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shufflenetv1

知识的搬运工又来了
论文地址：shufflenetv1论文地址
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices。
shufflenet是一篇关于降低深度学习计算量的论文，其可以运行在手机等移动设备端，发表在了CVPR2018上

摘要

此论文是一篇效率很高的cnn框架，可以运行在移动设备端，（例如，10-150 MFLOPs）而设计的，该结构利用分组逐点卷积（pointwise group convolution）和通道重排（channel shuffle）两种新的运算方法，ShuffleNet比AlexNet实现了约13倍的实际加速

介绍

我们会发现，例如在Xception和ResNeXt，由于运算代价高昂的11卷积，在极小的网络中效率变得非常低，于是就采用了分组逐点卷积来降低11卷积的计算复杂度。为了克服分组卷积带来的副作用，我们提出了一种新的通道重排操作来帮助信息在特征通道间流动。

创新点

1.设置了分组卷积的通道重排
2.设置了shuffleNet单元

通道重排

上图中，a是常规的分组卷积，但是其存在的问题是当分组比较多时，各个通道的信息就被隔离开来，此属性会阻塞通道组之间的信息流并削弱表征能力，所以我们做出了b方式的改进，将分组卷积卷积好的特征图进行通道重排，就是将分组后的特征图分成若干份，然后随机按照某一规则进行组合，组合好之后送入到下一次的卷积中，c是b的美观版本。

shuffleNet单元

利用通道重排设计出一种专门为小型网络设计的单元块，图中a图是一种由dw卷积的残差瓶颈结构，首先进行了11卷积+BN+RELU，然后进行33dw卷积，BN+relu，最后连接了11卷积+BN，再最后接了残差连接进行了Add操作。
b：是我们设计出的string==1的shufflenet单元，首先进行了11的Gconv(分组卷积)，然后接了通道重排，然后是33dw卷积，但是后边我们并没有接relu，最后再add操作。
c：我们设计出了string==2的shufflenet单元，其在残差边上使用了33的平均池化，注意最后是concat操作，而不是add操作，这样可以不增加计算量的前提下扩大特征维度，（add是通道数值相加，concat是通道堆叠）

模型总体结构

代码

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init


def conv3x3(in_channels, out_channels, stride=1, 
            padding=1, bias=True, groups=1):    
    """3x3 convolution with padding """
    return nn.Conv2d(
        in_channels, 
        out_channels, 
        kernel_size=3, 
        stride=stride,
        padding=padding,
        bias=bias,
        groups=groups)


def conv1x1(in_channels, out_channels, groups=1):
    """1x1 convolution with padding - Normal pointwise convolution When groups == 1 - Grouped pointwise convolution when groups > 1 """
    return nn.Conv2d(
        in_channels, 
        out_channels, 
        kernel_size=1, 
        groups=groups,
        stride=1)


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()

    channels_per_group = num_channels // groups# groups是分的组数
    
    # reshape
    x = x.view(batchsize, groups, 
        channels_per_group, height, width)

    # transpose
    # - contiguous() required if transpose() is used before view().
    # See https://github.com/pytorch/pytorch/issues/764
    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class ShuffleUnit(nn.Module):
    def __init__(self, in_channels, out_channels, groups=3,
                 grouped_conv=True, combine='add'):
        
        super(ShuffleUnit, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.grouped_conv = grouped_conv
        self.combine = combine
        self.groups = groups
        self.bottleneck_channels = self.out_channels // 4

        # define the type of ShuffleUnit
        if self.combine == 'add':
            # ShuffleUnit Figure 2b
            self.depthwise_stride = 1
            self._combine_func = self._add
        elif self.combine == 'concat':
            # ShuffleUnit Figure 2c
            self.depthwise_stride = 2
            self._combine_func = self._concat
            
            # ensure output of concat has the same channels as 
            # original output channels.
            self.out_channels -= self.in_channels
        else:
            raise ValueError("Cannot combine tensors with \"{}\"" \
                             "Only \"add\" and \"concat\" are" \
                             "supported".format(self.combine))

        # Use a 1x1 grouped or non-grouped convolution to reduce input channels
        # to bottleneck channels, as in a ResNet bottleneck module.
        # NOTE: Do not use group convolution for the first conv1x1 in Stage 2.
        self.first_1x1_groups = self.groups if grouped_conv else 1

        self.g_conv_1x1_compress = self._make_grouped_conv1x1(
            self.in_channels,
            self.bottleneck_channels,
            self.first_1x1_groups,
            batch_norm=True,
            relu=True
            )

        # 3x3 depthwise convolution followed by batch normalization
        self.depthwise_conv3x3 = conv3x3(
            self.bottleneck_channels, self.bottleneck_channels,
            stride=self.depthwise_stride, groups=self.bottleneck_channels)
        self.bn_after_depthwise = nn.BatchNorm2d(self.bottleneck_channels)

        # Use 1x1 grouped convolution to expand from 
        # bottleneck_channels to out_channels
        self.g_conv_1x1_expand = self._make_grouped_conv1x1(
            self.bottleneck_channels,
            self.out_channels,
            self.groups,
            batch_norm=True,
            relu=False
            )


    @staticmethod
    def _add(x, out):
        # residual connection
        return x + out


