preact.py

'''Pre-activation ResNet in PyTorch.
https://github.com/kuangliu/pytorch-cifar/blob/master/models/preact_resnet.py
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''


import torch.nn as nn
import torch.nn.functional as F


class PreActBlock(nn.Module):
    '''Pre-activation version of the BasicBlock.'''
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, bn=True):
        super(PreActBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes) if bn else nn.Identity()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
                               stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes) if bn else nn.Identity()
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)

        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(nn.Conv2d(
                in_planes, self.expansion * planes, kernel_size=1,
                stride=stride, bias=False))

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out += shortcut
        return out


class PreActBottleneck(nn.Module):
    '''Pre-activation version of the original Bottleneck module.'''
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)

        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(nn.Conv2d(
                in_planes, self.expansion * planes, kernel_size=1,
                stride=stride, bias=False))

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out = self.conv3(F.relu(self.bn3(out)))
        out += shortcut
        return out


class PreActBottleneck_depthwise(nn.Module):
    '''Pre-activation version of the original Bottleneck module.'''
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBottleneck_depthwise, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.group_num = in_planes if in_planes < planes else planes
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False, groups=self.group_num)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False, groups=self.group_num)
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1,
                               bias=False, groups=self.group_num)

        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False,
                    groups=self.group_num))

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out = self.conv3(F.relu(self.bn3(out)))
        out += shortcut
        return out