will not be a part of this module's
:attr:`state_dict`.
Buffers can be accessed as attributes using given names.
Args:
name (string): name of the buffer. The buffer can be accessed
from this module using the given name
tensor (Tensor or None): buffer to be registered. If ``None``, then operations
that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
the buffer is **not** included in the module's :attr:`state_dict`.
persistent (bool): whether the buffer is part of this module's
:attr:`state_dict`.
Example::
>>> self.register_buffer('running_mean', torch.zeros(num_features))
Fz4ScriptModule does not support non-persistent buffersrK z2cannot assign buffer before Module.__init__() callz&buffer name should be a string. Got {}.z buffer name can't contain "." z$buffer name can't be empty string ""z attribute '{}' already existsNzHcannot assign '{}' object to buffer '{}' (torch Tensor or None required))
isinstancerG jitZ ScriptModuler@ __dict__AttributeError_sixstring_classes TypeErrorformattypenameKeyErrorhasattrrK r rM discardadd)r rV rW rX r! r! r" register_buffer s*
zModule.register_buffer)rV paramr7 c C s d| j krtdnjt|tjjs8tdjtj|nFd|krJt dn4|dkr\t dn"t
| |r~|| j kr~t dj||d krd | j |< nBt|t std
jtj||n |j
rtd j|n
|| j |< d S ) a- Adds a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (string): name of the parameter. The parameter can be accessed
from this module using the given name
param (Parameter or None): parameter to be added to the module. If
``None``, then operations that run on parameters, such as :attr:`cuda`,
are ignored. If ``None``, the parameter is **not** included in the
module's :attr:`state_dict`.
rJ z5cannot assign parameter before Module.__init__() callz)parameter name should be a string. Got {}rY z parameter name can't contain "."rZ z'parameter name can't be empty string ""z attribute '{}' already existsNzQcannot assign '{}' object to parameter '{}' (torch.nn.Parameter or None required)zCannot assign non-leaf Tensor to parameter '{0}'. Model parameters must be created explicitly. To express '{0}' as a function of another Tensor, compute the value in the forward() method.)r] r^ r[ rG r_ r` ra rb rc rd re rJ r grad_fn
ValueError)r rV ri r! r! r" register_parameterD s,
zModule.register_parameter)rV moduler7 c C s t |t r*|dk r*tdjtj|npt |tjjsNtdjtj|nLt| |rr|| j krrt
dj|n(d|krt
dj|n|dkrt
d|| j |< dS ) a] Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (string): name of the child module. The child module can be
accessed from this module using the given name
module (Module): child module to be added to the module.
Nz{} is not a Module subclassz&module name should be a string. Got {}z attribute '{}' already existsrY z&module name can't contain ".", got: {}rZ z$module name can't be empty string "") r[ r ra rb rG rc r_ r` re rT rd )r rV rm r! r! r"
add_modulen s
zModule.add_module)targetr7 c C sv |dkr | S |j d}| }xV|D ]N}t||sFt|j d | d t||}t|tjjs td| d q W |S )a
Returns the submodule given by ``target`` if it exists,
otherwise throws an error.
For example, let's say you have an ``nn.Module`` ``A`` that
looks like this:
.. code-block::text
A(
(net_b): Module(
(net_c): Module(
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
)
(linear): Linear(in_features=100, out_features=200, bias=True)
)
)
(The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested
submodule ``net_b``, which itself has two submodules ``net_c``
and ``linear``. ``net_c`` then has a submodule ``conv``.)
To check whether or not we have the ``linear`` submodule, we
would call ``get_submodule("net_b.linear")``. To check whether
we have the ``conv`` submodule, we would call
``get_submodule("net_b.net_c.conv")``.
The runtime of ``get_submodule`` is bounded by the degree
of module nesting in ``target``. A query against
``named_modules`` achieves the same result, but it is O(N) in
the number of transitive modules. So, for a simple check to see
if some submodule exists, ``get_submodule`` should always be
used.
Args:
target: The fully-qualified string name of the submodule
to look for. (See above example for how to specify a
fully-qualified string.)
