coral_pytorch.losses
coral_pytorch version: 1.4.0
CornLoss
CornLoss(num_classes)
Computes the CORN loss described in our forthcoming 'Deep Neural Networks for Rank Consistent Ordinal Regression based on Conditional Probabilities' manuscript.
Parameters
-
num_classes: intNumber of unique class labels (class labels should start at 0).
Examples
>>> import torch
>>> from coral_pytorch.losses import corn_loss
>>> # Consider 8 training examples
>>> _ = torch.manual_seed(123)
>>> X_train = torch.rand(8, 99)
>>> y_train = torch.tensor([0, 1, 2, 2, 2, 3, 4, 4])
>>> NUM_CLASSES = 5
>>> #
>>> #
>>> # def __init__(self):
>>> corn_net = torch.nn.Linear(99, NUM_CLASSES-1)
>>> #
>>> #
>>> # def forward(self, X_train):
>>> logits = corn_net(X_train)
>>> logits.shape
torch.Size([8, 4])
>>> corn_loss(logits, y_train, NUM_CLASSES)
tensor(0.7127, grad_fn=<DivBackward0>)
Methods
add_module(name: str, module: Optional[ForwardRef('Module')]) -> None
Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): 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.
apply(self: ~T, fn: Callable[[ForwardRef('Module')], NoneType]) -> ~T
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.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
```
bfloat16(self: ~T) -> ~T
Casts all floating point parameters and buffers to bfloat16 datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
buffers(recurse: bool = True) -> Iterator[torch.Tensor]
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::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
```
children() -> Iterator[ForwardRef('Module')]
Returns an iterator over immediate children modules.
Yields:
Module: a child module
cpu(self: ~T) -> ~T
Moves all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
cuda(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
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
double(self: ~T) -> ~T
Casts all floating point parameters and buffers to double datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
eval(self: ~T) -> ~T
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) <torch.nn.Module.train>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
extra_repr() -> str
Set the extra representation of the module
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
float(self: ~T) -> ~T
Casts all floating point parameters and buffers to float datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
forward(logits, y_train)
Parameters
-
logits: torch.tensor, shape=(num_examples, num_classes-1)Outputs of the CORN layer.
-
y_train: torch.tensor, shape=(num_examples)Torch tensor containing the class labels.
Returns
-
loss: torch.tensorA torch.tensor containing a single loss value.
get_buffer(target: str) -> 'Tensor'
Returns the buffer given by target if it exists,
otherwise throws an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the buffer
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.Tensor: The buffer referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not a
buffer
get_extra_state() -> Any
Returns any extra state to include in the module's state_dict.
Implement this and a corresponding :func:set_extra_state for your module
if you need to store extra state. This function is called when building the
module's state_dict().
Note that extra state should be picklable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.
Returns:
object: Any extra state to store in the module's state_dict
get_parameter(target: str) -> 'Parameter'
Returns the parameter given by target if it exists,
otherwise throws an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the Parameter
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.nn.Parameter: The Parameter referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Parameter``
get_submodule(target: str) -> 'Module'
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``
half(self: ~T) -> ~T
Casts all floating point parameters and buffers to half datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
ipu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
Moves all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on IPU 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
load_state_dict(state_dict: Mapping[str, Any], strict: bool = True)
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.
modules() -> Iterator[ForwardRef('Module')]
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)
```
named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.Tensor]]
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, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
```
named_children() -> Iterator[Tuple[str, ForwardRef('Module')]]
Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
```
named_modules(memo: Optional[Set[ForwardRef('Module')]] = None, prefix: str = '', remove_duplicate: bool = True)
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:
(str, 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))
```
named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
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.
remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
Yields:
(str, Parameter): Tuple containing the name and parameter
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
```
parameters(recurse: bool = True) -> Iterator[torch.nn.parameter.Parameter]
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::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for param in model.parameters():
>>> print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
```
register_backward_hook(hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]) -> torch.utils.hooks.RemovableHandle
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()``
register_buffer(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) -> None
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 (str): 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::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> self.register_buffer('running_mean', torch.zeros(num_features))
```
register_forward_hook(hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]], , prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle*
Registers a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
If ``with_kwargs`` is ``False`` or not specified, 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. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If ``with_kwargs`` is ``True``, the forward hook will be passed the
``kwargs`` given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If ``True``, the provided ``hook`` will be fired
before all existing ``forward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``forward`` hooks registered with
:func:`register_module_forward_hook` will fire before all hooks
registered by this method.