    @staticmethod
    def _concat(x, out):
        # concatenate along channel axis
        return torch.cat((x, out), 1)


    def _make_grouped_conv1x1(self, in_channels, out_channels, groups,
        batch_norm=True, relu=False):

        modules = OrderedDict()

        conv = conv1x1(in_channels, out_channels, groups=groups)
        modules['conv1x1'] = conv

        if batch_norm:
            modules['batch_norm'] = nn.BatchNorm2d(out_channels)
        if relu:
            modules['relu'] = nn.ReLU()
        if len(modules) > 1:
            return nn.Sequential(modules)
        else:
            return conv


    def forward(self, x):
        # save for combining later with output
        residual = x

        if self.combine == 'concat':
            residual = F.avg_pool2d(residual, kernel_size=3, 
                stride=2, padding=1)

        out = self.g_conv_1x1_compress(x)
        out = channel_shuffle(out, self.groups)
        out = self.depthwise_conv3x3(out)
        out = self.bn_after_depthwise(out)
        out = self.g_conv_1x1_expand(out)
        
        out = self._combine_func(residual, out)
        return F.relu(out)


class ShuffleNet(nn.Module):
    """ShuffleNet implementation. """

    def __init__(self, groups=3, in_channels=3, num_classes=1000):
        """ShuffleNet constructor. Arguments: groups (int, optional): number of groups to be used in grouped 1x1 convolutions in each ShuffleUnit. Default is 3 for best performance according to original paper. in_channels (int, optional): number of channels in the input tensor. Default is 3 for RGB image inputs. num_classes (int, optional): number of classes to predict. Default is 1000 for ImageNet. """
        super(ShuffleNet, self).__init__()

        self.groups = groups
        self.stage_repeats = [3, 7, 3]
        self.in_channels =  in_channels
        self.num_classes = num_classes

        # index 0 is invalid and should never be called.
        # only used for indexing convenience.
        if groups == 1:
            self.stage_out_channels = [-1, 24, 144, 288, 567]
        elif groups == 2:
            self.stage_out_channels = [-1, 24, 200, 400, 800]
        elif groups == 3:
            self.stage_out_channels = [-1, 24, 240, 480, 960]
        elif groups == 4:
            self.stage_out_channels = [-1, 24, 272, 544, 1088]
        elif groups == 8:
            self.stage_out_channels = [-1, 24, 384, 768, 1536]
        else:
            raise ValueError(
                """{} groups is not supported for 1x1 Grouped Convolutions""".format(num_groups))
        
        # Stage 1 always has 24 output channels
        self.conv1 = conv3x3(self.in_channels,
                             self.stage_out_channels[1], # stage 1
                             stride=2)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # Stage 2
        self.stage2 = self._make_stage(2)
        # Stage 3
        self.stage3 = self._make_stage(3)
        # Stage 4
        self.stage4 = self._make_stage(4)

        # Global pooling:
        # Undefined as PyTorch's functional API can be used for on-the-fly
        # shape inference if input size is not ImageNet's 224x224

        # Fully-connected classification layer
        num_inputs = self.stage_out_channels[-1]
        self.fc = nn.Linear(num_inputs, self.num_classes)
        self.init_params()


    def init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant(m.weight, 1)
                init.constant(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant(m.bias, 0)


    def _make_stage(self, stage):
        modules = OrderedDict()
        stage_name = "ShuffleUnit_Stage{}".format(stage)
        
        # First ShuffleUnit in the stage
        # 1. non-grouped 1x1 convolution (i.e. pointwise convolution)
        # is used in Stage 2. Group convolutions used everywhere else.
        grouped_conv = stage > 2
        
        # 2. concatenation unit is always used.
        first_module = ShuffleUnit(
            self.stage_out_channels[stage-1],
            self.stage_out_channels[stage],
            groups=self.groups,
            grouped_conv=grouped_conv,
            combine='concat'
            )
        modules[stage_name+"_0"] = first_module

        # add more ShuffleUnits depending on pre-defined number of repeats
        for i in range(self.stage_repeats[stage-2]):
            name = stage_name + "_{}".format(i+1)
            module = ShuffleUnit(
                self.stage_out_channels[stage],
                self.stage_out_channels[stage],
                groups=self.groups,
                grouped_conv=True,
                combine='add'
                )
            modules[name] = module

        return nn.Sequential(modules)


    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)

        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)

        # global average pooling layer
        x = F.avg_pool2d(x, x.data.size()[-2:])
        
        # flatten for input to fully-connected layer
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return F.log_softmax(x, dim=1)


if __name__ == "__main__":
    model = ShuffleNet()

代码来源

最后总结

为了评估分组逐点卷积的重要性，我们比较了具有相同复杂度的ShuffleNet模型，其组数从1到8不等。如果组数等于1，则不涉及分组逐点卷积，则ShuffleNet单元成为一个“Xception-like”结构。为了更好地理解，我们还将网络的宽度扩展到3种不同的复杂性，并分别比较它们的分类性能。结果如表2所示。

表2. 分类误差VS组数g（较小的数字代表更好的性能）
 从结果中我们可以看出，有分组卷积（g>1）的模型始终比没有分组逐点卷积（g=1）的模型表现得更好，较小的模型往往从分组中获益更多。
 表2还显示，对于某些模型（(如ShuffleNet 0.5×），当组数变得相对较大时（例如g=8），分类分数饱和甚至下降。随着组数的增加（因此特征图的范围更广），每个卷积滤波器的输入通道变得更少，这可能会损害表示能力。有趣的是，我们也注意到，对于如ShuffleNet 0.25×这样较小的模型，**较大的组数往往会得到更好的一致性结果，这表明更宽的特征图为较小的模型带来了更多的好处。**我们在每次卷积之后都添加了一个批归一化层，使端到端的训练更加容易。**由于ShuffleNet的高效设计，我们可以在给定的计算预算下使用更多的通道，从而通常可以获得更好的性能。**浅模型仍然是明显好于相应的MobileNet，这意味着ShuffleNet的有效性主要是高效结构的结果，而不是深度。**根据经验，g=3通常在准确性和实际推理时间之间有一个适当的平衡。shufflenet比mobilenet效果要好