Returns:
torch.nn.Module: The submodule referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Module``
rZ rY z has no attribute ``z` is not an nn.Module) r/ re r^ _get_namegetattrr[ rG nnr )r ro Zatomsmoditemr! r! r"
get_submodule s 0
zModule.get_submoduler c C sh |j d\}}}| j|}t||s x| j D ]}|j| q
W dd }x| jj D ]\}}|d krBq0tj ||}W d Q R X |||}|rx||_|}n.t|tst |j
st t||j }|| j|< |j d k r0tj ||j } W d Q R X ||j | }|r| |j _q0|j j
st | j
|j j |_ q0W x0| jj D ]"\}}
|
d k r||
| j|< qW | S )Nc S s t j| |rt jj S dS d S )NF)rG Z!_has_compatible_shallow_copy_type
__future__Z)get_overwrite_module_params_on_conversion)rW Ztensor_appliedr! r! r" compute_should_use_set_data< s z2Module._apply..compute_should_use_set_data)children_applyrJ itemsrG no_graddatar[ r AssertionErrorZis_leaf
requires_gradgradrequires_grad_rK ) r fnrm r keyri Z
param_appliedZshould_use_set_dataZ out_paramZ grad_appliedbufr! r! r" r 8 s8
z
Module._apply)r r r7 c C s( x| j D ]}|j| q
W || | S )ab Applies ``fn`` recursively to every submodule (as returned by ``.children()``)
as well as self. Typical use includes initializing the parameters of a model
(see also :ref:`nn-init-doc`).
Args:
fn (:class:`Module` -> None): function to be applied to each submodule
Returns:
Module: self
Example::
>>> @torch.no_grad()
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.fill_(1.0)
>>> print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1., 1.],
[ 1., 1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1., 1.],
[ 1., 1.]])
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
)r apply)r r rm r! r! r" r l s &z Module.apply)r r r7 c s | j fddS )a Moves all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. note::
This method modifies the module in-place.
Args:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
c s
| j S )N)cuda)t)r r! r" s z Module.cuda..)r )r r r! )r r" r s z Module.cudac s | j fddS )a Moves all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on XPU while being optimized.
.. note::
This method modifies the module in-place.
Arguments:
device (int, optional): if specified, all parameters will be
copied to that device
Returns:
Module: self
c s
| j S )N)xpu)r )r r! r" r s z Module.xpu..)r )r r r! )r r" r s z
Module.xpu)r r7 c C s | j dd S )zMoves all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
c S s | j S )N)cpu)r r! r! r" r s z Module.cpu..)r )r r! r! r" r s z
Module.cpu)r dst_typer7 c s | j fddS )zCasts all parameters and buffers to :attr:`dst_type`.
.. note::
This method modifies the module in-place.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
c s
| j S )N)type)r )r r! r" r s z Module.type..)r )r r r! )r r" r s z Module.typec C s | j dd S )zCasts all floating point parameters and buffers to ``float`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
c S s | j r| j S | S )N)is_floating_pointfloat)r r! r! r" r s z Module.float..)r )r r! r! r" r s z Module.floatc C s | j dd S )zCasts all floating point parameters and buffers to ``double`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
c S s | j r| j S | S )N)r double)r r! r! r" r s z Module.double..)r )r r! r! r" r s z
Module.doublec C s | j dd S )zCasts all floating point parameters and buffers to ``half`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
c S s | j r| j S | S )N)r half)r r! r! r" r s z Module.half..)r )r r! r! r" r s z Module.halfc C s | j dd S )zCasts all floating point parameters and buffers to ``bfloat16`` datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
c S s | j r| j S | S )N)r bfloat16)r r! r! r" r s z!Module.bfloat16..)r )r r! r! r" r s zModule.bfloat16c s | j fddS )a Moves the parameters and buffers to the specified device without copying storage.
Args:
device (:class:`torch.device`): The desired device of the parameters
and buffers in this module.