Default: ``False``
with_kwargs (bool): If ``True``, the ``hook`` will be passed the
kwargs given to the forward function.
Default: ``False``
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_forward_pre_hook(hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]], , prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle*
Registers a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
If ``with_kwargs`` is false or not specified, 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). The hook should have the
following signature::
hook(module, args) -> None or modified input
If ``with_kwargs`` is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``forward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``forward_pre`` hooks registered with
:func:`register_module_forward_pre_hook` will fire before all
hooks registered by this method.
Default: ``False``
with_kwargs (bool): If true, the ``hook`` will be passed the kwargs
given to the forward function.
Default: ``False``
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_full_backward_hook(hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle
Registers a backward hook on the module.
The hook will be called every time the gradients with respect to a module
are computed, i.e. the hook will execute if and only if the gradients with
respect to module outputs 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.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``backward`` hooks registered with
:func:`register_module_full_backward_hook` will fire before
all hooks registered by this method.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_full_backward_pre_hook(hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle
Registers a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed.
The hook should have the following signature::
hook(module, grad_output) -> Tensor or None
The :attr:`grad_output` is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:`grad_output` in
subsequent computations. Entries in :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 inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``backward_pre`` hooks registered with
:func:`register_module_full_backward_pre_hook` will fire before
all hooks registered by this method.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_load_state_dict_post_hook(hook)
Registers a post hook to be run after module's load_state_dict
is called.
It should have the following signature::
hook(module, incompatible_keys) -> None
The ``module`` argument is the current module that this hook is registered
on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting
of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``
is a ``list`` of ``str`` containing the missing keys and
``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:`load_state_dict` with
``strict=True`` are affected by modifications the hook makes to
``missing_keys`` or ``unexpected_keys``, as expected. Additions to either
set of keys will result in an error being thrown when ``strict=True``, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_module(name: str, module: Optional[ForwardRef('Module')]) -> None
Alias for :func:add_module.
register_parameter(name: str, param: Optional[torch.nn.parameter.Parameter]) -> None
Adds a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (str): 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.
register_state_dict_pre_hook(hook)
These hooks will be called with arguments: self, prefix,
and keep_vars before calling state_dict on self. The registered
hooks can be used to perform pre-processing before the state_dict
call is made.
requires_grad_(self: ~T, requires_grad: bool = True) -> ~T
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
set_extra_state(state: Any)
This function is called from :func:load_state_dict to handle any extra state
found within the state_dict. Implement this function and a corresponding
:func:get_extra_state for your module if you need to store extra state within its
state_dict.
Args:
state (dict): Extra state from the `state_dict`
share_memory(self: ~T) -> ~T
See :meth:torch.Tensor.share_memory_
state_dict(args, destination=None, prefix='', keep_vars=False)*
Returns a dictionary containing references to the 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.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~torch.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
```
to(args, *kwargs)
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::
```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> 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)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> 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)
```
to_empty(self: ~T, , device: Union[str, torch.device]) -> ~T*
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
train(self: ~T, mode: bool = True) -> ~T
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
type(self: ~T, dst_type: Union[torch.dtype, str]) -> ~T
Casts 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
xpu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
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
zero_grad(set_to_none: bool = True) -> None
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.
Properties
CoralLoss
CoralLoss(reduction='mean')
Computes the CORAL loss described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
Parameters
-
reduction: str or None (default='mean')If 'mean' or 'sum', returns the averaged or summed loss value across all data points (rows) in logits. If None, returns a vector of shape (num_examples,)
Examples
>>> import torch
>>> from coral_pytorch.losses import CoralLoss
>>> levels = torch.tensor(
... [[1., 1., 0., 0.],
... [1., 0., 0., 0.],
... [1., 1., 1., 1.]])
