# ReflectionPad2d

Source: https://www.tensorplay.cn/docs/generated/tensorplay.nn.modules.padding.ReflectionPad2d.html

# ReflectionPad2d

class tensorplay.nn.modules.padding.ReflectionPad2d(padding: [int](https://docs.python.org/3/library/functions.html#int) | [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[int](https://docs.python.org/3/library/functions.html#int), [int](https://docs.python.org/3/library/functions.html#int), [int](https://docs.python.org/3/library/functions.html#int), [int](https://docs.python.org/3/library/functions.html#int)])[[source]](../_modules/tensorplay/nn/modules/padding.html#ReflectionPad2d)

Pads the input tensor using the reflection of the input boundary.

For N-dimensional padding, use torch.nn.functional.pad().

Parameters:

padding ([int](https://docs.python.org/3/library/functions.html#int), [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)) – the size of the padding. If it is int, uses the same
padding in all boundaries. If a 4-tuple, uses (\(\text{padding\_left}\),
\(\text{padding\_right}\), \(\text{padding\_top}\), \(\text{padding\_bottom}\))
Note that padding size should be less than the corresponding input dimension.

Shape:

- Input: \((N, C, H_{in}, W_{in})\) or \((C, H_{in}, W_{in})\) .

- Output: \((N, C, H_{out}, W_{out})\) or \((C, H_{out}, W_{out})\) where \(H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}\) \(W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}\)

Examples:

```
>>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
>>> m = nn.ReflectionPad2d(2)
>>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3)
>>> input
tensor([[[[0., 1., 2.],
          [3., 4., 5.],
          [6., 7., 8.]]]])
>>> m(input)
tensor([[[[8., 7., 6., 7., 8., 7., 6.],
          [5., 4., 3., 4., 5., 4., 3.],
          [2., 1., 0., 1., 2., 1., 0.],
          [5., 4., 3., 4., 5., 4., 3.],
          [8., 7., 6., 7., 8., 7., 6.],
          [5., 4., 3., 4., 5., 4., 3.],
          [2., 1., 0., 1., 2., 1., 0.]]]])
>>> # using different paddings for different sides
>>> m = nn.ReflectionPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[7., 6., 7., 8., 7.],
          [4., 3., 4., 5., 4.],
          [1., 0., 1., 2., 1.],
          [4., 3., 4., 5., 4.],
          [7., 6., 7., 8., 7.]]]])
```

add_module(name: [str](https://docs.python.org/3/library/stdtypes.html#str), module: [Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module) | [None](https://docs.python.org/3/library/constants.html#None)) → [None](https://docs.python.org/3/library/constants.html#None)

Add a child module to the current module.

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

Parameters:

- name ([str](https://docs.python.org/3/library/stdtypes.html#str)) – name of the child module. The child module can be accessed from this module using the given name

- module ([Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)) – child module to be added to the module.

apply(fn: [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)], [None](https://docs.python.org/3/library/constants.html#None)]) → Self

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 [nn.init documentation](../upstream_labels.html#nn-init-doc)).

Parameters:

fn (Module -> None) – function to be applied to each submodule

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

Example:

```
>>> @tensorplay.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
        [1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
        [1., 1.]], requires_grad=True)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
```

bfloat16() → Self

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

buffers(recurse: [bool](https://docs.python.org/3/library/functions.html#bool) = True) → [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[TensorBase]

Return an iterator over module buffers.

Parameters:

recurse ([bool](https://docs.python.org/3/library/functions.html#bool)) – if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module.

Yields:

tensorplay.Tensor – module buffer

Example:

```
>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)
```

children() &#x2192; [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.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 tensorplay.compile().

This Module’s __call__ method is compiled and all arguments are passed as-is
to tensorplay.compile().

See tensorplay.compile() for details on the arguments for this function.

cpu() &#x2192; Self

Move all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

cuda(device: [int](https://docs.python.org/3/library/functions.html#int) | [Device](tensorplay.Device.html#tensorplay.Device) | [None](https://docs.python.org/3/library/constants.html#None) = None) &#x2192; Self

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 the optimizer if the module will
live on GPU while being optimized.

Note

This method modifies the module in-place.

Parameters:

device ([int](https://docs.python.org/3/library/functions.html#int), optional) – if specified, all parameters will be
copied to that device

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

double() &#x2192; Self

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

eval() &#x2192; Self

Set the module in evaluation mode.

This has an effect only on certain modules. See the documentation of
particular modules for details of their behaviors in training/evaluation
mode, i.e. whether they are affected, e.g. Dropout, BatchNorm,
etc.

