astra.contrib.classifier.networks

Module Contents

Classes

OpticalCNN Base class for all neural network modules.
NIRCNN Base class for all neural network modules.
class astra.contrib.classifier.networks.OpticalCNN(in_channels=1, nb_channels=3, nb_classes=4)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Variables:training (bool) – Boolean represents whether this module is in training or evaluation mode.
dump_patches :bool = False

This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading.

If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.

_version :int = 1
training :bool
_is_full_backward_hook :Optional[bool]
__call__ :Callable[..., Any]
T_destination
forward(self, x)
register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True)

Adds a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Parameters:
  • name (string) – name of the buffer. The buffer can be accessed from this module using the given name
  • tensor (Tensor) – buffer to be registered.
  • persistent (bool) – whether the buffer is part of this module’s state_dict.

Example:

>>> self.register_buffer('running_mean', torch.zeros(num_features))
register_parameter(self, name: str, param: Optional[Parameter])

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:
  • name (string) – name of the parameter. The parameter can be accessed from this module using the given name
  • param (Parameter) – parameter to be added to the module.
add_module(self, name: str, module: Optional['Module'])

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:
  • name (string) – name of the child module. The child module can be accessed from this module using the given name
  • module (Module) – child module to be added to the module.
get_submodule(self, target: str)

Returns the submodule given by target if it exists, otherwise throws an error.

For example, let’s say you have an nn.Module A that looks like this:

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used.

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:torch.nn.Module – The submodule referenced by target
Raises:AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Module
get_parameter(self, target: str)

Returns the parameter given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

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:torch.nn.Parameter – The Parameter referenced by target
Raises:AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Parameter
get_buffer(self, target: str)

Returns the buffer given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

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:torch.Tensor – The buffer referenced by target
Raises:AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
_apply(self, fn)
apply(self: T, fn: Callable[['Module'], None])

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also nn-init-doc).

Parameters:fn (Module -> None) – function to be applied to each submodule
Returns:Module – self

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
cuda(self: T, device: Optional[Union[int, device]] = None)

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Note

This method modifies the module in-place.

Parameters:device (int, optional) – if specified, all parameters will be copied to that device
Returns:Module – self
xpu(self: T, device: Optional[Union[int, device]] = None)

Moves all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.

Note

This method modifies the module in-place.

Parameters:device (int, optional) – if specified, all parameters will be copied to that device
Returns:Module – self
cpu(self: T)

Moves all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns:Module – self
type(self: T, dst_type: Union[dtype, str])

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:Module – self
float(self: T)

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns:Module – self
double(self: T)

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns:Module – self
half(self: T)

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns:Module – self
bfloat16(self: T)

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:Module – self
to_empty(self: T, *, device: Union[str, device])

Moves 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.
Returns:Module – self
to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ..., non_blocking: bool = ...)
to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...)
to(self: T, tensor: Tensor, non_blocking: bool = ...)

Moves and/or casts 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 complex dtype`s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:`dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters:
  • device (torch.device) – the desired device of the parameters and buffers in this module
  • dtype (torch.dtype) – the desired floating point or complex dtype of the parameters and buffers in this module
  • tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
  • memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
Returns:

Module – self

Examples:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
register_backward_hook(self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]])

Registers a backward hook on the module.

This function is deprecated in favor of nn.Module.register_full_backward_hook() and the behavior of this function will change in future versions.

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
register_full_backward_hook(self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]])

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and grad_output will be None for all non-Tensor arguments.

Warning

Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
_get_backward_hooks(self)

Returns the backward hooks for use in the call function. It returns two lists, one with the full backward hooks and one with the non-full backward hooks.

_maybe_warn_non_full_backward_hook(self, inputs, result, grad_fn)
register_forward_pre_hook(self, hook: Callable[..., None])

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None or modified input

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
register_forward_hook(self, hook: Callable[..., None])

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None or modified output

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called.

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
_slow_forward(self, *input, **kwargs)
_call_impl(self, *input, **kwargs)
__setstate__(self, state)
__getattr__(self, name: str)
__setattr__(self, name: str, value: Union[Tensor, 'Module'])

Implement setattr(self, name, value).

__delattr__(self, name)

Implement delattr(self, name).

_register_state_dict_hook(self, hook)

These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set. Note that only parameters and buffers of self or its children are guaranteed to exist in state_dict. The hooks may modify state_dict inplace or return a new one.

_save_to_state_dict(self, destination, prefix, keep_vars)

Saves module state to destination dictionary, containing a state of the module, but not its descendants. This is called on every submodule in state_dict().

In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic.

Parameters:
  • destination (dict) – a dict where state will be stored
  • prefix (str) – the prefix for parameters and buffers used in this module
state_dict(self, destination: T_destination, prefix: str = ..., keep_vars: bool = ...)
state_dict(self, prefix: str = ..., keep_vars: bool = ...)

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns:dict – a dictionary containing a whole state of the module

Example:

>>> module.state_dict().keys()
['bias', 'weight']
_register_load_state_dict_pre_hook(self, hook)

These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self. These arguments are exactly the same as those of _load_from_state_dict.

