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利用GPU训练网络模型
2022-07-07 23:11:00 【booze-J】
本文章中使用的网络模型架构图:
GPU训练有两种方式:
方式一
使用gpu训练只要找到:网络模型、数据(输入和标注)、损失函数再调用.cuda()即可。
CPU训练代码:
import torch
import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import time
# 准备数据集
train_data = torchvision.datasets.CIFAR10(root='./CIFAR10',train=True,transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10(root='./CIFAR10',train=False,transform=torchvision.transforms.ToTensor(),download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为:{}".format(train_data_size))
print("测试数据集的长度为:{}".format(test_data_size))
# 利用dataloader 来加载数据集
train_dataloader = DataLoader(train_data,batch_size=64)
test_dataloader = DataLoader(test_data,batch_size=64)
# 创建网络模型
# 搭建神经网络(单独开一个文件存放网络模型)
class Booze(nn.Module):
def __init__(self):
super(Booze, self).__init__()
self.model = Sequential(
Conv2d(3, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, 5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024, 64),
Linear(64, 10)
)
def forward(self,x):
x = self.model(x)
return x
obj = Booze()
# 创建损失函数
loss_fn = nn.CrossEntropyLoss()
# 定义优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(obj.parameters(),lr = learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step=0
# 记录测试的次数
total_test_step=0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("logs")
start_time = time.time()
for i in range(epoch):
print("-------------第{}轮训练开始------------".format(i+1))
# 训练步骤开始 [train()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.train)
obj.train()
for data in train_dataloader:
imgs,targets = data
outputs = obj(imgs)
# 计算输出值与目标值的损失
loss = loss_fn(outputs,targets)
# 优化器优化模型:
# 利用优化器将梯度清零
optimizer.zero_grad()
# 利用反向传播得到每个参数节点的一个梯度
loss.backward()
optimizer.step()
total_train_step += 1
if total_train_step%100==0:
end_time = time.time()
print(end_time-start_time)
print("训练次数:{},Loss:{}".format(total_train_step,loss.item()))
writer.add_scalar("train_loss",loss.item(),total_train_step)
# 测试步骤开始:
# 注意在测试的过程中不需要对模型进行调优
obj.eval() # [eval()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval)
total_test_loss = 0
total_accuracy = 0
with torch.no_grad():
for data in test_dataloader:
imgs,targets = data
outputs = obj(imgs)
loss = loss_fn(outputs,targets)
total_test_loss+=loss
accurcay = (outputs.argmax(1)==targets).sum()
total_accuracy+=accurcay
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_accuracy",total_accuracy/test_data_size,total_test_step)
writer.add_scalar('test_loss',total_test_loss,total_test_step)
total_test_step+=1
torch.save(obj,"./model/obj_{}.pth".format(i))
print("模型已保存")
writer.close()
代码运行结果:
GPU训练代码:
import torch
import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import time
# 准备数据集
train_data = torchvision.datasets.CIFAR10(root='./CIFAR10',train=True,transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10(root='./CIFAR10',train=False,transform=torchvision.transforms.ToTensor(),download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为:{}".format(train_data_size))
print("测试数据集的长度为:{}".format(test_data_size))
# 利用dataloader 来加载数据集
train_dataloader = DataLoader(train_data,batch_size=64)
test_dataloader = DataLoader(test_data,batch_size=64)
# 创建网络模型
# 搭建神经网络(单独开一个文件存放网络模型)
class Booze(nn.Module):
def __init__(self):
super(Booze, self).__init__()
self.model = Sequential(
Conv2d(3, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, 5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024, 64),
Linear(64, 10)
)
def forward(self,x):
