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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1:  Week 5
Step2:  下面在图像上简单一下测试我们的conv函数,这里使用3*3的高斯核对下面的图像进行滤波.
Step4:  上面我们实现了实现了对单通道输入单通道输出的卷积.在CNN中,一般使用到的都是多通道输入多通道输出的卷积,要实现多通道的卷积, 我们只需要对循环调用上面的conv函数即可.
Step5:  接下来, 让我们测试我们写好的myconv2d函数.
Step6:  在Pytorch中,已经为我们提供了卷积和卷积层的实现.使用同样的input和weights,以及stride,padding,pytorch的卷积的结果应该和我们的一样.可以在下面的代码中进行验证.
Step7:  作业
Step8:  卷积层
Pytorch提供了卷积层和池化层供我们使用.
卷积层与上面相似, 而池化层与卷积层相似,Pooling layer的主要目的是缩小features的size.常用的有MaxPool(滑动窗口取最大值)与AvgPool(滑动窗口取均值)
Step9:  请问
Step11:  GPU
我们可以选择在cpu或gpu上来训练我们的模型. 
实验室提供了4卡的gpu服务器,要查看各个gpu设备的使用情况,可以在服务器上的jupyter主页点击new->terminal,在terminal中输入nvidia-smi即可查看每张卡的使用情况.如下图.
上图左边一栏显示了他们的设备id(0,1,2,3),风扇转速,温度,性能状态,能耗等信息,中间一栏显示他们的bus-id和显存使用量,右边一栏是GPU使用率等信息.注意到中间一栏的显存使用量,在训练模型前我们可以根据空余的显存来选择我们使用的gpu设备. 
在本次实验中我们将代码中的torch.device('cuda
Step15:  这样,一个简单的CNN模型就写好了.与前面的课堂内容相似,我们需要对完成网络进行训练与评估的代码.
Step16:  准备数据与训练模型
接下来，我们使用CIFAR10数据集来对我们的CNN模型进行训练.
CIFAR-10
Step18:  训练过程中使用交叉熵(cross-entropy)损失函数与Adam优化器来训练我们的分类器网络.
阅读下面的代码并在To-Do处,根据之前所学的知识,补充前向传播和反向传播的代码来实现分类网络的训练.
Step20:  ResNet
接下来,让我们完成更复杂的CNN的实现.
ResNet又叫做残差网络.在ResNet网络结构中会用到两种残差模块，一种是以两个3*3的卷积网络串接在一起作为一个残差模块，另外一种是1*1、3*3、1*1的3个卷积网络串接在一起作为一个残差模块。他们如下图所示。
我们以左边的模块为例实现一个ResidualBlock.注意到由于我们在两次卷积中可能会使输入的tensor的size与输出的tensor的size不相等,为了使它们能够相加,所以输出的tensor与输入的tensor size不同时,我们使用downsample(由外部传入)来使保持size相同
现在,试在To-Do补充代码完成下面的forward函数来完成ResidualBlock的实现,并运行它.
Step23:  下面是一份针对cifar10数据集的ResNet的实现. 
它先通过一个conv3x3,然后经过3个包含多个残差模块的layer(一个layer可能包括多个ResidualBlock, 由传入的layers列表中的数字决定), 然后经过一个全局平均池化层,最后通过一个线性层.
Step24:  使用fit函数训练实现的ResNet,观察结果变化.
Step25:  作业
尝试改变学习率lr,使用SGD或Adam优化器,训练10个epoch,提高ResNet在测试集上的accuracy.
Step26:  作业
下图表示将SE模块嵌入到ResNet的残差模块.
其中,global pooling表示全局池化层(将输入的size池化为1*1), 将c*h*w的输入变为c*1*1的输出.FC表示全连接层(线性层),两层FC之间使用ReLU作为激活函数.通过两层FC后使用sigmoid激活函数激活.最后将得到的c个值与原输入c*h*w按channel相乘,得到c*h*w的输出.
补充下方的代码完成SE-Resnet block的实现.
Step28:  Vgg
接下来让我们阅读vgg网络的实现代码.VGGNet全部使用3*3的卷积核和2*2的池化核，通过不断加深网络结构来提升性能。Vgg表明了卷积神经网络的深度增加和小卷积核的使用对网络的最终分类识别效果有很大的作用.
下面是一份用于训练cifar10的简化版的vgg代码. 
有时间的同学可以阅读并训练它.
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Python Code:
import numpy as np
def convolution(img, kernel, padding=1, stride=1):
    
    img: input image with one channel
    kernel: convolution kernel
    
    
    h, w = img.shape
    kernel_size = kernel.shape[0]
    
