# Source code for torch.nn.modules.instancenorm

```
from torch import Tensor
from .batchnorm import _LazyNormBase, _NormBase
from .. import functional as F
class _InstanceNorm(_NormBase):
def __init__(
self,
num_features: int,
eps: float = 1e-5,
momentum: float = 0.1,
affine: bool = False,
track_running_stats: bool = False,
device=None,
dtype=None
) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super(_InstanceNorm, self).__init__(
num_features, eps, momentum, affine, track_running_stats, **factory_kwargs)
def _check_input_dim(self, input):
raise NotImplementedError
def _get_no_batch_dim(self):
raise NotImplementedError
def _handle_no_batch_input(self, input):
return self._apply_instance_norm(input.unsqueeze(0)).squeeze(0)
def _apply_instance_norm(self, input):
return F.instance_norm(
input, self.running_mean, self.running_var, self.weight, self.bias,
self.training or not self.track_running_stats, self.momentum, self.eps)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
version = local_metadata.get('version', None)
# at version 1: removed running_mean and running_var when
# track_running_stats=False (default)
if version is None and not self.track_running_stats:
running_stats_keys = []
for name in ('running_mean', 'running_var'):
key = prefix + name
if key in state_dict:
running_stats_keys.append(key)
if len(running_stats_keys) > 0:
error_msgs.append(
'Unexpected running stats buffer(s) {names} for {klass} '
'with track_running_stats=False. If state_dict is a '
'checkpoint saved before 0.4.0, this may be expected '
'because {klass} does not track running stats by default '
'since 0.4.0. Please remove these keys from state_dict. If '
'the running stats are actually needed, instead set '
'track_running_stats=True in {klass} to enable them. See '
'the documentation of {klass} for details.'
.format(names=" and ".join('"{}"'.format(k) for k in running_stats_keys),
klass=self.__class__.__name__))
for key in running_stats_keys:
state_dict.pop(key)
super(_InstanceNorm, self)._load_from_state_dict(
state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs)
def forward(self, input: Tensor) -> Tensor:
self._check_input_dim(input)
if input.dim() == self._get_no_batch_dim():
return self._handle_no_batch_input(input)
return self._apply_instance_norm(input)
[docs]class InstanceNorm1d(_InstanceNorm):
r"""Applies Instance Normalization over a 2D (unbatched) or 3D (batched) input
as described in the paper
`Instance Normalization: The Missing Ingredient for Fast Stylization
<https://arxiv.org/abs/1607.08022>`__.
.. math::
y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
The mean and standard-deviation are calculated per-dimension separately
for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors
of size `C` (where `C` is the number of features or channels of the input) if :attr:`affine` is ``True``.
The standard-deviation is calculated via the biased estimator, equivalent to
`torch.var(input, unbiased=False)`.
By default, this layer uses instance statistics computed from input data in
both training and evaluation modes.
If :attr:`track_running_stats` is set to ``True``, during training this
layer keeps running estimates of its computed mean and variance, which are
then used for normalization during evaluation. The running estimates are
kept with a default :attr:`momentum` of 0.1.
.. note::
This :attr:`momentum` argument is different from one used in optimizer
classes and the conventional notion of momentum. Mathematically, the
update rule for running statistics here is
:math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
new observed value.
.. note::
:class:`InstanceNorm1d` and :class:`LayerNorm` are very similar, but
have some subtle differences. :class:`InstanceNorm1d` is applied
on each channel of channeled data like multidimensional time series, but
:class:`LayerNorm` is usually applied on entire sample and often in NLP
tasks. Additionally, :class:`LayerNorm` applies elementwise affine
transform, while :class:`InstanceNorm1d` usually don't apply affine
transform.
Args:
num_features: number of features or channels :math:`C` of the input
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to ``True``, this module has
learnable affine parameters, initialized the same way as done for batch normalization.
Default: ``False``.
