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