    # height and width of image with padding 
    ph, pw = h + 2 * padding, w + 2 * padding
    padding_img = np.zeros((ph, pw))
    padding_img[padding:h + padding, padding:w + padding] = img
    
    # height and width of output image
    result_h = (h + 2 * padding - kernel_size) // stride + 1
    result_w = (w + 2 * padding - kernel_size) // stride + 1
    
    result = np.zeros((result_h, result_w))
    
    # convolution
    x, y = 0, 0
    for i in range(0, ph - kernel_size + 1, stride):
        for j in range(0, pw - kernel_size + 1, stride):
            roi = padding_img[i:i+kernel_size, j:j+kernel_size]
            result[x, y] = np.sum(roi * kernel)
            y += 1
        y = 0
        x += 1
    return result
Explanation: Week 5: CNN-1
实验准备
熟悉python语言的使用和numpy,torch的基本用法
熟悉神经网络的训练过程与优化方法
结合理论课的内容,了解卷积与卷积神经网络(CNN)的内容和原理
了解常用的CNN模型的基本结构,如AlexNet,Vgg,ResNet
实验过程
1. 卷积与卷积层
numpy实现卷积
pytorch中的卷积层和池化层
2. CNN
实现并训练一个基本的CNN网络
ResNet
VGG
卷积
在实验课上我们已经了解过卷积运算的操作当我们对一张二维的图像做卷积时,将卷积核沿着图像进行滑动乘加即可(如上图所示).
下面的conv函数实现了对二维单通道图像的卷积.考虑输入的卷积核kernel的长宽相同,padding为对图像的四个边缘补0,stride为卷积核窗口滑动的步长.
End of explanation
from PIL import Image
import matplotlib.pyplot as plt
img = Image.open('pics/lena.jpg').convert('L')
plt.imshow(img, cmap='gray')
#  a Laplace kernel
laplace_kernel = np.array([[-1, -1, -1],
                           [-1, 8, -1],
                           [-1, -1, -1]])
# Gauss kernel with kernel_size=3
gauss_kernel3 = (1/ 16) * np.array([[1, 2, 1], 
                                   [2, 4, 2], 
                                   [1, 2, 1]])
# Gauss kernel with kernel_size=5
gauss_kernel5 = (1/ 84) * np.array([[1, 2, 3, 2, 1],
                                    [2, 5, 6, 5, 2], 
                                    [3, 6, 8, 6, 3],
                                    [2, 5, 6, 5, 2],
                                    [1, 2, 3, 2, 1]])
fig, ax = plt.subplots(1, 3, figsize=(12, 8))
laplace_img = convolution(np.array(img), laplace_kernel, padding=1, stride=1)
ax[0].imshow(Image.fromarray(laplace_img), cmap='gray')
ax[0].set_title('laplace')
gauss3_img = convolution(np.array(img), gauss_kernel3, padding=1, stride=1)
ax[1].imshow(Image.fromarray(gauss3_img), cmap='gray')
ax[1].set_title('gauss kernel_size=3')
gauss5_img = convolution(np.array(img), gauss_kernel5, padding=2, stride=1)
ax[2].imshow(Image.fromarray(gauss5_img), cmap='gray')
ax[2].set_title('gauss kernel_size=5')
Explanation: 下面在图像上简单一下测试我们的conv函数,这里使用3*3的高斯核对下面的图像进行滤波.
End of explanation
def myconv2d(features, weights,  padding=0, stride=1):
    
    features: input, in_channel * h * w
    weights: kernel, out_channel * in_channel * kernel_size * kernel_size
    return output with out_channel
    
    in_channel, h, w = features.shape
    out_channel, _, kernel_size, _ = weights.shape
    