>>> logits = torch.tensor(
... [[2.1, 1.8, -2.1, -1.8],
... [1.9, -1., -1.5, -1.3],
... [1.9, 1.8, 1.7, 1.6]])
>>> loss = CoralLoss()
>>> loss(logits, levels)
tensor(0.6920)
Methods
add_module(name: str, module: Optional[ForwardRef('Module')]) -> None
Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): 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.
apply(self: ~T, fn: Callable[[ForwardRef('Module')], NoneType]) -> ~T
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.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
[1., 1.]], requires_grad=True)
Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
```
bfloat16(self: ~T) -> ~T
Casts all floating point parameters and buffers to bfloat16 datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
buffers(recurse: bool = True) -> Iterator[torch.Tensor]
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::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>> print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
```
children() -> Iterator[ForwardRef('Module')]
Returns an iterator over immediate children modules.
Yields:
Module: a child module
cpu(self: ~T) -> ~T
Moves all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
cuda(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
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
double(self: ~T) -> ~T
Casts all floating point parameters and buffers to double datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
eval(self: ~T) -> ~T
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) <torch.nn.Module.train>`.
See :ref:`locally-disable-grad-doc` for a comparison between
`.eval()` and several similar mechanisms that may be confused with it.
Returns:
Module: self
extra_repr() -> str
Set the extra representation of the module
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
float(self: ~T) -> ~T
Casts all floating point parameters and buffers to float datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
forward(logits, levels, importance_weights=None)
Parameters
-
logits: torch.tensor, shape(num_examples, num_classes-1)Outputs of the CORAL layer.
-
levels: torch.tensor, shape(num_examples, num_classes-1)True labels represented as extended binary vectors (via
coral_pytorch.dataset.levels_from_labelbatch). -
importance_weights: torch.tensor, shape=(num_classes-1,) (default=None)Optional weights for the different labels in levels. A tensor of ones, i.e.,
torch.ones(num_classes-1, dtype=torch.float32)will result in uniform weights that have the same effect as None.
get_buffer(target: str) -> 'Tensor'
Returns the buffer given by target if it exists,
otherwise throws an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the buffer
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.Tensor: The buffer referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not a
buffer
get_extra_state() -> Any
Returns any extra state to include in the module's state_dict.
Implement this and a corresponding :func:set_extra_state for your module
if you need to store extra state. This function is called when building the
module's state_dict().
Note that extra state should be picklable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.
Returns:
object: Any extra state to store in the module's state_dict
get_parameter(target: str) -> 'Parameter'
Returns the parameter given by target if it exists,
otherwise throws an error.
See the docstring for ``get_submodule`` for a more detailed
explanation of this method's functionality as well as how to
correctly specify ``target``.
Args:
target: The fully-qualified string name of the Parameter
to look for. (See ``get_submodule`` for how to specify a
fully-qualified string.)
Returns:
torch.nn.Parameter: The Parameter referenced by ``target``
Raises:
AttributeError: If the target string references an invalid
path or resolves to something that is not an
``nn.Parameter``
get_submodule(target: str) -> 'Module'
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``
half(self: ~T) -> ~T
Casts all floating point parameters and buffers to half datatype.
.. note::
This method modifies the module in-place.
Returns:
Module: self
ipu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
Moves all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on IPU 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
load_state_dict(state_dict: Mapping[str, Any], strict: bool = True)
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.
modules() -> Iterator[ForwardRef('Module')]
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)
```
named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.Tensor]]
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, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
```
named_children() -> Iterator[Tuple[str, ForwardRef('Module')]]
Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
```
named_modules(memo: Optional[Set[ForwardRef('Module')]] = None, prefix: str = '', remove_duplicate: bool = True)
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:
(str, 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))
```
named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]
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.
remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
Yields:
(str, Parameter): Tuple containing the name and parameter
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>> if name in ['bias']:
>>> print(param.size())
```
parameters(recurse: bool = True) -> Iterator[torch.nn.parameter.Parameter]
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::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> for param in model.parameters():
>>> print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
```
register_backward_hook(hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]) -> torch.utils.hooks.RemovableHandle
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()``
register_buffer(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) -> None
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 (str): 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::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> self.register_buffer('running_mean', torch.zeros(num_features))
```
register_forward_hook(hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]], , prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle*
Registers a forward hook on the module.
