Pytorch学习笔记——ResNet模型

1.代码

import time
import torch
from torch import nn,optim
import torch.nn.functional as F
import torchvision

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

class Residual(nn.Module):
    def __init__(self,in_channels,out_channels,use_1x1conv=False,stride=1):
        super(Residual,self).__init__()
        self.conv1 = nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1,stride=stride)
        self.conv2 = nn.Conv2d(out_channels,out_channels,kernel_size=3,padding=1)
        if use_1x1conv:
            self.conv3 = nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=stride)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self,X):
        Y = F.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
            X = self.conv3(X)
        return F.relu(Y+X)

""" X = torch.rand((4,3,6,6))
blk = Residual(3,6,use_1x1conv=True,stride=2)
print(blk(X).shape) """

net = nn.Sequential(nn.Conv2d(1,64,kernel_size=7,stride=2,padding=3),
                    nn.BatchNorm2d(64),
                    nn.ReLU(),
                    nn.MaxPool2d(kernel_size=3,stride=2,padding=1))

def resnet_block(in_channels,out_channels,num_residuals,first_block=False):
    if first_block:
        assert in_channels == out_channels
    blk = []
    for i in range(num_residuals):
        if i == 0 and not first_block:
            blk.append(Residual(in_channels,out_channels,use_1x1conv=True,stride=2))
        else:
            blk.append(Residual(out_channels,out_channels))
    return nn.Sequential(*blk)

class FlattenLayer(nn.Module):
    def __init__(self):
        super(FlattenLayer,self).__init__()
    def forward(self,x):
        return x.view(x.shape[0],-1)

class GlobalAvgPool2d(nn.Module):
    def __init__(self):
        super(GlobalAvgPool2d,self).__init__()
    def forward(self,x):
        return F.avg_pool2d(x,kernel_size=x.size()[2:])

net.add_module("resnet_block1",resnet_block(64,64,2,first_block=True))
net.add_module("resnet_block2",resnet_block(64,128,2))
net.add_module("resnet_block3",resnet_block(128,256,2))
net.add_module("resnet_block4",resnet_block(256,512,2))

net.add_module("global_avg_pool",GlobalAvgPool2d())
net.add_module("fc",nn.Sequential(FlattenLayer(),
                                  nn.Linear(512,10)))

""" X = torch.rand((1,1,224,224))
for name,layer in net.named_children():
    X = layer(X)
    print(name,'output shape:\t',X.shape) """

def evaluate_accuracy(data_iter,net,device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):
    acc_sum,n = 0.0,0
    with torch.no_grad():
        for X,y in data_iter:
            if isinstance(net,torch.nn.Module):
                net.eval()
                acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()
                net.train()
            else:
                if('is_training' in net.__code__.co_varnames):
                    acc_sum += (net(X,is_training=False).argmax(dim=1) == y).float().sum().item()
                else:
                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
            n += y.shape[0]
    return acc_sum/n

def load_data_fashion_mnist(batch_size,resize=None,root='~/Datasets/FashionMNIST'):
    trans = []
    if resize:
        trans.append(torchvision.transforms.Resize(size=resize))
    trans.append(torchvision.transforms.ToTensor())

    transform = torchvision.transforms.Compose(trans)
    mnist_train = torchvision.datasets.FashionMNIST(root=root,train=True,download=True,transform=transform)
    mnist_test = torchvision.datasets.FashionMNIST(root=root,train=False,download=True,transform=transform)

    train_iter = torch.utils.data.DataLoader(mnist_train,batch_size=batch_size,shuffle=True,num_workers=4)
    test_iter = torch.utils.data.DataLoader(mnist_test,batch_size=batch_size,shuffle=False,num_workers=4)

    return train_iter,test_iter

def train_ch5(net,train_iter,test_iter,batch_size,optimizer,device,num_epochs):
    net = net.to(device)
    print("training on ",device)
    loss = torch.nn.CrossEntropyLoss()
    batch_count = 0
    for epoch in range(num_epochs):
        train_l_sum,train_acc_sum,n,start = 0.0,0.0,0,time.time()
        for X,y in train_iter:
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat,y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            train_l_sum += l.cpu().item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()
            n += y.shape[0]
            batch_count += 1
        test_acc = evaluate_accuracy(test_iter,net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' %(epoch+1,train_l_sum/batch_count,train_acc_sum/n,test_acc,time.time()-start))

batch_size = 256
train_iter,test_iter = load_data_fashion_mnist(batch_size,resize=96)
lr, num_epochs = 0.001,5
optimizer = torch.optim.Adam(net.parameters(),lr=lr)
train_ch5(net,train_iter,test_iter,batch_size,optimizer,device,num_epochs)

2.结果