摘要

MixConv 的主要思想是在单个深度卷积操作中混合多个不同大小的内核，以便它可以轻松地从输入图像中捕获不同类型的模式。大核来捕获高分辨率的特征(我理解是全局的特征)，又需要小核来捕获低分辨率的特征（我理解是图片的纹理特征），以提高模型的准确性和效率。网络结构如图:

mixnet_architecture

这种特征拼接和Inceptions 有很多相似的地方，但是卷积采用分组卷积的方式，所以参数的计算量比较小。想要理解MixNet，首先要理解大小卷积核的优缺点，然后，理解分组卷积。

大卷积核与小卷积核

究竟是大卷积核好，还是小的卷积核好，这个大家一直在争论。CNN的鼻祖LeNet和惊艳到大家的AlexNet都使用了大卷积核。后来，到VGG开始使用3×3的卷积核，再发展到YOLOV4、5里面使用了大量的1×1的卷积核。

卷积核越大，receptive field（感受野）越大，看到的图片信息越多，因此获得的特征越好。但是大的卷积核会导致计算量的暴增，不利于模型深度的增加，计算性能也会降低。

于是在VGG、Inception网络中，利用2个3×3卷积核的组合来代替1个5×5卷积核，感受野不变，计算量还得到降低。多个 3x3 的卷积层比一个大尺寸 filter卷积层有更多的非线性（更多层的非线性函数），使得判决函数更加具有判决性。

正因为这些因素，导致了人们越来越喜欢小卷积核。

最近，人们又开始重新审视大卷积核，比如MixNet使用了3×3、5×5、7×7和9×9等，还有更猛的RepLKNet，直接使用31×31大小的卷积核。但是都不再是普通的卷积了，比如MixNet使用的是分组卷积，这样大大降低模型的计算量。

分组卷积

分组卷积则是对输入feature map进行分组，然后每组分别卷积。如下图：

分组卷积则是对输入feature map进行分组，然后每组分别卷积。

假设输入feature map的尺寸仍为 $C_{0}\times H \times W$ ，输出feature map的数量为 $C_{1}$ 个，如果设定要分成G个groups，则每组的输入feature map数量为 $\frac{C_{0}}{G}$ ，每组的输出feature map数量为 $\frac{C{1}}{G}$ ，每个卷积核的尺寸为 $\frac{C_{0}}{G}\times K \times K$ ，卷积核的总数仍为 $C_{1}$ 个，每组的卷积核数量为 $\frac{C{1}}{G}$ ，卷积核只与其同组的输入map进行卷积，卷积核的总参数量为 $N\times \frac{C_{0}}{G}\times K \times K$ ，总参数量减少为原来的 $\frac{1}{G}$ 。

计算量公式：
$\left[\left(2 \times K^{2} \times C_{0} / g +1\right) \times H \times W \times C_{o} / g\right] \times g$
分组卷积的参数量为：
$\frac{C_{0}}{g} * \frac{C_{1}}{g} * g$
举例：

输入的尺寸是227×227×3，卷积核大小是11×11，输出是6，输出维度是55×55，group为3

我们带入公式可以计算出

参数量：

$11^2 \times \frac{3}{3} \times \frac{6}{3} \times 3$ =726

运算量：

$\left[\left(2 \times 11^{2} \times3 / 3 +1\right) \times 55 \times 55 \times 6 / 3\right] \times 3$ =2205225

