本文详细介绍了PyTorch的nn模块，包括Parameters、Containers、卷积层、池化层、激活函数、归一化层等。nn.Module是所有神经网络模块的基础类，可以包含其他子模块和参数。nn.Parameter是具有自动梯度功能的张量，用于模块参数。nn模块还提供了多种卷积层、池化层和非线性激活函数，如ReLU、LeakyReLU、Sigmoid等。此外，还涵盖了不同的归一化层，如BatchNorm1d、InstanceNorm1d等，这些在深度学习和计算机视觉任务中广泛应用。
目录
Non-linear activations (weighted sum, nonlinearity)
Non-linear activations (other)
torch.nn
Parameters
class torch.nn.Parameter[source]
A kind of Tensor that is to be considered a module parameter.
Parameters are Tensor subclasses, that have a very special property when used with Module s - when they’re assigned as Module attributes they are automatically added to the list of its parameters, and will appear e.g. in parameters() iterator. Assigning a Tensor doesn’t have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. If there was no such class as Parameter, these temporaries would get registered too.
Parameters
-
data (Tensor) – parameter tensor.
-
requires_grad (bool, optional) – if the parameter requires gradient. See Excluding subgraphs from backward for more details. Default: True
Containers
Module
class torch.nn.Module[source]
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.
add_module(name, module)[source]
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)[source]
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
self
Return type
Example:
>>> def init_weights(m):
>>> print(m)
>>> if type(m) == nn.Linear:
>>> m.weight.data.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)
)buffers(recurse=True)[source]
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.data), buf.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)children()[source]
Returns an iterator over immediate children modules.
Yields
Module – a child module
cpu()[source]
Moves all model parameters and buffers to the CPU.
Returns
self
Return type
cuda(device=None)[source]
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
self
Return type
double()[source]
Casts all floating point parameters and buffers to double datatype.
Returns
self
Return type
dump_patches = 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.
eval()[source]
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).
Returns
self
Return type
extra_repr()[source]
Set the extra representation of the module
To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable.
float()[source]
Casts all floating point parameters and buffers to float datatype.
Returns
self
Return type
forward(*input)[source]
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
half()[source]
Casts all floating point parameters and buffers to half datatype.
Returns
self
Return type
load_state_dict(state_dict, strict=True)[source]
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
modules()[source]
Returns an iterator over all modules in the network.
Yields
Module – a module in the network
Note
Duplicate modules are returned only once. In the following example, l will be returned only once.
Example:
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)named_buffers(prefix='', recurse=True)[source]
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()[source]
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='')[source]
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
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='', recurse=True)[source]
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)[source]
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.data), param.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)register_backward_hook(hook)[source]
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) -> Tensor or None
The grad_input and grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of grad_input in subsequent computations.
Returns
a handle that can be used to remove the added hook by calling handle.remove()
Return type
torch.utils.hooks.RemovableHandle
Warning
The current implementation will not have the presented behavior for complex Module that perform many operations. In some failure cases, grad_input and grad_output will only contain the gradients for a subset of the inputs and outputs. For such Module, you should use torch.Tensor.register_hook() directly on a specific input or output to get the required gradients.
register_buffer(name, tensor)[source]
Adds a persistent 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 persistent state.
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.
Example:
>>> self.register_buffer('running_mean', torch.zeros(num_features))register_forward_hook(hook)[source]
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 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)[source]
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 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_parameter(name, param)[source]
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)[source]
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
self
Return type
state_dict(destination=None, prefix='', keep_vars=False)[source]
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)[source]
Moves and/or casts the parameters and buffers.
This can be called as
to(device=None, dtype=None, non_blocking=False)[source]
to(dtype, non_blocking=False)[source]
to(tensor, non_blocking=False)[source]
Its signature is similar to torch.Tensor.to(), but only accepts floating point desired dtype s. In addition, this method will only cast the floating point parameters and buffers to 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 type of the floating point 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
Returns
self
Return type
Example:
>>> 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)train(mode=True)[source]
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
self
Return type
type(dst_type)[source]
Casts all parameters and buffers to dst_type.
Parameters
dst_type (type or string) – the desired type
Returns
self
Return type
zero_grad()[source]
Sets gradients of all model parameters to zero.
Sequential
class torch.nn.Sequential(*args)[source]
A sequential container. Modules will be added to it in the order they are passed in the constructor. Alternatively, an ordered dict of modules can also be passed in.
To make it easier to understand, here is a small example:
# Example of using Sequential
model = nn.Sequential(
nn.Conv2d(1,20,5),
nn.ReLU(),
nn.Conv2d(20,64,5),
nn.ReLU()
)
# Example of using Sequential with OrderedDict
model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(1,20,5)),
('relu1', nn.ReLU()),
('conv2', nn.Conv2d(20,64,5)),
('relu2', nn.ReLU())
]))ModuleList
class torch.nn.ModuleList(modules=None)[source]
Holds submodules in a list.
