댑덥딥 7주차 정리
Tags: ai, deeplearning, 스터디, 모두를위한딥러닝시즌2
Published: 2023-12-28 11:03:54

> '모두를 위한 딥러닝 시즌 2' 강의를 듣고 공부하는 스터디 입니다. https://deeplearningzerotoall.github.io/season2/lec_tensorflow.html
> 

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***비대면 24 May, 2023***

# 10-5 Advanced CNN(VGG)

### <case study>

**LeNet-5**: conv filter 5x5, stride 1/subsampling pool

**AlexNet**: conv1: 96 11x11, stride 4/pool1

**GoogLeNet**: 22layers. 1x1 conv를 병렬적으로 사용-inception module

**VGGNet**: 19 layers, 모든 conv 3x3, padding 1, stride1_깊이에 따른 성능 변화를 연구

**ResNet**: 152 layers-layer를 건너뛰듯이 → 두 개의 레이어가 합쳐지는 효과를 냄

⇒ 이미지 분류용 알고리즘들임. 텍스트 분류에 사용되기도 함

### VGG16

-vgg11~vgg19까지 만들 수 있음


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/f8v9a4o1wrizdwoff2s6.png)

torchvision.meodels.vgg: input 3x224x224 기준

```python
import torchvision.meodels.vgg as vgg
```

```python
class VGG(nn.Module):
    def __init__(self, features, num_classes=1000, init_weights=True):
        super(VGG, self).__init__()
        
        self.features = features #convolution
        
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),    #이미지 사이즈가 달라지면 수정 필요
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )#FC layer(1~3)
        
        if init_weights:
            self._initialize_weights()

    def forward(self, x):
        x = self.features(x) #Convolution 
        x = self.avgpool(x) # avgpool
        x = x.view(x.size(0), -1) #flatten
        x = self.classifier(x) #FC layer
        return x

    def _initialize_weights(self):
        for m in self.modules():    #feature값을 하나씩 리턴
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')    #He intialization
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)
```

```python
##feature값 만들기
# 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
    
def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v    #채널 수가 변경됨
                     
    return nn.Sequential(*layers)

cfg = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], #8 + 3 =11 == vgg11
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], # 10 + 3 = vgg 13
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], #13 + 3 = vgg 16
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], # 16 +3 =vgg 19
    'custom' : [64,64,64,'M',128,128,128,'M',256,256,256,'M']
}

conv = make_layers(cfg['custom'], batch_norm=True)
```

```python
CNN = VGG(make_layers(cfg['custom']), num_classes=10, init_weights=True)
```

-vgg뒤에 붙는 숫자는 어떻게 나오는 거지?  conv개수+classfier 개수

#### with CIFAR-10

-learning rate를 epoch 진행됨에 따라 줄어들게 할 수 있음

```python
lr_sche = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)    #epoch 5번마다 lr 줄어듦
```

```python
#training
epochs = 50

for epoch in range(epochs):  # loop over the dataset multiple times
    running_loss = 0.0
    lr_sche.step()
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data
        inputs = inputs.to(device)
        labels = labels.to(device)

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = vgg16(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 30 == 29:    # print every 30 mini-batches
            loss_tracker(loss_plt, torch.Tensor([running_loss/30]), torch.Tensor([i + epoch*len(trainloader) ]))
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 30))
            running_loss = 0.0
```

# 10-6 Advanced CNN(ResNet)


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/a70tyfm08kkxa40bz7fz.png)

-h(x)=x(가장 간단한 형태)가 가장 좋은 성능을 냄

torchvision.meodels.resnet: input 3x224x224 기준

```python
import torchvision.models.resnet as resnet
```

```python
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, strid
```

```python
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 = 2
        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)
#downsample이 없으면 stride가 2 이상이 됐을 때, out과 identity가 사이즈가 달라져 덧셈이 불가능함. 따라서 identity값을 낮춰주기 위해 사용함

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

        return out
```

```python
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)_planes를 곱해서 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
```

```python
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_근거가 나와있는 논문
        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)

#각주 layer2 기준임
def _make_layer(self, block, planes, blocks, stride=1):
        
        downsample = None
        
        if stride != 1 or self.inplanes != planes * block.expansion: 
            #resnet에서는 downsample을 channel을 맞추기 위해서 사용
            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
```

```python
def resnet18(pretrained=False, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) #=> 2*(2+2+2+2)(basicblock) +1(conv1) +1(fc)  = 16 +2 =resnet 18
    return model

def resnet50(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) #=> 3*(3+4+6+3)(bottleneck) +(conv1) +1(fc) = 48 +2 = 50
    return model

def resnet152(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) # 3*(3+8+36+3) +2 = 150+2 = resnet152    
    return mode
```

#### with CIFAR-10

-더 정확한 normalize_mean과 std 직접 계산해서 사용

```python

transform = transforms.Compose([
    transforms.ToTensor()
])

trainset = torchvision.datasets.CIFAR10(root='./cifar10', train=True, download=True, transform=transform)

print(trainset.train_data.shape)

train_data_mean = trainset.train_data.mean( axis=(0,1,2) )
train_data_std = trainset.train_data.std( axis=(0,1,2) )

print(train_data_mean)
print(train_data_std)

train_data_mean = train_data_mean / 255
train_data_std = train_data_std / 255

print(train_data_mean)
print(train_data_std)
```

```python
class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.inplanes = 16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        #self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.layer1 = self._make_layer(block, 16, layers[0], stride=1)
        self.layer2 = self._make_layer(block, 32, layers[1], stride=1)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 128, layers[3], stride=2)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(128 * 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),
                nn.BatchNorm2d(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        #x.shape =[1, 16, 32,32]
        x = self.bn1(x)
        x = self.relu(x)
        #x = self.maxpool(x)

        x = self.layer1(x)
        #x.shape =[1, 128, 32,32]
        x = self.layer2(x)
        #x.shape =[1, 256, 32,32]
        x = self.layer3(x)
        #x.shape =[1, 512, 16,16]
        x = self.layer4(x)
        #x.shape =[1, 1024, 8,8]
        
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x
```

```python
#training
epochs = 70

for epoch in range(epochs):  # loop over the dataset multiple times

    running_loss = 0.0
    lr_sche.step()
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data
        inputs = inputs.to(device)
        labels = labels.to(device)

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = resnet50(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 30 == 29:    # print every 30 mini-batches
            value_tracker(loss_plt, torch.Tensor([running_loss/30]), torch.Tensor([i + epoch*len(trainloader) ]))
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 30))
            running_loss = 0.0
    
    ##Check Accuracy
    #acc = acc_check(resnet50, testloader, epoch, save=1)
    #value_tracker(acc_plt, torch.Tensor([acc]), torch.Tensor([epoch]))
```

---

***대면 27 May, 2023***

###VGG, resNet
{% embed https://colab.research.google.com/drive/1h2PD5mw0_4THlxlcTO8f88JAAZMEzLyR?usp=sharing %}