_load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

Copies parameters and buffers from state_dict into only this module, but not its descendants. This is called on every submodule in load_state_dict(). Metadata saved for this module in input state_dict is provided as local_metadata. For state dicts without metadata, local_metadata is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get("version", None).

Note

state_dict is not the same object as the input state_dict to load_state_dict(). So it can be modified.

Parameters:
  • state_dict (dict) – a dict containing parameters and persistent buffers.
  • prefix (str) – the prefix for parameters and buffers used in this module
  • local_metadata (dict) – a dict containing the metadata for this module. See
  • strict (bool) – whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module
  • missing_keys (list of str) – if strict=True, add missing keys to this list
  • unexpected_keys (list of str) – if strict=True, add unexpected keys to this list
  • error_msgs (list of str) – error messages should be added to this list, and will be reported together in load_state_dict()
load_state_dict(self, state_dict: OrderedDict[str, Tensor], strict: bool = True)

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

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’s state_dict() function. Default: True
Returns:

NamedTuple with missing_keys and unexpected_keys fields – * missing_keys is a list of str containing the missing keys * unexpected_keys is a list of str containing the unexpected keys
_named_members(self, get_members_fn, prefix='', recurse=True)

Helper method for yielding various names + members of modules.

parameters(self, recurse: bool = True)

Returns 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:

>>> for param in model.parameters():
>>>     print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
named_parameters(self, prefix: str = '', recurse: bool = True)

Returns 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.
Yields:

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())
buffers(self, recurse: bool = True)

Returns 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:

>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
named_buffers(self, prefix: str = '', recurse: bool = True)

Returns 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) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:

(string, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())
children(self)

Returns an iterator over immediate children modules.

Yields:Module – a child module
named_children(self)

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields:(string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)
modules(self)

Returns an iterator over all modules in the network.

Yields:Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = '', remove_duplicate: bool = True)

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

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
  • not (or) –
Yields:

(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
train(self: T, mode: bool = True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters:mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns:Module – self
eval(self: T)

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

See locally-disable-grad-doc for a comparison between eval() and several similar mechanisms that may be confused with it.

Returns:Module – self
requires_grad_(self: T, requires_grad: bool = True)

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-disable-grad-doc 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:Module – self
zero_grad(self, set_to_none: bool = False)

Sets gradients of all model parameters to zero. 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.
share_memory(self: T)

See torch.Tensor.share_memory_()

_get_name(self)
extra_repr(self)

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.

__repr__(self)

Return repr(self).

__dir__(self)

__dir__() -> list default dir() implementation

_replicate_for_data_parallel(self)
class astra.contrib.classifier.networks.NIRCNN(nb_channels=3, nb_classes=4)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Variables:training (bool) – Boolean represents whether this module is in training or evaluation mode.
dump_patches :bool = False

This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading.

If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.

_version :int = 1
training :bool
_is_full_backward_hook :Optional[bool]
__call__ :Callable[..., Any]
T_destination
forward(self, x)
register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True)

Adds a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Parameters:
  • name (string) – name of the buffer. The buffer can be accessed from this module using the given name
  • tensor (Tensor) – buffer to be registered.
  • persistent (bool) – whether the buffer is part of this module’s state_dict.

Example:

>>> self.register_buffer('running_mean', torch.zeros(num_features))
register_parameter(self, name: str, param: Optional[Parameter])

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:
  • name (string) – name of the parameter. The parameter can be accessed from this module using the given name
  • param (Parameter) – parameter to be added to the module.
add_module(self, name: str, module: Optional['Module'])

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:
  • name (string) – name of the child module. The child module can be accessed from this module using the given name
  • module (Module) – child module to be added to the module.
get_submodule(self, target: str)

Returns the submodule given by target if it exists, otherwise throws an error.

For example, let’s say you have an nn.Module A that looks like this:

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used.

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:torch.nn.Module – The submodule referenced by target
Raises:AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Module
get_parameter(self, target: str)

Returns the parameter given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

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:torch.nn.Parameter – The Parameter referenced by target
Raises:AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Parameter
get_buffer(self, target: str)

Returns the buffer given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

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:torch.Tensor – The buffer referenced by target
Raises:AttributeError – If the target string references an invalid path or resolves to something that is not a buffer
_apply(self, fn)
apply(self: T, fn: Callable[['Module'], None])

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also nn-init-doc).

Parameters:fn (Module -> None) – function to be applied to each submodule
Returns:Module – self

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
cuda(self: T, device: Optional[Union[int, device]] = None)

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Note

This method modifies the module in-place.

Parameters:device (int, optional) – if specified, all parameters will be copied to that device
Returns:Module – self
xpu(self: T, device: Optional[Union[int, device]] = None)

Moves all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.

Note

This method modifies the module in-place.

Parameters:device (int, optional) – if specified, all parameters will be copied to that device
Returns:Module – self
cpu(self: T)

Moves all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns:Module – self
type(self: T, dst_type: Union[dtype, str])

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:Module – self
float(self: T)

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns:Module – self
double(self: T)

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns:Module – self
half(self: T)

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns:Module – self
bfloat16(self: T)

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:Module – self
to_empty(self: T, *, device: Union[str, device])

Moves 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.
Returns:Module – self
to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ..., non_blocking: bool = ...)
to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...)
to(self: T, tensor: Tensor, non_blocking: bool = ...)

