ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

具体见: https://github.com/kuangliu/pytorch-cifar/tree/master/models

class ShuffleNet(nn.Module):
    def __init__(self, cfg):
        super(ShuffleNet, self).__init__()
        out_planes = cfg['out_planes']
        num_blocks = cfg['num_blocks']
        groups = cfg['groups']

        self.conv1 = nn.Conv2d(3, 24, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(24)
        self.in_planes = 24
        self.layer1 = self._make_layer(out_planes[0], num_blocks[0], groups)
        self.layer2 = self._make_layer(out_planes[1], num_blocks[1], groups)
        self.layer3 = self._make_layer(out_planes[2], num_blocks[2], groups)
        self.linear = nn.Linear(out_planes[2], 10)

    def _make_layer(self, out_planes, num_blocks, groups):
        layers = []
        for i in range(num_blocks):
            stride = 2 if i == 0 else 1
            cat_planes = self.in_planes if i == 0 else 0
            layers.append(Bottleneck(self.in_planes, out_planes-cat_planes, stride=stride, groups=groups))
            self.in_planes = out_planes
        return nn.Sequential(*layers)

class Bottleneck(nn.Module):
    def __init__(self, in_planes, out_planes, stride, groups):
        super(Bottleneck, self).__init__()
        self.stride = stride

        mid_planes = out_planes/4
        g = 1 if in_planes==24 else groups
        self.conv1 = nn.Conv2d(in_planes, mid_planes, kernel_size=1, groups=g, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_planes)
        self.shuffle1 = ShuffleBlock(groups=g)
        self.conv2 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=stride, padding=1, groups=mid_planes, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_planes)
        self.conv3 = nn.Conv2d(mid_planes, out_planes, kernel_size=1, groups=groups, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1))