run.py

from ecanet import ResNet18
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torch
import torch.nn as nn
import pandas as pd

#check gpu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

#set hyperparameter
EPOCH = 50
pre_epoch = 0
BATCH_SIZE = 128
LR = 0.01

# data array
without = []

if __name__ == '__main__':
    # prepare dataset and preprocessing
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ])

    trainset = torchvision.datasets.CIFAR10(root='../data', train=True, download=True, transform=transform_train)
    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_test)
    testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

    # labels in CIFAR10
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

    # define ResNet18
    net = ResNet18().to(device)

    # define loss funtion & optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4)
    # train
    for epoch in range(pre_epoch, EPOCH):
        print('\nEpoch: %d' % (epoch + 1))
        net.train()
        sum_loss = 0.0
        correct = 0.0
        total = 0.0
        for i, data in enumerate(trainloader, 0):
            # prepare dataset
            length = len(trainloader)
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)
            optimizer.zero_grad()

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

            # print ac & loss in each batch
            sum_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += predicted.eq(labels.data).cpu().sum()
            print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '
                  % (epoch + 1, (i + 1 + epoch * length), sum_loss / (i + 1), (100. * float(correct) / total)))

        # get the ac with testdataset in each epoch
        print('Waiting Test...')
        with torch.no_grad():
            correct = 0
            total = 0
            for data in testloader:
                net.eval()
                images, labels = data
                images, labels = images.to(device), labels.to(device)
                outputs = net(images)
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum()
            print('Test\'s ac is: %.3f%%' % (100. * float(correct) / total))
            without.append((100. * float(correct) / total))

    print('Train has finished, total epoch is %d' % EPOCH)

    dataframe = pd.DataFrame({'with': without})
    dataframe.to_csv('withGhostEcanet.csv', index=False, sep=',')