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Source code for torch.nn.modules.normalization

import torch
import numbers
from torch.nn.parameter import Parameter
from .module import Module
from ._functions import CrossMapLRN2d as _cross_map_lrn2d
from .. import functional as F
from .. import init

from torch import Tensor, Size
from typing import Union, List, Tuple

__all__ = ['LocalResponseNorm', 'CrossMapLRN2d', 'LayerNorm', 'GroupNorm']

[docs]class LocalResponseNorm(Module): r"""Applies local response normalization over an input signal. The input signal is composed of several input planes, where channels occupy the second dimension. Applies normalization across channels. .. math:: b_{c} = a_{c}\left(k + \frac{\alpha}{n} \sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta} Args: size: amount of neighbouring channels used for normalization alpha: multiplicative factor. Default: 0.0001 beta: exponent. Default: 0.75 k: additive factor. Default: 1 Shape: - Input: :math:`(N, C, *)` - Output: :math:`(N, C, *)` (same shape as input) Examples:: >>> lrn = nn.LocalResponseNorm(2) >>> signal_2d = torch.randn(32, 5, 24, 24) >>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7) >>> output_2d = lrn(signal_2d) >>> output_4d = lrn(signal_4d) """ __constants__ = ['size', 'alpha', 'beta', 'k'] size: int alpha: float beta: float k: float def __init__(self, size: int, alpha: float = 1e-4, beta: float = 0.75, k: float = 1.) -> None: super().__init__() self.size = size self.alpha = alpha self.beta = beta self.k = k def forward(self, input: Tensor) -> Tensor: return F.local_response_norm(input, self.size, self.alpha, self.beta, self.k) def extra_repr(self): return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)
class CrossMapLRN2d(Module): size: int alpha: float beta: float k: float def __init__(self, size: int, alpha: float = 1e-4, beta: float = 0.75, k: float = 1) -> None: super().__init__() self.size = size self.alpha = alpha self.beta = beta self.k = k def forward(self, input: Tensor) -> Tensor: return _cross_map_lrn2d.apply(input, self.size, self.alpha, self.beta, self.k) def extra_repr(self) -> str: return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__) _shape_t = Union[int, List[int], Size]
[docs]class LayerNorm(Module): r"""Applies Layer Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Layer Normalization <https://arxiv.org/abs/1607.06450>`__ .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated over the last `D` dimensions, where `D` is the dimension of :attr:`normalized_shape`. For example, if :attr:`normalized_shape` is ``(3, 5)`` (a 2-dimensional shape), the mean and standard-deviation are computed over the last 2 dimensions of the input (i.e. ``input.mean((-2, -1))``). :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``. The standard-deviation is calculated via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. .. note:: Unlike Batch Normalization and Instance Normalization, which applies scalar scale and bias for each entire channel/plane with the :attr:`affine` option, Layer Normalization applies per-element scale and bias with :attr:`elementwise_affine`. This layer uses statistics computed from input data in both training and evaluation modes. Args: normalized_shape (int or list or torch.Size): input shape from an expected input of size .. math:: [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1] \times \ldots \times \text{normalized\_shape}[-1]] If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size. eps: a value added to the denominator for numerical stability. Default: 1e-5 elementwise_affine: a boolean value that when set to ``True``, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases). Default: ``True``. bias: If set to ``False``, the layer will not learn an additive bias (only relevant if :attr:`elementwise_affine` is ``True``). Default: ``True``. Attributes: weight: the learnable weights of the module of shape :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. The values are initialized to 1. bias: the learnable bias of the module of shape :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. The values are initialized to 0. Shape: - Input: :math:`(N, *)` - Output: :math:`(N, *)` (same shape as input) Examples:: >>> # NLP Example >>> batch, sentence_length, embedding_dim = 20, 5, 10 >>> embedding = torch.randn(batch, sentence_length, embedding_dim) >>> layer_norm = nn.LayerNorm(embedding_dim) >>> # Activate module >>> layer_norm(embedding) >>> >>> # Image Example >>> N, C, H, W = 20, 5, 10, 10 >>> input = torch.randn(N, C, H, W) >>> # Normalize over the last three dimensions (i.e. the channel and spatial dimensions) >>> # as shown in the image below >>> layer_norm = nn.LayerNorm([C, H, W]) >>> output = layer_norm(input) .. image:: ../_static/img/nn/layer_norm.jpg :scale: 50 % """ __constants__ = ['normalized_shape', 'eps', 'elementwise_affine'] normalized_shape: Tuple[int, ...] eps: float elementwise_affine: bool def __init__(self, normalized_shape: _shape_t, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() if isinstance(normalized_shape, numbers.Integral): # mypy error: incompatible types in assignment normalized_shape = (normalized_shape,) # type: ignore[assignment] self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type] self.eps = eps self.elementwise_affine = elementwise_affine if self.elementwise_affine: self.weight = Parameter(torch.empty(self.normalized_shape, **factory_kwargs)) if bias: self.bias = Parameter(torch.empty(self.normalized_shape, **factory_kwargs)) else: self.register_parameter('bias', None) else: self.register_parameter('weight', None) self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self) -> None: if self.elementwise_affine: init.ones_(self.weight) if self.bias is not None: init.zeros_(self.bias) def forward(self, input: Tensor) -> Tensor: return F.layer_norm( input, self.normalized_shape, self.weight, self.bias, self.eps) def extra_repr(self) -> str: return '{normalized_shape}, eps={eps}, ' \ 'elementwise_affine={elementwise_affine}'.format(**self.__dict__)
[docs]class GroupNorm(Module): r"""Applies Group Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Group Normalization <https://arxiv.org/abs/1803.08494>`__ .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The input channels are separated into :attr:`num_groups` groups, each containing ``num_channels / num_groups`` channels. :attr:`num_channels` must be divisible by :attr:`num_groups`. The mean and standard-deviation are calculated separately over the each group. :math:`\gamma` and :math:`\beta` are learnable per-channel affine transform parameter vectors of size :attr:`num_channels` if :attr:`affine` is ``True``. The standard-deviation is calculated via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. This layer uses statistics computed from input data in both training and evaluation modes. Args: num_groups (int): number of groups to separate the channels into num_channels (int): number of channels expected in input eps: a value added to the denominator for numerical stability. Default: 1e-5 affine: a boolean value that when set to ``True``, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: ``True``. Shape: - Input: :math:`(N, C, *)` where :math:`C=\text{num\_channels}` - Output: :math:`(N, C, *)` (same shape as input) Examples:: >>> input = torch.randn(20, 6, 10, 10) >>> # Separate 6 channels into 3 groups >>> m = nn.GroupNorm(3, 6) >>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm) >>> m = nn.GroupNorm(6, 6) >>> # Put all 6 channels into a single group (equivalent with LayerNorm) >>> m = nn.GroupNorm(1, 6) >>> # Activating the module >>> output = m(input) """ __constants__ = ['num_groups', 'num_channels', 'eps', 'affine'] num_groups: int num_channels: int eps: float affine: bool def __init__(self, num_groups: int, num_channels: int, eps: float = 1e-5, affine: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() if num_channels % num_groups != 0: raise ValueError('num_channels must be divisible by num_groups') self.num_groups = num_groups self.num_channels = num_channels self.eps = eps self.affine = affine if self.affine: self.weight = Parameter(torch.empty(num_channels, **factory_kwargs)) self.bias = Parameter(torch.empty(num_channels, **factory_kwargs)) else: self.register_parameter('weight', None) self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self) -> None: if self.affine: init.ones_(self.weight) init.zeros_(self.bias) def forward(self, input: Tensor) -> Tensor: return F.group_norm( input, self.num_groups, self.weight, self.bias, self.eps) def extra_repr(self) -> str: return '{num_groups}, {num_channels}, eps={eps}, ' \ 'affine={affine}'.format(**self.__dict__)
# TODO: ContrastiveNorm2d # TODO: DivisiveNorm2d # TODO: SubtractiveNorm2d

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