The hook will be called every time after :func:`forward` has computed an output.
If ``with_kwargs`` is ``False`` or not specified, 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. The hook
should have the following signature::
hook(module, args, output) -> None or modified output
If ``with_kwargs`` is ``True``, the forward hook will be passed the
``kwargs`` given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature::
hook(module, args, kwargs, output) -> None or modified output
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If ``True``, the provided ``hook`` will be fired
before all existing ``forward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``forward`` hooks registered with
:func:`register_module_forward_hook` will fire before all hooks
registered by this method.
Default: ``False``
with_kwargs (bool): If ``True``, the ``hook`` will be passed the
kwargs given to the forward function.
Default: ``False``
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_forward_pre_hook(hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]], , prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle*
Registers a forward pre-hook on the module.
The hook will be called every time before :func:`forward` is invoked.
If ``with_kwargs`` is false or not specified, 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). The hook should have the
following signature::
hook(module, args) -> None or modified input
If ``with_kwargs`` is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature::
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``forward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``forward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``forward_pre`` hooks registered with
:func:`register_module_forward_pre_hook` will fire before all
hooks registered by this method.
Default: ``False``
with_kwargs (bool): If true, the ``hook`` will be passed the kwargs
given to the forward function.
Default: ``False``
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_full_backward_hook(hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle
Registers a backward hook on the module.
The hook will be called every time the gradients with respect to a module
are computed, i.e. the hook will execute if and only if the gradients with
respect to module outputs 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.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward`` hooks on
this :class:`torch.nn.modules.Module`. Note that global
``backward`` hooks registered with
:func:`register_module_full_backward_hook` will fire before
all hooks registered by this method.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_full_backward_pre_hook(hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle
Registers a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed.
The hook should have the following signature::
hook(module, grad_output) -> Tensor or None
The :attr:`grad_output` is a tuple. The hook should
not modify its arguments, but it can optionally return a new gradient with
respect to the output that will be used in place of :attr:`grad_output` in
subsequent computations. Entries in :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 inplace is not allowed when using backward hooks and
will raise an error.
Args:
hook (Callable): The user-defined hook to be registered.
prepend (bool): If true, the provided ``hook`` will be fired before
all existing ``backward_pre`` hooks on this
:class:`torch.nn.modules.Module`. Otherwise, the provided
``hook`` will be fired after all existing ``backward_pre`` hooks
on this :class:`torch.nn.modules.Module`. Note that global
``backward_pre`` hooks registered with
:func:`register_module_full_backward_pre_hook` will fire before
all hooks registered by this method.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_load_state_dict_post_hook(hook)
Registers a post hook to be run after module's load_state_dict
is called.
It should have the following signature::
hook(module, incompatible_keys) -> None
The ``module`` argument is the current module that this hook is registered
on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting
of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``
is a ``list`` of ``str`` containing the missing keys and
``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.
The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling :func:`load_state_dict` with
``strict=True`` are affected by modifications the hook makes to
``missing_keys`` or ``unexpected_keys``, as expected. Additions to either
set of keys will result in an error being thrown when ``strict=True``, and
clearing out both missing and unexpected keys will avoid an error.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
register_module(name: str, module: Optional[ForwardRef('Module')]) -> None
Alias for :func:add_module.
register_parameter(name: str, param: Optional[torch.nn.parameter.Parameter]) -> None
Adds a parameter to the module.
The parameter can be accessed as an attribute using given name.
