import torch
import torchvision
import torchvision.transforms as transforms
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')import matplotlib.pyplot as plt
import numpy as npdef imshow(img):img = img / 2 + 0.5npimg = img.numpy()'''- (M, N): an image with scalar data. The data is visualizedusing a colormap.- (M, N, 3): an image with RGB values (0-1 float or 0-255 int).- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),i.e. including transparency.The first two dimensions (M, N) define the rows and columns ofthe image.'''plt.imshow(np.transpose(npimg, (1, 2, 0)))plt.savefig('./cafar11.png')print("trainloader===", trainloader)
dataiter = iter(trainloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print([labels[j] for j in range(4)])
'''
[tensor(1), tensor(5), tensor(3), tensor(0)]car dog cat plane
'''
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
import torch.nn as nn
import torch.nn.functional as Fclass Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(3, 6, 5)self.conv2 = nn.Conv2d(6, 16, 5)self.pool = nn.MaxPool2d(2, 2)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 xnet = Net()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(10):running_loss = 0.0for i, data in enumerate(trainloader, 0): inputs, labels = dataoptimizer.zero_grad()outputs = net(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item() if (i+1) % 2000 == 0:print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/2000) )running_loss = 0.0print("Finished Training")
PATH = './cifar_net.pth'
dataiter = iter(testloader)
images, labels = dataiter.next()
print("GroundTruth:", " ".join('%5s' % classes[labels[j]] for j in range(4)))net = Net()
net.load_state_dict(torch.load(PATH))outputs = net(images)
print("outputs===", outputs.shape)
_, predicted = torch.max(outputs, 1)
print('predicted===', predicted)
print("Predicted:", " ".join("%5s"%classes[predicted[j]] for j in range(4)))correct = 0
total = 0
with torch.no_grad():for data in testloader:images, labels = dataoutputs = 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))
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 = dataoutputs = net(images)_, predicted = torch.max(outputs, 1)c = (predicted == labels)print('c=====', c.size())c = (predicted == labels).squeeze()for i in range(4):label = labels[i]'''labels=== tensor([9, 3, 4, 4])c=== tensor([ True, True, False, False])label=== tensor(9)c[]=== tensor(True)c[]=== Truelabels=== tensor([9, 3, 4, 4])c=== tensor([ True, True, False, False])label=== tensor(3)c[]=== tensor(True)c[]item=== True'''class_correct[label] += c[i].item() class_total[label] += 1for i in range(10):print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))
