Pytorch CIFAR10图像分类 ShuffleNetv2篇

文章目录

Pytorch CIFAR10图像分类 ShuffleNetv2篇
- 4. 定义网络（ShuffleNetv2）
- - 高效网络设计实用指南
  - - 指南一：同等通道大小最小化内存访问量
    - 指南二：过量使用组卷积会增加MAC
    - 指南三：网络碎片化会降低并行度
    - 指南四：不能忽略元素级操作的负面影响
  - ShuffleNetv2的高效架构
  - 通道切分 channel split
  - ShuffleNetv2 网络结构
  - summary查看网络
  - 测试和定义网络
- 5. 定义损失函数和优化器
- 6. 训练及可视化（增加TensorBoard可视化）
- - 开始训练
  - 训练曲线可视化
  - - 损失函数曲线
    - 准确率曲线
    - 学习率曲线
- 7.测试
- - 查看准确率
  - 查看每一类的准确率
  - 抽样测试并可视化一部分结果
- 8. 保存模型
- 9. 预测
- - 读取本地图片进行预测
  - 读取图片地址进行预测
- 10.总结

再次介绍一下我的专栏，很适合大家初入深度学习或者是Pytorch和Keras，希望这能够帮助初学深度学习的同学一个入门Pytorch或者Keras的项目和在这之中更加了解Pytorch&Keras和各个图像分类的模型。

他有比较清晰的可视化结构和架构，除此之外，我是用jupyter写的，所以说在文章整体架构可以说是非常清晰，可以帮助你快速学习到各个模块的知识，而不是通过python脚本一行一行的看，这样的方式是符合初学者的。

除此之外，如果你需要变成脚本形式，也是很简单的。
这里贴一下汇总篇：汇总篇

4. 定义网络（ShuffleNetv2）

ShuffleNetv2是ECCV2018的文章，也是来源于旷视和清华研究组，它的效果同时是比ShuffleNetv1更好的。并且在同等复杂度下，ShuffleNetv2比ShuffleNet和MobileNetv2更准确。该论文最大的贡献在于看到了 GPU 访存带宽（内存访问代价 MAC）对于模型推理时间的影响，而不仅仅是模型复杂度，也就是 FLOPs 和参数量 Params 对于推理时间的影响，使用直接指标（如速度）而非间接指标（如FLOPs），并由此提出了 4 个轻量级网络设计的原则和一个新颖的卷积 block 架构-ShuffleNet v2。

这个网络的优势在于：

（1） 作为轻量级的卷积神经网络，ShuffleNetV2 相比其他轻量级模型速度稍快，准确率也更高；

（2） 轻量级不仅体现在速度上，还大大地减少了模型的参数量，并且通过设定每个单元的通道数便可以灵活地调整模型的复杂度。

在这里插入图片描述

浮点数运算量FLOPs被广泛用作计算复杂度的评估指标。然而，FLOPs是一项间接指标，无法与直接指标如速度和准确率等同。过往的工作也证明FLOPs相同的网络运算速度却不同。单一使用FLOPs作为评估指标可能导致次优方案。但在际运用中会发现，同一个FLOPS的网络运算速度却不同，只用FLOPS去进行衡量的话并不能完全代表模型速度。

直接指标与间接指标的矛盾可以归咎于两点原因。

FLOPs没有考虑影响速度的一些重要因素。例如内存访问成本，它在group convolution中占据大量运算时间，也是GPU运算时的潜在性能瓶颈，还有并行度。相同FLOPs的情况下，高度并行的网络执行起来会更加迅速。
FLOPs相同的操作，在不同平台下运行时不同。例如早期工作广泛使用张量分解来加速矩阵乘法。但是近期工作发现虽然它可以减少75%的FLOPs，但是在GPU上运算却更慢了。作者研究后发现这是因为最新的CUDNN（该文章发表于2018年）针对3×3卷积做了特殊优化。

通过如下图所示对比，作者发现Elemwise/Data IO等内存读写密集型操作也会极大的影响模型运算速度。

读懂ShuffleNet V2

高效网络设计实用指南

作者在特定的平台下研究ShuffleNetv1和MobileNetv2的运行时间

GPU。硬件为英伟达GTX 1080 Ti。软件CUDNN 7.0。
ARM。硬件为高通Snapdragon 810。软件基于Neon高度优化的实现，测试时单线程。

同时根据结合理论与实验得到了4条实用的指导原则，先总结一下，作者认为高效的网络设计应当：

同等通道大小最小化内存访问量
过量使用组卷积会增加MAC
网络碎片化会降低并行度
不能忽略元素级操作的负面影响

指南一：同等通道大小最小化内存访问量

在使用depthwise separable convolutions的网络中，pointwise卷积承担了大部分复杂度。输入通道数 $c 1$ 与输出通道数 $c 2$ 决定了网络形状。特征图的高度与宽度为 h 与 w，则1×1卷积的FLOPs为 $B=hwc_1c_2$ 。

假设缓存足够大能够储存下完整的特征图及参数。内存访问成本可以记为 $MAC=hw(c_1+c_2)+c_1c_2$ ，根据均值不等式可得：
$2\sqrt{hwB} + \frac{B}{hw}$
可见MAC的下界由FLOPs决定。当输入与输出通道相等时值最小。

这个结论是理论上的。实际的设备缓存通常不够大。现代运算库通常使用复杂的阻挡策略来最大化缓存的使用。为验证该指南，作者在测试网络中堆叠了10个block，每个block包含2个卷积层。第一个输入通道c1，输出通道c2；第二个则反过来。实验结果如下表，当c1:c2接近1:1时，MAC最小。

指南二：过量使用组卷积会增加MAC

分组卷积将稠密的卷积连接离散化，降低了FLOPs，使得使用更多的卷积通道成为可能。但是，这种操作增加了内存访问成本。

同指南一，1×1卷积的MAC公式可以记为：
$MAC=hw(c_1+c_2)+\frac{c_1c_2}{g}=hwc_1 + \frac{Bg}{c_1} + \frac{B}{hw}$
其中 $g$ 为分组的数量， $B=hwc_1c_2/g$ 为FLOPs。可见，当 $g$ 增大时，MAC增大。

