def escalate(init_size=32, sizes=[256, 512, 1024, 2048]):
"""Run the model multiple times with increasing resolution."""
first = True
for s in sizes:
if first:
ops.clean_filenames(path.rawC, rename='pic_%s')
ops.crop_square_resize(path.rawC, path.C, init_size, init_size, s,
s)
first = False
else:
# copy output to input
ops.output_as_input(path.test, path.tempC)
ops.clean_filenames(path.tempC, rename='pic_%s')
ops.blur_resize(path.tempC, path.C, s, s, blur=0)
ops.combine(path.C, path.C, path.val, s)
# run model on input
ops.test(path.model, path.val, path.test, s)
(vm.GPU_INSTANCE, path.GIT_REPO_URL, path.GIT_REPO_NAME))
# install packages
os.system(
'ssh %s "sudo apt-get install -y ffmpeg python-imaging python3-pil"' %
(vm.GPU_INSTANCE))
elif args.cmd == 'train_remote':
"""Run on GPU_INSTANCE via ssh and tmux.
To keep a process running I use:
tmux -d python script.py
Manually running tmux first works too. Detaching is done with [ctrl]-[b], [d].
And a running tmux session can be reatached with: tmux attach
"""
os.system('ssh %s "cd git; git pull"' % (vm.GPU_INSTANCE))
os.system(
'ssh %s "python git/pix2pix-tensorflow/project_enhance.py extract"' %
(vm.GPU_INSTANCE))
os.system(
'ssh %s "python git/pix2pix-tensorflow/project_enhance.py prep"' %
(vm.GPU_INSTANCE))
vm.call_remote_cmd_in_tmux(
vm.GPU_INSTANCE,
"python git/pix2pix-tensorflow/project_enhance.py train")
elif args.cmd == 'test':
ops.test(path.model, path.val, path.test, args.size)
elif args.cmd == 'push':
ops.push(PROJECT)
elif args.cmd == 'pull':
ops.pull(PROJECT)
elif args.cmd == 'pull_from_relay':
ops.pull_from_relay(PROJECT)
def main():
# Settings
parser = argparse.ArgumentParser(description='PyTorch Clothing1M')
parser.add_argument('--batch_size',
type=int,
default=256,
help='input batch size for training (default: 256)')
parser.add_argument('--test_batch_size',
type=int,
default=256,
help='input batch size for testing (default: 256)')
parser.add_argument('--epochs',
type=int,
default=10,
help='number of epochs to train (default: 120)')
parser.add_argument('--gpu_id',
type=int,
default=0,
help='index of gpu to use (default: 0)')
parser.add_argument('--lr',
type=float,
default=0.001,
help='init learning rate (default: 0.1)')
parser.add_argument('--seed',
type=int,
default=0,
help='random seed (default: 0)')
parser.add_argument('--save',
action='store_true',
default=False,
help='For saving softmax_out_avg')
parser.add_argument('--SEAL',
type=int,
default=0,
help='Phase of self-evolution')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device(
'cuda:' + str(args.gpu_id) if torch.cuda.is_available() else 'cpu')
# Datasets
root = './data/Clothing1M'
num_classes = 14
kwargs = {
'num_workers': 32,
'pin_memory': True
} if torch.cuda.is_available() else {}
transform_train = transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
train_dataset = Clothing1M(root, mode='train', transform=transform_train)
val_dataset = Clothing1M(root, mode='val', transform=transform_test)
test_dataset = Clothing1M(root, mode='test', transform=transform_test)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
softmax_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
def learning_rate(lr_init, epoch):
optim_factor = 0
if (epoch > 5):
optim_factor = 1
return lr_init * math.pow(0.1, optim_factor)
def load_pretrain(num_classes, device):
model_pre = resnet50(
num_classes=1000,
pretrained=True) # imagenet pretrained, numclasses=1000
if num_classes == 1000:
return model_pre.to(device)
else:
model = resnet50(num_classes=num_classes, pretrained=False)
params_pre = model_pre.state_dict().copy()
params = model.state_dict()
for i in params_pre:
if not i.startswith('fc'):
params[i] = params_pre[i]
model.load_state_dict(params)
return model.to(device)
# results
results_root = os.path.join('results', 'clothing')
if not os.path.isdir(results_root):
os.makedirs(results_root)
""" Test model """
if args.SEAL == -1:
model = resnet50().to(device)
model.load_state_dict(
torch.load(os.path.join(results_root, 'seed0_clothing_normal.pt')))
test(args, model, device, test_loader)
""" Get softmax_out_avg - normal training on noisy labels """
if args.SEAL == 0:
print(
'The DMI model is trained using the official pytorch implemention of L_DMI <https://github.com/Newbeeer/L_DMI>.\n'
)
""" Self Evolution - training on softmax_out_avg from DMI model """
if args.SEAL == 1:
# Loading softmax_out_avg of last phase
softmax_root = os.path.join(results_root, 'softmax_out_dmi.npy')
softmax_out_avg = np.load(softmax_root).reshape(
[-1, len(train_dataset), num_classes])
softmax_out_avg = softmax_out_avg[:5].mean(
axis=0
) # We found that the DMI model may not converged in the last 5 epochs.
