import torch
from torch import Tensor
[docs]class BatchNorm(torch.nn.Module):
r"""Applies batch normalization over a batch of node features as described
in the `"Batch Normalization: Accelerating Deep Network Training by
Reducing Internal Covariate Shift" <https://arxiv.org/abs/1502.03167>`_
paper
.. math::
\mathbf{x}^{\prime}_i = \frac{\mathbf{x} -
\textrm{E}[\mathbf{x}]}{\sqrt{\textrm{Var}[\mathbf{x}] + \epsilon}}
\odot \gamma + \beta
The mean and standard-deviation are calculated per-dimension over all nodes
inside the mini-batch.
Args:
in_channels (int): Size of each input sample.
eps (float, optional): A value added to the denominator for numerical
stability. (default: :obj:`1e-5`)
momentum (float, optional): The value used for the running mean and
running variance computation. (default: :obj:`0.1`)
affine (bool, optional): If set to :obj:`True`, this module has
learnable affine parameters :math:`\gamma` and :math:`\beta`.
(default: :obj:`True`)
track_running_stats (bool, optional): If set to :obj:`True`, this
module tracks the running mean and variance, and when set to
:obj:`False`, this module does not track such statistics and always
uses batch statistics in both training and eval modes.
(default: :obj:`True`)
allow_single_element (bool, optional): If set to :obj:`True`, batches
with only a single element will work as during in evaluation.
That is the running mean and variance will be used.
Requires :obj:`track_running_stats=True`. (default: :obj:`False`)
"""
def __init__(self, in_channels: int, eps: float = 1e-5,
momentum: float = 0.1, affine: bool = True,
track_running_stats: bool = True,
allow_single_element: bool = False):
super().__init__()
if allow_single_element and not track_running_stats:
raise ValueError("'allow_single_element' requires "
"'track_running_stats' to be set to `True`")
self.module = torch.nn.BatchNorm1d(in_channels, eps, momentum, affine,
track_running_stats)
self.in_channels = in_channels
self.allow_single_element = allow_single_element
[docs] def reset_parameters(self):
self.module.reset_parameters()
[docs] def forward(self, x: Tensor) -> Tensor:
""""""
if self.allow_single_element and x.size(0) <= 1:
return torch.nn.functional.batch_norm(
x,
self.module.running_mean,
self.module.running_var,
self.module.weight,
self.module.bias,
False, # bn_training
0.0, # momentum
self.module.eps,
)
return self.module(x)
def __repr__(self):
return f'{self.__class__.__name__}({self.module.num_features})'