DARTS代码分析（Pytorch）

最近在看DARTS的代码，有一个operations.py的文件，里面是对各类点与点之间操作的方法。

OPS = {
    'none': lambda C, stride, affine: Zero(stride),
    'avg_pool_3x3': lambda C, stride, affine: PoolBN('avg', C, 3, stride, 1, affine=affine),
    'max_pool_3x3': lambda C, stride, affine: PoolBN('max', C, 3, stride, 1, affine=affine),
    'skip_connect': lambda C, stride, affine: \
        Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
    'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
    'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
    'sep_conv_7x7': lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
    'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine), # 5x5
    'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine), # 9x9
    'conv_7x1_1x7': lambda C, stride, affine: FacConv(C, C, 7, stride, 3, affine=affine)
}

首先定义10个操作，依次解释：

class PoolBN(nn.Module):
    """
    AvgPool or MaxPool - BN
    """
    def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
        """
        Args:
            pool_type: 'max' or 'avg'
        """
        super().__init__()
        if pool_type.lower() == 'max':
            self.pool = nn.MaxPool2d(kernel_size, stride, padding)
        elif pool_type.lower() == 'avg':
            self.pool = nn.AvgPool2d(kernel_size, stride, padding, count_include_pad=False)
        else:
            raise ValueError()

        self.bn = nn.BatchNorm2d(C, affine=affine)

    def forward(self, x):
        out = self.pool(x)
        out = self.bn(out)
        return out

这是池化函数，有最大池化和平均池化方法，count_include_pad=False表示不把填充的0计算进去

class Identity(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x

这个表示skip conncet

class FactorizedReduce(nn.Module):
    """
    Reduce feature map size by factorized pointwise(stride=2).
    """
    def __init__(self, C_in, C_out, affine=True):
        super().__init__()
        self.relu = nn.ReLU()
        self.conv1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
        self.conv2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
        self.bn = nn.BatchNorm2d(C_out, affine=affine)

    def forward(self, x):
        x = self.relu(x)
        out = torch.cat([self.conv1(x), self.conv2(x[:, :, 1:, 1:])], dim=1)
        out = self.bn(out)
        return out

这个表示逐步减少特征图大小，通道数保持不变

DARTS代码分析（Pytorch）

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