Args:
name (str): 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.
register_state_dict_pre_hook(hook)
These hooks will be called with arguments: self, prefix,
and keep_vars before calling state_dict on self. The registered
hooks can be used to perform pre-processing before the state_dict
call is made.
requires_grad_(self: ~T, requires_grad: bool = True) -> ~T
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
set_extra_state(state: Any)
This function is called from :func:load_state_dict to handle any extra state
found within the state_dict. Implement this function and a corresponding
:func:get_extra_state for your module if you need to store extra state within its
state_dict.
Args:
state (dict): Extra state from the `state_dict`
share_memory(self: ~T) -> ~T
See :meth:torch.Tensor.share_memory_
state_dict(args, destination=None, prefix='', keep_vars=False)*
Returns a dictionary containing references to the 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.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~torch.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
```
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
```
to(args, *kwargs)
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::
```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> 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)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> 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)
```
to_empty(self: ~T, , device: Union[str, torch.device]) -> ~T*
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
train(self: ~T, mode: bool = True) -> ~T
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
type(self: ~T, dst_type: Union[torch.dtype, str]) -> ~T
Casts 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
xpu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T
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
zero_grad(set_to_none: bool = True) -> None
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.
Properties
corn_loss
corn_loss(logits, y_train, num_classes)
Computes the CORN loss described in our forthcoming 'Deep Neural Networks for Rank Consistent Ordinal Regression based on Conditional Probabilities' manuscript.
Parameters
-
logits: torch.tensor, shape=(num_examples, num_classes-1)Outputs of the CORN layer.
-
y_train: torch.tensor, shape=(num_examples)Torch tensor containing the class labels.
-
num_classes: intNumber of unique class labels (class labels should start at 0).
Returns
-
loss: torch.tensorA torch.tensor containing a single loss value.
Examples
>>> import torch
>>> from coral_pytorch.losses import corn_loss
>>> # Consider 8 training examples
>>> _ = torch.manual_seed(123)
>>> X_train = torch.rand(8, 99)
>>> y_train = torch.tensor([0, 1, 2, 2, 2, 3, 4, 4])
>>> NUM_CLASSES = 5
>>> #
>>> #
>>> # def __init__(self):
>>> corn_net = torch.nn.Linear(99, NUM_CLASSES-1)
>>> #
>>> #
>>> # def forward(self, X_train):
>>> logits = corn_net(X_train)
>>> logits.shape
torch.Size([8, 4])
>>> corn_loss(logits, y_train, NUM_CLASSES)
tensor(0.7127, grad_fn=<DivBackward0>)
coral_loss
coral_loss(logits, levels, importance_weights=None, reduction='mean')
Computes the CORAL loss described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
Parameters
-
logits: torch.tensor, shape(num_examples, num_classes-1)Outputs of the CORAL layer.
-
levels: torch.tensor, shape(num_examples, num_classes-1)True labels represented as extended binary vectors (via
coral_pytorch.dataset.levels_from_labelbatch). -
importance_weights: torch.tensor, shape=(num_classes-1,) (default=None)Optional weights for the different labels in levels. A tensor of ones, i.e.,
torch.ones(num_classes-1, dtype=torch.float32)will result in uniform weights that have the same effect as None. -
reduction: str or None (default='mean')If 'mean' or 'sum', returns the averaged or summed loss value across all data points (rows) in logits. If None, returns a vector of shape (num_examples,)
Returns
-
loss: torch.tensorA torch.tensor containing a single loss value (if
reduction='mean'or 'sum') or a loss value for each data record (ifreduction=None).
Examples
>>> import torch
>>> from coral_pytorch.losses import coral_loss
>>> levels = torch.tensor(
... [[1., 1., 0., 0.],
... [1., 0., 0., 0.],
... [1., 1., 1., 1.]])
>>> logits = torch.tensor(
... [[2.1, 1.8, -2.1, -1.8],
... [1.9, -1., -1.5, -1.3],
... [1.9, 1.8, 1.7, 1.6]])
>>> coral_loss(logits, levels)
tensor(0.6920)