홈 [MachineLearning] Machine Learning 공부노트 - CIFAR-10 데이터 셋을 활용한 CNN
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[MachineLearning] Machine Learning 공부노트 - CIFAR-10 데이터 셋을 활용한 CNN

게시 2023/03/17 업데이트 2023/03/17
By Beomki Chung
9 분읽는 시간

CIFAR-10 데이터 셋을 활용한 CNN

  • CIFAR-10 데이터 셋과 CNN을 사용하여 Image Classification을 해보자.

Index

  • torchvision과 CIFAR-10이란 무엇인가?
  • CIFAR-10을 활용한 분류기(Classifier) 학습하기


torchvision과 CIFAR-10이란 무엇인가?

  • torchvision
    • 영상 분야를 위해 특화된 torch package
  • CIFAR-10
    • MNIST, IMAGENET 같은 머신러닝을 위해 사용되는 데이터셋(Data-Set)
    • 다음과같은 Label을 가짐
      • 비행기 -airplane
      • 자동차 -automobile
      • 새 -bird
      • 고양이 -cat
      • 사슴 -deer
      • 개 -dog
      • 개구리 -frog
      • 말 -horse
      • 배 -ship
      • 트럭 -truck

CIFAR-10


CIFAR-10을 활용한 분류기(Classifier) 학습하기

1. CIFAR10을 불러오고 정규화하기

  • 주의사항
    • torchvision data set output : [0,1] 범위를 갖는 PILImage
    • [0,1] PILImage -> [-1,1] 범위로 정규화된 Tensor 변환 필요
    • 모든 테스트는 pycharm 환경에서 진행


1) CIFAR-10 Data Load

  • CIFAR-10 데이터셋은 한번만 받고 사용하면 된다.
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#python sample code : https://tutorials.pytorch.kr/beginner/blitz/cifar10_tutorial.html#cifar10
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))])

batch_size = 4

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

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

2) CIFAR-10 Image Data Viewer

  • CIFAR-10 데이터를 matplotlib을 통해 직접 확인해보자.
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#python sample code : https://tutorials.pytorch.kr/beginner/blitz/cifar10_tutorial.html#cifar10
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# 이미지를 보여주기 위한 함수

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

if __name__ == '__main__':

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

    batch_size = 4

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

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

    # 학습용 이미지를 무작위로 가져오기
    dataiter = iter(trainloader)
    images, labels = next(dataiter)

    # 이미지 보여주기
    imshow(torchvision.utils.make_grid(images))
    # 정답(GroundTruth label) 출력
    print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))

CIFAR-10 Image View

  • Label : Plane / Cat / Truck / Plane


2. 시험용 데이터로 신경망 트레이닝 및 검사하기

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#python sample code

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np

import torch.optim as optim

# 이미지를 보여주기 위한 함수

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

# Set CNN Model
class Net(nn.Module):
    def __init__(self):
        super().__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 = torch.flatten(x, 1) # 배치를 제외한 모든 차원을 평탄화(flatten)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

if __name__ == '__main__':

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

    batch_size = 4

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

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


    # Set CNN Model
    net = Net()

    # Set Criterion / Optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    # Training CNN

    for epoch in range(2):   # 데이터셋을 수차례 반복합니다.

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # [inputs, labels]의 목록인 data로부터 입력을 받은 후;
            inputs, labels = data

            # 변화도(Gradient) 매개변수를 0으로 만들고
            optimizer.zero_grad()

            # 순전파 + 역전파 + 최적화를 한 후
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

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

    print('Finished Training')

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

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


    # Save trained model
    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)

3. 학습된 모델 로드 및 검증

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import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np

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

# Set CNN Model
class Net(nn.Module):
    def __init__(self):
        super().__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 = torch.flatten(x, 1) # 배치를 제외한 모든 차원을 평탄화(flatten)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

if __name__ == '__main__':

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

    batch_size = 4

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

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

    net = Net()

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

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


    PATH = "./cifar_net.pth"
    net.load_state_dict(torch.load(PATH))

    outputs = net(images)

    # 가장 높은 ground truth label값을 뽑아보자
    _, predicted = torch.max(outputs, 1)

    print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'
                                  for j in range(4)))

    correct = 0
    total = 0
    # 학습 중이 아니므로, 출력에 대한 변화도를 계산할 필요가 없습니다
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            # 신경망에 이미지를 통과시켜 출력을 계산합니다
            outputs = net(images)
            # 가장 높은 값(energy)를 갖는 분류(class)를 정답으로 선택하겠습니다
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')

    # 각 분류(class)에 대한 예측값 계산을 위해 준비
    correct_pred = {classname: 0 for classname in classes}
    total_pred = {classname: 0 for classname in classes}

    # 변화도는 여전히 필요하지 않습니다
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predictions = torch.max(outputs, 1)
            # 각 분류별로 올바른 예측 수를 모읍니다
            for label, prediction in zip(labels, predictions):
                if label == prediction:
                    correct_pred[classes[label]] += 1
                total_pred[classes[label]] += 1

    # 각 분류별 정확도(accuracy)를 출력합니다
    for classname, correct_count in correct_pred.items():
        accuracy = 100 * float(correct_count) / total_pred[classname]
        print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')

Reference List

MachineLearning, PyTorch
MachineLearning AI
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