    # height and width of output image
    output_h = (h + 2 * padding - kernel_size) // stride + 1
    output_w = (w + 2 * padding - kernel_size) // stride + 1
    output = np.zeros((out_channel, output_h, output_w))
    
    # call convolution out_channel * in_channel times
    for i in range(out_channel):
        weight = weights[i]
        for j in range(in_channel):
            feature_map = features[j]
            kernel = weight[j]
            output[i] += convolution(feature_map, kernel, padding, stride)
    return output
Explanation: 上面我们实现了实现了对单通道输入单通道输出的卷积.在CNN中,一般使用到的都是多通道输入多通道输出的卷积,要实现多通道的卷积, 我们只需要对循环调用上面的conv函数即可.
End of explanation
input_data=[
           [[0,0,2,2,0,1],
            [0,2,2,0,0,2],
            [1,1,0,2,0,0],
            [2,2,1,1,0,0],
            [2,0,1,2,0,1],
            [2,0,2,1,0,1]],
           [[2,0,2,1,1,1],
            [0,1,0,0,2,2],
            [1,0,0,2,1,0],
            [1,1,1,1,1,1],
            [1,0,1,1,1,2],
            [2,1,2,1,0,2]]
            ]
weights_data=[[ 
               [[ 0, 1, 0],
                [ 1, 1, 1],
                [ 0, 1, 0]],
    
               [[-1, -1, -1],
                [ -1, 8, -1],
                [ -1, -1, -1]] 
           ]]
# numpy array
input_data   = np.array(input_data)
weights_data = np.array(weights_data)
# show the result
print(myconv2d(input_data, weights_data, padding=3, stride=3))
Explanation: 接下来, 让我们测试我们写好的myconv2d函数.
End of explanation
import torch
import torch.nn.functional as F
input_tensor = torch.tensor(input_data).unsqueeze(0).float()
F.conv2d(input_tensor, weight=torch.tensor(weights_data).float(), bias=None, stride=3, padding=3)
Explanation: 在Pytorch中,已经为我们提供了卷积和卷积层的实现.使用同样的input和weights,以及stride,padding,pytorch的卷积的结果应该和我们的一样.可以在下面的代码中进行验证.
End of explanation
def convolutionV2(img, kernel, padding=(0,0), stride=(1,1)):
    h, w = img.shape
    kh, kw = kernel.shape
    # height and width of image with padding 
    ph, pw = h + 2 * padding[0], w + 2 * padding[1]
    padding_img = np.zeros((ph, pw))
    padding_img[padding[0]:h + padding[0], padding[1]:w + padding[1]] = img
    
    # height and width of output image
    result_h = (h + 2 * padding[0] - kh) // stride[0] + 1
    result_w = (w + 2 * padding[1] - kw) // stride[1] + 1
    
    result = np.zeros((result_h, result_w))
    