Moves and/or casts 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 complex dtype`s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:`dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters:
  • device (torch.device) – the desired device of the parameters and buffers in this module
  • dtype (torch.dtype) – the desired floating point or complex dtype of the parameters and buffers in this module
  • tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
  • memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
Returns:

Module – self

Examples:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)
register_backward_hook(self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]])

Registers a backward hook on the module.

This function is deprecated in favor of nn.Module.register_full_backward_hook() and the behavior of this function will change in future versions.

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
register_full_backward_hook(self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]])

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and grad_output will be None for all non-Tensor arguments.

Warning

Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
_get_backward_hooks(self)

Returns the backward hooks for use in the call function. It returns two lists, one with the full backward hooks and one with the non-full backward hooks.

_maybe_warn_non_full_backward_hook(self, inputs, result, grad_fn)
register_forward_pre_hook(self, hook: Callable[..., None])

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None or modified input

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
register_forward_hook(self, hook: Callable[..., None])

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None or modified output

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called.

Returns:torch.utils.hooks.RemovableHandle – a handle that can be used to remove the added hook by calling handle.remove()
_slow_forward(self, *input, **kwargs)
_call_impl(self, *input, **kwargs)
__setstate__(self, state)
__getattr__(self, name: str)
__setattr__(self, name: str, value: Union[Tensor, 'Module'])

Implement setattr(self, name, value).

__delattr__(self, name)

Implement delattr(self, name).

_register_state_dict_hook(self, hook)

These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set. Note that only parameters and buffers of self or its children are guaranteed to exist in state_dict. The hooks may modify state_dict inplace or return a new one.

_save_to_state_dict(self, destination, prefix, keep_vars)

Saves module state to destination dictionary, containing a state of the module, but not its descendants. This is called on every submodule in state_dict().

In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic.

Parameters:
  • destination (dict) – a dict where state will be stored
  • prefix (str) – the prefix for parameters and buffers used in this module
state_dict(self, destination: T_destination, prefix: str = ..., keep_vars: bool = ...)
state_dict(self, prefix: str = ..., keep_vars: bool = ...)

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns:dict – a dictionary containing a whole state of the module

Example:

>>> module.state_dict().keys()
['bias', 'weight']
_register_load_state_dict_pre_hook(self, hook)

These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self. These arguments are exactly the same as those of _load_from_state_dict.

_load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

Copies parameters and buffers from state_dict into only this module, but not its descendants. This is called on every submodule in load_state_dict(). Metadata saved for this module in input state_dict is provided as local_metadata. For state dicts without metadata, local_metadata is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get("version", None).

Note

state_dict is not the same object as the input state_dict to load_state_dict(). So it can be modified.

Parameters:
  • state_dict (dict) – a dict containing parameters and persistent buffers.
  • prefix (str) – the prefix for parameters and buffers used in this module
  • local_metadata (dict) – a dict containing the metadata for this module. See
  • strict (bool) – whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module
  • missing_keys (list of str) – if strict=True, add missing keys to this list
  • unexpected_keys (list of str) – if strict=True, add unexpected keys to this list
  • error_msgs (list of str) – error messages should be added to this list, and will be reported together in load_state_dict()
load_state_dict(self, state_dict: OrderedDict[str, Tensor], strict: bool = True)

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

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’s state_dict() function. Default: True
Returns:

NamedTuple with missing_keys and unexpected_keys fields – * missing_keys is a list of str containing the missing keys * unexpected_keys is a list of str containing the unexpected keys
_named_members(self, get_members_fn, prefix='', recurse=True)

Helper method for yielding various names + members of modules.

parameters(self, recurse: bool = True)

Returns 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:

>>> for param in model.parameters():
>>>     print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
named_parameters(self, prefix: str = '', recurse: bool = True)

Returns 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.
Yields:

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())
buffers(self, recurse: bool = True)

Returns 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:

>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
named_buffers(self, prefix: str = '', recurse: bool = True)

Returns 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) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:

(string, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())
children(self)

Returns an iterator over immediate children modules.

Yields:Module – a child module
named_children(self)

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields:(string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)
modules(self)

Returns an iterator over all modules in the network.

Yields:Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = '', remove_duplicate: bool = True)

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

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
  • not (or) –
Yields:

(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
train(self: T, mode: bool = True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters:mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns:Module – self
eval(self: T)

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

See locally-disable-grad-doc for a comparison between eval() and several similar mechanisms that may be confused with it.

Returns:Module – self
requires_grad_(self: T, requires_grad: bool = True)

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-disable-grad-doc 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:Module – self
zero_grad(self, set_to_none: bool = False)

Sets gradients of all model parameters to zero. 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.
share_memory(self: T)

See torch.Tensor.share_memory_()

_get_name(self)
extra_repr(self)

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.

__repr__(self)

Return repr(self).

__dir__(self)

__dir__() -> list default dir() implementation

_replicate_for_data_parallel(self)