>>> input = torch.randn(32, 1, 5, 5)
>>> m = nn.Sequential(
>>> nn.Conv2d(1, 32, 5, 1, 1),
>>> nn.Flatten()
>>> output = m(input)
>>> output.size()
torch.Size([32, 288])
add_module(name, 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.
apply(fn)
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 torch.nn.init).
Parameters
fn (Module -> None) – function to be applied to each submodule
Returns
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.]])
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)
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)
cuda(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.
Parameters
device (int, optional) – if specified, all parameters will be
copied to that device
Returns
Return type
Module
eval()
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,
This is equivalent with self.train(False).
Returns
Return type
Module
load_state_dict(state_dict, strict=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
missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys
Return type
NamedTuple with missing_keys and unexpected_keys fields
Duplicate modules are returned only once. In the following
example, l will be returned only once.
Example:
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
1 -> Linear(in_features=2, out_features=2, bias=True)
named_buffers(prefix='', recurse=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())
named_children()
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)
named_modules(memo=None, prefix='')
Returns an iterator over all modules in the network, yielding
both the name of the module as well as the module itself.
Yields
(string, Module) – Tuple of name and module
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='', recurse=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())
parameters(recurse=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)
register_backward_hook(hook)
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
a handle that can be used to remove the added hook by calling
handle.remove()
Return type
torch.utils.hooks.RemovableHandle
register_buffer(name, tensor, persistent=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_forward_hook(hook)
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
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)
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
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)
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
a handle that can be used to remove the added hook by calling
handle.remove()
Return type
torch.utils.hooks.RemovableHandle
register_parameter(name, param)
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.
requires_grad_(requires_grad=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).
Parameters
requires_grad (bool) – whether autograd should record operations on
parameters in this module. Default: True.
Returns
Return type
Module
state_dict(destination=None, prefix='', keep_vars=False)
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
a dictionary containing a whole state of the module
Return type
Example:
>>> module.state_dict().keys()
['bias', 'weight']
to(*args, **kwargs)
Moves and/or casts the parameters and buffers.
This can be called as
to(device=None, dtype=None, non_blocking=False)
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.
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
Return type
Module
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)
train(mode=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,
Parameters
mode (bool) – whether to set training mode (True) or evaluation
mode (False). Default: True.
Returns
Return type
Module
xpu(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.
Parameters
device (int, optional) – if specified, all parameters will be
copied to that device
Returns
Return type
Module
zero_grad(set_to_none=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.