This is equivalent with self.train(False).

See [Locally disabling gradient computation](../upstream_labels.html#locally-disable-grad-doc) for a comparison between
.eval() and several similar mechanisms that may be confused with it.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

float() &#x2192; Self

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

get_buffer(target: [str](https://docs.python.org/3/library/stdtypes.html#str)) &#x2192; TensorBase

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 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:

The buffer referenced by target

Return type:

tensorplay.Tensor

Raises:

[AttributeError](https://docs.python.org/3/library/exceptions.html#AttributeError) – If the target string references an invalid
    path or resolves to something that is not a
    buffer

get_extra_state() &#x2192; [Any](https://docs.python.org/3/library/typing.html#typing.Any)

Return any extra state to include in the module’s state_dict.

Implement this and a corresponding [set_extra_state()](#tensorplay.nn.modules.padding.ReflectionPad2d.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 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](https://docs.python.org/3/library/functions.html#object)

get_parameter(target: [str](https://docs.python.org/3/library/stdtypes.html#str)) &#x2192; [Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.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 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:

The Parameter referenced by target

Return type:

[tensorplay.nn.Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.Parameter)

Raises:

[AttributeError](https://docs.python.org/3/library/exceptions.html#AttributeError) – If the target string references an invalid
    path or resolves to something that is not an
    nn.Parameter

get_submodule(target: [str](https://docs.python.org/3/library/stdtypes.html#str)) &#x2192; [Module](tensorplay.nn.modules.module.Module.html#tensorplay.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 which 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:

The submodule referenced by target

Return type:

tensorplay.nn.Module

Raises:

[AttributeError](https://docs.python.org/3/library/exceptions.html#AttributeError) – If at any point along the path resulting from
    the target string the (sub)path resolves to a non-existent
    attribute name or an object that is not an instance of nn.Module.

half() &#x2192; Self

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

load_state_dict(state_dict: [Mapping](https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)], strict: [bool](https://docs.python.org/3/library/functions.html#bool) = True, assign: [bool](https://docs.python.org/3/library/functions.html#bool) = False)

Copy parameters and buffers from [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict) into this module and its descendants.

If strict is True, then
the keys of [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict) must exactly match the keys returned
by this module’s state_dict() function.

Warning

If assign is True the optimizer must be created after
the call to [load_state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.load_state_dict) unless
get_swap_module_params_on_conversion() is True.

Parameters:

- state_dict ([dict](https://docs.python.org/3/library/stdtypes.html#dict)) – a dict containing parameters and persistent buffers.

- strict ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – whether to strictly enforce that the keys in [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict) match the keys returned by this module’s state_dict() function. Default: True

- assign ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – When set to False , the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of [Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.Parameter) for which the value from the module is preserved. Default: 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 with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key
exists in [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict), [load_state_dict()](#tensorplay.nn.modules.padding.ReflectionPad2d.load_state_dict) will raise a
RuntimeError.

modules() &#x2192; [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.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)
```

named_buffers(prefix: [str](https://docs.python.org/3/library/stdtypes.html#str) = '', recurse: [bool](https://docs.python.org/3/library/functions.html#bool) = True, remove_duplicate: [bool](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[str](https://docs.python.org/3/library/stdtypes.html#str), TensorBase]]

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

- prefix ([str](https://docs.python.org/3/library/stdtypes.html#str)) – prefix to prepend to all buffer names.

- recurse ([bool](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/functions.html#bool) , optional ) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, tensorplay.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() &#x2192; [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Module](tensorplay.nn.modules.module.Module.html#tensorplay.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: [set](https://docs.python.org/3/library/stdtypes.html#set)[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)] | [None](https://docs.python.org/3/library/constants.html#None) = None, prefix: [str](https://docs.python.org/3/library/stdtypes.html#str) = '', remove_duplicate: [bool](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/stdtypes.html#str) = '', recurse: [bool](https://docs.python.org/3/library/functions.html#bool) = True, remove_duplicate: [bool](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[[tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.Parameter)]]

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

- prefix ([str](https://docs.python.org/3/library/stdtypes.html#str)) – prefix to prepend to all parameter names.

- recurse ([bool](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; [Iterator](https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator)[[Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.Parameter)]

Return an iterator over module parameters.

This is typically passed to an optimizer.

Parameters:

recurse ([bool](https://docs.python.org/3/library/functions.html#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 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)
```

register_backward_hook(hook: [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module), [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase, [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase], [None](https://docs.python.org/3/library/constants.html#None) | [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase]) &#x2192; 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:

tensorplay.utils.hooks.RemovableHandle

register_buffer(name: [str](https://docs.python.org/3/library/stdtypes.html#str), tensor: TensorBase | [None](https://docs.python.org/3/library/constants.html#None), persistent: [bool](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; [None](https://docs.python.org/3/library/constants.html#None)

Add a buffer to the module.

