必须在mysql上创建，不能在mycat上，否则报错java.sql.SQLSyntaxErrorException: op table not in schema----VIEW
然后配xml
就可以select了。
show table status where comment =‘view’;

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
 PDF: https://arxiv.org/pdf/1803.02579v2.pdf
 PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks
 PyTorch代码: https://github.com/shanglianlm0525/CvPytorch
 
1 概述
 
本文对SE模块进行了改进，设计了三种 SE 变形结构cSE、sSE、scSE，在 MRI 脑分割 和 CT 器官分割任务上取得了可观的改进。
 
 
2 Spatial Squeeze and Channel Excitation Block (cSE)
 
即原始的SE Block , 详细见 Attention论文:Squeeze-and-Excitation Networks及其PyTorch实现
 PyTorch代码:
 
class SE_Module(nn.Module):
    def __init__(self, channel,ratio = 16):
        super(SE_Module, self).__init__()
        self.squeeze = nn.AdaptiveAvgPool2d(1)
        self.excitation = nn.Sequential(
                nn.Linear(in_features=channel, out_features=channel // ratio),
                nn.ReLU(inplace=True),
                nn.Linear(in_features=channel // ratio, out_features=channel),
                nn.Sigmoid()
            )
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.squeeze(x).view(b, c)
        z = self.excitation(y).view(b, c, 1, 1)
        return x * z.expand_as(x)
 
3 Channel Squeeze and Spatial Excitation Block (sSE)
 
PyTorch代码:
 
class sSE_Module(nn.Module):
    def __init__(self, channel):
        super(sSE_Module, self).__init__()
        self.spatial_excitation = nn.Sequential(
                nn.Conv2d(in_channels=channel, out_channels=1, kernel_size=1,stride=1,padding=0),
                nn.Sigmoid()
            )
    def forward(self, x):
        z = self.spatial_excitation(x)
        return x * z.expand_as(x)
 
4 Spatial and Channel Squeeze & Excitation Block (scSE)
 
PyTorch代码:
 
class scSE_Module(nn.Module):
    def __init__(self, channel,ratio = 16):
        super(scSE_Module, self).__init__()
        self.cSE = cSE_Module(channel,ratio)
        self.sSE = sSE_Module(channel)

    def forward(self, x):
        return self.cSE(x) + self.sSE(x)
 
5 实验结果
 

源码
 
ResNet封装 在torchvision中封装了Resnet的源码，下面通过对ResNet源码的分析进一步了解ResNet网络结构，方便对ResNet结构进行修改，同时学习网络源码的组织方式，方便日后搭建自己的神经网络
 
 
源码解析
 
def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model
 
从源码的入口出发，通过 model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) 构造网络结构，主要分成两个部分即 Bottleneck 和 [3,4,6,3] 由这两个参数共同决定了ResNet50的网络结构 ，当pretrained 为true时，为model加载imageNet中预训练的参数。
 这里涉及到了Bottleneck这个类，[3,4,6,3]对应于上图中ResNet50中 conv2_x中 有三个（1164,3364,11256）卷积层的堆叠 ，同理conv3_中有4个（11128,33128,11512）卷积层的堆叠,resnet将卷积层分为4个大层，[3,4,6,3]代表每一个大层中11,33,1*1 卷积层组合的重复次数 总共1（第一个卷积层）+1（第一个池化层）+（3+4+6+3）*3=50层
 
这里涉及到一个Bottleneck类，可以把一个Bottleneck当成一个基础的block就是对应上图的（11,33,11）卷积核大小的卷积层的组合
 
 解释一下为什么输入Bottleneck之前是56 * 56 * 64 的，因为ResNet接受的图像大小为224 * 224 经过第一层卷积层
 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
 floor((224-7+23)/2)+1=112
 经过第一层池化之后
 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 floor（(112+2*1-3)/2）+1=56
 
因此在输入到Bottleneck之前得到一个56（height）*56(weight)*64(channel)大小的feature map
 

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
 
因为1 * 1卷积核不改变feature map的大小，3 * 3卷积核padding=1 也不改变输入feature map的大小，因此经过一个Bottleneck组成的卷积层组操作后feature map大小不会改变
 
下面看一下Bottleneck的forward函数
 
def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out
 
这里要留意一下downsample，因为feature map的大小不变 但是在经过Bottleneck 之后channel变成了原来的四倍，因此想要和原始的feature map相加 需要将原始的feature map 也变为原来的四倍 ，downsample 作用是residual+当前feature map时将维度统一
 
接下来分析ResNet 类的具体构成
 
class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = [ ]
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

 
Resnet 也是由__init__ 和forward构成 ，为了方便分析 这里首先分析init函数，在init中最重要的是_make_layer 函数，以layer1为例
 block为Bottleneck，planes=64（即channel数目）blocks=3 （[3,4,6,3] 分别代表每一层的blocks数目）这里要注意layer1的stride为1 其他layer的stride为2
 对于layer1而言 inplanes=64 planes=64 block.expansion=4,因此需要经过downsample 才能够使得残差和经过该层的feature map能够相加，downsample即为右路部分
 

 这样说其实还是不太清晰 ，最直观的方法就是分析每一层究竟发生了什么变化
 layer1：
 输入 ：[batch_size,56,56,64]
 此时self.inplanes=64 planes * block.expansion=644不相等 （之所以要二者相等时在Bottleneck最后一个卷积层会将channel变为 planes乘block.expansion 如果inplanes（实际就是输入的channel）与之不相等 不可相加）
 构造右路downsample （11卷积核的卷积层 扩展channel +BN层）
 