    # convolution
    x, y = 0, 0
    for i in range(0, ph - kh + 1, stride[0]):
        for j in range(0, pw - kw + 1, stride[1]):
            roi = padding_img[i:i+kh, j:j+kw]
            result[x, y] = np.sum(roi * kernel)
            y += 1
        y = 0
        x += 1
    return result
# test input
test_input = np.array([[1, 1, 2, 1],
                       [0, 1, 0, 2],
                       [2, 2, 0, 2],
                       [2, 2, 2, 1],
                       [2, 3, 2, 3]])
test_kernel = np.array([[1, 0], [0, 1], [0, 0]])
# output
print(convolutionV2(test_input, test_kernel, padding=(1, 0), stride=(1, 1)))
print(convolutionV2(test_input, test_kernel, padding=(2, 1), stride=(1, 2)))
Explanation: 作业:
上述代码中convolution的实现只考虑卷积核以及padding和stride长宽一致的情况,若输入的卷积核可能长宽不一致,padding与stride的输入可能为两个元素的元祖(代表两个维度上的padding与stride)并使用下面test input对你的convolutionV2进行测试.
End of explanation
import torch
import torch.nn as nn
x = torch.randn(1, 1, 32, 32)
conv_layer = nn.Conv2d(in_channels=1, out_channels=3, kernel_size=3, stride=1, padding=0)
y = conv_layer(x)
print(x.shape)
print(y.shape)
Explanation: 卷积层
Pytorch提供了卷积层和池化层供我们使用.
卷积层与上面相似, 而池化层与卷积层相似,Pooling layer的主要目的是缩小features的size.常用的有MaxPool(滑动窗口取最大值)与AvgPool(滑动窗口取均值)
End of explanation
x = torch.randn(1, 1, 32, 32)
conv_layer = nn.Conv2d(in_channels=1, out_channels=3, kernel_size=5, stride=2, padding=2)
y = conv_layer(x)
print(x.shape)
print(y.shape)
# input N * C * H * W
x = torch.randn(1, 1, 4, 4)
# maxpool
maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
y = maxpool(x)
# avgpool
avgpool = nn.AvgPool2d(kernel_size=2, stride=2)
z = avgpool(x)
#avgpool
print(x)
print(y)
print(z)
Explanation: 请问:
1. 输入与输出的tensor的size分别是多少?该卷积层的参数量是多少?
2. 若kernel_size=5,stride=2,padding=2, 输出的tensor的size是多少?在上述代码中改变参数后试验后并回答.
3. 若输入的tensor size为N*C*H*W,若第5行中卷积层的参数为in_channels=C,out_channels=Cout,kernel_size=k,stride=s,padding=p,那么输出的tensor size是多少? 
答: 
1. 输入的tensor的大小为$1 * 1 * 32 * 32$，输出的tensor的大小为$1 * 3 * 30 * 30$，这说明卷积核是$1 * 1 * 3 * 3$的规模，一共有3个卷积核。
2. 输出的tensor的大小为$1 * 3 * 16 * 16$，代码验证如下。
3. 输出的tensor大小为$N * C_{out} * ((H+2p-k)//s+1) * ((W+2p-k)//s+1)$。
End of explanation
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
class MyCNN(nn.Module):
    
    def __init__(self, image_size, num_classes):
        super(MyCNN, self).__init__()
        # conv1: Conv2d -> BN -> ReLU -> MaxPool
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(), 
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        # conv2: Conv2d -> BN -> ReLU -> MaxPool
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        # fully connected layer
        self.fc = nn.Linear(32 * (image_size // 4) * (image_size // 4), num_classes)
        
    def forward(self, x):
        
        input: N * 3 * image_size * image_size
        output: N * num_classes
        
        x = self.conv1(x)
        x = self.conv2(x)
        # view(x.size(0), -1): change tensor size from (N ,H , W) to (N, H*W)
        x = x.view(x.size(0), -1)
        output = self.fc(x)
        return output
Explanation: GPU
我们可以选择在cpu或gpu上来训练我们的模型. 
实验室提供了4卡的gpu服务器,要查看各个gpu设备的使用情况,可以在服务器上的jupyter主页点击new->terminal,在terminal中输入nvidia-smi即可查看每张卡的使用情况.如下图.
上图左边一栏显示了他们的设备id(0,1,2,3),风扇转速,温度,性能状态,能耗等信息,中间一栏显示他们的bus-id和显存使用量,右边一栏是GPU使用率等信息.注意到中间一栏的显存使用量,在训练模型前我们可以根据空余的显存来选择我们使用的gpu设备. 
在本次实验中我们将代码中的torch.device('cuda:0')的0更换成所需的设备id即可选择在相应的gpu设备上运行程序.
CNN(卷积神经网络)
一个简单的CNN
接下来,让我们建立一个简单的CNN分类器.
这个CNN的整体流程是  
卷积(Conv2d) -> BN(batch normalization) -> 激励函数(ReLU) -> 池化(MaxPooling) ->   
卷积(Conv2d) -> BN(batch normalization) -> 激励函数(ReLU) -> 池化(MaxPooling) ->  
全连接层(Linear) -> 输出.
End of explanation
def train(model, train_loader, loss_func, optimizer, device):
    
    train model using loss_fn and optimizer in an epoch.
    model: CNN networks
    train_loader: a Dataloader object with training data
    loss_func: loss function
    device: train on cpu or gpu device
    
    total_loss = 0
    # train the model using minibatch
    for i, (images, targets) in enumerate(train_loader):
        images = images.to(device)
        targets = targets.to(device)
        # forward
        outputs = model(images)
        loss = loss_func(outputs, targets)
        # backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
        