This is typically used to register a buffer that should not 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](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict).

Buffers can be accessed as attributes using given names.

Parameters:

- name ([str](https://docs.python.org/3/library/stdtypes.html#str)) – name of the buffer. The buffer can be accessed from this module using the given name

- tensor ( Tensor or None ) – buffer to be registered. If None , then operations that run on buffers, such as [cuda](#tensorplay.nn.modules.padding.ReflectionPad2d.cuda), are ignored. If None , the buffer is not included in the module’s [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict).

- persistent ([bool](https://docs.python.org/3/library/functions.html#bool)) – whether the buffer is part of this module’s [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_dict).

Example:

```
>>> # xdoctest: +SKIP("undefined vars")
>>> self.register_buffer('running_mean', tensorplay.zeros(num_features))
```

register_forward_hook(hook: [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[T, [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [Any](https://docs.python.org/3/library/typing.html#typing.Any)], [Any](https://docs.python.org/3/library/typing.html#typing.Any) | [None](https://docs.python.org/3/library/constants.html#None)] | [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[T, [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dict](https://docs.python.org/3/library/stdtypes.html#dict)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)], [Any](https://docs.python.org/3/library/typing.html#typing.Any)], [Any](https://docs.python.org/3/library/typing.html#typing.Any) | [None](https://docs.python.org/3/library/constants.html#None)], *, prepend: [bool](https://docs.python.org/3/library/functions.html#bool) = False, with_kwargs: [bool](https://docs.python.org/3/library/functions.html#bool) = False, always_call: [bool](https://docs.python.org/3/library/functions.html#bool) = False) &#x2192; RemovableHandle

Register a forward hook on the module.

The hook will be called every time after forward() has computed an output.

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

```
hook(module, args, output) -> None or modified output
```

If with_kwargs is True, the forward hook will be passed the
kwargs given to the forward function and be expected to return the
output possibly modified. The hook should have the following signature:

```
hook(module, args, kwargs, output) -> None or modified output
```

Parameters:

- hook ( Callable ) – The user defined hook to be registered.

- prepend ([bool](https://docs.python.org/3/library/functions.html#bool)) – If True , the provided hook will be fired before all existing forward hooks on this tensorplay.nn.Module . Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module . Note that global forward hooks registered with register_module_forward_hook() will fire before all hooks registered by this method. Default: False

- with_kwargs ([bool](https://docs.python.org/3/library/functions.html#bool)) – If True , the hook will be passed the kwargs given to the forward function. Default: False

- always_call ([bool](https://docs.python.org/3/library/functions.html#bool)) – If True the hook 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:

tensorplay.utils.hooks.RemovableHandle

register_forward_pre_hook(hook: [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[T, [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...]], [Any](https://docs.python.org/3/library/typing.html#typing.Any) | [None](https://docs.python.org/3/library/constants.html#None)] | [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[T, [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dict](https://docs.python.org/3/library/stdtypes.html#dict)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)]], [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), [dict](https://docs.python.org/3/library/stdtypes.html#dict)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)]] | [None](https://docs.python.org/3/library/constants.html#None)], *, prepend: [bool](https://docs.python.org/3/library/functions.html#bool) = False, with_kwargs: [bool](https://docs.python.org/3/library/functions.html#bool) = False) &#x2192; 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 the forward. The hook can modify the
input. User can either return a tuple or a single modified value in the
hook. We will wrap the value into a tuple if a single value is returned
(unless that value is already a tuple). The hook should have the
following signature:

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

If with_kwargs is true, the forward pre-hook will be passed the
kwargs given to the forward function. And if the hook modifies the
input, both the args and kwargs should be returned. The hook should have
the following signature:

```
hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
```

Parameters:

- hook ( Callable ) – The user defined hook to be registered.

- prepend ([bool](https://docs.python.org/3/library/functions.html#bool)) – If true, the provided hook will be fired before all existing forward_pre hooks on this tensorplay.nn.Module . Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module . Note that global forward_pre hooks registered with register_module_forward_pre_hook() will fire before all hooks registered by this method. Default: False

- with_kwargs ([bool](https://docs.python.org/3/library/functions.html#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:

tensorplay.utils.hooks.RemovableHandle

register_full_backward_hook(hook: [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module), [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase, [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase], [None](https://docs.python.org/3/library/constants.html#None) | [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase], prepend: [bool](https://docs.python.org/3/library/functions.html#bool) = False) &#x2192; RemovableHandle

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

- Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.

