Pytorch学习笔记——VGG模型

1.代码

import time
import torch
from torch import nn,optim
import torchvision

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

def vgg_block(num_convs,in_channels,out_channels):
    blk = []
    for i in range(num_convs):
        if i == 0:
            blk.append(nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1))
        else:
            blk.append(nn.Conv2d(out_channels,out_channels,kernel_size=3,padding=1))
        blk.append(nn.ReLU())
    blk.append(nn.MaxPool2d(kernel_size=2,stride=2))
    return nn.Sequential(*blk)

conv_arch = ((1,1,64),(1,64,128),(2,128,256),(2,256,512),(2,512,512))
fc_features = 512*7*7
fc_hidden_units = 4096

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

def vgg(conv_arch,fc_features,fc_hidden_units=4096):
    net = nn.Sequential()
    for i,(num_convs,in_channels,out_channels) in enumerate(conv_arch):
        net.add_module("vgg_block_"+str(i+1),vgg_block(num_convs,in_channels,out_channels))
    net.add_module('fc',nn.Sequential(FlattenLayer(),
                                      nn.Linear(fc_features,fc_hidden_units),
                                      nn.ReLU(),
                                      nn.Dropout(0.5),
                                      nn.Linear(fc_hidden_units,fc_hidden_units),
                                      nn.ReLU(),
                                      nn.Dropout(0.5),
                                      nn.Linear(fc_hidden_units,10)))
    return net

""" net = vgg(conv_arch,fc_features,fc_hidden_units)
X = torch.rand(1,1,224,224)

for name,blk in net.named_children():
    X = blk(X)
    print(name,'output shape: ',X.shape) """

ratio = 8
small_conv_arch = [(1,1,64//ratio),(1,64//ratio,128//ratio),(2,128//ratio,256//ratio),(2,256//ratio,512//ratio),(2,512//ratio,512//ratio)]
net = vgg(small_conv_arch,fc_features//ratio,fc_hidden_units//ratio)

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 = 64
train_iter,test_iter = load_data_fashion_mnist(batch_size,resize=224)

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