        # every 100 iteration, print loss
        if (i + 1) % 100 == 0:
            print ("Step [{}/{}] Train Loss: {:.4f}"
                   .format(i+1, len(train_loader), loss.item()))
    return total_loss / len(train_loader)
def evaluate(model, val_loader, device):
    
    model: CNN networks
    val_loader: a Dataloader object with validation data
    device: evaluate on cpu or gpu device
    return classification accuracy of the model on val dataset
    
    # evaluate the model
    model.eval()
    # context-manager that disabled gradient computation
    with torch.no_grad():
        correct = 0
        total = 0
        
        for i, (images, targets) in enumerate(val_loader):
            # device: cpu or gpu
            images = images.to(device)
            targets = targets.to(device)
            
            
            outputs = model(images)
            
            # return the maximum value of each row of the input tensor in the 
            # given dimension dim, the second return vale is the index location
            # of each maxium value found(argmax)
            _, predicted = torch.max(outputs.data, dim=1)
            
            
            correct += (predicted == targets).sum().item()
            
            total += targets.size(0)
            
        accuracy = correct / total
        print('Accuracy on Test Set: {:.4f} %'.format(100 * accuracy))
        return accuracy
def save_model(model, save_path):
    # save model
    torch.save(model.state_dict(), save_path)
import matplotlib.pyplot as plt
def show_curve(ys, title):
    
    plot curlve for Loss and Accuacy
    Args:
        ys: loss or acc list
        title: loss or accuracy
    
    x = np.array(range(len(ys)))
    y = np.array(ys)
    plt.plot(x, y, c='b')
    plt.axis()
    plt.title('{} curve'.format(title))
    plt.xlabel('epoch')
    plt.ylabel('{}'.format(title))
    plt.show()
Explanation: 这样,一个简单的CNN模型就写好了.与前面的课堂内容相似,我们需要对完成网络进行训练与评估的代码.
End of explanation
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# mean and std of cifar10 in 3 channels 
cifar10_mean = (0.49, 0.48, 0.45)
cifar10_std = (0.25, 0.24, 0.26)
# define transform operations of train dataset 
train_transform = transforms.Compose([
    # data augmentation
    transforms.Pad(4),
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32),
    transforms.ToTensor(),
    transforms.Normalize(cifar10_mean, cifar10_std)])
test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(cifar10_mean, cifar10_std)])
# torchvision.datasets provide CIFAR-10 dataset for classification
train_dataset = torchvision.datasets.CIFAR10(root='./data/',
                                             train=True, 
                                             transform=train_transform,
                                             download=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data/',
                                            train=False, 
                                            transform=test_transform)
# Data loader: provides single- or multi-process iterators over the dataset.
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=100, 
                                           shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=100, 
                                          shuffle=False)
Explanation: 准备数据与训练模型
接下来，我们使用CIFAR10数据集来对我们的CNN模型进行训练.
CIFAR-10:该数据集共有60000张彩色图像,这些图像是32*32,分为10个类,每类6000张图.这里面有50000张用于训练,构成了5个训练批,每一批10000张图;另外10000用于测试,单独构成一批.在本次实验中,使用CIFAR-10数据集来训练我们的模型.我们可以用torchvision.datasets.CIFAR10来直接使用CIFAR10数据集.
End of explanation
def fit(model, num_epochs, optimizer, device):
    
    train and evaluate an classifier num_epochs times.
    We use optimizer and cross entropy loss to train the model. 
    Args: 
        model: CNN network
        num_epochs: the number of training epochs
        optimizer: optimize the loss function
    