Returns:
Module: self
c s t j| dS )N)r )rG Z
empty_like)r )r r! r" r s z!Module.to_empty..)r )r r r! )r r" to_empty s
zModule.to_empty)r r r non_blockingr7 c C s d S )Nr! )r r r r r! r! r" to s z Module.to)r r r r7 c C s d S )Nr! )r r r r! r! r" r s )r rW r r7 c C s d S )Nr! )r rW r r! r! r" r s c sf t jjj||\ dk rJjp*js:tdjjrJtj d fdd}| j
|S )a=
Moves and/or casts the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
:noindex:
.. function:: to(dtype, non_blocking=False)
:noindex:
.. function:: to(tensor, non_blocking=False)
:noindex:
.. function:: to(memory_format=torch.channels_last)
:noindex:
Its signature is similar to :meth:`torch.Tensor.to`, but only accepts
floating point or complex :attr:`dtype`\ s. In addition, this method will
only cast the floating point or complex parameters and buffers to :attr:`dtype`
(if given). The integral parameters and buffers will be moved
:attr:`device`, if that is given, but with dtypes unchanged. When
:attr:`non_blocking` is set, it tries to convert/move asynchronously
with respect to the host if possible, e.g., moving CPU Tensors with
pinned memory to CUDA devices.
See below for examples.
.. note::
This method modifies the module in-place.
Args:
device (:class:`torch.device`): the desired device of the parameters
and buffers in this module
dtype (:class:`torch.dtype`): the desired floating point or complex dtype of
the parameters and buffers in this module
tensor (torch.Tensor): Tensor whose dtype and device are the desired
dtype and device for all parameters and buffers in this module
memory_format (:class:`torch.memory_format`): the desired memory
format for 4D parameters and buffers in this module (keyword
only argument)
Returns:
Module: self
Examples::
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
[-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
[-0.5112, -0.2324]], dtype=torch.float16)
>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j, 0.2382+0.j],
[ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j],
[0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
NzTnn.Module.to only accepts floating point or complex dtypes, but got desired dtype={}a@ Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.md if a complex module does not work as expected.c s\ d k r:| j dkr:| j| j s*| j r.nd dS | j| j sP| j rTnd S )N )Z
memory_format)r r )Zdimr r
is_complex)r )convert_to_formatr r r r! r" convert} s
zModule.to..convert) rG rH Z_nnZ _parse_tor r ra rb warningswarnr )r argskwargsr r! )r r r r r" r s U )r6 r7 c C s4 | j dkrtdd| _ tj| j}|| j|j< |S )a Registers a backward hook on the module.
This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and
the behavior of this function will change in future versions.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
TzoCannot use both regular backward hooks and full backward hooks on a single Module. Please use only one of them.F)rO r@ r8 r rN r: )r r6 r; r! r! r" register_backward_hook s
z Module.register_backward_hookc C s4 | j dkrtdd| _ tj| j}|| j|j< |S )a Registers a backward hook on the module.
The hook will be called every time the gradients with respect to module
inputs are computed. The hook should have the following signature::
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients
with respect to the inputs and outputs respectively. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the input that will be used in place of :attr:`grad_input` in
subsequent computations. :attr:`grad_input` will only correspond to the inputs given
as positional arguments and all kwarg arguments are ignored. Entries
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor
arguments.
For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module's forward function.
.. warning ::
Modifying inputs or outputs inplace is not allowed when using backward hooks and
will raise an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
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The hook will be called every time before :func:`forward` is invoked.
It should have the following signature::
hook(module, input) -> None or modified input
The input contains only the positional arguments given to the module.
Keyword arguments won't be passed to the hooks and only to the ``forward``.
The hook can modify the input. User can either return a tuple or a
single modified value in the hook. We will wrap the value into a tuple
if a single value is returned(unless that value is already a tuple).
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
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The hook will be called every time after :func:`forward` has computed an output.
It should have the following signature::
hook(module, input, output) -> None or modified output
The input contains only the positional arguments given to the module.
Keyword arguments won't be passed to the hooks and only to the ``forward``.