print('softmax_out_avg loaded from', softmax_root, ', shape: ',
softmax_out_avg.shape)
# Dataset with soft targets
train_dataset_soft = Clothing1M_soft(root,
targets_soft=torch.Tensor(
softmax_out_avg.copy()),
mode='train',
transform=transform_train)
train_loader_soft = torch.utils.data.DataLoader(
train_dataset_soft,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
# Building model
model = load_pretrain(num_classes, device)
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])
model.load_state_dict(
torch.load(os.path.join(results_root, 'clothing_dmi.pt')))
print('Initialize the model using DMI model.')
# Training
best_val_acc = 0
save_path = os.path.join(
results_root, 'seed' + str(args.seed) + '_clothing_dmi_SEAL1.pt')
softmax_out = []
for epoch in range(1, args.epochs + 1):
optimizer = optim.SGD(model.parameters(),
lr=learning_rate(args.lr, epoch),
momentum=0.9,
weight_decay=1e-3)
train_soft(args, model, device, train_loader_soft, optimizer,
epoch)
best_val_acc = val_test(args, model, device, val_loader,
test_loader, best_val_acc, save_path)
softmax_out.append(get_softmax_out(model, softmax_loader, device))
if args.save:
softmax_root = os.path.join(
results_root,
'seed' + str(args.seed) + '_softmax_out_dmi_SEAL1.npy')
softmax_out = np.concatenate(softmax_out)
np.save(softmax_root, softmax_out)
print('new softmax_out saved to', softmax_root, ', shape: ',
softmax_out.shape)
if args.SEAL >= 2:
# Loading softmax_out_avg of last phase
softmax_root = os.path.join(
results_root, 'seed' + str(args.seed) + '_softmax_out_dmi_SEAL' +
str(args.SEAL - 1) + '.npy')
softmax_out_avg = np.load(softmax_root).reshape(
[-1, len(train_dataset), num_classes])
softmax_out_avg = softmax_out_avg.mean(axis=0)
print('softmax_out_avg loaded from', softmax_root, ', shape: ',
softmax_out_avg.shape)
# Dataset with soft targets
train_dataset_soft = Clothing1M_soft(root,
targets_soft=torch.Tensor(
softmax_out_avg.copy()),
mode='train',
transform=transform_train)
train_loader_soft = torch.utils.data.DataLoader(
train_dataset_soft,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
# Building model
model = load_pretrain(num_classes, device)
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])
model_path = os.path.join(
results_root, 'seed' + str(args.seed) + '_clothing_dmi_SEAL' +
str(args.SEAL - 1) + '.pt')
model.load_state_dict(torch.load(model_path))
print('Initialize the model using {}.'.format(model_path))
# Training
best_val_acc = 0
save_path = os.path.join(
results_root, 'seed' + str(args.seed) + '_clothing_dmi_SEAL' +
str(args.SEAL) + '.pt')
softmax_out = []
for epoch in range(1, args.epochs + 1):
optimizer = optim.SGD(model.parameters(),
lr=learning_rate(args.lr, epoch),
momentum=0.9,
weight_decay=1e-3)
train_soft(args, model, device, train_loader_soft, optimizer,
epoch)
best_val_acc = val_test(args, model, device, val_loader,
test_loader, best_val_acc, save_path)
softmax_out.append(get_softmax_out(model, softmax_loader, device))
if args.save:
softmax_root = os.path.join(
results_root, 'seed' + str(args.seed) +
'_softmax_out_dmi_SEAL' + str(args.SEAL) + '.npy')
softmax_out = np.concatenate(softmax_out)
np.save(softmax_root, softmax_out)
print('new softmax_out saved to', softmax_root, ', shape: ',
softmax_out.shape)
def main():
# Settings
parser = argparse.ArgumentParser(description='PyTorch MNIST')
parser.add_argument('--batch_size', type=int, default=64, help='input batch size for training (default: 64)')
parser.add_argument('--test_batch_size', type=int, default=1000, help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=50, help='number of epochs to train (default: 50)')
parser.add_argument('--gpu_id', type=int, default=0, help='index of gpu to use (default: 0)')
parser.add_argument('--lr', type=float, default=0.01, help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, help='SGD momentum (default: 0.5)')
parser.add_argument('--seed', type=int, default=0, help='random seed (default: 0)')
parser.add_argument('--noise_pattern', type=str, default='dependent', help='Noise pattern (default: dependent)')
