Hello,
So you can find my code here :
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import time
tmps1=time.time()

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

def imshow(img):
    img = img / 2 + 0.5    
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

device = torch.device("cuda:0")
print(device)
 
 
  #Transfert du modèle (réseau de neurones)
if device:
    net.to(device)
    print("Transfert du réseau sur le GPU")
print("Transfert réseau sur GPU fait.")

for epoch in range(1):

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):

        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches♠
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')


dataiter = iter(testloader)
images, labels = dataiter.next()


# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

print("debug1")
nb_cuda = torch.cuda.device_count()
print(nb_cuda)

print("Crash here")
outputs = net(images)
print("debug2")

_, predicted = torch.max(outputs, 1)
print("debug3")
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))
print("debug4")

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data


        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
print("debug5")

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data

        outputs = net(images)

        _, predicted = torch.max(outputs, 1)

        c = (predicted == labels).squeeze()


        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

tmps2=time.time()-tmps1
print ("Temps d'execution = %f secondes" %tmps2)

As you can see, I check the progress of the program with differents print(“debug x”). Finally, as I have seen in the tutorial and differents forum, I have to push to the GPU, my model, and my inputs/labels.
When you run this code, the program stops just after print(“crash here”) with the error : RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 ‘weight’
So I understand the problem, why it says to me this answer, but I don’t understand what is the solution, because I push my model and my inputs/labels… 
I hope you can help me 
Thanks