def main():
print('==> Preparing data..')
transforms_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
transforms_test = transforms.Compose([
transforms.ToTensor()
])
mode = {'train': True, 'test': True}
rate = np.squeeze([0.45, 0.4])
for iter in range(1):
image_datasets = {'train': Cifar10(root='./datasets', train=True, transform = None, download=True),
'test': Cifar10(root='./datasets', train=False, transform = None, download=True)}
trainData = image_datasets['train'].train_data
trainLabel = image_datasets['train'].train_labels
testData = image_datasets['test'].test_data
testLabel = image_datasets['test'].test_labels
true_label = np.squeeze(trainLabel).copy()
trainLabel, actual_noise_rate = GN.noisify(nb_classes=args.num_class, train_labels=np.squeeze(trainLabel), noise_type='pairflip', noise_rate=rate[iter])
trainData = np.array(trainData)
trainLabel = np.squeeze(trainLabel)
testData = np.array(testData)
testLabel = np.squeeze(testLabel)
train_data = DT(trainData= trainData, trainLabel = trainLabel, transform=transforms_train)
train_data_test = DT(trainData= trainData, trainLabel = trainLabel, transform=transforms_test)
test_data = DT(trainData= testData, trainLabel = testLabel, transform=transforms_test)
train_loader = torch.utils.data.DataLoader(train_data, batch_size = args.batch_size, shuffle=True, num_workers=args.workers)
train_loader_test = torch.utils.data.DataLoader(train_data_test, batch_size = args.batch_size, shuffle=False, num_workers=args.workers)
test_loader = torch.utils.data.DataLoader(test_data, batch_size = args.batch_size, shuffle=False, num_workers=args.workers)
train(train_loader, test_loader, train_loader_test, true_label, rate[iter])
def main():
print('==> Preparing data..')
transforms_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
transforms_test = transforms.Compose([transforms.ToTensor()])
mode = {'train': True, 'test': True}
rate = np.squeeze([0.2, 0.5, 0.8])
for iter in range(rate.size):
model = ResNet(num_classes=args.num_class)
if use_gpu:
model = model.cuda()
model = torch.nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
image_datasets = {
'train':
Cifar10(root='./datasets',
train=True,
transform=None,
download=True),
'test':
Cifar10(root='./datasets',
train=False,
transform=None,
download=True)
}
trainData = image_datasets['train'].train_data
trainLabel = image_datasets['train'].train_labels
testData = image_datasets['test'].test_data
testLabel = image_datasets['test'].test_labels
true_label = np.squeeze(trainLabel).copy()
trainLabel, actual_noise_rate = GN.noisify(
nb_classes=args.num_class,
train_labels=np.squeeze(trainLabel),
noise_type='symmetric',
noise_rate=rate[iter])
trainData = np.array(trainData)
trainLabel = np.squeeze(trainLabel)
testData = np.array(testData)
testLabel = np.squeeze(testLabel)
train_data = DT(trainData=trainData,
trainLabel=trainLabel,
transform=transforms_train)
train_data_test = DT(trainData=trainData,
trainLabel=trainLabel,
transform=transforms_test)
test_data = DT(trainData=testData,
trainLabel=testLabel,
transform=transforms_test)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
train_loader_test = torch.utils.data.DataLoader(
train_data_test,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
train(model, optimizer, train_loader, test_loader, train_loader_test,
true_label, rate[iter])