调试手写数字识别代码时出现的问题，将cpu的代码改用gpu训练时虽然可以训练，详见上一条博客(Mnist手写数字识别cpu训练与gpu训练)，但是会出现Error。查找资料后以下是解决过程。
一、调整前代码&调整后代码
1、前
import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from datetime import datetime
# 添加
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 添加
class Config:
batch_size = 64
epoch = 10
momentum = 0.9
alpha = 1e-3
print_per_step = 100
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
# 3*3的卷积
self.conv1 = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, 2), #kernel_size卷积核大小 stride卷积步长 padding特征图填充
nn.ReLU(),
nn.MaxPool2d(2, 2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, 5),
nn.ReLU(),
nn.MaxPool2d(2, 2) #2*2的最大池化层
)
self.fc1 = nn.Sequential(
nn.Linear(64 * 5 * 5, 128),
nn.BatchNorm1d(128),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(128, 64),
nn.BatchNorm1d(64), # 加快收敛速度的方法（注：批标准化一般放在全连接层后面，激活函数层的前面）
nn.ReLU()
)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
class TrainProcess:
def __init__(self):
self.train, self.test = self.load_data()
#修改
self.net = LeNet().to(device)
#修改
self.criterion = nn.CrossEntropyLoss() # 定义损失函数
self.optimizer = optim.SGD(self.net.parameters(), lr=Config.alpha, momentum=Config.momentum)
@staticmethod
def load_data():
print("Loading Data......")
"""加载MNIST数据集，本地数据不存在会自动下载"""
train_data = datasets.MNIST(root='./data/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_data = datasets.MNIST(root='./data/',
train=False,
transform=transforms.ToTensor())
# 返回一个数据迭代器
# shuffle：是否打乱顺序
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=Config.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_data,
batch_size=Config.batch_size,
shuffle=False)
return train_loader, test_loader
def train_step(self):
steps = 0
start_time = datetime.now()
print("Training & Evaluating......")
for epoch in range(Config.epoch):
print("Epoch {:3}".format(epoch + 1))
for data, label in self.train:
# 修改
data, label = data.to(device),label.to(device)
# 修改
self.optimizer.zero_grad() # 将梯度归零
outputs = self.net(data) # 将数据传入网络进行前向运算
loss = self.criterion(outputs, label) # 得到损失函数
loss.backward() # 反向传播
self.optimizer.step() # 通过梯度做一步参数更新
# 每100次打印一次结果
if steps % Config.print_per_step == 0:
_, predicted = torch.max(outputs, 1)
correct = int(sum(predicted == label))
accuracy = correct / Config.batch_size # 计算准确率
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
time_usage = '{:3}m{:3}s'.format(int(time_diff / 60), time_diff % 60)
msg = "Step {:5}, Loss:{:6.2f}, Accuracy:{:8.2%}, Time usage:{:9}."
print(msg.format(steps, loss, accuracy, time_usage))
steps += 1
test_loss = 0.
test_correct = 0
for data, label in self.test:
# 修改
data, label = data.to(device),label.to(device)
# 修改
outputs = self.net(data)
loss = self.criterion(outputs, label)
test_loss += loss * Config.batch_size
_, predicted = torch.max(outputs, 1)
correct = int(sum(predicted == label))
test_correct += correct
accuracy = test_correct / len(self.test.dataset)
loss = test_loss / len(self.test.dataset)
print("Test Loss: {:5.2f}, Accuracy: {:6.2%}".format(loss, accuracy))
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
print("Time Usage: {:5.2f} mins.".format(time_diff / 60.))
