fiftyone.utils.clip.model¶
CLIP model from https://github.com/openai/CLIP.
Classes:
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A ResNet class that is similar to torchvision’s but contains the following changes: |
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Subclass torch’s LayerNorm to handle fp16. |
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Functions:
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Converts applicable model parameters to fp16. |
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-
class
fiftyone.utils.clip.model.
Bottleneck
(inplanes, planes, stride=1)¶ Bases:
torch.nn.modules.module.Module
Attributes:
alias of TypeVar(‘T_destination’)
Methods:
forward
(x)Define the computation performed at every call.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module’s forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module’s state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, …])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, …])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, …])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, …])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post-hook to be run after module’s
load_state_dict()
is called.Register a pre-hook to be run before module’s
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a post-hook for the
state_dict()
method.Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.state_dict
(*args[, destination, prefix, …])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
-
expansion
= 4¶
-
forward
(x: torch.Tensor)¶ Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
T_destination
¶ alias of TypeVar(‘T_destination’)
-
add_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
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
(fn: Callable[[torch.nn.modules.module.Module], None]) → T¶ Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
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
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Return an iterator over module buffers.
- Parameters
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)
-
call_super_init
: bool = False¶
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Return an iterator over immediate children modules.
- Yields
Module – a child module
-
compile
(*args, **kwargs)¶ Compile this Module’s forward using
torch.compile()
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile()
.See
torch.compile()
for details on the arguments for this function.
-
cpu
() → T¶ Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
dump_patches
: bool = False¶
-
eval
() → T¶ Set 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.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns
self
- Return type
Module
-
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
() → T¶ Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
get_buffer
(target: str) → torch.Tensor¶ Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The buffer referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
-
get_extra_state
() → Any¶ Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
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
Any extra state to store in the module’s state_dict
- Return type
object
-
get_parameter
(target: str) → torch.nn.parameter.Parameter¶ Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The Parameter referenced by
target
- Return type
torch.nn.Parameter
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
-
get_submodule
(target: str) → torch.nn.modules.module.Module¶ Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_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.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns
The submodule referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
ipu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)¶ Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
unlessget_swap_module_params_on_conversion()
isTrue
.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
assign (bool, optional) – When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. The only exception is therequires_grad
field ofDefault: ``False`
- Returns
- missing_keys is a list of str containing any keys that are expected
by this module but missing from the provided
state_dict
.
- unexpected_keys is a list of str containing the keys that are not
expected by this module but present in the provided
state_dict
.
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict()
will raise aRuntimeError
.
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Return 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)
-
mtia
(device: Optional[Union[int, torch.device]] = None) → T¶ Move all model parameters and buffers to the MTIA.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.
Note
This method modifies the module in-place.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
named_buffers
(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
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, torch.nn.modules.module.Module]]¶ Return 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[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)¶ Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters
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]]¶ Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
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]¶ Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
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[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()
and the behavior of this function will change in future versions.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None¶ Add 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 settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
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 ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
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, always_call: bool = False) → torch.utils.hooks.RemovableHandle¶ Register a forward hook on the module.
The hook will be called every time after
forward()
has computed an output.If
with_kwargs
isFalse
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 theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook()
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
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¶ Register a forward pre-hook on the module.
The hook will be called every time before
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 theforward
. 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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_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
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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
grad_input
andgrad_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 ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_pre_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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) -> tuple[Tensor] or None
The
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 ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_post_hook
(hook)¶ Register 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 theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_pre_hook
(hook)¶ Register a pre-hook to be run before module’s
load_state_dict()
is called.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950
- Parameters
hook (Callable) – Callable hook that will be invoked before loading the state dict.
-
register_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Alias for
add_module()
.
-
register_parameter
(name: str, param: Optional[torch.nn.parameter.Parameter]) → None¶ Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
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 ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
-
register_state_dict_post_hook
(hook)¶ Register a post-hook for the
state_dict()
method.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata) -> None
The registered hooks can modify the
state_dict
inplace.
-
register_state_dict_pre_hook
(hook)¶ Register a pre-hook for the
state_dict()
method.- It should have the following signature::
hook(module, prefix, keep_vars) -> None
The registered hooks can be used to perform pre-processing before the
state_dict
call is made.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
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 Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
set_extra_state
(state: Any) → None¶ Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within its state_dict.- Parameters
state (dict) – Extra state from the state_dict
-
set_submodule
(target: str, module: torch.nn.modules.module.Module) → None¶ Set the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To overide the
Conv2d
with a new submoduleLinear
, you would callset_submodule("net_b.net_c.conv", nn.Linear(33, 16))
.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module – The module to set the submodule to.