The hook can modify the output. It can modify the input inplace but
it will not have effect on forward since this is called after
:func:`forward` is called.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
)r8 r rP r: )r r6 r; r! r! r" register_forward_hook s z Module.register_forward_hookc
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zModule._call_implc C sh | j j| d| j kr t | _d| j kr0t | _d| j krBt | _d| j krTt | _d| j krdd | _d S )NrQ rR rS rM rO ) r] updater rQ rR rS rL rM rO )r r r! r! r" __setstate__~ s
zModule.__setstate__)rV r7 c C s d| j kr$| j d }||kr$|| S d| j krH| j d }||krH|| S d| j krl| j d }||krl|| S tdjt| j|d S )NrJ rK rT z!'{}' object has no attribute '{}')r] r^ rb r r# )r rV rJ rK modulesr! r! r" __getattr__ s
zModule.__getattr__)rV valuer7 c s fdd}| j jd}t|trX|d kr2td|| j | j| j| j | j | n&|d k r |kr|d k rt dj
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zModule.__delattr__c C s t j| j}|| j|j< |S )aV These hooks will be called with arguments: `self`, `state_dict`,
`prefix`, `local_metadata`, after the `state_dict` of `self` is set.
Note that only parameters and buffers of `self` or its children are
guaranteed to exist in `state_dict`. The hooks may modify `state_dict`
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)r8 r rR r: )r r6 r; r! r! r" _register_state_dict_hook s z Module._register_state_dict_hookc C s x6| j j D ](\}}|dk r |r$|n|j ||| < q W x@| jj D ]2\}}|dk rD|| jkrD|rf|n|j ||| < qDW |t }t| jdtj tj k r| j ||< dS )a Saves module state to `destination` dictionary, containing a state
of the module, but not its descendants. This is called on every
submodule in :meth:`~torch.nn.Module.state_dict`.
In rare cases, subclasses can achieve class-specific behavior by
overriding this method with custom logic.
Args:
destination (dict): a dict where state will be stored
prefix (str): the prefix for parameters and buffers used in this
module
Nr~ )
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zModule._save_to_state_dict
T_destination)r )r r r r7 c C s d S )Nr! )r r r r r! r! r"
state_dict s zModule.state_dictzOrderedDict[str, Tensor])r r r7 c C s d S )Nr! )r r r r! r! r" r s rZ c C s |dkrt }t |_t| jd |j|dd < }| j||| x6| jj D ](\}}|dk rN|j||| d |d qNW x,| jj D ] }|| |||}|dk r|}qW |S )a Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> module.state_dict().keys()
['bias', 'weight']
N)versionr) rY )r )
r _metadatar _versionr rT r r rR r ) r r r r local_metadatarV rm r6 r r! r! r" r s c C s, t j| j}|r tj|| }|| j|j< |S )a These hooks will be called with arguments: `state_dict`, `prefix`,
`local_metadata`, `strict`, `missing_keys`, `unexpected_keys`,
`error_msgs`, before loading `state_dict` into `self`. These arguments
are exactly the same as those of `_load_from_state_dict`.
If ``with_module`` is ``True``, then the first argument to the hook is
an instance of the module.
Arguments:
hook (Callable): Callable hook that will be invoked before
loading the state dict.
with_module (bool, optional): Whether or not to pass the module
instance to the hook as the first parameter.
)r8 r rS r r r: )r r6 Z with_moduler; r! r! r" "_register_load_state_dict_pre_hook% s
z)Module._register_load_state_dict_pre_hookc sN x& j j D ]}|||||||| q W fdd jj D } tj jj | j }
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D } x | j D ]\} }
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|
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dd }| j kr|| kr|j
| qW dS ) ay Copies parameters and buffers from :attr:`state_dict` into only
this module, but not its descendants. This is called on every submodule
in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this
module in input :attr:`state_dict` is provided as :attr:`local_metadata`.
For state dicts without metadata, :attr:`local_metadata` is empty.
Subclasses can achieve class-specific backward compatible loading using
the version number at `local_metadata.get("version", None)`.
.. note::
:attr:`state_dict` is not the same object as the input
:attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So
it can be modified.
Args:
state_dict (dict): a dict containing parameters and
persistent buffers.
prefix (str): the prefix for parameters and buffers used in this
module
local_metadata (dict): a dict containing the metadata for this module.