MinNet核心代码

mixnet_s参数列表：

	mixnet_s = [(16,  16,  [3],              [1],    [1],    1, 1, 'ReLU',  0.0),
                (16,  24,  [3],              [1, 1], [1, 1], 2, 6, 'ReLU',  0.0),
                (24,  24,  [3],              [1, 1], [1, 1], 1, 3, 'ReLU',  0.0),
                (24,  40,  [3, 5, 7],        [1],    [1],    2, 6, 'Swish', 0.5),
                (40,  40,  [3, 5],           [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40,  40,  [3, 5],           [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40,  40,  [3, 5],           [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40,  80,  [3, 5, 7],        [1],    [1, 1], 2, 6, 'Swish', 0.25),
                (80,  80,  [3, 5],           [1],    [1, 1], 1, 6, 'Swish', 0.25),
                (80,  80,  [3, 5],           [1],    [1, 1], 1, 6, 'Swish', 0.25),
                (80,  120, [3, 5, 7],        [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9],     [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9],     [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 200, [3, 5, 7, 9, 11], [1],    [1],    2, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9],     [1],    [1, 1], 1, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9],     [1],    [1, 1], 1, 6, 'Swish', 0.5)]

列的含义

第一列：in_channels,输入的通道。

第二列：out_channels，输出的通道。

第三列：卷积核的大小。

第四列：信道扩张，应用在MixNetBlock的扩展阶段。

第五列：信道映射，应用在MixNetBlock的末尾，映射输出通道。

第六列：stride，特征图缩放的倍数。

第七列：信道扩张的倍数。

第八列：激活函数

第九列：SE注意力机制放大的倍率。0代表没有SE。

行代表每个MixNetBlock的配置,MixNetBlock的代码如下：

class MixNetBlock(nn.Module):
    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size=[3],
            expand_ksize=[1],
            project_ksize=[1],
            stride=1,
            expand_ratio=1,
            non_linear='ReLU',
            se_ratio=0.0
    ):
        super(MixNetBlock, self).__init__()
        expand = (expand_ratio != 1)
        expand_channels = in_channels * expand_ratio
        se = (se_ratio != 0.0)
        self.residual_connection = (stride == 1 and in_channels == out_channels)
        conv = []
        if expand:
            # 扩展阶段
            pw_expansion = nn.Sequential(
                GroupedConv2d(in_channels, expand_channels, expand_ksize),
                nn.BatchNorm2d(expand_channels),
                NON_LINEARITY[non_linear]
            )
            conv.append(pw_expansion)
        # depthwise convolution phase
        dw = nn.Sequential(
            MDConv(expand_channels, kernel_size, stride),
            nn.BatchNorm2d(expand_channels),
            NON_LINEARITY[non_linear]
        )
        conv.append(dw)
        if se:
            # squeeze and excite
            squeeze_excite = SqueezeAndExcite(expand_channels, in_channels, se_ratio)
            conv.append(squeeze_excite)
        # projection phase
        pw_projection = nn.Sequential(
            GroupedConv2d(expand_channels, out_channels, project_ksize),
            nn.BatchNorm2d(out_channels)
        )
        conv.append(pw_projection)
        self.conv = nn.Sequential(*conv)
    def forward(self, x):
        if self.residual_connection:
            return x + self.conv(x)
        else:
            return self.conv(x)

我们将网络打印出来，选择“(80, 120, [3, 5, 7], [1, 1], [1, 1], 1, 6, ‘Swish’, 0.5),”这组配置，结合MixNetBlock的代码来学习。

 (10): MixNetBlock(
      (conv): Sequential(
        (0): Sequential(
          (0): GroupedConv2d(
            (grouped_conv): ModuleList(
              (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
            )
          )
          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): Swish(
            (sigmoid): Sigmoid()
          )
        )
        (1): Sequential(
          (0): MDConv(
            (mixed_depthwise_conv): ModuleList(
              (0): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=160, bias=False)
              (1): Conv2d(160, 160, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=160, bias=False)
              (2): Conv2d(160, 160, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=160, bias=False)
            )
          )
          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): Swish(
            (sigmoid): Sigmoid()
          )
        )
        (2): SqueezeAndExcite(
          (se_reduce): Conv2d(480, 40, kernel_size=(1, 1), stride=(1, 1))
          (non_linear1): Swish(
            (sigmoid): Sigmoid()
          )
          (se_expand): Conv2d(40, 480, kernel_size=(1, 1), stride=(1, 1))
          (non_linear2): Sigmoid()
        )
        (3): Sequential(
          (0): GroupedConv2d(
            (grouped_conv): ModuleList(
              (0): Conv2d(240, 60, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): Conv2d(240, 60, kernel_size=(1, 1), stride=(1, 1), bias=False)
            )
          )
          (1): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
    )

pw_expansion:通道扩展，将80个通道拆为两个40的channel作为卷积的输出，输入的channel×expand_ratio作为扩张的输出，然后拼接位480channel的特征图。