- Raises
ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
state_dict
(*args, destination=None, prefix='', keep_vars=False)¶ Return 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 fordestination
,prefix
andkeep_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.- Parameters
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
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Move and/or cast the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)
-
to
(dtype, non_blocking=False)
-
to
(tensor, non_blocking=False)
-
to
(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_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.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
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
(*, device: Optional[Union[int, str, torch.device]], recurse: bool = True) → T¶ Move the parameters and buffers to the specified device without copying storage.
- Parameters
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns
self
- Return type
Module
-
train
(mode: bool = True) → T¶ Set 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.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
xpu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
zero_grad
(set_to_none: bool = True) → None¶ Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizer
for more context.- Parameters
set_to_none (bool) – instead of setting to zero, set the grads to None. See
torch.optim.Optimizer.zero_grad()
for details.
-
training
: bool¶
-
-
class
fiftyone.utils.clip.model.
AttentionPool2d
(spacial_dim: int, embed_dim: int, num_heads: int, output_dim: Optional[int] = None)¶ Bases:
torch.nn.modules.module.Module
Methods:
forward
(x)Define the computation performed at every call.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module’s forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module’s state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, …])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, …])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, …])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, …])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post-hook to be run after module’s
load_state_dict()
is called.Register a pre-hook to be run before module’s
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a post-hook for the
state_dict()
method.Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.state_dict
(*args[, destination, prefix, …])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes:
alias of TypeVar(‘T_destination’)
-
forward
(x)¶ Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
T_destination
¶ alias of TypeVar(‘T_destination’)
-
add_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
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
(fn: Callable[[torch.nn.modules.module.Module], None]) → T¶ Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
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
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Return an iterator over module buffers.
- Parameters
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)
-
call_super_init
: bool = False¶
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Return an iterator over immediate children modules.
- Yields
Module – a child module
-
compile
(*args, **kwargs)¶ Compile this Module’s forward using
torch.compile()
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile()
.See
torch.compile()
for details on the arguments for this function.
-
cpu
() → T¶ Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
dump_patches
: bool = False¶
-
eval
() → T¶ Set 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.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns
self
- Return type
Module
-
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
() → T¶ Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
get_buffer
(target: str) → torch.Tensor¶ Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The buffer referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
-
get_extra_state
() → Any¶ Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
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
Any extra state to store in the module’s state_dict
- Return type
object
-
get_parameter
(target: str) → torch.nn.parameter.Parameter¶ Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The Parameter referenced by
target
- Return type
torch.nn.Parameter
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
-
get_submodule
(target: str) → torch.nn.modules.module.Module¶ Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_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.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns
The submodule referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
ipu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)¶ Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
unlessget_swap_module_params_on_conversion()
isTrue
.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
assign (bool, optional) – When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. The only exception is therequires_grad
field ofDefault: ``False`
- Returns
- missing_keys is a list of str containing any keys that are expected
by this module but missing from the provided
state_dict
.
- unexpected_keys is a list of str containing the keys that are not
expected by this module but present in the provided
state_dict
.
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict()
will raise aRuntimeError
.
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Return 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)
-
mtia
(device: Optional[Union[int, torch.device]] = None) → T¶ Move all model parameters and buffers to the MTIA.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.
Note
This method modifies the module in-place.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
named_buffers
(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
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, torch.nn.modules.module.Module]]¶ Return 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[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)¶ Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters
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]]¶ Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
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]¶ Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
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[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()
and the behavior of this function will change in future versions.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None¶ Add 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 settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
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 ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
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, always_call: bool = False) → torch.utils.hooks.RemovableHandle¶ Register a forward hook on the module.
The hook will be called every time after
forward()
has computed an output.If
with_kwargs
isFalse
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 theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook()
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
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¶ Register a forward pre-hook on the module.
The hook will be called every time before
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 theforward
. 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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_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
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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
grad_input
andgrad_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 ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_pre_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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) -> tuple[Tensor] or None
The
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 ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_post_hook
(hook)¶ Register 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 theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_pre_hook
(hook)¶ Register a pre-hook to be run before module’s
load_state_dict()
is called.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950
- Parameters
hook (Callable) – Callable hook that will be invoked before loading the state dict.
-
register_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Alias for
add_module()
.
-
register_parameter
(name: str, param: Optional[torch.nn.parameter.Parameter]) → None¶ Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
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 ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
-
register_state_dict_post_hook
(hook)¶ Register a post-hook for the
state_dict()
method.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata) -> None
The registered hooks can modify the
state_dict
inplace.