See
strict (bool): whether to strictly enforce that the keys in
:attr:`state_dict` with :attr:`prefix` match the names of
parameters and buffers in this module
missing_keys (list of str): if ``strict=True``, add missing keys to
this list
unexpected_keys (list of str): if ``strict=True``, add unexpected
keys to this list
error_msgs (list of str): error messages should be added to this
list, and will be reported together in
:meth:`~torch.nn.Module.load_state_dict`
c s i | ]\}}| j kr||qS r! )rM )r+ kr )r r! r"
^ s z0Module._load_from_state_dict..c S s i | ]\}}|d k r||qS )Nr! )r+ r r r! r! r" r ` s r r) zfsize mismatch for {}: copying a param with shape {} from checkpoint, the shape in current model is {}.NzWhile copying the parameter named "{}", whose dimensions in the model are {} and whose dimensions in the checkpoint are {}, an exception occurred : {}.r rY ) rS r rK r itertoolschainrJ rG rs parameterZis_lazyr0 shapeappendrb r Zcopy_ Exceptionsizer r rr r r r keys
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S )ab Copies parameters and buffers from :attr:`state_dict` into
this module and its descendants. If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~torch.nn.Module.state_dict` function.
Args:
state_dict (dict): a dict containing parameters and
persistent buffers.
strict (bool, optional): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``True``
Returns:
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
* **missing_keys** is a list of str containing the missing keys
* **unexpected_keys** is a list of str containing the unexpected keys
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
r NrZ c sl d kr i nj |d d i }| j||d x0| jj D ]"\}}|d k rB||| d qBW d S )Nr) TrY r )r r rT r )rm r r rV child)r loadmetadatar r r r! r" r s z$Module.load_state_dict..loadr z%Unexpected key(s) in state_dict: {}. z, c s s | ]}d j |V qdS )z"{}"N)rb )r+ r r! r! r" r s z)Module.load_state_dict..z"Missing key(s) in state_dict: {}. c s s | ]}d j |V qdS )z"{}"N)rb )r+ r r! r! r" r s z*Error(s) in loading state_dict for {}:
{}z
)rZ ) rr copyr r0 insertrb r2 r@ r r# r )r r r r! )r r r r r r r" load_state_dict s. zModule.load_state_dictc c s t }|r| j|dn|| fg}xd|D ]\\}}||}xJ|D ]B\} }
|
dks<|
|krVq<|j|
||rjdnd | } | |
fV qHelper method for yielding various names + members of modules.)r NrY rZ )rL
named_modulesrg ) r Zget_members_fnr recursememor Z
module_prefixrm membersr r rV r! r! r" _named_members s
zModule._named_members)r r7 c c s$ x | j |dD ]\}}|V qW dS )aR Returns an iterator over module parameters.
This is typically passed to an optimizer.
Args:
recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that
are direct members of this module.
Yields:
Parameter: module parameter
Example::
>>> for param in model.parameters():
>>> print(type(param), param.size())
(20L,)
(20L, 1L, 5L, 5L)
)r N)named_parameters)r r rV ri r! r! r"
parameters s zModule.parameters)r r r7 c c s, | j dd ||d}x|D ]
}|V qW dS )a Returns an iterator over module parameters, yielding both the
name of the parameter as well as the parameter itself.
Args:
prefix (str): prefix to prepend to all parameter names.
recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that
are direct members of this module.
Yields:
(string, Parameter): Tuple containing the name and parameter
Example::
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
c S s
| j j S )N)rJ r )rm r! r! r" r s z)Module.named_parameters..)r r N)r )r r r genelemr! r! r" r s
zModule.named_parametersc c s$ x | j |dD ]\}}|V qW dS )a
Returns an iterator over module buffers.
Args:
recurse (bool): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module.
Yields:
torch.Tensor: module buffer
Example::
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
(20L,)
(20L, 1L, 5L, 5L)
)r N)
named_buffers)r r ry r r! r! r" r
s zModule.buffersc c s, | j dd ||d}x|D ]
}|V qW dS )a Returns an iterator over module buffers, yielding both the
name of the buffer as well as the buffer itself.
Args:
prefix (str): prefix to prepend to all buffer names.
recurse (bool): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module.
Yields:
(string, torch.Tensor): Tuple containing the name and buffer
Example::
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
c S s
| j j S )N)rK r )rm r! r! r" r 8 s z&Module.named_buffers..)r r N)r )r r r r r r! r! r" r # s
zModule.named_buffersc c s x| j D ]\}}|V q
W dS )zqReturns an iterator over immediate children modules.
