Source code for torch_geometric.nn.norm.instance_norm

from torch_geometric.typing import OptTensor

import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.instancenorm import _InstanceNorm
from torch_scatter import scatter
from torch_geometric.utils import degree

[docs]class InstanceNorm(_InstanceNorm): r"""Applies instance normalization over each individual example in a batch of node features as described in the `"Instance Normalization: The Missing Ingredient for Fast Stylization" <>`_ 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 separately for each object in a 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:`False`) 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 instance statistics in both training and eval modes. (default: :obj:`False`) """ def __init__(self, in_channels, eps=1e-5, momentum=0.1, affine=False, track_running_stats=False): super(InstanceNorm, self).__init__(in_channels, eps, momentum, affine, track_running_stats)
[docs] def forward(self, x: Tensor, batch: OptTensor = None) -> Tensor: """""" if batch is None: out = F.instance_norm( x.t().unsqueeze(0), self.running_mean, self.running_var, self.weight, self.bias, or not self.track_running_stats, self.momentum, self.eps) return out.squeeze(0).t() batch_size = int(batch.max()) + 1 mean = var = unbiased_var = x # Dummies. if or not self.track_running_stats: norm = degree(batch, batch_size, dtype=x.dtype).clamp_(min=1) norm = norm.view(-1, 1) unbiased_norm = (norm - 1).clamp_(min=1) mean = scatter(x, batch, dim=0, dim_size=batch_size, reduce='add') / norm x = x - mean[batch] var = scatter(x * x, batch, dim=0, dim_size=batch_size, reduce='add') unbiased_var = var / unbiased_norm var = var / norm momentum = self.momentum if self.running_mean is not None: self.running_mean = ( 1 - momentum) * self.running_mean + momentum * mean.mean(0) if self.running_var is not None: self.running_var = ( 1 - momentum ) * self.running_var + momentum * unbiased_var.mean(0) else: if self.running_mean is not None: mean = self.running_mean.view(1, -1).expand(batch_size, -1) if self.running_var is not None: var = self.running_var.view(1, -1).expand(batch_size, -1) x = x - mean[batch] out = x / (var + self.eps).sqrt()[batch] if self.weight is not None and self.bias is not None: out = out * self.weight.view(1, -1) + self.bias.view(1, -1) return out
def __repr__(self): return f'{self.__class__.__name__}({self.num_features})'