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#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch.nn.init as init
__all__ = ['BatchNormReimpl']
class BatchNorm2dReimpl(nn.Module):
"""
A re-implementation of batch normalization, used for testing the numerical
stability.
Author: acgtyrant
See also:
https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14
"""
def __init__(self, num_features, eps=1e-5, momentum=0.1):
super().__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.weight = nn.Parameter(torch.empty(num_features))
self.bias = nn.Parameter(torch.empty(num_features))
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_running_stats(self):
self.running_mean.zero_()
self.running_var.fill_(1)
def reset_parameters(self):
self.reset_running_stats()
init.uniform_(self.weight)
init.zeros_(self.bias)
def forward(self, input_):
batchsize, channels, height, width = input_.size()
numel = batchsize * height * width
input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel)
sum_ = input_.sum(1)
sum_of_square = input_.pow(2).sum(1)
mean = sum_ / numel
sumvar = sum_of_square - sum_ * mean
self.running_mean = (
(1 - self.momentum) * self.running_mean
+ self.momentum * mean.detach()
)
unbias_var = sumvar / (numel - 1)
self.running_var = (
(1 - self.momentum) * self.running_var
+ self.momentum * unbias_var.detach()
)
bias_var = sumvar / numel
inv_std = 1 / (bias_var + self.eps).pow(0.5)
output = (
(input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) *
self.weight.unsqueeze(1) + self.bias.unsqueeze(1))
return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous()
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