parser.add_argument('--noise_rate', type=float, default=0.0, help='Noise rate (default: 0.0)')
parser.add_argument('--save', action='store_true', default=False, help='For saving softmax_out_avg')
parser.add_argument('--SEAL', type=int, default=0, help='Phase of self-evolution')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device('cuda:'+str(args.gpu_id) if torch.cuda.is_available() else 'cpu')
# Datasets
root = './data'
kwargs = {'num_workers': 4, 'pin_memory': True} if torch.cuda.is_available() else {}
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) #0.1307, 0.3081 are the mean and std of mnist
train_dataset = datasets.MNIST(root, train=True, download=True, transform=transform)
train_dataset_noisy = datasets.MNIST(root, train=True, transform=transform)
test_dataset = datasets.MNIST(root, train=False, transform=transform)
targets_noisy = torch.Tensor(pd.read_csv(os.path.join('./data/MNIST/label_noisy', args.noise_pattern+str(args.noise_rate)+'.csv'))['label_noisy'].values.astype(int))
train_dataset_noisy.targets = targets_noisy
train_loader = torch.utils.data.DataLoader(train_dataset_noisy, batch_size=args.batch_size, shuffle=True, **kwargs)
softmax_loader = torch.utils.data.DataLoader(train_dataset_noisy, batch_size=args.test_batch_size, shuffle=False, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
# results
results_root = os.path.join('results', 'mnist_'+args.noise_pattern+str(args.noise_rate))
if not os.path.isdir(results_root):
os.makedirs(results_root)
""" Get softmax_out_avg - normal training on noisy labels """
if args.SEAL==0:
# Building model
model = MNIST_CNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
# Training
softmax_out_avg = np.zeros([len(train_dataset_noisy), 10])
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test(args, model, device, test_loader)
softmax_out_avg += get_softmax_out(model, softmax_loader, device)
softmax_out_avg /= args.epochs
if args.save:
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_avg_'+args.noise_pattern+str(args.noise_rate)+'_normal.npy')
np.save(softmax_root, softmax_out_avg)
print('new softmax_out_avg saved to', softmax_root, ', shape: ', softmax_out_avg.shape)
""" Self Evolution - training on softmax_out_avg """
if args.SEAL>=1:
# Loading softmax_out_avg of last phase
if args.SEAL==1:
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_avg_'+args.noise_pattern+str(args.noise_rate)+'_normal.npy')
else:
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_avg_'+args.noise_pattern+str(args.noise_rate)+'_SEAL'+str(args.SEAL-1)+'.npy')
softmax_out_avg = np.load(softmax_root)
print('softmax_out_avg loaded from', softmax_root, ', shape: ', softmax_out_avg.shape)
# Dataset with soft targets
train_dataset_soft = MNIST_soft(root, targets_soft=torch.Tensor(softmax_out_avg.copy()), train=True, transform=transform)
train_dataset_soft.targets = targets_noisy
train_loader_soft = torch.utils.data.DataLoader(train_dataset_soft, batch_size=args.batch_size, shuffle=True, **kwargs)
# Building model
model = MNIST_CNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
# Training
softmax_out_avg = np.zeros([len(train_dataset_noisy), 10])
for epoch in range(1, args.epochs + 1):
train_soft(args, model, device, train_loader_soft, optimizer, epoch)
test(args, model, device, test_loader)
softmax_out_avg += get_softmax_out(model, softmax_loader, device)
softmax_out_avg /= args.epochs
if args.save:
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_avg_'+args.noise_pattern+str(args.noise_rate)+'_SEAL'+str(args.SEAL)+'.npy')
np.save(softmax_root, softmax_out_avg)
print('new softmax_out_avg saved to', softmax_root, ', shape: ', softmax_out_avg.shape)
def main():
# Settings
parser = argparse.ArgumentParser(description='PyTorch cifar10')
parser.add_argument('--batch_size',
type=int,
default=128,
help='input batch size for training (default: 128)')
parser.add_argument('--test_batch_size',
type=int,
default=1000,