将480的channel拆解位3个160的channel，分别输入到混合卷积中，混合卷积由3×3、5×5和7×7构成的分组卷积中，分组为160，计算完成后拼接成480channel的特征图。

将特征图数据SE注意力中，计算完成后得到480channel的特征图。

最后，将480channel的特征图拆为两个240的特征图，分别输入到1×1的卷积中，得到60channel的特征图，然后，做拼接，得到120channel的特征图。

完整代码：

import math

import torch
import torch.nn as nn
from torch.autograd import Variable


class Swish(nn.Module):
    def __init__(self):
        super(Swish, self).__init__()

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        return x * self.sigmoid(x)


NON_LINEARITY = {
    
    
    'ReLU': nn.ReLU(inplace=True),
    'Swish': Swish(),
}


def _RoundChannels(c, divisor=8, min_value=None):
    if min_value is None:
        min_value = divisor
    new_c = max(min_value, int(c + divisor / 2) // divisor * divisor)
    if new_c < 0.9 * c:
        new_c += divisor
    return new_c


def _SplitChannels(channels, num_groups):
    split_channels = [channels // num_groups for _ in range(num_groups)]
    split_channels[0] += channels - sum(split_channels)
    return split_channels


def Conv3x3Bn(in_channels, out_channels, stride, non_linear='ReLU'):
    return nn.Sequential(
        nn.Conv2d(in_channels, out_channels, 3, stride, 1, bias=False),
        nn.BatchNorm2d(out_channels),
        NON_LINEARITY[non_linear]
    )


def Conv1x1Bn(in_channels, out_channels, non_linear='ReLU'):
    return nn.Sequential(
        nn.Conv2d(in_channels, out_channels, 1, 1, 0, bias=False),
        nn.BatchNorm2d(out_channels),
        NON_LINEARITY[non_linear]
    )


class SqueezeAndExcite(nn.Module):
    def __init__(self, channels, squeeze_channels, se_ratio):
        super(SqueezeAndExcite, self).__init__()

        squeeze_channels = squeeze_channels * se_ratio
        if not squeeze_channels.is_integer():
            raise ValueError('channels must be divisible by 1/ratio')

        squeeze_channels = int(squeeze_channels)
        self.se_reduce = nn.Conv2d(channels, squeeze_channels, 1, 1, 0, bias=True)
        self.non_linear1 = NON_LINEARITY['Swish']
        self.se_expand = nn.Conv2d(squeeze_channels, channels, 1, 1, 0, bias=True)
        self.non_linear2 = nn.Sigmoid()

    def forward(self, x):
        y = torch.mean(x, (2, 3), keepdim=True)
        y = self.non_linear1(self.se_reduce(y))
        y = self.non_linear2(self.se_expand(y))
        y = x * y

        return y


class GroupedConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(GroupedConv2d, self).__init__()

        self.num_groups = len(kernel_size)
        self.split_in_channels = _SplitChannels(in_channels, self.num_groups)
        self.split_out_channels = _SplitChannels(out_channels, self.num_groups)
        print(self.split_in_channels)
        self.grouped_conv = nn.ModuleList()
        for i in range(self.num_groups):
            self.grouped_conv.append(nn.Conv2d(
                self.split_in_channels[i],
                self.split_out_channels[i],
                kernel_size[i],
                stride=stride,
                padding=padding,
                bias=False
            ))