作者的实验显示，相比标准卷积，分组数为8的卷积运算耗时在GPU上增加了一倍，在ARM上增加了30%。因此分组卷积带来的准确率上升是以速度为代价的，需要谨慎设计。

指南三：网络碎片化会降低并行度

碎片化运算使用多个较小的运算取代单个较大的运算。尽管这样可以提升准确率，但是却降低了网络的并行度。这尤其不利于强并行运算资源如GPU，还会带来额外的kernel载入与同步损耗。

为了量化碎片化带来的影响，作者设计了上图所示block。实验中，GPU下采用图c的网络比采用图a的慢3倍。在ARM下的影响则有限。

指南四：不能忽略元素级操作的负面影响

逐元素操作包括ReLu、AddTensor、AddBias等。他们的FLOPs相对较小，但是内存访问成本却很高。（作者将depthwise convolution也看做逐元素操作）。试验中，去除ReLU与shortcut连接的ResNet在GPU和ARM上的速度都提升了20%。

ShuffleNetv2的高效架构

ShuffleNet V1使用了pointwise group convolution以及类bottleneck结构，还引入了channel shuffle操作来促进不同群组间的信息交换。从上述的四个指南出发，作者提出了改进的ShuffleNet V2。

在ShuffleNetv1的模块中，大量使用了1x1组卷积，这违背了G2原则

在这里插入图片描述

另外v1采用了类似ResNet中的瓶颈层（bottleneck layer），输入和输出通道数不同，这违背了G1原则。同时使用过多的组，也违背了G3原则。

在这里插入图片描述

短路连接中存在大量的元素级Add运算，这违背了G4原则。

在这里插入图片描述

通道切分 channel split

为了改善v1的缺陷，v2版本引入了一种新的运算：channel split。

卷积块分为基本模块（左图）和下采样模块（右图），操作如下图©所示。在每个单元前输入通道c被划分为c-c’与c’两个分支。为避免碎片化，一个分支保持不变；另一个分支遵循指南一，包含3个恒等通道数的卷积层，且1×1卷积不再分组。之后两个分支拼接为一个，通道数不变；再执行通道随机化操作。对于具备降采样功能的单元如图(d)所示，去掉了通道切分后，输出通道数扩增一倍。

图© Channel Spilt 模块将输入图像的通道数平均分成两份，一份用于残差连接，一份用于特征提取。

图(d) Channel Shuffle 模块将堆叠的特征图的通道重新排序，实现各分组之间的特征融合。

在基本模块中，特征图size不变，通道数不变。在下采样模块中，特征图的长宽减半，通道数加倍。

在这种网络设计中，左边分支做同等映射，右边的分支包含3个连续的卷积，并且输入和输出通道相同，这符合G1。而且两个1x1卷积不再是组卷积，这符合G2，另外两个分支相当于已经分成两组。两个分支的输出不再是Add元素，而是concat在一起，紧接着是对两个分支concat结果进行channle shuffle，以保证两个分支信息交流。其实concat和channel shuffle可以和下一个模块单元的channel split合成一个元素级运算，这符合原则G4。

在这里插入图片描述

ShuffleNetv2 网络结构

ShuffleNet V2就是基于以上两种单元构建的。为了简化操作，模型的c’=c/2。网络整体架构类似ShuffleNet V1，具体参数如下表所示。有一点不同之处是在global averaged pooling前增加了1×1卷积以融合特征。每个单元的channel数是可变的，以便统一缩放改变网络的复杂度。

ShuffleNet v2 | PyTorch

首先我们还是得判断是否可以利用GPU，因为GPU的速度可能会比我们用CPU的速度快20-50倍左右，特别是对卷积神经网络来说，更是提升特别明显。

device = 'cuda' if torch.cuda.is_available() else 'cpu'

第一步我们需要构建我们的ShuffleBlock，这一部分其实是跟v1一样的，里面就用到了我们的核心理念，Channel Shuffle，其实在程序上实现channel shuffle是非常容易的：

假定将输入层分为 g 组，总通道数为 $g \times n$ ，首先你将通道那个维度拆分为 $(g, n)$ 两个维度，然后将这两个维度转置变成 $(n, g)$ ，最后重新 reshape 成一个维度。如果你不太理解这个操作，你可以试着动手去试一下，发现仅需要简单的维度操作和转置就可以实现均匀的shuffle。利用channel shuffle就可以充分发挥group convolution的优点，而避免其缺点。

class ShuffleBlock(nn.Module):
    def __init__(self, groups=2):
        super(ShuffleBlock, self).__init__()
        self.groups = groups

    def forward(self, x):
        '''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
        N,C,H,W = x.size()
        g = self.groups
        return x.view(N,g,C//g,H,W).permute(0,2,1,3,4).reshape(N,C,H,W)

还有就是上述提到的通道切分模块，也是比较容易实现的

class SplitBlock(nn.Module):
    def __init__(self, ratio):
        super(SplitBlock, self).__init__()
        self.ratio = ratio

    def forward(self, x):
        c = int(x.size(1) * self.ratio)
        return x[:, :c, :, :], x[:, c:, :, :]

然后先设计一个基础的模块，后续可以进行复用方便

class BasicBlock(nn.Module):
    def __init__(self, in_channels, split_ratio=0.5):
        super(BasicBlock, self).__init__()
        self.split = SplitBlock(split_ratio)
        in_channels = int(in_channels * split_ratio)
        self.conv1 = nn.Conv2d(in_channels, in_channels,
                               kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv2 = nn.Conv2d(in_channels, in_channels,
                               kernel_size=3, stride=1, padding=1, groups=in_channels, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels)
        self.conv3 = nn.Conv2d(in_channels, in_channels,
                               kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(in_channels)
        self.shuffle = ShuffleBlock()

    def forward(self, x):
        x1, x2 = self.split(x)
        out = F.relu(self.bn1(self.conv1(x2)))
        out = self.bn2(self.conv2(out))
        out = F.relu(self.bn3(self.conv3(out)))
        out = torch.cat([x1, out], 1)
        out = self.shuffle(out)
        return out