        
    # loss and optimizer
    loss_func = nn.CrossEntropyLoss()
    
    model.to(device)
    loss_func.to(device)
    
    # log train loss and test accuracy
    losses = []
    accs = []
    
    for epoch in range(num_epochs):
        
        print('Epoch {}/{}:'.format(epoch + 1, num_epochs))
        # train step
        loss = train(model, train_loader, loss_func, optimizer, device)
        losses.append(loss)
        
        # evaluate step
        accuracy = evaluate(model, test_loader, device)
        accs.append(accuracy)
        
    
    # show curve
    show_curve(losses, "train loss")
    show_curve(accs, "test accuracy")
# hyper parameters
num_epochs = 10
lr = 0.01
image_size = 32
num_classes = 10
# declare and define an objet of MyCNN
mycnn = MyCNN(image_size, num_classes)
print(mycnn)
# Device configuration, cpu, cuda:0/1/2/3 available
device = torch.device('cuda:0')
optimizer = torch.optim.Adam(mycnn.parameters(), lr=lr)
# start training on cifar10 dataset
fit(mycnn, num_epochs, optimizer, device)
Explanation: 训练过程中使用交叉熵(cross-entropy)损失函数与Adam优化器来训练我们的分类器网络.
阅读下面的代码并在To-Do处,根据之前所学的知识,补充前向传播和反向传播的代码来实现分类网络的训练.
End of explanation
# 3x3 convolution
def conv3x3(in_channels, out_channels, stride=1):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3, 
                     stride=stride, padding=1, bias=False)
# Residual block
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels, stride)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
        
    def forward(self, x):
        
        Defines the computation performed at every call.
        x: N * C * H * W
        
        residual = x
        # if the size of input x changes, using downsample to change the size of residual
        if self.downsample:
            residual = self.downsample(x)
        out = self.conv1(x)
        out = self.bn1(out)
        
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        
        out += residual
        out = self.relu(out)
        return out
Explanation: ResNet
接下来,让我们完成更复杂的CNN的实现.
ResNet又叫做残差网络.在ResNet网络结构中会用到两种残差模块，一种是以两个3*3的卷积网络串接在一起作为一个残差模块，另外一种是1*1、3*3、1*1的3个卷积网络串接在一起作为一个残差模块。他们如下图所示。
我们以左边的模块为例实现一个ResidualBlock.注意到由于我们在两次卷积中可能会使输入的tensor的size与输出的tensor的size不相等,为了使它们能够相加,所以输出的tensor与输入的tensor size不同时,我们使用downsample(由外部传入)来使保持size相同
现在,试在To-Do补充代码完成下面的forward函数来完成ResidualBlock的实现,并运行它.
End of explanation
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        
        block: ResidualBlock or other block
        layers: a list with 3 positive num.
        
        super(ResNet, self).__init__()
        self.in_channels = 16
        self.conv = conv3x3(3, 16)
        self.bn = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        # layer1: image size 32
        self.layer1 = self.make_layer(block, 16, num_blocks=layers[0])
        # layer2: image size 32 -> 16
        self.layer2 = self.make_layer(block, 32, num_blocks=layers[1], stride=2)
        # layer1: image size 16 -> 8
        self.layer3 = self.make_layer(block, 64, num_blocks=layers[2], stride=2)
        # global avg pool: image size 8 -> 1
        self.avg_pool = nn.AvgPool2d(8)
    
        self.fc = nn.Linear(64, num_classes)
        
    def make_layer(self, block, out_channels, num_blocks, stride=1):
        
        make a layer with num_blocks blocks.
        
        
        downsample = None
        if (stride != 1) or (self.in_channels != out_channels):
            # use Conv2d with stride to downsample
            downsample = nn.Sequential(
                conv3x3(self.in_channels, out_channels, stride=stride),
                nn.BatchNorm2d(out_channels))
        
        # first block with downsample
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        
        self.in_channels = out_channels
        # add num_blocks - 1 blocks
        for i in range(1, num_blocks):
            layers.append(block(out_channels, out_channels))
            