-
register_state_dict_pre_hook
(hook)¶ Register a pre-hook for the
state_dict()
method.- It should have the following signature::
hook(module, prefix, keep_vars) -> None
The registered hooks can be used to perform pre-processing before the
state_dict
call is made.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
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 Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
set_extra_state
(state: Any) → None¶ Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within its state_dict.- Parameters
state (dict) – Extra state from the state_dict
-
set_submodule
(target: str, module: torch.nn.modules.module.Module) → None¶ Set the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To overide the
Conv2d
with a new submoduleLinear
, you would callset_submodule("net_b.net_c.conv", nn.Linear(33, 16))
.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module – The module to set the submodule to.
- Raises
ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
state_dict
(*args, destination=None, prefix='', keep_vars=False)¶ Return 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 fordestination
,prefix
andkeep_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.- Parameters
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
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Move and/or cast the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)
-
to
(dtype, non_blocking=False)
-
to
(tensor, non_blocking=False)
-
to
(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_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.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
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
(*, device: Optional[Union[int, str, torch.device]], recurse: bool = True) → T¶ Move the parameters and buffers to the specified device without copying storage.
- Parameters
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns
self
- Return type
Module
-
train
(mode: bool = True) → T¶ Set 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.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
xpu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
zero_grad
(set_to_none: bool = True) → None¶ Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizer
for more context.- Parameters
set_to_none (bool) – instead of setting to zero, set the grads to None. See
torch.optim.Optimizer.zero_grad()
for details.
-
training
: bool¶
-
-
class
fiftyone.utils.clip.model.
ModifiedResNet
(layers, output_dim, heads, input_resolution=224, width=64)¶ Bases:
torch.nn.modules.module.Module
A ResNet class that is similar to torchvision’s but contains the following changes:
There are now 3 “stem” convolutions as opposed to 1, with an average pool instead of a max pool.
Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
The final pooling layer is a QKV attention instead of an average pool
Methods:
forward
(x)Define the computation performed at every call.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module’s forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module’s state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, …])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, …])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, …])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, …])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post-hook to be run after module’s
load_state_dict()
is called.Register a pre-hook to be run before module’s
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a post-hook for the
state_dict()
method.Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.state_dict
(*args[, destination, prefix, …])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes:
alias of TypeVar(‘T_destination’)
-
forward
(x)¶ Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
T_destination
¶ alias of TypeVar(‘T_destination’)
-
add_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
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
(fn: Callable[[torch.nn.modules.module.Module], None]) → T¶ Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
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
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Return an iterator over module buffers.
- Parameters
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)
-
call_super_init
: bool = False¶
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Return an iterator over immediate children modules.
- Yields
Module – a child module
-
compile
(*args, **kwargs)¶ Compile this Module’s forward using
torch.compile()
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile()
.See
torch.compile()
for details on the arguments for this function.
-
cpu
() → T¶ Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
dump_patches
: bool = False¶
-
eval
() → T¶ Set 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.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns
self
- Return type
Module
-
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
() → T¶ Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
get_buffer
(target: str) → torch.Tensor¶ Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The buffer referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
-
get_extra_state
() → Any¶ Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
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
Any extra state to store in the module’s state_dict
- Return type
object
-
get_parameter
(target: str) → torch.nn.parameter.Parameter¶ Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The Parameter referenced by
target
- Return type
torch.nn.Parameter
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
-
get_submodule
(target: str) → torch.nn.modules.module.Module¶ Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_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.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns
The submodule referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
ipu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)¶ Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
unlessget_swap_module_params_on_conversion()
isTrue
.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
assign (bool, optional) – When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. The only exception is therequires_grad
field ofDefault: ``False`
- Returns
- missing_keys is a list of str containing any keys that are expected
by this module but missing from the provided
state_dict
.
- unexpected_keys is a list of str containing the keys that are not
expected by this module but present in the provided
state_dict
.
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict()
will raise aRuntimeError
.
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Return 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)
-
mtia
(device: Optional[Union[int, torch.device]] = None) → T¶ Move all model parameters and buffers to the MTIA.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.
Note
This method modifies the module in-place.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
named_buffers
(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
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, torch.nn.modules.module.Module]]¶ Return 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[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)¶ Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters
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]]¶ Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
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]¶ Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
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[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()
and the behavior of this function will change in future versions.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None¶ Add 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 settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
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 ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
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, always_call: bool = False) → torch.utils.hooks.RemovableHandle¶ Register a forward hook on the module.