然后我们再设计我们的下采样的模块，对于下采样模块，不再有channel split，而是每个分支都是直接copy一份输入，每个分支都有stride=2的下采样，最后concat在一起后，特征图空间大小减半，但是通道数翻倍。

class DownBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DownBlock, self).__init__()
        mid_channels = out_channels // 2
        # left
        self.conv1 = nn.Conv2d(in_channels, in_channels,
                               kernel_size=3, stride=2, padding=1, groups=in_channels, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv2 = nn.Conv2d(in_channels, mid_channels,
                               kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        # right
        self.conv3 = nn.Conv2d(in_channels, mid_channels,
                               kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(mid_channels)
        self.conv4 = nn.Conv2d(mid_channels, mid_channels,
                               kernel_size=3, stride=2, padding=1, groups=mid_channels, bias=False)
        self.bn4 = nn.BatchNorm2d(mid_channels)
        self.conv5 = nn.Conv2d(mid_channels, mid_channels,
                               kernel_size=1, bias=False)
        self.bn5 = nn.BatchNorm2d(mid_channels)

        self.shuffle = ShuffleBlock()

    def forward(self, x):
        # left
        out1 = self.bn1(self.conv1(x))
        out1 = F.relu(self.bn2(self.conv2(out1)))
        # right
        out2 = F.relu(self.bn3(self.conv3(x)))
        out2 = self.bn4(self.conv4(out2))
        out2 = F.relu(self.bn5(self.conv5(out2)))
        # concat
        out = torch.cat([out1, out2], 1)
        out = self.shuffle(out)
        return out

基于网络结构表，我们就可以定义我们的ShuffleNetv2了，基本与ShuffleNetv1相同，这里优于shape不同，所以用的池化卷积也稍微有一点不同，用了4x4的，跟ShuffleNet一样

class ShuffleNetV2(nn.Module):
    def __init__(self, net_size):
        super(ShuffleNetV2, self).__init__()
        out_channels = configs[net_size]['out_channels']
        num_blocks = configs[net_size]['num_blocks']

        self.conv1 = nn.Conv2d(3, 24, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(24)
        self.in_channels = 24
        self.layer1 = self._make_layer(out_channels[0], num_blocks[0])
        self.layer2 = self._make_layer(out_channels[1], num_blocks[1])
        self.layer3 = self._make_layer(out_channels[2], num_blocks[2])
        self.conv2 = nn.Conv2d(out_channels[2], out_channels[3],
                               kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels[3])
        self.linear = nn.Linear(out_channels[3], 10)

    def _make_layer(self, out_channels, num_blocks):
        layers = [DownBlock(self.in_channels, out_channels)]
        for i in range(num_blocks):
            layers.append(BasicBlock(out_channels))
            self.in_channels = out_channels
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        # out = F.max_pool2d(out, 3, stride=2, padding=1)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.relu(self.bn2(self.conv2(out)))
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out
    
# 根据表中的定义
configs = {
    0.5: {
        'out_channels': (48, 96, 192, 1024),
        'num_blocks': (3, 7, 3)
    },

    1: {
        'out_channels': (116, 232, 464, 1024),
        'num_blocks': (3, 7, 3)
    },
    1.5: {
        'out_channels': (176, 352, 704, 1024),
        'num_blocks': (3, 7, 3)
    },
    2: {
        'out_channels': (224, 488, 976, 2048),
        'num_blocks': (3, 7, 3)
    }
}

net = ShuffleNetV2(net_size=0.5, num_classes=10)

summary查看网络

我们可以通过summary来看到，模型的维度的变化，这个也是和论文是匹配的，经过层后shape的变化，是否最后也是输出(batch,shape)

summary(net,(2,3,32,32))