        # return a layer containing layers
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = self.conv(x)
        out = self.bn(out)
        out = self.relu(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avg_pool(out)
        # view: here change output size from 4 dimensions to 2 dimensions
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out
resnet = ResNet(ResidualBlock, [2, 2, 2])
print(resnet)
Explanation: 下面是一份针对cifar10数据集的ResNet的实现. 
它先通过一个conv3x3,然后经过3个包含多个残差模块的layer(一个layer可能包括多个ResidualBlock, 由传入的layers列表中的数字决定), 然后经过一个全局平均池化层,最后通过一个线性层.
End of explanation
# Hyper-parameters
num_epochs = 10
lr = 0.001
# Device configuration
device = torch.device('cuda:0')
# optimizer
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
fit(resnet, num_epochs, optimizer, device)
Explanation: 使用fit函数训练实现的ResNet,观察结果变化.
End of explanation
resnet = ResNet(ResidualBlock, [2, 2, 2])
num_epochs = 10
lr = 0.0009
device = torch.device('cuda:0')
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
fit(resnet, num_epochs, optimizer, device)
Explanation: 作业
尝试改变学习率lr,使用SGD或Adam优化器,训练10个epoch,提高ResNet在测试集上的accuracy.
End of explanation
from torch import nn
class SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        # The output of AdaptiveAvgPool2d is of size H x W, for any input size.
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(channel, channel // reduction)
        self.fc2 = nn.Linear(channel // reduction, channel)
        self.sigmoid = nn.Sigmoid()
    def forward(self, x):
        b, c, _, _ = x.shape
        out = self.avg_pool(x).view(b, c)
        out = self.fc1(out)
        out = self.fc2(out)
        out = self.sigmoid(out).view(b, c, 1, 1)
        return out * x
class SEResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None, reduction=16):
        super(SEResidualBlock, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels, stride)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.se = SELayer(out_channels, reduction)
        self.downsample = downsample
        
    def forward(self, x):
        residual = x
        if self.downsample:
            residual = self.downsample(x)
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.se(out)
        out += residual
        out = self.relu(out)
        return out
se_resnet = ResNet(SEResidualBlock, [2, 2, 2])
print(se_resnet)
# Hyper-parameters
num_epochs = 10
lr = 0.001
# Device configuration
device = torch.device('cuda:0')
# optimizer
optimizer = torch.optim.Adam(se_resnet.parameters(), lr=lr)
fit(se_resnet, num_epochs, optimizer, device) 
Explanation: 作业
下图表示将SE模块嵌入到ResNet的残差模块.
其中,global pooling表示全局池化层(将输入的size池化为1*1), 将c*h*w的输入变为c*1*1的输出.FC表示全连接层(线性层),两层FC之间使用ReLU作为激活函数.通过两层FC后使用sigmoid激活函数激活.最后将得到的c个值与原输入c*h*w按channel相乘,得到c*h*w的输出.
补充下方的代码完成SE-Resnet block的实现.
End of explanation
import math
class VGG(nn.Module):
    def __init__(self, cfg):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg)
        # linear layer
        self.classifier = nn.Linear(512, 10)
    def forward(self, x):
        out = self.features(x)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out
    def _make_layers(self, cfg):
        
        cfg: a list define layers this layer contains
            'M': MaxPool, number: Conv2d(out_channels=number) -> BN -> ReLU
        
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)
cfg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
vggnet = VGG(cfg['VGG11'])
print(vggnet)
# Hyper-parameters
num_epochs = 10
lr = 1e-3
# Device configuration
device = torch.device('cuda:0')
# optimizer
optimizer = torch.optim.Adam(vggnet.parameters(), lr=lr)
fit(vggnet, num_epochs, optimizer, device)
Explanation: Vgg
接下来让我们阅读vgg网络的实现代码.VGGNet全部使用3*3的卷积核和2*2的池化核，通过不断加深网络结构来提升性能。Vgg表明了卷积神经网络的深度增加和小卷积核的使用对网络的最终分类识别效果有很大的作用.
下面是一份用于训练cifar10的简化版的vgg代码. 
有时间的同学可以阅读并训练它.
End of explanation
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