- If none of the module inputs require gradients, the hook will fire when the gradients are computed with respect to module outputs.

- If none of the module outputs require gradients, then the hooks will not fire.

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.

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](https://docs.python.org/3/library/functions.html#bool)) – If true, the provided hook will be fired before all existing backward hooks on this tensorplay.nn.Module . Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module . Note that global backward hooks registered with register_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:

tensorplay.utils.hooks.RemovableHandle

register_full_backward_pre_hook(hook: [Callable](https://docs.python.org/3/library/typing.html#typing.Callable)[[[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module), [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase], [None](https://docs.python.org/3/library/constants.html#None) | [tuple](https://docs.python.org/3/library/stdtypes.html#tuple)[TensorBase, ...] | TensorBase], prepend: [bool](https://docs.python.org/3/library/functions.html#bool) = False) &#x2192; 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 of grad_output in
subsequent computations. Entries in grad_output will be None for
all non-Tensor arguments.

For technical reasons, when this hook is applied to a Module, its forward function will
receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
of each Tensor returned by the Module’s forward function.

Warning

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

Parameters:

- hook ( Callable ) – The user-defined hook to be registered.

- prepend ([bool](https://docs.python.org/3/library/functions.html#bool)) – If true, the provided hook will be fired before all existing backward_pre hooks on this tensorplay.nn.Module . Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module . Note that global backward_pre hooks registered with register_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:

tensorplay.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 the incompatible_keys argument is a NamedTuple consisting
of attributes missing_keys and unexpected_keys. missing_keys
is a list of str containing the missing keys and
unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling [load_state_dict()](#tensorplay.nn.modules.padding.ReflectionPad2d.load_state_dict) with
strict=True are affected by modifications the hook makes to
missing_keys or unexpected_keys, as expected. Additions to either
set of keys will result in an error being thrown when strict=True, and
clearing out both missing and unexpected keys will avoid an error.

Returns:

a handle that can be used to remove the added hook by calling
handle.remove()

Return type:

tensorplay.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](https://docs.python.org/3/library/stdtypes.html#str), module: [Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module) | [None](https://docs.python.org/3/library/constants.html#None)) &#x2192; [None](https://docs.python.org/3/library/constants.html#None)

Alias for [add_module()](#tensorplay.nn.modules.padding.ReflectionPad2d.add_module).

register_parameter(name: [str](https://docs.python.org/3/library/stdtypes.html#str), param: [Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.Parameter) | [None](https://docs.python.org/3/library/constants.html#None)) &#x2192; [None](https://docs.python.org/3/library/constants.html#None)

Add a parameter to the module.

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

Parameters:

- name ([str](https://docs.python.org/3/library/stdtypes.html#str)) – name of the parameter. The parameter can be accessed from this module using the given name

- param ([Parameter](tensorplay.nn.Parameter.html#tensorplay.nn.Parameter) or None ) – parameter to be added to the module. If None , then operations that run on parameters, such as [cuda](#tensorplay.nn.modules.padding.ReflectionPad2d.cuda), are ignored. If None , the parameter is not included in the module’s [state_dict](#tensorplay.nn.modules.padding.ReflectionPad2d.state_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](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; Self

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](../upstream_labels.html#locally-disable-grad-doc) for a comparison between
.requires_grad_() and several similar mechanisms that may be confused with it.

Parameters:

requires_grad ([bool](https://docs.python.org/3/library/functions.html#bool)) – whether autograd should record operations on
parameters in this module. Default: True.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

set_extra_state(state: [Any](https://docs.python.org/3/library/typing.html#typing.Any)) &#x2192; [None](https://docs.python.org/3/library/constants.html#None)

Set extra state contained in the loaded state_dict.

This function is called from [load_state_dict()](#tensorplay.nn.modules.padding.ReflectionPad2d.load_state_dict) to handle any extra state
found within the state_dict. Implement this function and a corresponding
[get_extra_state()](#tensorplay.nn.modules.padding.ReflectionPad2d.get_extra_state) for your module if you need to store extra state within its
state_dict.

Parameters:

state ([dict](https://docs.python.org/3/library/stdtypes.html#dict)) – Extra state from the state_dict

set_submodule(target: [str](https://docs.python.org/3/library/stdtypes.html#str), module: [Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module), strict: [bool](https://docs.python.org/3/library/functions.html#bool) = False) &#x2192; [None](https://docs.python.org/3/library/constants.html#None)

Set the submodule given by target if it exists, otherwise throw an error.