x = self.model(x)
return x
obj = Booze()
if torch.cuda.is_available():
# 网络模型 调用cuda()方法之后再进行返回
obj = obj.cuda()
# 创建损失函数
loss_fn = nn.CrossEntropyLoss()
# 要先判断cuda可不可以用,然后才可以转移过去
if torch.cuda.is_available():
# 损失函数 调用cuda()方法之后再进行返回
loss_fn = loss_fn.cuda()
# 定义优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(obj.parameters(),lr = learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step=0
# 记录测试的次数
total_test_step=0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("logs")
start_time = time.time()
for i in range(epoch):
print("-------------第{}轮训练开始------------".format(i+1))
# 训练步骤开始 [train()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.train)
obj.train()
for data in train_dataloader:
imgs,targets = data
if torch.cuda.is_available():
# 数据 调用cuda()方法之后再进行返回
imgs = imgs.cuda()
# 数据 调用cuda()方法之后再进行返回
targets = targets.cuda()
outputs = obj(imgs)
# 计算输出值与目标值的损失
loss = loss_fn(outputs,targets)
# 优化器优化模型:
# 利用优化器将梯度清零
optimizer.zero_grad()
# 利用反向传播得到每个参数节点的一个梯度
loss.backward()
optimizer.step()
total_train_step += 1
if total_train_step%100==0:
end_time = time.time()
print(end_time-start_time)
print("训练次数:{},Loss:{}".format(total_train_step,loss.item()))
writer.add_scalar("train_loss",loss.item(),total_train_step)
# 测试步骤开始:
# 注意在测试的过程中不需要对模型进行调优
obj.eval() # [eval()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval)
total_test_loss = 0
total_accuracy = 0
with torch.no_grad():
for data in test_dataloader:
imgs,targets = data
if torch.cuda.is_available():
# 数据 调用cuda()方法之后再进行返回
imgs = imgs.cuda()
# 数据 调用cuda()方法之后再进行返回
targets = targets.cuda()
outputs = obj(imgs)
loss = loss_fn(outputs,targets)
total_test_loss+=loss
accurcay = (outputs.argmax(1)==targets).sum()
total_accuracy+=accurcay
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_accuracy",total_accuracy/test_data_size,total_test_step)
writer.add_scalar('test_loss',total_test_loss,total_test_step)
total_test_step+=1
torch.save(obj,"./model/obj_{}.pth".format(i))
print("模型已保存")
writer.close()
代码运行结果:
相比于CPU训练代码,GPU训练代码做出了以下的改变。
CPU中的网络模型
# 创建神经网络
obj = Booze()
GPU中的网络模型
obj = Booze()
# 要先判断cuda可不可以用,然后才可以转移过去
if torch.cuda.is_available():
# 网络模型 调用cuda()方法之后再进行返回
obj = obj.cuda()
CPU中的损失函数
# 创建损失函数
loss_fn = nn.CrossEntropyLoss()
GPU中的损失函数
# 创建损失函数
loss_fn = nn.CrossEntropyLoss()
# 要先判断cuda可不可以用,然后才可以转移过去
if torch.cuda.is_available():
# 损失函数 调用cuda()方法之后再进行返回
loss_fn = loss_fn.cuda()
CPU中的数据
imgs,targets = data
GPU中的数据
imgs,targets = data
if torch.cuda.is_available():
# 数据 调用cuda()方法之后再进行返回
imgs = imgs.cuda()
# 数据 调用cuda()方法之后再进行返回
targets = targets.cuda()
另外,有些笔记本可能没有显卡,然后又想体验显卡的快感,其实网上也有一些线上的显卡可以使用,像是谷歌的colab也可以线上使用显卡进行GPU训练。
方式二
使用gpu训练只要找到:网络模型、数据(输入和标注)、损失函数再调用.to(device)即可。
前提是得先定义device
例如:
# 使用CPU进行训练
device = torch.device("cpu")
# 使用GPU进行训练
device = torch.device("cuda")
# 如果有多张显卡时,使用第一张显卡进行训练
device = torch.device("cuda:0")
# 如果有多张显卡时,使用第二张显卡进行训练
device = torch.device("cuda:1")
# 若cuda可以使用则使用GPU进行训练,否则使用cpu作为设备进行训练
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
定义完device之后只要对网络模型、数据(输入和标注)、损失函数调用.to(device)即可
相比于CPU训练代码,GPU训练代码做出了以下的改变。
CPU中的网络模型
# 创建神经网络
obj = Booze()
GPU中的网络模型
obj = Booze()
# 调用.to(device)方法
obj = obj.to(device)
CPU中的损失函数
# 创建损失函数
loss_fn = nn.CrossEntropyLoss()
GPU中的损失函数
# 创建损失函数
loss_fn = nn.CrossEntropyLoss()
# 调用.to(device)方法
loss_fn = loss_fn.to(device)
CPU中的数据
imgs,targets = data
GPU中的数据
imgs,targets = data
# 调用.to(device)方法
imgs = imgs.to(device)
targets = targets.to(device)
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