----------------------------------------------------------------
     Layer (type)               Output Shape         Param #
================================================================
         Conv2d-1           [-1, 24, 32, 32]             648
    BatchNorm2d-2           [-1, 24, 32, 32]              48
         Conv2d-3           [-1, 24, 16, 16]             216
    BatchNorm2d-4           [-1, 24, 16, 16]              48
         Conv2d-5           [-1, 24, 16, 16]             576
    BatchNorm2d-6           [-1, 24, 16, 16]              48
         Conv2d-7           [-1, 24, 32, 32]             576
    BatchNorm2d-8           [-1, 24, 32, 32]              48
         Conv2d-9           [-1, 24, 16, 16]             216
   BatchNorm2d-10           [-1, 24, 16, 16]              48
        Conv2d-11           [-1, 24, 16, 16]             576
   BatchNorm2d-12           [-1, 24, 16, 16]              48
  ShuffleBlock-13           [-1, 48, 16, 16]               0
     DownBlock-14           [-1, 48, 16, 16]               0
    SplitBlock-15  [[-1, 24, 16, 16], [-1, 24, 16, 16]]               0
        Conv2d-16           [-1, 24, 16, 16]             576
   BatchNorm2d-17           [-1, 24, 16, 16]              48
        Conv2d-18           [-1, 24, 16, 16]             216
   BatchNorm2d-19           [-1, 24, 16, 16]              48
        Conv2d-20           [-1, 24, 16, 16]             576
   BatchNorm2d-21           [-1, 24, 16, 16]              48
  ShuffleBlock-22           [-1, 48, 16, 16]               0
    BasicBlock-23           [-1, 48, 16, 16]               0
    SplitBlock-24  [[-1, 24, 16, 16], [-1, 24, 16, 16]]               0
        Conv2d-25           [-1, 24, 16, 16]             576
   BatchNorm2d-26           [-1, 24, 16, 16]              48
        Conv2d-27           [-1, 24, 16, 16]             216
   BatchNorm2d-28           [-1, 24, 16, 16]              48
        Conv2d-29           [-1, 24, 16, 16]             576
   BatchNorm2d-30           [-1, 24, 16, 16]              48
  ShuffleBlock-31           [-1, 48, 16, 16]               0
    BasicBlock-32           [-1, 48, 16, 16]               0
    SplitBlock-33  [[-1, 24, 16, 16], [-1, 24, 16, 16]]               0
        Conv2d-34           [-1, 24, 16, 16]             576
   BatchNorm2d-35           [-1, 24, 16, 16]              48
        Conv2d-36           [-1, 24, 16, 16]             216
   BatchNorm2d-37           [-1, 24, 16, 16]              48
        Conv2d-38           [-1, 24, 16, 16]             576
   BatchNorm2d-39           [-1, 24, 16, 16]              48
  ShuffleBlock-40           [-1, 48, 16, 16]               0
    BasicBlock-41           [-1, 48, 16, 16]               0
        Conv2d-42             [-1, 48, 8, 8]             432
   BatchNorm2d-43             [-1, 48, 8, 8]              96
        Conv2d-44             [-1, 48, 8, 8]           2,304
   BatchNorm2d-45             [-1, 48, 8, 8]              96
        Conv2d-46           [-1, 48, 16, 16]           2,304
   BatchNorm2d-47           [-1, 48, 16, 16]              96
        Conv2d-48             [-1, 48, 8, 8]             432
   BatchNorm2d-49             [-1, 48, 8, 8]              96
        Conv2d-50             [-1, 48, 8, 8]           2,304
   BatchNorm2d-51             [-1, 48, 8, 8]              96
  ShuffleBlock-52             [-1, 96, 8, 8]               0
     DownBlock-53             [-1, 96, 8, 8]               0
    SplitBlock-54  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
        Conv2d-55             [-1, 48, 8, 8]           2,304
   BatchNorm2d-56             [-1, 48, 8, 8]              96
        Conv2d-57             [-1, 48, 8, 8]             432
   BatchNorm2d-58             [-1, 48, 8, 8]              96
        Conv2d-59             [-1, 48, 8, 8]           2,304
   BatchNorm2d-60             [-1, 48, 8, 8]              96
  ShuffleBlock-61             [-1, 96, 8, 8]               0
    BasicBlock-62             [-1, 96, 8, 8]               0
    SplitBlock-63  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
        Conv2d-64             [-1, 48, 8, 8]           2,304
   BatchNorm2d-65             [-1, 48, 8, 8]              96
        Conv2d-66             [-1, 48, 8, 8]             432
   BatchNorm2d-67             [-1, 48, 8, 8]              96
        Conv2d-68             [-1, 48, 8, 8]           2,304
   BatchNorm2d-69             [-1, 48, 8, 8]              96
  ShuffleBlock-70             [-1, 96, 8, 8]               0
    BasicBlock-71             [-1, 96, 8, 8]               0
    SplitBlock-72  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
        Conv2d-73             [-1, 48, 8, 8]           2,304
   BatchNorm2d-74             [-1, 48, 8, 8]              96
        Conv2d-75             [-1, 48, 8, 8]             432
   BatchNorm2d-76             [-1, 48, 8, 8]              96
        Conv2d-77             [-1, 48, 8, 8]           2,304
   BatchNorm2d-78             [-1, 48, 8, 8]              96
  ShuffleBlock-79             [-1, 96, 8, 8]               0
    BasicBlock-80             [-1, 96, 8, 8]               0
    SplitBlock-81  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
        Conv2d-82             [-1, 48, 8, 8]           2,304
   BatchNorm2d-83             [-1, 48, 8, 8]              96
        Conv2d-84             [-1, 48, 8, 8]             432
   BatchNorm2d-85             [-1, 48, 8, 8]              96
        Conv2d-86             [-1, 48, 8, 8]           2,304
   BatchNorm2d-87             [-1, 48, 8, 8]              96
  ShuffleBlock-88             [-1, 96, 8, 8]               0
    BasicBlock-89             [-1, 96, 8, 8]               0
    SplitBlock-90  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
        Conv2d-91             [-1, 48, 8, 8]           2,304
   BatchNorm2d-92             [-1, 48, 8, 8]              96
        Conv2d-93             [-1, 48, 8, 8]             432
   BatchNorm2d-94             [-1, 48, 8, 8]              96
        Conv2d-95             [-1, 48, 8, 8]           2,304
   BatchNorm2d-96             [-1, 48, 8, 8]              96
  ShuffleBlock-97             [-1, 96, 8, 8]               0
    BasicBlock-98             [-1, 96, 8, 8]               0
    SplitBlock-99  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
       Conv2d-100             [-1, 48, 8, 8]           2,304
  BatchNorm2d-101             [-1, 48, 8, 8]              96
       Conv2d-102             [-1, 48, 8, 8]             432
  BatchNorm2d-103             [-1, 48, 8, 8]              96
       Conv2d-104             [-1, 48, 8, 8]           2,304
  BatchNorm2d-105             [-1, 48, 8, 8]              96
 ShuffleBlock-106             [-1, 96, 8, 8]               0
   BasicBlock-107             [-1, 96, 8, 8]               0
   SplitBlock-108  [[-1, 48, 8, 8], [-1, 48, 8, 8]]               0
       Conv2d-109             [-1, 48, 8, 8]           2,304
  BatchNorm2d-110             [-1, 48, 8, 8]              96
       Conv2d-111             [-1, 48, 8, 8]             432
  BatchNorm2d-112             [-1, 48, 8, 8]              96
       Conv2d-113             [-1, 48, 8, 8]           2,304
  BatchNorm2d-114             [-1, 48, 8, 8]              96
 ShuffleBlock-115             [-1, 96, 8, 8]               0
   BasicBlock-116             [-1, 96, 8, 8]               0
       Conv2d-117             [-1, 96, 4, 4]             864
  BatchNorm2d-118             [-1, 96, 4, 4]             192
       Conv2d-119             [-1, 96, 4, 4]           9,216
  BatchNorm2d-120             [-1, 96, 4, 4]             192
       Conv2d-121             [-1, 96, 8, 8]           9,216
  BatchNorm2d-122             [-1, 96, 8, 8]             192
       Conv2d-123             [-1, 96, 4, 4]             864
  BatchNorm2d-124             [-1, 96, 4, 4]             192
       Conv2d-125             [-1, 96, 4, 4]           9,216
  BatchNorm2d-126             [-1, 96, 4, 4]             192
 ShuffleBlock-127            [-1, 192, 4, 4]               0
    DownBlock-128            [-1, 192, 4, 4]               0
   SplitBlock-129  [[-1, 96, 4, 4], [-1, 96, 4, 4]]               0
       Conv2d-130             [-1, 96, 4, 4]           9,216
  BatchNorm2d-131             [-1, 96, 4, 4]             192
       Conv2d-132             [-1, 96, 4, 4]             864
  BatchNorm2d-133             [-1, 96, 4, 4]             192
       Conv2d-134             [-1, 96, 4, 4]           9,216
  BatchNorm2d-135             [-1, 96, 4, 4]             192
 ShuffleBlock-136            [-1, 192, 4, 4]               0
   BasicBlock-137            [-1, 192, 4, 4]               0
   SplitBlock-138  [[-1, 96, 4, 4], [-1, 96, 4, 4]]               0
       Conv2d-139             [-1, 96, 4, 4]           9,216
  BatchNorm2d-140             [-1, 96, 4, 4]             192
       Conv2d-141             [-1, 96, 4, 4]             864
  BatchNorm2d-142             [-1, 96, 4, 4]             192
       Conv2d-143             [-1, 96, 4, 4]           9,216
  BatchNorm2d-144             [-1, 96, 4, 4]             192
 ShuffleBlock-145            [-1, 192, 4, 4]               0
   BasicBlock-146            [-1, 192, 4, 4]               0
   SplitBlock-147  [[-1, 96, 4, 4], [-1, 96, 4, 4]]               0
       Conv2d-148             [-1, 96, 4, 4]           9,216
  BatchNorm2d-149             [-1, 96, 4, 4]             192
       Conv2d-150             [-1, 96, 4, 4]             864
  BatchNorm2d-151             [-1, 96, 4, 4]             192
       Conv2d-152             [-1, 96, 4, 4]           9,216
  BatchNorm2d-153             [-1, 96, 4, 4]             192
 ShuffleBlock-154            [-1, 192, 4, 4]               0
   BasicBlock-155            [-1, 192, 4, 4]               0
       Conv2d-156           [-1, 1024, 4, 4]         196,608
  BatchNorm2d-157           [-1, 1024, 4, 4]           2,048
       Linear-158                   [-1, 10]          10,250
================================================================
Total params: 352,042
Trainable params: 352,042
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 1416.34
Params size (MB): 1.34
Estimated Total Size (MB): 1417.69
----------------------------------------------------------------

