PyTorch GPU加速 PyTorch-GPU加速实例

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')

#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()

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()