因為torchvision對resnet18-resnet152進行了封裝實現，因而想跟蹤下原始碼(^▽^)

首先看張核心的resnet層次結構圖(圖1)，它詮釋了resnet18-152是如何搭建的，其中resnet18和resnet34結構類似，而resnet50-resnet152結構類似。下面先看resnet18的原始碼

圖1

resnet18
首先是models.resnet18函式的呼叫

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]和結構圖[]X2是對應的
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained: #載入模型權重
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model

這裡涉及到了一個BasicBlock類(resnet18和34)，這樣的一個結構我們稱為一個block，因為在block內部的conv都使用了padding，輸入的in_img_size和out_img_size都是56x56，在圖2右邊的shortcut只需要改變輸入的channel的大小，輸入bloack的輸入tensor和輸出tensor就可以相加(

詳細內容)

圖2

事實上圖2是Bottleneck類(用於resnet50-152，稍後分析)，其和BasicBlock差不多，圖3為圖2的精簡版(ps:可以把下圖視為為一個box_block，即多個block疊加在一起，x3說明有3個上圖一樣的結構串起來)：

圖3

BasicBlock類，可以對比結構圖中的resnet18和resnet34，類中expansion =1，其表示box_block中最後一個block的channel比上第一個block的channel，即：

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其實就是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的維度統一
        if self.downsample is not None:
            residual = self.downsample(x)

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

        return out

接下來是ResNet類，其和我們通常定義的模型差不多一個init()+forward()，程式碼有點長，我們一步步來分析：

1. 參考前面的結構圖，所有的resnet的第一個conv層都是一樣的，輸出channel=64
2. 然後到了self.layer1 = self._make_layer(block, 64, layers[0])，這裡的layers[0]=2，然後我們進入到_make_layer函式，由於stride=1或當前的輸入channel和上一個塊的輸出channel一樣，因而可以直接相加
3. self.layer2 = self._make_layer(block, 128, layers[1], stride=2)，此時planes=128而self.inplanes=64為上box_block的輸出channel，此時channel不一致，需要對輸出的x擴維後才能相加，downsample 實現的就是該功能(ps：這裡只有box_block中的第一個block需要downsample，為何？請看下圖)
4. self.layer3 = self._make_layer(block, 256, layers[2], stride=2)，此時planes=256而self.inplanes=128為，此時也需要擴維後才能相加，layer4 同理。
   
   圖4

圖4中下標2,3,4和上面的步驟對應，圖中箭頭旁數值表示box_block輸入或者輸出的channel數。
具體看圖4-2，上一個box_block的最後一個block輸出channel為64(也是下一個box_block的輸入channel)，而當前的box_block的第一個block的輸出為128，在此需要擴維才能相加。然後到了當前box_block的第2個block,其輸入channel和輸出channel是一致的，因此無需擴維。
也就是說在box_block內部，只需要對第1個block進行擴維，因為在box_block內，第一個block輸出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 主要用來處理H(x)=F(x)+x中F(x)和xchannel維度不匹配問題
        downsample = None
        #self.inplanes為上個box_block的輸出channel,planes為當前box_block塊的輸入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 = []
        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類替換了BasicBlock，[3, 8, 36, 3]也和上面結構圖對應。

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類，這裡需要注意的是 expansion = 4，前面2個block的channel沒有變，最後一個變成了第一個的4倍，具體可看本文的第2個圖。

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

影象輸入大小問題：
首先pytorch輸入的大小固定為224*224，超過這個大小就會報錯，比如輸入大小256*256

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

首先我們看下，resnet在哪些地方改變了輸出影象的大小

conv和pool層的輸出大小都可以根據下面公式計算得出
$H_{out} = floor((H_{in} + 2 * padding[0] - kernel\_size[0]) / stride[0]) + 1$
$W_{out} = floor((W_{in} + 2 * padding[1] -kernel\_size[1] ) / stride[1] )+ 1$

但是resnet裡面的卷積層太多了，就resnet152的height而言，其最後avgpool後的大小為$h_{out}=ceil( h_{in}/32-7+1)$,因此修改原始碼把影象的height和width傳遞進去，從而相容非224的圖片大小：

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

也可以在外面替換跳最後一個fc層，這裡的2048即本文圖1中resnet152對應的最後layer的輸出channel，若是resnet18或resnet34則為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) 

還有另外一種暴力的方法，就是不管卷積層的輸出大小，取其平均值做為輸出，比如：

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

第一次研究pytorch，請大神門輕噴

reference:
resnet詳細介紹
deeper bottleneck Architectures詳細介紹