首先从我们summary可以看到，我们输入的是（batch，3，32，32）的张量，并且这里也能看到每一层后我们的图像输出大小的变化，最后输出10个参数，再通过softmax函数就可以得到我们每个类别的概率了。

我们也可以打印出我们的模型观察一下

ShuffleNetV2(
  (conv1): Conv2d(3, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (layer1): Sequential(
    (0): DownBlock(
      (conv1): Conv2d(24, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=24, bias=False)
      (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv4): Conv2d(24, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=24, bias=False)
      (bn4): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv5): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn5): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (1): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
      (bn2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (2): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
      (bn2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (3): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
      (bn2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
  )
  (layer2): Sequential(
    (0): DownBlock(
      (conv1): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv4): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)
      (bn4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv5): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn5): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (1): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (2): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (3): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (4): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (5): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (6): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (7): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)
      (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
  )
  (layer3): Sequential(
    (0): DownBlock(
      (conv1): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
      (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv4): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
      (bn4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv5): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn5): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (1): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
      (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (2): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
      (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
    (3): BasicBlock(
      (split): SplitBlock()
      (conv1): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
      (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shuffle): ShuffleBlock()
    )
  )
  (conv2): Conv2d(192, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (linear): Linear(in_features=1024, out_features=10, bias=True)
)

测试和定义网络

接下来可以简单测试一下，是否输入后能得到我们的正确的维度shape

test_x = torch.randn(2,3,32,32).to(device)
test_y = net(test_x)
print(test_y.shape)

torch.Size([2, 10])

定义网络和设置类别

net = ShuffleNetV2(net_size=0.5, num_classes=10)

5. 定义损失函数和优化器

pytorch将深度学习中常用的优化方法全部封装在torch.optim之中，所有的优化方法都是继承基类optim.Optimizier
损失函数是封装在神经网络工具箱nn中的,包含很多损失函数

这里我使用的是SGD + momentum算法，并且我们损失函数定义为交叉熵函数，除此之外学习策略定义为动态更新学习率，如果5次迭代后，训练的损失并没有下降，那么我们便会更改学习率，会变为原来的0.5倍，最小降低到0.00001

如果想更加了解优化器和学习率策略的话，可以参考以下资料

Pytorch Note15 优化算法1 梯度下降（Gradient descent varients）
Pytorch Note16 优化算法2 动量法(Momentum)
Pytorch Note34 学习率衰减

这里决定迭代10次

import torch.optim as optim
optimizer = optim.SGD(net.parameters(), lr=1e-1, momentum=0.9, weight_decay=5e-4)
criterion = nn.CrossEntropyLoss()
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.94 ,patience = 1,min_lr = 0.000001) # 动态更新学习率
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[75, 150], gamma=0.5)
import time
epoch = 10

6. 训练及可视化（增加TensorBoard可视化）

首先定义模型保存的位置

import os
if not os.path.exists('./model'):
    os.makedirs('./model')
else:
    print('文件已存在')
save_path = './model/ShuffleNetv2.pth'

这次更新了tensorboard的可视化，可以得到更好看的图片，并且能可视化出不错的结果

# 使用tensorboard
from torch.utils.tensorboard import SummaryWriter
os.makedirs("./logs", exist_ok=True)
tbwriter = SummaryWriter(log_dir='./logs/ShuffleNetv2', comment='ShuffleNetv2')  # 使用tensorboard记录中间输出
tbwriter.add_graph(model= net, input_to_model=torch.randn(size=(1, 3, 32, 32)))