    def forward(self, x):
        if self.num_groups == 1:
            return self.grouped_conv[0](x)

        x_split = torch.split(x, self.split_in_channels, dim=1)
        x = [conv(t) for conv, t in zip(self.grouped_conv, x_split)]
        x = torch.cat(x, dim=1)

        return x


class MDConv(nn.Module):
    def __init__(self, channels, kernel_size, stride):
        super(MDConv, self).__init__()

        self.num_groups = len(kernel_size)
        self.split_channels = _SplitChannels(channels, self.num_groups)

        self.mixed_depthwise_conv = nn.ModuleList()
        for i in range(self.num_groups):
            self.mixed_depthwise_conv.append(nn.Conv2d(
                self.split_channels[i],
                self.split_channels[i],
                kernel_size[i],
                stride=stride,
                padding=kernel_size[i] // 2,
                groups=self.split_channels[i],
                bias=False
            ))

    def forward(self, x):
        if self.num_groups == 1:
            return self.mixed_depthwise_conv[0](x)

        x_split = torch.split(x, self.split_channels, dim=1)
        x = [conv(t) for conv, t in zip(self.mixed_depthwise_conv, x_split)]
        x = torch.cat(x, dim=1)

        return x


class MixNetBlock(nn.Module):
    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size=[3],
            expand_ksize=[1],
            project_ksize=[1],
            stride=1,
            expand_ratio=1,
            non_linear='ReLU',
            se_ratio=0.0
    ):

        super(MixNetBlock, self).__init__()

        expand = (expand_ratio != 1)
        expand_channels = in_channels * expand_ratio
        se = (se_ratio != 0.0)
        self.residual_connection = (stride == 1 and in_channels == out_channels)

        conv = []

        if expand:
            # expansion phase
            pw_expansion = nn.Sequential(
                GroupedConv2d(in_channels, expand_channels, expand_ksize),
                nn.BatchNorm2d(expand_channels),
                NON_LINEARITY[non_linear]
            )
            conv.append(pw_expansion)

        # depthwise convolution phase
        dw = nn.Sequential(
            MDConv(expand_channels, kernel_size, stride),
            nn.BatchNorm2d(expand_channels),
            NON_LINEARITY[non_linear]
        )
        conv.append(dw)

        if se:
            # squeeze and excite
            squeeze_excite = SqueezeAndExcite(expand_channels, in_channels, se_ratio)
            conv.append(squeeze_excite)

        # projection phase
        pw_projection = nn.Sequential(
            GroupedConv2d(expand_channels, out_channels, project_ksize),
            nn.BatchNorm2d(out_channels)
        )
        conv.append(pw_projection)

        self.conv = nn.Sequential(*conv)

    def forward(self, x):
        if self.residual_connection:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MixNet(nn.Module):
    # [in_channels, out_channels, kernel_size, expand_ksize, project_ksize, stride, expand_ratio, non_linear, se_ratio]
    mixnet_s = [(16, 16, [3], [1], [1], 1, 1, 'ReLU', 0.0),
                (16, 24, [3], [1, 1], [1, 1], 2, 6, 'ReLU', 0.0),
                (24, 24, [3], [1, 1], [1, 1], 1, 3, 'ReLU', 0.0),
                (24, 40, [3, 5, 7], [1], [1], 2, 6, 'Swish', 0.5),
                (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40, 80, [3, 5, 7], [1], [1, 1], 2, 6, 'Swish', 0.25),
                (80, 80, [3, 5], [1], [1, 1], 1, 6, 'Swish', 0.25),
                (80, 80, [3, 5], [1], [1, 1], 1, 6, 'Swish', 0.25),
                (80, 120, [3, 5, 7], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9], [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9], [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 200, [3, 5, 7, 9, 11], [1], [1], 2, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9], [1], [1, 1], 1, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9], [1], [1, 1], 1, 6, 'Swish', 0.5)]

