pytorch笔记：04)resnet网络&解决输入图像大小问题

因为torchvision对resnet18-resnet152进行了封装实现，因而想跟踪下源码(^▽)

首先看张核心的resnet层次结构图(图1)，它诠释了resnet18-152是如何搭建的，其中resnet18和resnet34结构类似，而resnet50-resnet152结构类似。下面先看resnet18的源码

**图1**

resnet18
首先是models.resnet18函数的调用

def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    #[2, 2, 2, 2]和结构图[]X2是对应的
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained: #加载模型权重
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model

这里涉及到了一个BasicBlock类(resnet18和34)，这样的一个结构我们称为一个block，因为在block内部的conv都使用了padding，输入的in_img_size和out_img_size都是56x56，在图2右边的shortcut只需要改变输入的channel的大小，输入bloack的输入tensor和输出tensor就可以相加(详细内容)

 **图2**

事实上图2是Bottleneck类(用于resnet50-152，稍后分析)，其和BasicBlock差不多，图3为图2的精简版(ps:可以把下图视为为一个box_block，即多个block叠加在一起，x3说明有3个上图一样的结构串起来)：

 **图3**

BasicBlock类，可以对比结构图中的resnet18和resnet34，类中expansion =1，其表示box_block中最后一个block的channel比上第一个block的channel，即：
$$expansion=last\_block\_channel/first\_block\_channel$$

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)
                     
class BasicBlock(nn.Module):
    expansion = 1
	#inplanes其实就是channel,叫法不同
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    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)
		#把shortcut那的channel的维度统一
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

接下来是ResNet类，其和我们通常定义的模型差不多一个__init__()+forward()，代码有点长，我们一步步来分析：

1. 参考前面的结构图，所有的resnet的第一个conv层都是一样的，输出channel=64
2. 然后到了self.layer1 = self._make_layer(block, 64, layers[0])，这里的layers[0]=2，然后我们进入到_make_layer函数，由于stride=1或当前的输入channel和上一个块的输出channel一样，因而可以直接相加
3. self.layer2 = self._make_layer(block, 128, layers[1], stride=2)，此时planes=128而self.inplanes=64为上box_block的输出channel，此时channel不一致，需要对输出的x扩维后才能相加，downsample 实现的就是该功能(ps：这里只有box_block中的第一个block需要downsample，为何？请看下图)
4. self.layer3 = self._make_layer(block, 256, layers[2], stride=2)，此时planes=256而self.inplanes=128为，此时也需要扩维后才能相加，layer4 同理。
   

**图4**

图4中下标2,3,4和上面的步骤对应，图中箭头旁数值表示box_block输入或者输出的channel数。
具体看图4-2，上一个box_block的最后一个block输出channel为64(也是下一个box_block的输入channel)，而当前的box_block的第一个block的输出为128，在此需要扩维才能相加。然后到了当前box_block的第2个block,其输入channel和输出channel是一致的，因此无需扩维。
也就是说在box_block内部，只需要对第1个block进行扩维，因为在box_block内，第一个block输出channel和剩下的保持一致了。

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):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        #downsample 主要用来处理H(x)=F(x)+x中F(x)和xchannel维度不匹配问题
        downsample = None
        #self.inplanes为上个box_block的输出channel,planes为当前box_block块的输入channel
        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 = []
        #只在这里传递了stride=2的参数，因而一个box_block中的图片大小只在第一次除以2
        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)

    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

resnet152
resnet152和resnet18差不多，Bottleneck类替换了BasicBlock，[3, 8, 36, 3]也和上面结构图对应。

def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model

Bottleneck类，这里需要注意的是 expansion = 4，前面2个block的channel没有变，最后一个变成了第一个的4倍，具体可看本文的第2个图。

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 * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    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

图像输入大小问题：
首先pytorch输入的大小固定为224224，超过这个大小就会报错，比如输入大小256256

RuntimeError: size mismatch, m1: [1 x 8192], m2: [2048 x 1000] at c:\miniconda2\conda-bld\pytorch-cpu_1519449358620\work\torch\lib\th\generic/THTensorMath.c:1434

首先我们看下，resnet在哪些地方改变了输出图像的大小

conv和pool层的输出大小都可以根据下面公式计算得出
$H_{out}=floor((H_{in}+2\ast padding[0]-kernel\_size[0])/stride[0])+1$
$W_{out}=floor((W_{in}+2\ast padding[1]-kernel\_size[1])/stride[1])+1$

但是resnet里面的卷积层太多了，就resnet152的height而言，其最后avgpool后的大小为$h_{out}=ceil(h_{in}/32-7+1)$,因此修改源码把图像的height和width传递进去，从而兼容非224的图片大小：

self.avgpool = nn.AvgPool2d(7, stride=1)
f = lambda x:math.ceil(x /32 - 7 + 1)
self.fc = nn.Linear(512 * block.expansion * f(w) * f(h), num_classes) #block.expansion=4

也可以在外面替换跳最后一个fc层，这里的2048即本文图1中resnet152对应的最后layer的输出channel，若是resnet18或resnet34则为512

model_ft = models.resnet152(pretrained=True)
f = lambda x:math.ceil(x /32 - 7 + 1)
model_ft.fc = nn.Linear(f(target_w) * f(target_h) * 2048, nb_classes) 

还有另外一种暴力的方法，就是不管卷积层的输出大小，取其平均值做为输出，比如：

self.main = torchvision.models.resnet152(pretrained)
self.main.avgpool = nn.AdaptiveAvgPool2d((1, 1))

第一次研究pytorch，请大神门轻喷

reference:
resnet详细介绍
deeper bottleneck Architectures详细介绍