The hook will be called every time after
forward()
has computed an output.If
with_kwargs
isFalse
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 theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook()
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
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¶ Register a forward pre-hook on the module.
The hook will be called every time before
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 theforward
. 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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_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
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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
grad_input
andgrad_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 ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_pre_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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) -> tuple[Tensor] or None
The
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 ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_post_hook
(hook)¶ Register 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 theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_pre_hook
(hook)¶ Register a pre-hook to be run before module’s
load_state_dict()
is called.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950
- Parameters
hook (Callable) – Callable hook that will be invoked before loading the state dict.
-
register_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Alias for
add_module()
.
-
register_parameter
(name: str, param: Optional[torch.nn.parameter.Parameter]) → None¶ Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
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 ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
-
register_state_dict_post_hook
(hook)¶ Register a post-hook for the
state_dict()
method.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata) -> None
The registered hooks can modify the
state_dict
inplace.
-
register_state_dict_pre_hook
(hook)¶ Register a pre-hook for the
state_dict()
method.- It should have the following signature::
hook(module, prefix, keep_vars) -> None
The registered hooks can be used to perform pre-processing before the
state_dict
call is made.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
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 Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
set_extra_state
(state: Any) → None¶ Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within its state_dict.- Parameters
state (dict) – Extra state from the state_dict
-
set_submodule
(target: str, module: torch.nn.modules.module.Module) → None¶ Set the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To overide the
Conv2d
with a new submoduleLinear
, you would callset_submodule("net_b.net_c.conv", nn.Linear(33, 16))
.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module – The module to set the submodule to.
- Raises
ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
state_dict
(*args, destination=None, prefix='', keep_vars=False)¶ Return 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 fordestination
,prefix
andkeep_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.- Parameters
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
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Move and/or cast the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)
-
to
(dtype, non_blocking=False)
-
to
(tensor, non_blocking=False)
-
to
(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_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.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
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
(*, device: Optional[Union[int, str, torch.device]], recurse: bool = True) → T¶ Move the parameters and buffers to the specified device without copying storage.
- Parameters
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns
self
- Return type
Module
-
train
(mode: bool = True) → T¶ Set 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.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
xpu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
zero_grad
(set_to_none: bool = True) → None¶ Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizer
for more context.- Parameters
set_to_none (bool) – instead of setting to zero, set the grads to None. See
torch.optim.Optimizer.zero_grad()
for details.
-
training
: bool¶
-
class
fiftyone.utils.clip.model.
LayerNorm
(normalized_shape: Union[int, List[int], torch.Size], eps: float = 1e-05, elementwise_affine: bool = True, bias: bool = True, device=None, dtype=None)¶ Bases:
torch.nn.modules.normalization.LayerNorm
Subclass torch’s LayerNorm to handle fp16.
Methods:
forward
(x)Define the computation performed at every call.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module’s forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module’s state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, …])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, …])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, …])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, …])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post-hook to be run after module’s
load_state_dict()
is called.Register a pre-hook to be run before module’s
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a post-hook for the
state_dict()
method.Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.state_dict
(*args[, destination, prefix, …])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes:
alias of TypeVar(‘T_destination’)
-
forward
(x: torch.Tensor)¶ Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
T_destination
¶ alias of TypeVar(‘T_destination’)
-
add_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
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
(fn: Callable[[torch.nn.modules.module.Module], None]) → T¶ Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
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
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Return an iterator over module buffers.
- Parameters
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)
-
call_super_init
: bool = False¶
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Return an iterator over immediate children modules.
- Yields
Module – a child module
-
compile
(*args, **kwargs)¶ Compile this Module’s forward using
torch.compile()
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile()
.See
torch.compile()
for details on the arguments for this function.
-
cpu
() → T¶ Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
dump_patches
: bool = False¶
-
eval
() → T¶ Set 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.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns
self
- Return type
Module
-
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
() → T¶ Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
get_buffer
(target: str) → torch.Tensor¶ Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The buffer referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
-
get_extra_state
() → Any¶ Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
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
Any extra state to store in the module’s state_dict
- Return type
object
-
get_parameter
(target: str) → torch.nn.parameter.Parameter¶ Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The Parameter referenced by
target
- Return type
torch.nn.Parameter
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
-
get_submodule
(target: str) → torch.nn.modules.module.Module¶ Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_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.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns
The submodule referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
ipu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)¶ Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
unlessget_swap_module_params_on_conversion()
isTrue
.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
assign (bool, optional) – When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. The only exception is therequires_grad
field ofDefault: ``False`
- Returns
- missing_keys is a list of str containing any keys that are expected
by this module but missing from the provided
state_dict
.