如果存在GPU可以选择使用GPU进行运行，并且可以设置并行运算

if device == 'cuda':
    net.to(device)
    net = nn.DataParallel(net) # 使用并行运算

开始训练

我定义了一个train函数，在train函数中进行一个训练，并保存我们训练后的模型，这一部分一定要注意，这里的utils文件是我个人写的，所以需要下载下来

或者可以参考我们的工具函数篇，我还更新了结果和方法，利用tqdm更能可视化我们的结果。

from utils import plot_history
from utils import train
Acc, Loss, Lr = train(net, trainloader, testloader, epoch, optimizer, criterion, scheduler, save_path, tbwriter, verbose = True)

Train Epoch 1/20: 100%|██████████| 390/390 [00:29<00:00, 13.40it/s, Train Acc=0.202, Train Loss=2.18]
Test Epoch 1/20: 100%|██████████| 78/78 [00:01<00:00, 51.88it/s, Test Acc=0.32, Test Loss=1.81] 
Epoch [  1/ 20]  Train Loss:2.181060  Train Acc:20.16% Test Loss:1.812469  Test Acc:32.01%  Learning Rate:0.100000
Train Epoch 2/20: 100%|██████████| 390/390 [00:29<00:00, 13.14it/s, Train Acc=0.399, Train Loss=1.56]
Test Epoch 2/20: 100%|██████████| 78/78 [00:01<00:00, 55.32it/s, Test Acc=0.454, Test Loss=1.48]
Epoch [  2/ 20]  Train Loss:1.558131  Train Acc:39.95% Test Loss:1.481740  Test Acc:45.44%  Learning Rate:0.100000
Train Epoch 3/20: 100%|██████████| 390/390 [00:30<00:00, 12.97it/s, Train Acc=0.49, Train Loss=1.38] 
Test Epoch 3/20: 100%|██████████| 78/78 [00:01<00:00, 53.75it/s, Test Acc=0.474, Test Loss=1.44]
Epoch [  3/ 20]  Train Loss:1.375844  Train Acc:48.95% Test Loss:1.436291  Test Acc:47.38%  Learning Rate:0.100000
Train Epoch 4/20: 100%|██████████| 390/390 [00:27<00:00, 14.12it/s, Train Acc=0.546, Train Loss=1.25]
Test Epoch 4/20: 100%|██████████| 78/78 [00:01<00:00, 52.70it/s, Test Acc=0.511, Test Loss=1.39]
Epoch [  4/ 20]  Train Loss:1.249782  Train Acc:54.56% Test Loss:1.389962  Test Acc:51.11%  Learning Rate:0.100000
Train Epoch 5/20: 100%|██████████| 390/390 [00:28<00:00, 13.72it/s, Train Acc=0.581, Train Loss=1.17]
Test Epoch 5/20: 100%|██████████| 78/78 [00:01<00:00, 56.07it/s, Test Acc=0.572, Test Loss=1.21]
Epoch [  5/ 20]  Train Loss:1.168995  Train Acc:58.10% Test Loss:1.210948  Test Acc:57.17%  Learning Rate:0.100000
Train Epoch 6/20: 100%|██████████| 390/390 [00:28<00:00, 13.47it/s, Train Acc=0.6, Train Loss=1.11]  
Test Epoch 6/20: 100%|██████████| 78/78 [00:01<00:00, 51.49it/s, Test Acc=0.57, Test Loss=1.2]  
Epoch [  6/ 20]  Train Loss:1.109439  Train Acc:60.02% Test Loss:1.204134  Test Acc:57.04%  Learning Rate:0.100000
Train Epoch 7/20: 100%|██████████| 390/390 [00:28<00:00, 13.89it/s, Train Acc=0.624, Train Loss=1.06]
Test Epoch 7/20: 100%|██████████| 78/78 [00:01<00:00, 55.05it/s, Test Acc=0.608, Test Loss=1.15]
Epoch [  7/ 20]  Train Loss:1.057093  Train Acc:62.45% Test Loss:1.154943  Test Acc:60.77%  Learning Rate:0.100000
Train Epoch 8/20: 100%|██████████| 390/390 [00:29<00:00, 13.16it/s, Train Acc=0.636, Train Loss=1.02] 
Test Epoch 8/20: 100%|██████████| 78/78 [00:01<00:00, 56.74it/s, Test Acc=0.612, Test Loss=1.11]
Epoch [  8/ 20]  Train Loss:1.024675  Train Acc:63.65% Test Loss:1.113078  Test Acc:61.22%  Learning Rate:0.100000
Train Epoch 9/20: 100%|██████████| 390/390 [00:28<00:00, 13.72it/s, Train Acc=0.651, Train Loss=0.987]
Test Epoch 9/20: 100%|██████████| 78/78 [00:01<00:00, 54.63it/s, Test Acc=0.614, Test Loss=1.14]
Epoch [  9/ 20]  Train Loss:0.986667  Train Acc:65.11% Test Loss:1.142446  Test Acc:61.44%  Learning Rate:0.100000
Train Epoch 10/20: 100%|██████████| 390/390 [00:30<00:00, 12.86it/s, Train Acc=0.663, Train Loss=0.954]
Test Epoch 10/20: 100%|██████████| 78/78 [00:01<00:00, 57.09it/s, Test Acc=0.652, Test Loss=15.3]
Epoch [ 10/ 20]  Train Loss:0.953566  Train Acc:66.32% Test Loss:15.277926  Test Acc:65.19%  Learning Rate:0.100000
Train Epoch 11/20: 100%|██████████| 390/390 [00:28<00:00, 13.90it/s, Train Acc=0.678, Train Loss=0.922]
Test Epoch 11/20: 100%|██████████| 78/78 [00:01<00:00, 57.69it/s, Test Acc=0.614, Test Loss=1.28]
Epoch [ 11/ 20]  Train Loss:0.921855  Train Acc:67.84% Test Loss:1.284624  Test Acc:61.44%  Learning Rate:0.100000
Train Epoch 12/20: 100%|██████████| 390/390 [00:27<00:00, 14.22it/s, Train Acc=0.689, Train Loss=0.881]
Test Epoch 12/20: 100%|██████████| 78/78 [00:01<00:00, 59.13it/s, Test Acc=0.667, Test Loss=1.01] 
Epoch [ 12/ 20]  Train Loss:0.881344  Train Acc:68.95% Test Loss:1.005071  Test Acc:66.70%  Learning Rate:0.100000
Train Epoch 13/20: 100%|██████████| 390/390 [00:28<00:00, 13.61it/s, Train Acc=0.701, Train Loss=0.858]
Test Epoch 13/20: 100%|██████████| 78/78 [00:01<00:00, 52.73it/s, Test Acc=0.661, Test Loss=0.996]
Epoch [ 13/ 20]  Train Loss:0.858123  Train Acc:70.07% Test Loss:0.996488  Test Acc:66.10%  Learning Rate:0.100000
Train Epoch 14/20: 100%|██████████| 390/390 [00:27<00:00, 13.97it/s, Train Acc=0.712, Train Loss=0.83] 
Test Epoch 14/20: 100%|██████████| 78/78 [00:01<00:00, 54.49it/s, Test Acc=0.711, Test Loss=0.855]
Epoch [ 14/ 20]  Train Loss:0.830285  Train Acc:71.23% Test Loss:0.855183  Test Acc:71.07%  Learning Rate:0.100000
Train Epoch 15/20: 100%|██████████| 390/390 [00:27<00:00, 14.20it/s, Train Acc=0.727, Train Loss=0.799]
Test Epoch 15/20: 100%|██████████| 78/78 [00:01<00:00, 55.80it/s, Test Acc=0.664, Test Loss=260]  
Epoch [ 15/ 20]  Train Loss:0.799097  Train Acc:72.67% Test Loss:260.248212  Test Acc:66.43%  Learning Rate:0.100000
Train Epoch 16/20: 100%|██████████| 390/390 [00:26<00:00, 14.62it/s, Train Acc=0.731, Train Loss=0.78] 
Test Epoch 16/20: 100%|██████████| 78/78 [00:01<00:00, 53.76it/s, Test Acc=0.699, Test Loss=116]  
Epoch [ 16/ 20]  Train Loss:0.780266  Train Acc:73.08% Test Loss:115.791749  Test Acc:69.94%  Learning Rate:0.100000
Train Epoch 17/20: 100%|██████████| 390/390 [00:28<00:00, 13.52it/s, Train Acc=0.746, Train Loss=0.747]
Test Epoch 17/20: 100%|██████████| 78/78 [00:01<00:00, 56.72it/s, Test Acc=0.724, Test Loss=3.52] 
Epoch [ 17/ 20]  Train Loss:0.747231  Train Acc:74.64% Test Loss:3.520048  Test Acc:72.45%  Learning Rate:0.100000
Train Epoch 18/20: 100%|██████████| 390/390 [00:32<00:00, 11.97it/s, Train Acc=0.759, Train Loss=0.708]
Test Epoch 18/20: 100%|██████████| 78/78 [00:01<00:00, 53.87it/s, Test Acc=0.725, Test Loss=0.845]
Epoch [ 18/ 20]  Train Loss:0.708474  Train Acc:75.88% Test Loss:0.845017  Test Acc:72.53%  Learning Rate:0.100000
Train Epoch 19/20: 100%|██████████| 390/390 [00:27<00:00, 14.27it/s, Train Acc=0.773, Train Loss=0.672]
Test Epoch 19/20: 100%|██████████| 78/78 [00:01<00:00, 55.64it/s, Test Acc=0.722, Test Loss=1.83e+5]
Epoch [ 19/ 20]  Train Loss:0.671686  Train Acc:77.30% Test Loss:183250.281071  Test Acc:72.23%  Learning Rate:0.100000
Train Epoch 20/20: 100%|██████████| 390/390 [00:27<00:00, 14.41it/s, Train Acc=0.781, Train Loss=0.644]
Test Epoch 20/20: 100%|██████████| 78/78 [00:01<00:00, 56.07it/s, Test Acc=0.721, Test Loss=0.861]
Epoch [ 20/ 20]  Train Loss:0.644469  Train Acc:78.11% Test Loss:0.860960  Test Acc:72.06%  Learning Rate:0.100000

