硬件：NVIDIA-GTX1080

软件：Windows7、python3.6.5、pytorch-gpu-0.4.1

一、基础知识

将数据和网络都推到GPU，接上.cuda()

二、代码展示

import torch

import torch.nn as nn

import torch.utils.data as Data

import torchvision

# torch.manual_seed(1)

EPOCH = 1

BATCH_SIZE = 50

LR = 0.001

DOWNLOAD_MNIST = False

train_data = torchvision.datasets.MNIST(root='./mnist/', train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)

train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)

# !!!!!!!! Change in here !!!!!!!!! #

test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU

test_y = test_data.test_labels[:2000].cuda()

class CNN(nn.Module):

def __init__(self):

super(CNN, self).__init__()

self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,),

nn.ReLU(), nn.MaxPool2d(kernel_size=2),)

self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2),)

self.out = nn.Linear(32 * 7 * 7, 10)

def forward(self, x):

x = self.conv1(x)

x = self.conv2(x)

x = x.view(x.size(0), -1)

output = self.out(x)

return output

cnn = CNN()

# !!!!!!!! Change in here !!!!!!!!! #

cnn.cuda() # Moves all model parameters and buffers to the GPU.

optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)

loss_func = nn.CrossEntropyLoss()

for epoch in range(EPOCH):

for step, (x, y) in enumerate(train_loader):

# !!!!!!!! Change in here !!!!!!!!! #

b_x = x.cuda() # Tensor on GPU

b_y = y.cuda() # Tensor on GPU

output = cnn(b_x)

loss = loss_func(output, b_y)

optimizer.zero_grad()

loss.backward()

optimizer.step()

if step % 50 == 0:

test_output = cnn(test_x)

# !!!!!!!! Change in here !!!!!!!!! #

pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU

accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)

print('Epoch: ', epoch, '| train loss: %.4f' % loss, '| test accuracy: %.2f' % accuracy)

test_output = cnn(test_x[:10])

# !!!!!!!! Change in here !!!!!!!!! #

pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU

print(pred_y, 'prediction number')

print(test_y[:10], 'real number')

三、结果展示

补充知识：pytorch使用gpu对网络计算进行加速

1.基本要求

你的电脑里面有合适的GPU显卡(NVIDA),并且需要支持CUDA模块

你必须安装GPU版的Torch，(详细安装方法请移步pytorch官网)

2.使用GPU训练CNN

利用pytorch使用GPU进行加速方法主要就是将数据的形式变成GPU能读的形式，然后将CNN也变成GPU能读的形式，具体办法就是在后面加上.cuda()。

例如：

#如何检查自己电脑是否支持cuda

print torch.cuda.is_available()

# 返回True代表支持，False代表不支持

'''

注意在进行某种运算的时候使用.cuda()

'''

test_data=test_data.test_labels[:2000].cuda()

'''

对于CNN与损失函数利用cuda加速

'''

class CNN(nn.Module):

...

cnn=CNN()

cnn.cuda()

loss_f = t.nn.CrossEntropyLoss()

loss_f = loss_f.cuda()

而在train时，对于train_data训练过程进行GPU加速。也同样+.cuda()。

for epoch ..:

for step, ...:

1

'''

若你的train_data在训练时需要进行操作

若没有其他操作仅仅只利用cnn()则无需另加.cuda()

'''

#eg

train_data = torch.max(teain_data, 1)[1].cuda()

补充：取出数据需要从GPU切换到CPU上进行操作

eg：

loss = loss.cpu()

acc = acc.cpu()

理解并不全，如有纰漏或者错误还望各位大佬指点迷津

以上这篇PyTorch-GPU加速实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。