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1. 개요

2.

3.1 BasicBlok (For ResNet 18/34)

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)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

class BasicBlock(nn.Module):
    expansion = 1

    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):

        identity = x

        out = self.conv1(x) # 3x3 stride = stride
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out) # 3x3 stride = 1
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

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

        return out

3.1 Bottlenet (For ResNet 50/101/152)

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes) #conv1x1(64,64)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)#conv3x3(64,64)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion) #conv1x1(64,256)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x) # 1x1 stride = 1
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out) # 3x3 stride = stride 
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out) # 1x1 stride = 1
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

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

        return out

RESNET 50

class ResNet(nn.Module):
    # model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) #resnet 50 
    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
        super(ResNet, self).__init__()

        self.inplanes = 64

        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]'''3''')
        self.layer2 = self._make_layer(block, 128, layers[1]'''4''', stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2]'''6''', stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3]'''3''', stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 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)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):

        downsample = None

        if stride != 1 or self.inplanes != planes * block.expansion: 

            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride), #conv1x1(256, 512, 2)
                nn.BatchNorm2d(planes * block.expansion), #batchnrom2d(512)
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))

        self.inplanes = planes * block.expansion #self.inplanes = 128 * 4

        for _ in range(1, blocks): 
            layers.append(block(self.inplanes, planes)) # * 3

        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

• RESNET수정하여 cifar10 적용하기: youtube