构造layer1 3个Bottleneck中的第一个：
 
主体分支
 .
 downsample 分支
 
 更新 inplanes=64*4
 
构造layer1 3个Bottleneck中的第2个
 
self.conv1 = nn.Conv2d(256, 64, kernel_size=1, bias=False)
 self.bn1 = nn.BatchNorm2d(64)
 self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=stride,
 padding=1, bias=False)
 self.bn2 = nn.BatchNorm2d(64)
 self.conv3 = nn.Conv2d(64,256, kernel_size=1, bias=False)
 self.bn3 = nn.BatchNorm2d(planes * self.expansion)
 self.relu = nn.ReLU(inplace=True)
 self.downsample = None
 self.stride = 1
 
构造layer1 3个Bottleneck中的第3个
 
self.conv1 = nn.Conv2d(256, 64, kernel_size=1, bias=False)
 self.bn1 = nn.BatchNorm2d(64)
 self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=stride,
 padding=1, bias=False)
 self.bn2 = nn.BatchNorm2d(64)
 self.conv3 = nn.Conv2d(64,256, kernel_size=1, bias=False)
 self.bn3 = nn.BatchNorm2d(planes * self.expansion)
 self.relu = nn.ReLU(inplace=True)
 self.downsample = None
 self.stride = 1
 
构造layer2 4个Bottleneck中的第一个
 
此时stride=2 self.inplanes=256 planes * block.expansion=128*4
 需要生成downsample层
 downsample = nn.Sequential(
 nn.Conv2d(256 ，512
 kernel_size=1, stride=2, bias=False),
 nn.BatchNorm2d(512),
 )
 
生成第一个Bottleneck的主干
 self.conv1 = nn.Conv2d(256, 128, kernel_size=1, bias=False)
 self.bn1 = nn.BatchNorm2d(planes)
 self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, //此时feature map 大小由56变成28
 padding=1, bias=False)
 self.bn2 = nn.BatchNorm2d(planes)
 self.conv3 = nn.Conv2d(128, 512, kernel_size=1, bias=False)
 self.bn3 = nn.BatchNorm2d(planes * self.expansion)
 self.relu = nn.ReLU(inplace=True)
 self.downsample = downsample
 self.stride = stride
 
构造layer2 4个Bottleneck中的第2,3，4个
 
更新inplanes=512
 self.conv1 = nn.Conv2d(512, 128, kernel_size=1, bias=False)
 self.bn1 = nn.BatchNorm2d(64)
 self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
 padding=1, bias=False)
 self.bn2 = nn.BatchNorm2d(64)
 self.conv3 = nn.Conv2d(128,256, kernel_size=1, bias=False)
 self.bn3 = nn.BatchNorm2d(planes * self.expansion)
 self.relu = nn.ReLU(inplace=True)
 self.downsample = None
 self.stride = 1
 
构造layer3 6 个Bottleneck中的第1个
 
对于之后的layer3和layer4 都同理
 第一层会将feature map缩小2倍
 同时生成一个11卷积核 inchannel =inplanes outchannel=4planes的支路用来进行对输入的feature mapdownsample操作
 生成一个11 33（stride=2） 1*1 的卷积层组 对featuremap进行卷积操作 并与支路相加
 
(0): Bottleneck(
 (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
 (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
 (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (relu): ReLU(inplace)
 (downsample): Sequential(
 (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
 (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 )
 )
 剩余的Bottleneck均生成如下结构;
 (1): Bottleneck(
 (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
 (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (relu): ReLU(inplace)
 )
 
整体结构如下：
 
(net): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer2): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer3): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)  
    (fc): Linear(in_features=2048, out_features=1000, bias=True)
  )
)

最后看一下Resnet的forward函数：

 
def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.maxpool(x)

    x = self.layer1(x)
    x = self.layer2(x)
    x = self.layer3(x)
    x = self.layer4(x)

    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.fc(x)

    return x
 
   self.avgpool = nn.AvgPool2d(7, stride=1)
  x = self.avgpool(x)  因为最后feature map在输入为224时 经过layer4之后大小为7乘7   ，此时经过 nn.AvgPool2d(7, stride=1)大小变为1乘1  再经过全连接层时
self.fc = nn.Linear(512 * block.expansion, num_classes) 前者是输出的所有channel数目 实际应该为channel * 1 * 1 后者为分类数
  x = x.view(x.size(0), -1) 将数据拉伸成batchsize * channel * 1 * 1    
  如果输入大小不为224  那么相应的可以修改AvgPool2d 或者在全连接层第一个参数中乘上最终的width 和height
 
Ref：[https://blog.csdn.net/jiangpeng59/article/details/79609392](https://blog.csdn.net/jiangpeng59/article/details/79609392)

这是我的报错：
 Could not resolve view with name ‘/ getPersonal’ in servlet with name ‘springMvc’
 at org.springframework.web.servlet.DispatcherServlet.render(DispatcherServlet.java:1190)
 
我的解决方法：检查后台是否有这个方法，如果有但是还报错那就重启项目， 因为注解和方法名，参数在项目初始化才会被注入。如果没有这个方法查看html或jsp是否写错请求的方法名