- unexpected_keys is a list of str containing the keys that are not
expected by this module but present in the provided
state_dict
.
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict()
will raise aRuntimeError
.
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Return 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)
-
mtia
(device: Optional[Union[int, torch.device]] = None) → T¶ Move all model parameters and buffers to the MTIA.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.
Note
This method modifies the module in-place.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
named_buffers
(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
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, torch.nn.modules.module.Module]]¶ Return 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[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)¶ Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters
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]]¶ Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
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]¶ Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
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[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()
and the behavior of this function will change in future versions.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None¶ Add 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 settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
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 ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
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, always_call: bool = False) → torch.utils.hooks.RemovableHandle¶ Register a forward hook on the module.
The hook will be called every time after
forward()
has computed an output.If
with_kwargs
isFalse
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 theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook()
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
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¶ Register a forward pre-hook on the module.
The hook will be called every time before
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 theforward
. 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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_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
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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
grad_input
andgrad_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 ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_pre_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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) -> tuple[Tensor] or None
The
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 ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_post_hook
(hook)¶ Register 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 theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_pre_hook
(hook)¶ Register a pre-hook to be run before module’s
load_state_dict()
is called.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950
- Parameters
hook (Callable) – Callable hook that will be invoked before loading the state dict.
-
register_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Alias for
add_module()
.
-
register_parameter
(name: str, param: Optional[torch.nn.parameter.Parameter]) → None¶ Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
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 ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
-
register_state_dict_post_hook
(hook)¶ Register a post-hook for the
state_dict()
method.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata) -> None
The registered hooks can modify the
state_dict
inplace.
-
register_state_dict_pre_hook
(hook)¶ Register a pre-hook for the
state_dict()
method.- It should have the following signature::
hook(module, prefix, keep_vars) -> None
The registered hooks can be used to perform pre-processing before the
state_dict
call is made.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
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 Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
reset_parameters
() → None¶
-
set_extra_state
(state: Any) → None¶ Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within its state_dict.- Parameters
state (dict) – Extra state from the state_dict
-
set_submodule
(target: str, module: torch.nn.modules.module.Module) → None¶ Set the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To overide the
Conv2d
with a new submoduleLinear
, you would callset_submodule("net_b.net_c.conv", nn.Linear(33, 16))
.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module – The module to set the submodule to.
- Raises
ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
state_dict
(*args, destination=None, prefix='', keep_vars=False)¶ Return 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 fordestination
,prefix
andkeep_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.- Parameters
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
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Move and/or cast the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)
-
to
(dtype, non_blocking=False)
-
to
(tensor, non_blocking=False)
-
to
(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_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.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
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
(*, device: Optional[Union[int, str, torch.device]], recurse: bool = True) → T¶ Move the parameters and buffers to the specified device without copying storage.
- Parameters
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns
self
- Return type
Module
-
train
(mode: bool = True) → T¶ Set 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.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
xpu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
zero_grad
(set_to_none: bool = True) → None¶ Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizer
for more context.- Parameters
set_to_none (bool) – instead of setting to zero, set the grads to None. See
torch.optim.Optimizer.zero_grad()
for details.
-
normalized_shape
: Tuple[int, …]¶
-
elementwise_affine
: bool¶
-
training
: bool¶
-
-
class
fiftyone.utils.clip.model.
QuickGELU
(*args, **kwargs)¶ Bases:
torch.nn.modules.module.Module
Methods:
forward
(x)Define the computation performed at every call.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module’s forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module’s state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, …])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, …])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, …])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, …])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post-hook to be run after module’s
load_state_dict()
is called.Register a pre-hook to be run before module’s
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a post-hook for the
state_dict()
method.Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.state_dict
(*args[, destination, prefix, …])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes:
alias of TypeVar(‘T_destination’)
-
forward
(x: torch.Tensor)¶ Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
T_destination
¶ alias of TypeVar(‘T_destination’)
-
add_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
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
(fn: Callable[[torch.nn.modules.module.Module], None]) → T¶ Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
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
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Return an iterator over module buffers.
- Parameters
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)
-
call_super_init
: bool = False¶
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Return an iterator over immediate children modules.
- Yields
Module – a child module
-
compile
(*args, **kwargs)¶ Compile this Module’s forward using
torch.compile()
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile()
.See
torch.compile()
for details on the arguments for this function.