训练曲线可视化

接着可以分别打印，损失函数曲线，准确率曲线和学习率曲线

plot_history(epoch ,Acc, Loss, Lr)

损失函数曲线

在这里插入图片描述

准确率曲线

在这里插入图片描述

学习率曲线

在这里插入图片描述

可以运行以下代码进行可视化

tensorboard --logdir logs

7.测试

查看准确率

correct = 0   # 定义预测正确的图片数，初始化为0
total = 0     # 总共参与测试的图片数，也初始化为0
# testloader = torch.utils.data.DataLoader(testset, batch_size=32,shuffle=True, num_workers=2)
for data in testloader:  # 循环每一个batch
    images, labels = data
    images = images.to(device)
    labels = labels.to(device)
    net.eval()  # 把模型转为test模式
    if hasattr(torch.cuda, 'empty_cache'):
        torch.cuda.empty_cache()
    outputs = net(images)  # 输入网络进行测试
    
    # outputs.data是一个4x10张量，将每一行的最大的那一列的值和序号各自组成一个一维张量返回，第一个是值的张量，第二个是序号的张量。
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)          # 更新测试图片的数量
    correct += (predicted == labels).sum() # 更新正确分类的图片的数量

print('Accuracy of the network on the 10000 test images: %.2f %%' % (100 * correct / total))

Accuracy of the network on the 10000 test images: 72.06 %

可以看到ShuffleNetv2的模型在测试集中准确率达到72.06%左右

程序中的 torch.max(outputs.data, 1) ，返回一个tuple (元组)

而这里很明显，这个返回的元组的第一个元素是image data，即是最大的值，第二个元素是label，即是最大的值的索引！我们只需要label（最大值的索引），所以就会有_,predicted这样的赋值语句，表示忽略第一个返回值，把它赋值给 _，就是舍弃它的意思；

查看每一类的准确率

 # 定义2个存储每类中测试正确的个数的 列表，初始化为0
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
# testloader = torch.utils.data.DataLoader(testset, batch_size=64,shuffle=True, num_workers=2)
net.eval()
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images = images.to(device)
        labels = labels.to(device)
        if hasattr(torch.cuda, 'empty_cache'):
            torch.cuda.empty_cache()
        outputs = net(images)