Yields:
Module: a child module
N)named_children)r rV rm r! r! r" r = s zModule.childrenc c sF t }x:| jj D ],\}}|dk r||kr|j| ||fV qW dS )a Returns an iterator over immediate children modules, yielding both
the name of the module as well as the module itself.
Yields:
(string, Module): Tuple containing a name and child module
Example::
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
N)rL rT r rg )r r rV rm r! r! r" r F s
zModule.named_childrenc c s x| j D ]\}}|V q
W dS )a Returns an iterator over all modules in the network.
Yields:
Module: a module in the network
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
N)r )r ry rm r! r! r" r Z s zModule.modules)r r remove_duplicatec c s |dkrt }| |kr|r$|j| || fV xR| jj D ]D\}}|dkrLq:||rVdnd | }x |j|||D ]
}|V qpW q:W dS )aD Returns an iterator over all modules in the network, yielding
both the name of the module as well as the module itself.
Args:
memo: a memo to store the set of modules already added to the result
prefix: a prefix that will be added to the name of the module
remove_duplicate: whether to remove the duplicated module instances in the result
or not
Yields:
(string, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
Example::
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
print(idx, '->', m)
0 -> ('', Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
NrY rZ )rL rg rT r r )r r r r rV rm Zsubmodule_prefixmr! r! r" r u s
zModule.named_modules)r moder7 c C s8 t |tstd|| _x| j D ]}|j| q"W | S )a Sets the module in training mode.
This has any effect only on certain modules. See documentations of
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
etc.
Args:
mode (bool): whether to set training mode (``True``) or evaluation
mode (``False``). Default: ``True``.
Returns:
Module: self
z'training mode is expected to be boolean)r[ boolrk rI r train)r r rm r! r! r" r
s
z Module.trainc C s
| j dS )a9 Sets the module in evaluation mode.
This has any effect only on certain modules. See documentations of
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
etc.
This is equivalent with :meth:`self.train(False) `.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
F)r
)r r! r! r" eval s z Module.eval)r r r7 c C s x| j D ]}|j| q
W | S )a Change if autograd should record operations on parameters in this
module.
This method sets the parameters' :attr:`requires_grad` attributes
in-place.
This method is helpful for freezing part of the module for finetuning
or training parts of a model individually (e.g., GAN training).
See :ref:`locally-disable-grad-doc` for a comparison between
`.requires_grad_()` and several similar mechanisms that may be confused with it.
Args:
requires_grad (bool): whether autograd should record operations on
parameters in this module. Default: ``True``.
Returns:
Module: self
)r r )r r pr! r! r" r s zModule.requires_grad_) set_to_noner7 c C sp t | ddrtjd xT| j D ]H}|jdk r |r:d|_q |jjdk rR|jj n |jjd |jj q W dS )a7 Sets gradients of all model parameters to zero. See similar function
under :class:`torch.optim.Optimizer` for more context.
Args:
set_to_none (bool): instead of setting to zero, set the grads to None.
See :meth:`torch.optim.Optimizer.zero_grad` for details.
_is_replicaFaG Calling .zero_grad() from a module created with nn.DataParallel() has no effect. The parameters are copied (in a differentiable manner) from the original module. This means they are not leaf nodes in autograd and so don't accumulate gradients. If you need gradients in your forward method, consider using autograd.grad instead.N) rr r r r r rj Zdetach_r Zzero_)r r
r r! r! r" zero_grad s
zModule.zero_gradc C s | j dd S )z&See :meth:`torch.Tensor.share_memory_`c S s | j S )N)Z
share_memory_)r r! r! r" r s z%Module.share_memory..)r )r r! r! r" share_memory s zModule.share_memoryc C s | j jS )N)r r# )r r! r! r" rq s zModule._get_namec C s dS )zSet the extra representation of the module
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this method in your own modules. Both single-line and multi-line
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rZ r! )r r! r! r"
extra_repr s zModule.extra_reprc C s g }| j }|r|jd}g }x>| jj D ]0\}}t|}t|d}|jd| d | q*W || }| j d }|rt|dkr| r||d 7 }n|ddj | d 7 }|d7 }|S ) Nr( r (z): r) r z
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