-
cpu
() → T¶ Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
dump_patches
: bool = False¶
-
eval
() → T¶ Set 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.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns
self
- Return type
Module
-
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
() → T¶ Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
get_buffer
(target: str) → torch.Tensor¶ Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The buffer referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
-
get_extra_state
() → Any¶ Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
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
Any extra state to store in the module’s state_dict
- Return type
object
-
get_parameter
(target: str) → torch.nn.parameter.Parameter¶ Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The Parameter referenced by
target
- Return type
torch.nn.Parameter
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
-
get_submodule
(target: str) → torch.nn.modules.module.Module¶ Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_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.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns
The submodule referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
ipu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)¶ Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
unlessget_swap_module_params_on_conversion()
isTrue
.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
assign (bool, optional) – When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. The only exception is therequires_grad
field ofDefault: ``False`
- Returns
- missing_keys is a list of str containing any keys that are expected
by this module but missing from the provided
state_dict
.
- unexpected_keys is a list of str containing the keys that are not
expected by this module but present in the provided
state_dict
.
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict()
will raise aRuntimeError
.
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Return 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)
-
mtia
(device: Optional[Union[int, torch.device]] = None) → T¶ Move all model parameters and buffers to the MTIA.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.
Note
This method modifies the module in-place.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
named_buffers
(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
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, torch.nn.modules.module.Module]]¶ Return 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[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)¶ Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters
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]]¶ Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
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]¶ Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
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[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()
and the behavior of this function will change in future versions.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None¶ Add 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 settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
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 ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
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, always_call: bool = False) → torch.utils.hooks.RemovableHandle¶ Register a forward hook on the module.
The hook will be called every time after
forward()
has computed an output.If
with_kwargs
isFalse
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 theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook()
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
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¶ Register a forward pre-hook on the module.
The hook will be called every time before
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 theforward
. 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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_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
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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
grad_input
andgrad_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 ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_pre_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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) -> tuple[Tensor] or None
The
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 ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_post_hook
(hook)¶ Register 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 theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_pre_hook
(hook)¶ Register a pre-hook to be run before module’s
load_state_dict()
is called.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950
- Parameters
hook (Callable) – Callable hook that will be invoked before loading the state dict.
-
register_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Alias for
add_module()
.
-
register_parameter
(name: str, param: Optional[torch.nn.parameter.Parameter]) → None¶ Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
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 ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
-
register_state_dict_post_hook
(hook)¶ Register a post-hook for the
state_dict()
method.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata) -> None
The registered hooks can modify the
state_dict
inplace.
-
register_state_dict_pre_hook
(hook)¶ Register a pre-hook for the
state_dict()
method.- It should have the following signature::
hook(module, prefix, keep_vars) -> None
The registered hooks can be used to perform pre-processing before the
state_dict
call is made.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
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 Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
set_extra_state
(state: Any) → None¶ Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within its state_dict.- Parameters
state (dict) – Extra state from the state_dict
-
set_submodule
(target: str, module: torch.nn.modules.module.Module) → None¶ Set the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To overide the
Conv2d
with a new submoduleLinear
, you would callset_submodule("net_b.net_c.conv", nn.Linear(33, 16))
.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module – The module to set the submodule to.
- Raises
ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
state_dict
(*args, destination=None, prefix='', keep_vars=False)¶ Return 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 fordestination
,prefix
andkeep_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.- Parameters
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
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Move and/or cast the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)
-
to
(dtype, non_blocking=False)
-
to
(tensor, non_blocking=False)
-
to
(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_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.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
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
(*, device: Optional[Union[int, str, torch.device]], recurse: bool = True) → T¶ Move the parameters and buffers to the specified device without copying storage.
- Parameters
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns
self
- Return type
Module
-
train
(mode: bool = True) → T¶ Set 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.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
xpu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
zero_grad
(set_to_none: bool = True) → None¶ Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizer
for more context.- Parameters
set_to_none (bool) – instead of setting to zero, set the grads to None. See
torch.optim.Optimizer.zero_grad()
for details.
-
training
: bool¶
-
-
class
fiftyone.utils.clip.model.
ResidualAttentionBlock
(d_model: int, n_head: int, attn_mask: Optional[torch.Tensor] = None)¶ Bases:
torch.nn.modules.module.Module
Methods:
attention
(x)forward
(x)Define the computation performed at every call.
add_module
(name, module)Add a child module to the current module.
apply
(fn)Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
()Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module’s forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])Move all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Set the module in evaluation mode.
Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.get_buffer
(target)Return the buffer given by
target
if it exists, otherwise throw an error.Return any extra state to include in the module’s state_dict.
get_parameter
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
mtia
([device])Move all model parameters and buffers to the MTIA.
named_buffers
([prefix, recurse, …])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, …])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, …])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, …])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
Register a post-hook to be run after module’s
load_state_dict()
is called.Register a pre-hook to be run before module’s
load_state_dict()
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
Register a post-hook for the
state_dict()
method.Register a pre-hook for the
state_dict()
method.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
set_submodule
(target, module)Set the submodule given by
target
if it exists, otherwise throw an error.state_dict
(*args[, destination, prefix, …])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes:
alias of TypeVar(‘T_destination’)
-
attention
(x: torch.Tensor)¶
-
forward
(x: torch.Tensor)¶ Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
T_destination
¶ alias of TypeVar(‘T_destination’)
-
add_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
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
(fn: Callable[[torch.nn.modules.module.Module], None]) → T¶ Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
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
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Return an iterator over module buffers.
- Parameters
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)
-
call_super_init
: bool = False¶
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Return an iterator over immediate children modules.
- Yields
Module – a child module
-
compile
(*args, **kwargs)¶ Compile this Module’s forward using
torch.compile()
.This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile()
.See
torch.compile()
for details on the arguments for this function.
-
cpu
() → T¶ Move all model parameters and buffers to the CPU.
Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
dump_patches
: bool = False¶
-
eval
() → T¶ Set 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.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns
self
- Return type
Module
-
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
() → T¶ Casts all floating point parameters and buffers to
float
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
get_buffer
(target: str) → torch.Tensor¶ Return the buffer given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the buffer to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The buffer referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
-
get_extra_state
() → Any¶ Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
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
Any extra state to store in the module’s state_dict
- Return type
object
-
get_parameter
(target: str) → torch.nn.parameter.Parameter¶ Return the parameter given by
target
if it exists, otherwise throw an error.See the docstring for
get_submodule
for a more detailed explanation of this method’s functionality as well as how to correctly specifytarget
.- Parameters
target – The fully-qualified string name of the Parameter to look for. (See
get_submodule
for how to specify a fully-qualified string.)- Returns
The Parameter referenced by
target
- Return type
torch.nn.Parameter
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Parameter
-
get_submodule
(target: str) → torch.nn.modules.module.Module¶ Return the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To check whether or not we have the
linear
submodule, we would callget_submodule("net_b.linear")
. To check whether we have theconv
submodule, we would callget_submodule("net_b.net_c.conv")
.The runtime of
get_submodule
is bounded by the degree of module nesting intarget
. A query againstnamed_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.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
- Returns
The submodule referenced by
target
- Return type
- Raises
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.Note
This method modifies the module in-place.
- Returns
self
- Return type
Module
-
ipu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)¶ Copy parameters and buffers from
state_dict
into this module and its descendants.If
strict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.Warning
If
assign
isTrue
the optimizer must be created after the call toload_state_dict
unlessget_swap_module_params_on_conversion()
isTrue
.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
assign (bool, optional) – When
False
, the properties of the tensors in the current module are preserved while whenTrue
, the properties of the Tensors in the state dict are preserved. The only exception is therequires_grad
field ofDefault: ``False`
- Returns
- missing_keys is a list of str containing any keys that are expected
by this module but missing from the provided
state_dict
.
- unexpected_keys is a list of str containing the keys that are not
expected by this module but present in the provided
state_dict
.
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
Note
If a parameter or buffer is registered as
None
and its corresponding key exists instate_dict
,load_state_dict()
will raise aRuntimeError
.
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Return 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)
-
mtia
(device: Optional[Union[int, torch.device]] = None) → T¶ Move all model parameters and buffers to the MTIA.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.
Note
This method modifies the module in-place.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
named_buffers
(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
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, torch.nn.modules.module.Module]]¶ Return 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[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)¶ Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Parameters
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]]¶ Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
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]¶ Return an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
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[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()
and the behavior of this function will change in future versions.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None¶ Add 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 settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
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 ascuda
, are ignored. IfNone
, the buffer is not included in the module’sstate_dict
.persistent (bool) – whether the buffer is part of this module’s
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, always_call: bool = False) → torch.utils.hooks.RemovableHandle¶ Register a forward hook on the module.
The hook will be called every time after
forward()
has computed an output.If
with_kwargs
isFalse
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 theforward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargs
isTrue
, the forward hook will be passed thekwargs
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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If
True
, the providedhook
will be fired before all existingforward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward
hooks on thistorch.nn.modules.Module
. Note that globalforward
hooks registered withregister_module_forward_hook()
will fire before all hooks registered by this method. Default:False
with_kwargs (bool) – If
True
, thehook
will be passed the kwargs given to the forward function. Default:False
always_call (bool) – If
True
thehook
will be run regardless of whether an exception is raised while calling the Module. Default:False
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
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¶ Register a forward pre-hook on the module.