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=150,
help='number of epochs to train (default: 150)')
parser.add_argument('--gpu_id',
type=int,
default=0,
help='index of gpu to use (default: 0)')
parser.add_argument('--lr',
type=float,
default=0.1,
help='init learning rate (default: 0.1)')
parser.add_argument('--dp',
type=float,
default=0.0,
help='dropout rate (default: 0.0)')
parser.add_argument('--seed',
type=int,
default=0,
help='random seed (default: 0)')
parser.add_argument('--noise_pattern',
type=str,
default='dependent',
help='Noise pattern (default: dependent)')
parser.add_argument('--noise_rate',
type=float,
default=0.0,
help='Noise rate (default: 0.0)')
parser.add_argument('--save',
action='store_true',
default=False,
help='For saving softmax_out_avg')
parser.add_argument('--SEAL',
type=int,
default=0,
help='Phase of self-evolution')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device(
'cuda:' + str(args.gpu_id) if torch.cuda.is_available() else 'cpu')
# Datasets
root = './data/CIFAR10'
num_classes = 10
kwargs = {
'num_workers': 4,
'pin_memory': True
} if torch.cuda.is_available() else {}
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))
])
train_dataset = datasets.CIFAR10(root,
train=True,
download=True,
transform=transform_train)
train_dataset_noisy = datasets.CIFAR10(root,
train=True,
transform=transform_train)
test_dataset = datasets.CIFAR10(root,
train=False,
transform=transform_test)
targets_noisy = list(
pd.read_csv(
os.path.join('./data/CIFAR10/label_noisy',
args.noise_pattern + str(args.noise_rate) +
'.csv'))['label_noisy'].values.astype(int))
train_dataset_noisy.targets = targets_noisy
train_loader = torch.utils.data.DataLoader(train_dataset_noisy,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
softmax_loader = torch.utils.data.DataLoader(
train_dataset_noisy,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
def learning_rate(lr_init, epoch):
optim_factor = 0
if (epoch > 120):
optim_factor = 2
elif (epoch > 60):
optim_factor = 1
return lr_init * math.pow(0.2, optim_factor)
# results
results_root = os.path.join(
'results', 'cifar10_' + args.noise_pattern + str(args.noise_rate))
if not os.path.isdir(results_root):
os.makedirs(results_root)
""" Get softmax_out_avg - normal training on noisy labels """
if args.SEAL == 0:
# Building model
model = Wide_ResNet(depth=28,
widen_factor=10,
dropout_rate=args.dp,
num_classes=num_classes).to(device)
# Training
softmax_out_avg = np.zeros([len(train_dataset_noisy), num_classes])
for epoch in range(1, args.epochs + 1):
optimizer = optim.SGD(model.parameters(),
lr=learning_rate(args.lr, epoch),
momentum=0.9,
weight_decay=5e-4)
train(args, model, device, train_loader, optimizer, epoch)
test(args, model, device, test_loader)
softmax_out_avg += get_softmax_out(model, softmax_loader, device)
softmax_out_avg /= args.epochs
if args.save:
softmax_root = os.path.join(
results_root, 'seed' + str(args.seed) + '_softmax_out_avg_' +
args.noise_pattern + str(args.noise_rate) + '_normal.npy')
np.save(softmax_root, softmax_out_avg)
print('new softmax_out_avg saved to', softmax_root, ', shape: ',
softmax_out_avg.shape)
""" Self Evolution - training on softmax_out_avg """
if args.SEAL >= 1:
# Loading softmax_out_avg of last phase
if args.SEAL == 1:
softmax_root = os.path.join(
results_root, 'seed' + str(args.seed) + '_softmax_out_avg_' +
args.noise_pattern + str(args.noise_rate) + '_normal.npy')
else:
softmax_root = os.path.join(
results_root, 'seed' + str(args.seed) + '_softmax_out_avg_' +
args.noise_pattern + str(args.noise_rate) + '_SEAL' +
str(args.SEAL - 1) + '.npy')
softmax_out_avg = np.load(softmax_root)
print('softmax_out_avg loaded from', softmax_root, ', shape: ',
softmax_out_avg.shape)
# Dataset with soft targets
train_dataset_soft = CIFAR10_soft(root,
targets_soft=torch.Tensor(
softmax_out_avg.copy()),
train=True,
transform=transform_train)
train_dataset_soft.targets = targets_noisy
train_loader_soft = torch.utils.data.DataLoader(
train_dataset_soft,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
