DropBlock: A regularization method for convolutional networks

DropBlock: A regularization method for convolutional networks

一. 论文简介

正则化卷积层，防止过拟合

主要做的贡献如下（可能之前有人已提出）：

1. 正则化卷积层的模块（正则化Conv层），类似dropout（正则化FC层）

二. 模块详解

2.1 论文思路简介

1. 正常的DropOut是对FC层做随机失活，如何对卷积层做随机失活？
2. 按照DropOut的思路，直接对卷积层的feature做随机失活，如下图（b）所示，试验效果并不理想，作者猜测是由于卷积层对局部敏感，而随机失活导致局部某些信息得以保留，造成效果不好。
3. DropOut的思想融合到卷积之中，局部块随机失活，试验效果随机块失活明显优于随机点失活

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2.2 具体实现

2.2.1 具体实现

其实按照上面的分析，我们就可以大概猜到怎么做了。

需要哪些参数：

• 随机失活块的大小，这里按照卷积一样，\(Kernel=K*K\)
• 创建一块 \(Mask\) 符合 \(Bernoulli\) 分布
• 循环 \(Mask\) 对于每一个 \(M_{ij}=0\) 的点，使得其周围 \(Kernel\) 个点也为0
• 价格 \(Mask\) 作用于 \(feature\) 上，\(feature=M*Feature\)
• 归一化特征图：\(feature=feature*count(M)/count\_ones(M)\)

其实上面的公式很简单，就是2.1节说的那样，安装随机块失活即可，什么方法都可以。以下代码主要使用\(maxpooling\)进行\(block\)的操作，其它地方都一样。

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


class DropBlock2D(nn.Module):
    r"""Randomly zeroes 2D spatial blocks of the input tensor.
    As described in the paper
    `DropBlock: A regularization method for convolutional networks`_ ,
    dropping whole blocks of feature map allows to remove semantic
    information as compared to regular dropout.
    Args:
        drop_prob (float): probability of an element to be dropped.
        block_size (int): size of the block to drop
    Shape:
        - Input: `(N, C, H, W)`
        - Output: `(N, C, H, W)`
    .. _DropBlock: A regularization method for convolutional networks:
       https://arxiv.org/abs/1810.12890
    """

    def __init__(self, drop_prob, block_size):
        super(DropBlock2D, self).__init__()

        self.drop_prob = drop_prob
        self.block_size = block_size

    def forward(self, x):
        # shape: (bsize, channels, height, width)

        assert x.dim() == 4, \
            "Expected input with 4 dimensions (bsize, channels, height, width)"

        if not self.training or self.drop_prob == 0.:
            return x
        else:
            # get gamma value
            gamma = self._compute_gamma(x)

            # sample mask
            mask = (torch.rand(x.shape[0], *x.shape[2:]) < gamma).float()

            # place mask on input device
            mask = mask.to(x.device)

            # compute block mask
            block_mask = self._compute_block_mask(mask)

            # apply block mask
            out = x * block_mask[:, None, :, :]

            # scale output
            out = out * block_mask.numel() / block_mask.sum() # 归一化

            return out

    def _compute_block_mask(self, mask):
        # 使用maxpooling代替block计算
        block_mask = F.max_pool2d(input=mask[:, None, :, :],
                                  kernel_size=(self.block_size, self.block_size),
                                  stride=(1, 1),
                                  padding=self.block_size // 2) # 由于使用padding，边界概率计算不准确

        if self.block_size % 2 == 0:
            block_mask = block_mask[:, :, :-1, :-1]

        block_mask = 1 - block_mask.squeeze(1)

        return block_mask

    def _compute_gamma(self, x):
        return self.drop_prob / (self.block_size ** 2)


class DropBlock3D(DropBlock2D):
    r"""Randomly zeroes 3D spatial blocks of the input tensor.
    An extension to the concept described in the paper
    `DropBlock: A regularization method for convolutional networks`_ ,
    dropping whole blocks of feature map allows to remove semantic
    information as compared to regular dropout.
    Args:
        drop_prob (float): probability of an element to be dropped.
        block_size (int): size of the block to drop
    Shape:
        - Input: `(N, C, D, H, W)`
        - Output: `(N, C, D, H, W)`
    .. _DropBlock: A regularization method for convolutional networks:
       https://arxiv.org/abs/1810.12890
    """

    def __init__(self, drop_prob, block_size):
        super(DropBlock3D, self).__init__(drop_prob, block_size)

    def forward(self, x):
        # shape: (bsize, channels, depth, height, width)

        assert x.dim() == 5, \
            "Expected input with 5 dimensions (bsize, channels, depth, height, width)"

        if not self.training or self.drop_prob == 0.:
            return x
        else:
            # get gamma value
            gamma = self._compute_gamma(x)

            # sample mask
            mask = (torch.rand(x.shape[0], *x.shape[2:]) < gamma).float()

            # place mask on input device
            mask = mask.to(x.device)

            # compute block mask
            block_mask = self._compute_block_mask(mask)

            # apply block mask
            out = x * block_mask[:, None, :, :, :]

            # scale output
            out = out * block_mask.numel() / block_mask.sum()

            return out

    def _compute_block_mask(self, mask):
        block_mask = F.max_pool3d(input=mask[:, None, :, :, :],
                                  kernel_size=(self.block_size, self.block_size, self.block_size),
                                  stride=(1, 1, 1),
                                  padding=self.block_size // 2)

        if self.block_size % 2 == 0:
            block_mask = block_mask[:, :, :-1, :-1, :-1]

        block_mask = 1 - block_mask.squeeze(1)

        return block_mask

    def _compute_gamma(self, x):
        return self.drop_prob / (self.block_size ** 3)


if __name__ == "__main__":
    x = torch.ones(size=(10,256,64,64),dtype=torch.float32)
    layer = DropBlock2D(0.1, 5)
    y = layer(x)

三. 参考文献

• 原始论文
• code