ModuleList can be indexed like a regular Python list, but modules it contains are properly registered, and will be visible by all Module methods.
Parameters
modules (iterable, optional) – an iterable of modules to add
Example:
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])
def forward(self, x):
# ModuleList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return xappend(module)[source]
Appends a given module to the end of the list.
Parameters
module (nn.Module) – module to append
extend(modules)[source]
Appends modules from a Python iterable to the end of the list.
Parameters
modules (iterable) – iterable of modules to append
insert(index, module)[source]
Insert a given module before a given index in the list.
Parameters
ModuleDict
class torch.nn.ModuleDict(modules=None)[source]
Holds submodules in a dictionary.
ModuleDict can be indexed like a regular Python dictionary, but modules it contains are properly registered, and will be visible by all Module methods.
ModuleDict is an ordered dictionary that respects
-
the order of insertion, and
-
in update(), the order of the merged
OrderedDictor another ModuleDict (the argument to update()).
Note that update() with other unordered mapping types (e.g., Python’s plain dict) does not preserve the order of the merged mapping.
Parameters
modules (iterable, optional) – a mapping (dictionary) of (string: module) or an iterable of key-value pairs of type (string, module)
Example:
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.choices = nn.ModuleDict({
'conv': nn.Conv2d(10, 10, 3),
'pool': nn.MaxPool2d(3)
})
self.activations = nn.ModuleDict([
['lrelu', nn.LeakyReLU()],
['prelu', nn.PReLU()]
])
def forward(self, x, choice, act):
x = self.choices[choice](x)
x = self.activations[act](x)
return xclear()[source]
Remove all items from the ModuleDict.
items()[source]
Return an iterable of the ModuleDict key/value pairs.
keys()[source]
Return an iterable of the ModuleDict keys.
pop(key)[source]
Remove key from the ModuleDict and return its module.
Parameters
key (string) – key to pop from the ModuleDict
update(modules)[source]
Update the ModuleDict with the key-value pairs from a mapping or an iterable, overwriting existing keys.
Note
If modules is an OrderedDict, a ModuleDict, or an iterable of key-value pairs, the order of new elements in it is preserved.
Parameters
modules (iterable) – a mapping (dictionary) from string to Module, or an iterable of key-value pairs of type (string, Module)
values()[source]
Return an iterable of the ModuleDict values.
ParameterList
class torch.nn.ParameterList(parameters=None)[source]
Holds parameters in a list.
ParameterList can be indexed like a regular Python list, but parameters it contains are properly registered, and will be visible by all Module methods.
Parameters
parameters (iterable, optional) – an iterable of Parameter to add
Example:
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.params = nn.ParameterList([nn.Parameter(torch.randn(10, 10)) for i in range(10)])
def forward(self, x):
# ParameterList can act as an iterable, or be indexed using ints
for i, p in enumerate(self.params):
x = self.params[i // 2].mm(x) + p.mm(x)
return xappend(parameter)[source]
Appends a given parameter at the end of the list.
Parameters
parameter (nn.Parameter) – parameter to append
extend(parameters)[source]
Appends parameters from a Python iterable to the end of the list.
Parameters
parameters (iterable) – iterable of parameters to append
ParameterDict
class torch.nn.ParameterDict(parameters=None)[source]
Holds parameters in a dictionary.
ParameterDict can be indexed like a regular Python dictionary, but parameters it contains are properly registered, and will be visible by all Module methods.
ParameterDict is an ordered dictionary that respects
-
the order of insertion, and
-
in update(), the order of the merged
OrderedDictor another ParameterDict (the argument to update()).
Note that update() with other unordered mapping types (e.g., Python’s plain dict) does not preserve the order of the merged mapping.
Parameters
parameters (iterable, optional) – a mapping (dictionary) of (string : Parameter) or an iterable of key-value pairs of type (string, Parameter)
Example:
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.params = nn.ParameterDict({
'left': nn.Parameter(torch.randn(5, 10)),
'right': nn.Parameter(torch.randn(5, 10))
})
def forward(self, x, choice):
x = self.params[choice].mm(x)
return xclear()[source]
Remove all items from the ParameterDict.
items()[source]
Return an iterable of the ParameterDict key/value pairs.
keys()[source]
Return an iterable of the ParameterDict keys.
pop(key)[source]
Remove key from the ParameterDict and return its parameter.
Parameters
key (string) – key to pop from the ParameterDict
update(parameters)
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