        _, predicted = torch.max(outputs.data, 1)
    #4组(batch_size)数据中，输出于label相同的，标记为1，否则为0
        c = (predicted == labels).squeeze()
        for i in range(len(images)):      # 因为每个batch都有4张图片，所以还需要一个4的小循环
            label = labels[i]   # 对各个类的进行各自累加
            class_correct[label] += c[i]
            class_total[label] += 1
 
 
for i in range(10):
    print('Accuracy of %5s : %.2f %%' % (classes[i], 100 * class_correct[i] / class_total[i]))

Accuracy of airplane : 74.12 %
Accuracy of automobile : 89.19 %
Accuracy of  bird : 58.43 %
Accuracy of   cat : 74.20 %
Accuracy of  deer : 61.20 %
Accuracy of   dog : 49.75 %
Accuracy of  frog : 57.70 %
Accuracy of horse : 73.70 %
Accuracy of  ship : 90.09 %
Accuracy of truck : 92.18 %

抽样测试并可视化一部分结果

dataiter = iter(testloader)
images, labels = dataiter.next()
images_ = images
#images_ = images_.view(images.shape[0], -1)
images_ = images_.to(device)
labels = labels.to(device)
val_output = net(images_)
_, val_preds = torch.max(val_output, 1)

fig = plt.figure(figsize=(25,4))

correct = torch.sum(val_preds == labels.data).item()

val_preds = val_preds.cpu()
labels = labels.cpu()

print("Accuracy Rate = {}%".format(correct/len(images) * 100))

fig = plt.figure(figsize=(25,25))
for idx in np.arange(64):    
    ax = fig.add_subplot(8, 8, idx+1, xticks=[], yticks=[])
    #fig.tight_layout()
#     plt.imshow(im_convert(images[idx]))
    imshow(images[idx])
    ax.set_title("{}, ({})".format(classes[val_preds[idx].item()], classes[labels[idx].item()]), 
                 color = ("green" if val_preds[idx].item()==labels[idx].item() else "red"))

Accuracy Rate = 72.65625%

在这里插入图片描述

8. 保存模型

torch.save(net,save_path[:-4]+'_'+str(epoch)+'.pth')

9. 预测

读取本地图片进行预测

import torch
from PIL import Image
from torch.autograd import Variable
import torch.nn.functional as F
from torchvision import datasets, transforms
import numpy as np
 
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = net = ShuffleNetV2(net_size=0.5, num_classes=10)

model = torch.load(save_path, map_location="cpu")  # 加载模型
model.to(device)
model.eval()  # 把模型转为test模式

并且为了方便，定义了一个predict函数，简单思想就是，先resize成网络使用的shape，然后进行变化tensor输入即可，不过这里有一个点，我们需要对我们的图片也进行transforms，因为我们的训练的时候，对每个图像也是进行了transforms的，所以我们需要保持一致

def predict(img):
    trans = transforms.Compose([transforms.Resize((32,32)),
                            transforms.ToTensor(),
                            transforms.Normalize(mean=(0.5, 0.5, 0.5), 
                                                 std=(0.5, 0.5, 0.5)),
                           ])
 
    img = trans(img)
    img = img.to(device)
    # 图片扩展多一维,因为输入到保存的模型中是4维的[batch_size,通道,长，宽]，而普通图片只有三维，[通道,长，宽]
    img = img.unsqueeze(0)  # 扩展后，为[1，3，32，32]
    output = model(img)
    prob = F.softmax(output,dim=1) #prob是10个分类的概率
    print("概率",prob)
    value, predicted = torch.max(output.data, 1)
    print("类别",predicted.item())
    print(value)
    pred_class = classes[predicted.item()]
    print("分类",pred_class)

# 读取要预测的图片
img = Image.open("./airplane.jpg").convert('RGB') # 读取图像
img

在这里插入图片描述

predict(img)

概率 tensor([[9.6835e-01, 3.9856e-03, 7.3570e-03, 9.4723e-05, 5.8031e-04, 3.3879e-06,
         3.0444e-04, 3.3827e-05, 1.9141e-02, 1.5289e-04]], device='cuda:0',
       grad_fn=<SoftmaxBackward0>)
类别 0
tensor([7.0580], device='cuda:0')
分类 plane

这里就可以看到，我们最后的结果，分类为plane，我们的置信率大概是99.8%，看起来是很不错的，置信度很高，说明预测的还是比较准确的。

读取图片地址进行预测

我们也可以通过读取图片的url地址进行预测，这里我找了多个不同的图片进行预测

import requests
from PIL import Image
url = 'https://dss2.bdstatic.com/70cFvnSh_Q1YnxGkpoWK1HF6hhy/it/u=947072664,3925280208&fm=26&gp=0.jpg'
url = 'https://ss0.bdstatic.com/70cFuHSh_Q1YnxGkpoWK1HF6hhy/it/u=2952045457,215279295&fm=26&gp=0.jpg'
url = 'https://ss0.bdstatic.com/70cFvHSh_Q1YnxGkpoWK1HF6hhy/it/u=2838383012,1815030248&fm=26&gp=0.jpg'
url = 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fwww.goupuzi.com%2Fnewatt%2FMon_1809%2F1_179223_7463b117c8a2c76.jpg&refer=http%3A%2F%2Fwww.goupuzi.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1624346733&t=36ba18326a1e010737f530976201326d'
url = 'https://ss3.bdstatic.com/70cFv8Sh_Q1YnxGkpoWK1HF6hhy/it/u=2799543344,3604342295&fm=224&gp=0.jpg'
# url = 'https://ss1.bdstatic.com/70cFuXSh_Q1YnxGkpoWK1HF6hhy/it/u=2032505694,2851387785&fm=26&gp=0.jpg'
response = requests.get(url, stream=True)
print (response)
img = Image.open(response.raw)
img

在这里插入图片描述

这里和前面是一样的

predict(img)

概率 tensor([[0.2931, 0.0125, 0.5008, 0.0560, 0.0500, 0.0200, 0.0140, 0.0412, 0.0107,
         0.0017]], device='cuda:0', grad_fn=<SoftmaxBackward0>)
类别 2
tensor([1.3903], device='cuda:0')
分类 bird