pytorch中如何将CPU上运行的数据模型转到GPU上运行（mnist举例）
作者：互联网
首先贴一份在cpu上运行的代码
1 import torch
2 from torchvision import transforms
3 from torchvision import datasets
4 from torch.utils.data import DataLoader
5 import torch.nn.functional as F
6 import torch.optim as optim
7
8 batch_size = 64
9 transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
10 train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)
11 train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
12 test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)
13 test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)
14
15
16 class Net(torch.nn.Module):
17 def __init__(self):
18 super(Net, self).__init__()
19 self.l1 = torch.nn.Linear(784, 512)
20 self.l2 = torch.nn.Linear(512, 256)
21 self.l3 = torch.nn.Linear(256, 128)
22 self.l4 = torch.nn.Linear(128, 64)
23 self.l5 = torch.nn.Linear(64, 10)
24
25 def forward(self, x):
26 x = x.view(-1, 784)
27 x = F.relu(self.l1(x))
28 x = F.relu(self.l2(x))
29 x = F.relu(self.l3(x))
30 x = F.relu(self.l4(x))
31 return self.l5(x)
32
33
34 model = Net()
35
36 criterion = torch.nn.CrossEntropyLoss()
37 optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
38
39
40 def train(epoch):
41 running_loss = 0.0
42 for batch_idx, data in enumerate(train_loader, 0):
43 inputs, target = data
44 optimizer.zero_grad()
45 # forward + backward + update
46 outputs = model(inputs)
47 loss = criterion(outputs, target)
48 loss.backward()
49 optimizer.step()
50 running_loss += loss.item()
51 if batch_idx % 300 == 299:
52 print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
53 running_loss = 0.0
54
55
56 def test():
57 correct = 0
58 total = 0
59 with torch.no_grad():
60 for data in test_loader:
61 images, labels = data
62 outputs = model(images)
63 _, predicted = torch.max(outputs.data, dim=1)
64 total += labels.size(0)
65 correct += (predicted == labels).sum().item()
66 print('Accuracy on test set: %d %%' % (100 * correct / total))
67
68
69 if __name__ == '__main__':
70 for epoch in range(10):
71 train(epoch)
72 test()
View Code
要在GPU上运行数据需要把一些相关的参数和模型转到GPU上
需要转换的有：model，数据，criterion（loss函数）
其中optimizer不需要转换
首先定义
1 device = t.device('cuda:0')
将model和criterion to(device)
1 #cuda 2 model = model.to(device) 3 criterion = criterion.to(device)
再将43行的inputs、target，46行的outputs to(device)到GPU上训练
1 def train(epoch):
2 running_loss = 0.0
3 for batch_idx, data in enumerate(train_loader, 0):
4 inputs, target = data
5 #cuda inputs and target
6 inputs = inputs.to(device) ###
7 target = target.to(device) ###
8 optimizer.zero_grad() # noticed: the grad return to zero before starting the loop
9
10 # forward + backward +update
11 outputs = model(inputs)
12 outputs = outputs.to(device) ###
13 loss = criterion(outputs, target)
14 loss.backward()
15 optimizer.step()
16
17 running_loss += loss.item()
18 if batch_idx % 300 == 299:
19 print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
20 running_loss = 0.0
再将61行的images、labels，62行的outputs to(device)到GPU上测试
1 def test():
2 correct = 0
3 total = 0
4 with t.no_grad(): # ensuring grad can not updating
5 for data in test_loader:
6 images, label = data
7 #cuda images, label
8 images = images.to(device) ###
9 label = label.to(device) ###
10 outputs = model(images)
11 outputs = outputs.to(device) ###
12 _, predicted = t.max(outputs.data, dim=1) # taking the max num from one_hot code
13 total += label.size(0)
14 # label's format in array that N*1 like [1,8,5,8,3,2,1,4,.....,5]. so label.size() return a array [N,1] and label.size(0) is N
15 correct += (predicted == label).sum().item()
16 print('Accurary on test set: %d %%' % (100 * correct / total))
全部代码（需要cuda而修改或者添加的地方用###在代码后标出）
1 import torch as t
2 from torchvision import transforms
3 from torchvision import datasets
4 from torch.utils.data import DataLoader
5 import torch.optim as op
6 import torch.nn.functional as f
7
8 use_gpu = t.cuda.is_available() ###
9 device = t.device('cuda:0') ###
10
11 batch_size = 64
12 transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
13 # notice : Compose([]) there is []!!!!!!!!!!!!!!!!!!!!!!!!
