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