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``False``
Shape:
- Input: :math:`(N, C, L)` or :math:`(C, L)`
- Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input)
Examples::
>>> # Without Learnable Parameters
>>> m = nn.InstanceNorm1d(100)
>>> # With Learnable Parameters
>>> m = nn.InstanceNorm1d(100, affine=True)
>>> input = torch.randn(20, 100, 40)
>>> output = m(input)
"""
def _get_no_batch_dim(self):
return 2
def _check_input_dim(self, input):
if input.dim() not in (2, 3):
raise ValueError('expected 2D or 3D input (got {}D input)'
.format(input.dim()))
[docs]class LazyInstanceNorm1d(_LazyNormBase, _InstanceNorm):
r"""A :class:`torch.nn.InstanceNorm1d` module with lazy initialization of
the ``num_features`` argument of the :class:`InstanceNorm1d` that is inferred
from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight`, `bias`,
`running_mean` and `running_var`.
Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.
Args:
num_features: :math:`C` from an expected input of size
:math:`(N, C, L)` or :math:`(C, L)`
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to ``True``, this module has
learnable affine parameters, initialized the same way as done for batch normalization.
Default: ``False``.
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``False``
Shape:
- Input: :math:`(N, C, L)` or :math:`(C, L)`
- Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input)
"""
cls_to_become = InstanceNorm1d # type: ignore[assignment]
def _get_no_batch_dim(self):
return 2
def _check_input_dim(self, input):
if input.dim() not in (2, 3):
raise ValueError('expected 2D or 3D input (got {}D input)'
.format(input.dim()))
[docs]class InstanceNorm2d(_InstanceNorm):
r"""Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs
with additional channel dimension) as described in the paper
`Instance Normalization: The Missing Ingredient for Fast Stylization
<https://arxiv.org/abs/1607.08022>`__.
.. math::
y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
The mean and standard-deviation are calculated per-dimension separately
for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors
of size `C` (where `C` is the input size) if :attr:`affine` is ``True``.
The standard-deviation is calculated via the biased estimator, equivalent to
`torch.var(input, unbiased=False)`.
By default, this layer uses instance statistics computed from input data in
both training and evaluation modes.
If :attr:`track_running_stats` is set to ``True``, during training this
layer keeps running estimates of its computed mean and variance, which are
then used for normalization during evaluation. The running estimates are
kept with a default :attr:`momentum` of 0.1.
.. note::
This :attr:`momentum` argument is different from one used in optimizer
classes and the conventional notion of momentum. Mathematically, the
update rule for running statistics here is
:math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
new observed value.
.. note::
:class:`InstanceNorm2d` and :class:`LayerNorm` are very similar, but
have some subtle differences. :class:`InstanceNorm2d` is applied
on each channel of channeled data like RGB images, but
:class:`LayerNorm` is usually applied on entire sample and often in NLP
tasks. Additionally, :class:`LayerNorm` applies elementwise affine
transform, while :class:`InstanceNorm2d` usually don't apply affine
transform.
Args:
num_features: :math:`C` from an expected input of size
:math:`(N, C, H, W)` or :math:`(C, H, W)`
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to ``True``, this module has
learnable affine parameters, initialized the same way as done for batch normalization.
Default: ``False``.
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``False``
Shape:
- Input: :math:`(N, C, H, W)` or :math:`(C, H, W)`
- Output: :math:`(N, C, H, W)` or :math:`(C, H, W)` (same shape as input)
Examples::
>>> # Without Learnable Parameters
>>> m = nn.InstanceNorm2d(100)
>>> # With Learnable Parameters
>>> m = nn.InstanceNorm2d(100, affine=True)
>>> input = torch.randn(20, 100, 35, 45)
>>> output = m(input)
"""
def _get_no_batch_dim(self):
return 3
def _check_input_dim(self, input):
if input.dim() not in (3, 4):
raise ValueError('expected 3D or 4D input (got {}D input)'
.format(input.dim()))
[docs]class LazyInstanceNorm2d(_LazyNormBase, _InstanceNorm):
r"""A :class:`torch.nn.InstanceNorm2d` module with lazy initialization of
the ``num_features`` argument of the :class:`InstanceNorm2d` that is inferred
from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight`, `bias`,
`running_mean` and `running_var`.
Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.
Args:
num_features: :math:`C` from an expected input of size
:math:`(N, C, H, W)` or :math:`(C, H, W)`
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to ``True``, this module has
learnable affine parameters, initialized the same way as done for batch normalization.
Default: ``False``.
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``False``
Shape:
- Input: :math:`(N, C, H, W)` or :math:`(C, H, W)`
- Output: :math:`(N, C, H, W)` or :math:`(C, H, W)` (same shape as input)
"""
cls_to_become = InstanceNorm2d # type: ignore[assignment]
def _get_no_batch_dim(self):
return 3
def _check_input_dim(self, input):
if input.dim() not in (3, 4):
raise ValueError('expected 3D or 4D input (got {}D input)'
.format(input.dim()))
[docs]class InstanceNorm3d(_InstanceNorm):
r"""Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs
with additional channel dimension) as described in the paper
`Instance Normalization: The Missing Ingredient for Fast Stylization
<https://arxiv.org/abs/1607.08022>`__.
.. math::
y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
The mean and standard-deviation are calculated per-dimension separately
for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors
of size C (where C is the input size) if :attr:`affine` is ``True``.
The standard-deviation is calculated via the biased estimator, equivalent to
`torch.var(input, unbiased=False)`.
By default, this layer uses instance statistics computed from input data in
both training and evaluation modes.
If :attr:`track_running_stats` is set to ``True``, during training this
layer keeps running estimates of its computed mean and variance, which are
then used for normalization during evaluation. The running estimates are
kept with a default :attr:`momentum` of 0.1.
.. note::
This :attr:`momentum` argument is different from one used in optimizer
classes and the conventional notion of momentum. Mathematically, the
update rule for running statistics here is
:math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
new observed value.
.. note::
:class:`InstanceNorm3d` and :class:`LayerNorm` are very similar, but
have some subtle differences. :class:`InstanceNorm3d` is applied
on each channel of channeled data like 3D models with RGB color, but
:class:`LayerNorm` is usually applied on entire sample and often in NLP
tasks. Additionally, :class:`LayerNorm` applies elementwise affine
transform, while :class:`InstanceNorm3d` usually don't apply affine
transform.
Args:
num_features: :math:`C` from an expected input of size
:math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to ``True``, this module has
learnable affine parameters, initialized the same way as done for batch normalization.
Default: ``False``.
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``False``
Shape:
- Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`
- Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input)
Examples::
>>> # Without Learnable Parameters
>>> m = nn.InstanceNorm3d(100)
>>> # With Learnable Parameters
>>> m = nn.InstanceNorm3d(100, affine=True)
>>> input = torch.randn(20, 100, 35, 45, 10)
>>> output = m(input)
"""
def _get_no_batch_dim(self):
return 4
def _check_input_dim(self, input):
if input.dim() not in (4, 5):
raise ValueError('expected 4D or 5D input (got {}D input)'
.format(input.dim()))
[docs]class LazyInstanceNorm3d(_LazyNormBase, _InstanceNorm):
r"""A :class:`torch.nn.InstanceNorm3d` module with lazy initialization of
the ``num_features`` argument of the :class:`InstanceNorm3d` that is inferred
from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight`, `bias`,
`running_mean` and `running_var`.
Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.
Args:
num_features: :math:`C` from an expected input of size
:math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`
eps: a value added to the denominator for numerical stability. Default: 1e-5
momentum: the value used for the running_mean and running_var computation. Default: 0.1
affine: a boolean value that when set to ``True``, this module has
learnable affine parameters, initialized the same way as done for batch normalization.
Default: ``False``.
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``False``
Shape:
- Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`
- Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input)
"""
cls_to_become = InstanceNorm3d # type: ignore[assignment]
def _get_no_batch_dim(self):
return 4
def _check_input_dim(self, input):
if input.dim() not in (4, 5):
raise ValueError('expected 4D or 5D input (got {}D input)'
.format(input.dim()))
```