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POSTECH-CVLab
GitHub Repository: POSTECH-CVLab/PyTorch-StudioGAN
 Path: blob/master/src/sync_batchnorm/batchnorm_reimpl.py
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"""

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-*- coding: utf-8 -*-

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File   : batchnorm_reimpl.py

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Author : Jiayuan Mao

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Email  : [email protected]

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Date   : 27/01/2018

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This file is part of Synchronized-BatchNorm-PyTorch.

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https://github.com/vacancy/Synchronized-BatchNorm-PyTorch

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Distributed under MIT License.

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MIT License

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Copyright (c) 2018 Jiayuan MAO

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Permission is hereby granted, free of charge, to any person obtaining a copy

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of this software and associated documentation files (the "Software"), to deal

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in the Software without restriction, including without limitation the rights

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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

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copies of the Software, and to permit persons to whom the Software is

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furnished to do so, subject to the following conditions:

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The above copyright notice and this permission notice shall be included in all

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copies or substantial portions of the Software.

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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

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SOFTWARE.

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"""

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import torch

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import torch.nn as nn

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import torch.nn.init as init

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__all__ = ['BatchNorm2dReimpl']

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class BatchNorm2dReimpl(nn.Module):

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    """

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    A re-implementation of batch normalization, used for testing the numerical

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    stability.

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    Author: acgtyrant

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    See also:

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    https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14

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    """

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    def __init__(self, num_features, eps=1e-5, momentum=0.1):

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        super().__init__()

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        self.num_features = num_features

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        self.eps = eps

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        self.momentum = momentum

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        self.weight = nn.Parameter(torch.empty(num_features))

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        self.bias = nn.Parameter(torch.empty(num_features))

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        self.register_buffer('running_mean', torch.zeros(num_features))

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        self.register_buffer('running_var', torch.ones(num_features))

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        self.reset_parameters()

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    def reset_running_stats(self):

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        self.running_mean.zero_()

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        self.running_var.fill_(1)

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    def reset_parameters(self):

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        self.reset_running_stats()

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        init.uniform_(self.weight)

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        init.zeros_(self.bias)

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    def forward(self, input_):

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        batchsize, channels, height, width = input_.size()

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        numel = batchsize * height * width

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        input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel)

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        sum_ = input_.sum(1)

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        sum_of_square = input_.pow(2).sum(1)

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        mean = sum_ / numel

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        sumvar = sum_of_square - sum_ * mean

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        self.running_mean = ((1 - self.momentum) * self.running_mean + self.momentum * mean.detach())

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        unbias_var = sumvar / (numel - 1)

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        self.running_var = ((1 - self.momentum) * self.running_var + self.momentum * unbias_var.detach())

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        bias_var = sumvar / numel

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        inv_std = 1 / (bias_var + self.eps).pow(0.5)

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        output = ((input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) * self.weight.unsqueeze(1) +

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                  self.bias.unsqueeze(1))

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        return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous()

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