VGG网络结构
基本的单元(vgg_block)是几个卷积再加上一个池化,这个单元结构反复出现,用一个函数封装(vgg_stack).
import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
from torchvision.datasets import CIFAR10
from torchvision import transforms
import torch.nn.functional as F
from utils import train
def vgg_block(num_convs, in_channels, out_channels):
net = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(True)] # 定义第一层卷积层
for i in range(num_convs-1): # 定义后面的num_convs-1层卷积层
net.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))
net.append(nn.ReLU(True))
net.append(nn.MaxPool2d(2, 2)) # 定义池化层
return nn.Sequential(*net)
# 每个vgg_block的卷积层层数是num_convs,输入、输出通道数保存在channels中
def vgg_stack(num_convs, channels):
net = []
for n, c in zip(num_convs, channels):
in_c = c[0]
out_c = c[1]
net.append(vgg_block(n, in_c, out_c))
return nn.Sequential(*net)
# 定义一个vgg结构,其中有五个卷积层,每个vgg_block的卷积层分别是1,1,2,2,2
# 每个模块的输入、输出通道数如下
vgg_net = vgg_stack((1, 1, 2, 2, 2), ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))
# print(vgg_net)
class vgg(nn.Module):
def __init__(self):
super(vgg, self).__init__()
self.feature = vgg_net
self.fc = nn.Sequential(
nn.Linear(512, 100),
nn.ReLU(True),
nn.Linear(100, 10)
)
def forward(self, x):
x = self.feature(x)
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x
def data_tf(x):
x = np.array(x, dtype='float32') / 255
x = (x-0.5)/0.5 # 标准化
x = x.transpose((2, 0, 1)) # 将channel放到第一维,只是pytorch要求的输入方式
x = torch.from_numpy(x)
return x
train_set = CIFAR10('./data', train=True, transform=data_tf, download=False)
train_data = torch.utils.data.DataLoader(train_set, batch_size=128, shuffle=True)
test_set = CIFAR10('./data', train=False, transform=data_tf, download=False)
test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)
net = vgg().cuda()
optimizer = torch.optim.SGD(net.parameters(), lr=1e-2)
criterion = nn.CrossEntropyLoss()
train(net, train_data, test_data, 20, optimizer, criterion)
utils.py
from datetime import datetime
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
def get_acc(output, label):
total = output.shape[0]
_, pred_label = output.max(1)
num_correct = (pred_label == label).sum().data[0]
return num_correct / total
def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
if torch.cuda.is_available():
net = net.cuda()
prev_time = datetime.now()
for epoch in range(num_epochs):
train_loss = 0
train_acc = 0
net = net.train()
for im, label in train_data:
if torch.cuda.is_available():
im = Variable(im.cuda()) # (bs, 3, h, w)
label = Variable(label.cuda()) # (bs, h, w)
else:
im = Variable(im)
label = Variable(label)
# forward
output = net(im)
loss = criterion(output, label)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.data[0]
train_acc += get_acc(output, label)
cur_time = datetime.now()
h, remainder = divmod((cur_time - prev_time).seconds, 3600)
m, s = divmod(remainder, 60)
time_str = "Time %02d:%02d:%02d" % (h, m, s)
if valid_data is not None:
valid_loss = 0
valid_acc = 0
net = net.eval()
for im, label in valid_data:
if torch.cuda.is_available():
im = Variable(im.cuda(), volatile=True)
label = Variable(label.cuda(), volatile=True)
else:
im = Variable(im, volatile=True)
label = Variable(label, volatile=True)
output = net(im)
loss = criterion(output, label)
valid_loss += loss.data[0]
valid_acc += get_acc(output, label)
epoch_str = (
"Epoch %d. Train Loss: %f, Train Acc: %f, Valid Loss: %f, Valid Acc: %f, "
% (epoch, train_loss / len(train_data),
train_acc / len(train_data), valid_loss / len(valid_data),
valid_acc / len(valid_data)))
else:
epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " %
(epoch, train_loss / len(train_data),
train_acc / len(train_data)))
prev_time = cur_time
print(epoch_str + time_str)
上述代码用的batch_size是128,训练一次数据集大概需要三分钟多,如果改成64,大概需要五分钟多。
训练结果:
训练到第八次的时候,训练集的准确率就达到了99.1%,测试集达到了98.9%