# Building model
model = Wide_ResNet(depth=28,
widen_factor=10,
dropout_rate=args.dp,
num_classes=num_classes).to(device)
# Training
softmax_out_avg = np.zeros([len(train_dataset_noisy), num_classes])
for epoch in range(1, args.epochs + 1):
optimizer = optim.SGD(model.parameters(),
lr=learning_rate(args.lr, epoch),
momentum=0.9,
weight_decay=5e-4)
train_soft(args, model, device, train_loader_soft, optimizer,
epoch)
test(args, model, device, test_loader)
softmax_out_avg += get_softmax_out(model, softmax_loader, device)
softmax_out_avg /= args.epochs
if args.save:
softmax_root = os.path.join(
results_root, 'seed' + str(args.seed) + '_softmax_out_avg_' +
args.noise_pattern + str(args.noise_rate) + '_SEAL' +
str(args.SEAL) + '.npy')
np.save(softmax_root, softmax_out_avg)
print('new softmax_out_avg saved to', softmax_root, ', shape: ',
softmax_out_avg.shape)
import ops
import sys
import convert_to_midi
import os
if __name__ == "__main__":
ops.test(sys.argv[1])
a = convert_to_midi.transcribe(sys.argv[1])
command = "open /Applications/MuseScore\ 3.app " + a
os.system(command)
def main():
# Settings
parser = argparse.ArgumentParser(description='PyTorch MNIST')
parser.add_argument('--batch_size',
type=int,
default=64,
help='input batch size for training (default: 64)')
parser.add_argument('--test_batch_size',
type=int,
default=1000,
help='input batch size for testing (default: 1000)')
parser.add_argument(
'--epochs',
type=int,
default=20,
help='number of epochs to train (default: 20)'
) # On clean data, 20 is sufficiently large to achiece 100% training accuracy.
parser.add_argument('--gpu_id',
type=int,
default=0,
help='index of gpu to use (default: 0)')
parser.add_argument('--lr',
type=float,
default=0.01,
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
help='SGD momentum (default: 0.5)')
parser.add_argument('--seed',
type=int,
default=0,
help='random seed (default: 0)')
parser.add_argument('--noise_rate',
type=float,
default=0.0,
help='Noise rate (default: 0.0)')
parser.add_argument('--load',
action='store_true',
default=False,
help='Load existing averaged softmax')
parser.add_argument('--gen',
action='store_true',
default=False,
help='Generate noisy labels')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device(
'cuda:' + str(args.gpu_id) if torch.cuda.is_available() else 'cpu')
# Datasets
root = './data'
kwargs = {
'num_workers': 4,
'pin_memory': True
} if torch.cuda.is_available() else {}
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]) #0.1307, 0.3081 are the mean and std of mnist
train_dataset = datasets.MNIST(root,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(root, train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
if args.load:
softmax_out_avg = np.load('data/MNIST/label_noisy/softmax_out_avg.npy')
print('softmax_out_avg loaded, shape: ', softmax_out_avg.shape)
else:
# Building model
model = MNIST_CNN().to(device)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
# Training
softmax_out_avg = np.zeros([len(train_dataset), 10])
softmax_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test(args, model, device, test_loader)
softmax_out_avg += get_softmax_out(model, softmax_loader, device)
softmax_out_avg /= args.epochs
np.save('data/MNIST/label_noisy/softmax_out_avg.npy', softmax_out_avg)
if args.gen:
print('Generating noisy labels according to softmax_out_avg...')
label = np.array(train_dataset.targets)
label_noisy_cand, label_noisy_prob = [], []
for i in range(len(label)):
pred = softmax_out_avg[i, :].copy()
pred[label[i]] = -1
label_noisy_cand.append(np.argmax(pred))
label_noisy_prob.append(np.max(pred))
label_noisy = label.copy()
index = np.argsort(label_noisy_prob)[-int(args.noise_rate *
len(label)):]
label_noisy[index] = np.array(label_noisy_cand)[index]
save_pth = os.path.join('./data/MNIST/label_noisy',
'dependent' + str(args.noise_rate) + '.csv')
pd.DataFrame.from_dict({
'label': label,
'label_noisy': label_noisy
}).to_csv(save_pth, index=False)
print('Noisy label data saved to ', save_pth)
