4_training_image_classifier.py

import torch
import torchvision as tv
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 = tv.datasets.CIFAR10(root='./data', train=True,
                               download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=0)

testset = tv.datasets.CIFAR10(root='./data', train=False,
                               download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                          shuffle=False, num_workers=0)

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

import matplotlib.pyplot as plt
import numpy as np


def imshow(img):
    img = img / 2 + 0.5     # unnormalize (-1 ~ 1 => 0 ~ 1)
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0))) # CHW -> HWC
    plt.show()


dataiter = iter(trainloader)
images, labels = dataiter.next()
print(images)

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


## Define Covolution Neural Network
import torch.nn as nn
import torch.nn.functional as F

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 = F.softmax(self.fc3(x), dim=1) # range => 0 ~ 1
        x = self.fc3(x) # range => -infinite ~ infinite
        return x

net = Net()

## Define loss function and optimizer
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

## Train
for epoch in range(2):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()

        outputs = net.forward(inputs)
        loss = criterion(outputs, labels)
        loss.backward() # loss에 대해 gradient 계산
        optimizer.step() # gradient update

        running_loss += loss.item()
        if (i + 1) % 2000 == 0:
            print('[%d, %5d] loss: %.3f' %
                    (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0
            print(outputs)

print('Finished Training')

PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH) # save parameters only
# torch.save(net, PATH) will save entire model

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

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

net = Net()
net.load_state_dict(torch.load(PATH))

outputs = net.forward(images)
_, predicted = torch.max(outputs, 1) # returns value list, index of max value list
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 = data
        outputs = net.forward(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 = data
        outputs = net.forward(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze() # [True False True False]
        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]))

## Train using GPU
#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#print(device)

#net.to(device)
# input과 label도 GPU로 보내야한다.
#input, labels = data[0].to(device), data[1].to(device)