Note

If strict is set to False (default), the method will replace an existing submodule
or create a new submodule if the parent module exists. If strict is set to True,
the method will only attempt to replace an existing submodule and throw an error if
the submodule does not exist.

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

```
A(
    (net_b): Module(
        (net_c): Module(
            (conv): Conv2d(3, 3, 3)
        )
        (linear): Linear(3, 3)
    )
)
```

(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 override the Conv2d with a new submodule Linear, you
could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1))
where strict could be True or False

To add a new submodule Conv2d to the existing net_b module,
you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call
set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError
will be raised because net_b does not have a submodule named conv.

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.

- strict – If False , the method will replace an existing submodule or create a new submodule if the parent module exists. If True , the method will only attempt to replace an existing submodule and throw an error if the submodule doesn’t already exist.

Raises:

- [ValueError](https://docs.python.org/3/library/exceptions.html#ValueError) – If the target string is empty or if module is not an instance of nn.Module .

- [AttributeError](https://docs.python.org/3/library/exceptions.html#AttributeError) – If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module .

share_memory() &#x2192; Self

See tensorplay.Tensor.share_memory_().

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 for
destination, prefix and keep_vars in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.

Warning

Please avoid the use of argument destination as it is not
designed for end-users.

Parameters:

- destination ([dict](https://docs.python.org/3/library/stdtypes.html#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](https://docs.python.org/3/library/stdtypes.html#str) , optional ) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: '' .

- keep_vars ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – by default the [Tensor](tensorplay.Tensor.html#tensorplay.Tensor) s returned in the state dict are detached from autograd. If it’s set to True , detaching will not be performed. Default: False .

Returns:

a dictionary containing a whole state of the module

Return type:

[dict](https://docs.python.org/3/library/stdtypes.html#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=tensorplay.channels_last)

Its signature is similar to tensorplay.Tensor.to(), but only accepts
floating point or complex dtypes. In addition, this method will
only cast the floating point or complex 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 ([tensorplay.device](tensorplay.device.html#tensorplay.device)) – the desired device of the parameters and buffers in this module

- dtype ([tensorplay.dtype](tensorplay.dtype.html#tensorplay.dtype)) – the desired floating point or complex dtype of the parameters and buffers in this module

- tensor ( tensorplay.Tensor ) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module

- memory_format ( tensorplay.memory_format ) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
>>> # xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
>>> gpu1 = tensorplay.device("cuda:1")
>>> linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
>>> cpu = tensorplay.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=tensorplay.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
>>> linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)
```

to_empty(*, device: [str](https://docs.python.org/3/library/stdtypes.html#str) | [Device](tensorplay.Device.html#tensorplay.Device) | [int](https://docs.python.org/3/library/functions.html#int) | [None](https://docs.python.org/3/library/constants.html#None), recurse: [bool](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; Self

Move the parameters and buffers to the specified device without copying storage.

Parameters:

- device ([tensorplay.device](tensorplay.device.html#tensorplay.device)) – The desired device of the parameters and buffers in this module.

- recurse ([bool](https://docs.python.org/3/library/functions.html#bool)) – Whether parameters and buffers of submodules should be recursively moved to the specified device.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

train(mode: [bool](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; Self

Set the module in training mode.

This has an effect only on certain modules. See the documentation of
particular modules for details of their behaviors in training/evaluation
mode, i.e., whether they are affected, e.g. Dropout, BatchNorm,
etc.

Parameters:

mode ([bool](https://docs.python.org/3/library/functions.html#bool)) – whether to set training mode (True) or evaluation
mode (False). Default: True.

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

type(dst_type: [DType](tensorplay.nn.functional.DType.html#tensorplay.nn.functional.DType) | [str](https://docs.python.org/3/library/stdtypes.html#str)) &#x2192; Self

Casts all parameters and buffers to dst_type.

Note

This method modifies the module in-place.

Parameters:

dst_type ([type](https://docs.python.org/3/library/functions.html#type) or string) – the desired type

Returns:

self

Return type:

[Module](tensorplay.nn.modules.module.Module.html#tensorplay.nn.modules.module.Module)

zero_grad(set_to_none: [bool](https://docs.python.org/3/library/functions.html#bool) = True) &#x2192; [None](https://docs.python.org/3/library/constants.html#None)

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Parameters:

set_to_none ([bool](https://docs.python.org/3/library/functions.html#bool)) – instead of setting to zero, set the grads to None.
See tensorplay.optim.Optimizer.zero_grad() for details.