The hook will be called every time before
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 theforward
. 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
- Parameters
hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingforward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingforward_pre
hooks on thistorch.nn.modules.Module
. Note that globalforward_pre
hooks registered withregister_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
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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
grad_input
andgrad_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 ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_input
andgrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward
hooks on thistorch.nn.modules.Module
. Note that globalbackward
hooks registered withregister_module_full_backward_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_full_backward_pre_hook
(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, Tuple[torch.Tensor, …], torch.Tensor]], prepend: bool = False) → torch.utils.hooks.RemovableHandle¶ Register 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) -> tuple[Tensor] or None
The
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 ofgrad_output
in subsequent computations. Entries ingrad_output
will beNone
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.
- Parameters
hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided
hook
will be fired before all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Otherwise, the providedhook
will be fired after all existingbackward_pre
hooks on thistorch.nn.modules.Module
. Note that globalbackward_pre
hooks registered withregister_module_full_backward_pre_hook()
will fire before all hooks registered by this method.
- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_post_hook
(hook)¶ Register 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 theincompatible_keys
argument is aNamedTuple
consisting of attributesmissing_keys
andunexpected_keys
.missing_keys
is alist
ofstr
containing the missing keys andunexpected_keys
is alist
ofstr
containing the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()
withstrict=True
are affected by modifications the hook makes tomissing_keys
orunexpected_keys
, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True
, and clearing out both missing and unexpected keys will avoid an error.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_load_state_dict_pre_hook
(hook)¶ Register a pre-hook to be run before module’s
load_state_dict()
is called.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950
- Parameters
hook (Callable) – Callable hook that will be invoked before loading the state dict.
-
register_module
(name: str, module: Optional[torch.nn.modules.module.Module]) → None¶ Alias for
add_module()
.
-
register_parameter
(name: str, param: Optional[torch.nn.parameter.Parameter]) → None¶ Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
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 ascuda
, are ignored. IfNone
, the parameter is not included in the module’sstate_dict
.
-
register_state_dict_post_hook
(hook)¶ Register a post-hook for the
state_dict()
method.- It should have the following signature::
hook(module, state_dict, prefix, local_metadata) -> None
The registered hooks can modify the
state_dict
inplace.
-
register_state_dict_pre_hook
(hook)¶ Register a pre-hook for the
state_dict()
method.- It should have the following signature::
hook(module, prefix, keep_vars) -> None
The registered hooks can be used to perform pre-processing before the
state_dict
call is made.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
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 Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
set_extra_state
(state: Any) → None¶ Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()
to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()
for your module if you need to store extra state within its state_dict.- Parameters
state (dict) – Extra state from the state_dict
-
set_submodule
(target: str, module: torch.nn.modules.module.Module) → None¶ Set the submodule given by
target
if it exists, otherwise throw an error.For example, let’s say you have an
nn.Module
A
that looks like this: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 submodulenet_b
, which itself has two submodulesnet_c
andlinear
.net_c
then has a submoduleconv
.)To overide the
Conv2d
with a new submoduleLinear
, you would callset_submodule("net_b.net_c.conv", nn.Linear(33, 16))
.- Parameters
target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module – The module to set the submodule to.
- Raises
ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an
nn.Module
-
state_dict
(*args, destination=None, prefix='', keep_vars=False)¶ Return 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 fordestination
,prefix
andkeep_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.- Parameters
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
Tensor
s returned in the state dict are detached from autograd. If it’s set toTrue
, detaching will not be performed. Default:False
.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Move and/or cast the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)
-
to
(dtype, non_blocking=False)
-
to
(tensor, non_blocking=False)
-
to
(memory_format=torch.channels_last)
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point or complexdtype
s. In addition, this method will only cast the floating point or complex parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_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.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point or complex dtype of the parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
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
(*, device: Optional[Union[int, str, torch.device]], recurse: bool = True) → T¶ Move the parameters and buffers to the specified device without copying storage.
- Parameters
device (
torch.device
) – The desired device of the parameters and buffers in this module.recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.
- Returns
self
- Return type
Module
-
train
(mode: bool = True) → T¶ Set 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.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.Note
This method modifies the module in-place.
- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
xpu
(device: Optional[Union[int, torch.device]] = None) → T¶ Move 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.
- Paramet
-