if __name__ == "__main__":
p = TrainProcess()
p.train_step()
报错：RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.44 GiB already allocated; 0 bytes free; 2.45 GiB reserved in total by PyTorch)
[图片上传失败...(image-433dd1-1649150131900)]
2、后
import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from datetime import datetime
# 添加
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 添加
class Config:
batch_size = 64
epoch = 10
momentum = 0.9
alpha = 1e-3
print_per_step = 100
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
# 3*3的卷积
self.conv1 = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, 2), #kernel_size卷积核大小 stride卷积步长 padding特征图填充
nn.ReLU(),
nn.MaxPool2d(2, 2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, 5),
nn.ReLU(),
nn.MaxPool2d(2, 2) #2*2的最大池化层
)
self.fc1 = nn.Sequential(
nn.Linear(64 * 5 * 5, 128),
nn.BatchNorm1d(128),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(128, 64),
nn.BatchNorm1d(64), # 加快收敛速度的方法（注：批标准化一般放在全连接层后面，激活函数层的前面）
nn.ReLU()
)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
class TrainProcess:
def __init__(self):
self.train, self.test = self.load_data()
#修改
self.net = LeNet().to(device)
#修改
self.criterion = nn.CrossEntropyLoss() # 定义损失函数
self.optimizer = optim.SGD(self.net.parameters(), lr=Config.alpha, momentum=Config.momentum)
@staticmethod
def load_data():
print("Loading Data......")
"""加载MNIST数据集，本地数据不存在会自动下载"""
train_data = datasets.MNIST(root='./data/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_data = datasets.MNIST(root='./data/',
train=False,
transform=transforms.ToTensor())
# 返回一个数据迭代器
# shuffle：是否打乱顺序
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=Config.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_data,
batch_size=Config.batch_size,
shuffle=False)
return train_loader, test_loader
def train_step(self):
steps = 0
start_time = datetime.now()
print("Training & Evaluating......")
for epoch in range(Config.epoch):
print("Epoch {:3}".format(epoch + 1))
for data, label in self.train:
# 修改
data, label = data.to(device),label.to(device)
# 修改
self.optimizer.zero_grad() # 将梯度归零
outputs = self.net(data) # 将数据传入网络进行前向运算
loss = self.criterion(outputs, label) # 得到损失函数
loss.backward() # 反向传播
self.optimizer.step() # 通过梯度做一步参数更新
# 每100次打印一次结果
if steps % Config.print_per_step == 0:
_, predicted = torch.max(outputs, 1)
correct = int(sum(predicted == label))
accuracy = correct / Config.batch_size # 计算准确率
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
time_usage = '{:3}m{:3}s'.format(int(time_diff / 60), time_diff % 60)
msg = "Step {:5}, Loss:{:6.2f}, Accuracy:{:8.2%}, Time usage:{:9}."
print(msg.format(steps, loss, accuracy, time_usage))
steps += 1
test_loss = 0.
test_correct = 0
for data, label in self.test:
with torch.no_grad():# 修改
data, label = data.to(device),label.to(device)
outputs = self.net(data)
loss = self.criterion(outputs, label)
test_loss += loss * Config.batch_size
_, predicted = torch.max(outputs, 1)
correct = int(sum(predicted == label))
test_correct += correct
accuracy = test_correct / len(self.test.dataset)
loss = test_loss / len(self.test.dataset)
print("Test Loss: {:5.2f}, Accuracy: {:6.2%}".format(loss, accuracy))
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
print("Time Usage: {:5.2f} mins.".format(time_diff / 60.))
if __name__ == "__main__":
print(device)
p = TrainProcess()
p.train_step()
运行结果：
[图片上传失败...(image-14b58-1649150131900)]
做了修改后便不会报该错误了。
二、解决方法
方法一：调整batch_size大小
网上的解决方法大多让调整batch_size大小，但是我在调整后，并没有解决问题。
方法二：不计算梯度
使用with torch.no_grad():
给出一篇博主写的博客：pytorch运行错误：CUDA out of memory.
注：本文使用的就是方法二解决了问题。
方法三：释放内存
在报错代码前加上以下代码，释放无关内存：
if hasattr(torch.cuda, 'empty_cache'):
torch.cuda.empty_cache()
参考博客：解决：RuntimeError: CUDA out of memory. Tried to allocate 2.00 MiB