14 # mnist mean : 0.1307, std :0.3081
15 # convert the PIL to Tensor and then normalized so that the gray level is [0,1]
16
17 train_dataset = datasets.MNIST(root='../dataset/mnist', train=True, download=True, transform=transform)
18 train_loader = DataLoader(train_dataset, batch_size=batch_size)
19 test_dataset = datasets.MNIST(root='../dataset/mnist', train=False, download=True, transform=transform)
20 test_loader = DataLoader(test_dataset, batch_size=batch_size)
21
22 test_dataset = test_dataset
23 train_loader = train_loader
24 test_dataset = test_dataset
25 test_loader = test_loader
26
27
28 class Net(t.nn.Module):
29 def __init__(self):
30 super(Net, self).__init__()
31 self.linear1 = t.nn.Linear(784, 512)
32 self.linear2 = t.nn.Linear(512, 256)
33 self.linear3 = t.nn.Linear(256, 128)
34 self.linear4 = t.nn.Linear(128, 64)
35 self.linear5 = t.nn.Linear(64, 10)
36
37 def forward(self, x):
38 x = x.view(-1, 784) # 1*28*28 Tensor to 1*784 vectors, -1 can auto_transform to pic nums
39 x = f.relu(self.linear1(x))
40 x = f.relu(self.linear2(x))
41 x = f.relu(self.linear3(x))
42 x = f.relu(self.linear4(x))
43 return self.linear5(x)
44
45
46 model = Net() # there is ()!
47 criterion = t.nn.CrossEntropyLoss()
48 optimizer = op.SGD(model.parameters(), lr=0.01, momentum=0.5)
49
50 # cuda
51 model = model.to(device) ###
52 criterion = criterion.to(device) ###
53
54
55 def train(epoch):
56 running_loss = 0.0
57 for batch_idx, data in enumerate(train_loader, 0):
58 inputs, target = data
59 # cuda inputs and target
60 inputs = inputs.to(device) ###
61 target = target.to(device) ###
62 optimizer.zero_grad() # noticed: the grad return to zero before starting the loop
63
64 # forward + backward +update
65 outputs = model(inputs)
66 outputs = outputs.to(device) ###
67 loss = criterion(outputs, target)
68 loss.backward()
69 optimizer.step()
70
71 running_loss += loss.item()
72 if batch_idx % 300 == 299:
73 print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
74 running_loss = 0.0
75
76
77 def test():
78 correct = 0
79 total = 0
80 with t.no_grad(): # ensuring grad can not updating
81 for data in test_loader:
82 images, label = data
83 # cuda images, label
84 images = images.to(device) ###
85 label = label.to(device) ###
86 outputs = model(images)
87 outputs = outputs.to(device) ###
88 _, predicted = t.max(outputs.data, dim=1) # taking the max num from one_hot code
89 total += label.size(0)
90 # label's format in array that N*1 like [1,8,5,8,3,2,1,4,.....,5]. so label.size() return a array [N,1] and label.size(0) is N
91 correct += (predicted == label).sum().item()
92 print('Accurary on test set: %d %%' % (100 * correct / total))
93
94
95 if __name__ == '__main__':
96 for epoch in range(10):
97 train(epoch)
98 test()
View Code
标签：loss,outputs,self,device,pytorch,train,test,GPU,数据模型 来源： https://www.cnblogs.com/luoyoucode/p/15171113.html