    mixnet_m = [(24, 24, [3], [1], [1], 1, 1, 'ReLU', 0.0),
                (24, 32, [3, 5, 7], [1, 1], [1, 1], 2, 6, 'ReLU', 0.0),
                (32, 32, [3], [1, 1], [1, 1], 1, 3, 'ReLU', 0.0),
                (32, 40, [3, 5, 7, 9], [1], [1], 2, 6, 'Swish', 0.5),
                (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5),
                (40, 80, [3, 5, 7], [1], [1], 2, 6, 'Swish', 0.25),
                (80, 80, [3, 5, 7, 9], [1, 1], [1, 1], 1, 6, 'Swish', 0.25),
                (80, 80, [3, 5, 7, 9], [1, 1], [1, 1], 1, 6, 'Swish', 0.25),
                (80, 80, [3, 5, 7, 9], [1, 1], [1, 1], 1, 6, 'Swish', 0.25),
                (80, 120, [3], [1], [1], 1, 6, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9], [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9], [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 120, [3, 5, 7, 9], [1, 1], [1, 1], 1, 3, 'Swish', 0.5),
                (120, 200, [3, 5, 7, 9], [1], [1], 2, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9], [1], [1, 1], 1, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9], [1], [1, 1], 1, 6, 'Swish', 0.5),
                (200, 200, [3, 5, 7, 9], [1], [1, 1], 1, 6, 'Swish', 0.5)]

    def __init__(self, net_type='mixnet_s', input_size=224, num_classes=1000, stem_channels=16, feature_size=1536,
                 depth_multiplier=1.0):
        super(MixNet, self).__init__()

        if net_type == 'mixnet_s':
            config = self.mixnet_s
            stem_channels = 16
            dropout_rate = 0.2
        elif net_type == 'mixnet_m':
            config = self.mixnet_m
            stem_channels = 24
            dropout_rate = 0.25
        elif net_type == 'mixnet_l':
            config = self.mixnet_m
            stem_channels = 24
            depth_multiplier *= 1.3
            dropout_rate = 0.25
        else:
            raise TypeError('Unsupported MixNet type')

        assert input_size % 32 == 0

        # depth multiplier
        if depth_multiplier != 1.0:
            stem_channels = _RoundChannels(stem_channels * depth_multiplier)

            for i, conf in enumerate(config):
                conf_ls = list(conf)
                conf_ls[0] = _RoundChannels(conf_ls[0] * depth_multiplier)
                conf_ls[1] = _RoundChannels(conf_ls[1] * depth_multiplier)
                config[i] = tuple(conf_ls)

        # stem convolution
        self.stem_conv = Conv3x3Bn(3, stem_channels, 2)

        # building MixNet blocks
        layers = []
        for in_channels, out_channels, kernel_size, expand_ksize, project_ksize, stride, expand_ratio, non_linear, se_ratio in config:
            layers.append(MixNetBlock(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                expand_ksize=expand_ksize,
                project_ksize=project_ksize,
                stride=stride,
                expand_ratio=expand_ratio,
                non_linear=non_linear,
                se_ratio=se_ratio
            ))
        self.layers = nn.Sequential(*layers)

        # last several layers
        self.head_conv = Conv1x1Bn(config[-1][1], feature_size)

        self.avgpool = nn.AvgPool2d(input_size // 32, stride=1)
        self.dropout = nn.Dropout(dropout_rate)
        self.classifier = nn.Linear(feature_size, num_classes)

        self._initialize_weights()

    def forward(self, x):
        x = self.stem_conv(x)
        x = self.layers(x)
        x = self.head_conv(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.classifier(x)

        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()


if __name__ == '__main__':
    net = MixNet()
    x_image = Variable(torch.randn(1, 3, 224, 224))
    y = net(x_image)

MixNet解析以及pytorch源码

文章目录

摘要

大卷积核与小卷积核

分组卷积

MinNet核心代码

完整代码：

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