def load_mod_dl(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
imsize, in_channel, num_classes = 32, 3, 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(
args.batch_size,
val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size, args.test_size])
elif args.dataset == 'cifar100':
imsize, in_channel, num_classes = 32, 3, 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(
args.batch_size,
val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size, args.test_size])
elif args.dataset == 'mnist':
imsize, in_channel, num_classes = 28, 1, 10
num_train = 50000
train_loader, val_loader, test_loader = data_loaders.load_mnist(
args.batch_size,
subset=[args.train_size, args.val_size, args.test_size],
num_train=num_train,
only_split_train=False)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'cbr':
cnn = CBRStudent(in_channel, num_classes)
# This essentially does no mixup.
mixup_mat = -100 * torch.ones([num_classes, num_classes]).cuda()
checkpoint = None
if args.load_checkpoint:
checkpoint = torch.load(args.load_checkpoint)
mixup_mat = checkpoint['mixup_grid']
print(f"loaded mixupmat from {args.load_checkpoint}")
if args.rand_mixup:
# Randomise mixup grid
rng = np.random.RandomState(args.seed)
mixup_mat = rng.uniform(
0.5, 1.0, (num_classes, num_classes)).astype(np.float32)
print("Randomised the mixup mat")
mixup_mat = torch.from_numpy(
mixup_mat.reshape(num_classes, num_classes)).cuda()
model = cnn.cuda()
model.train()
return model, mixup_mat, train_loader, val_loader, test_loader, checkpoint
def load_mod_dl(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
imsize, in_channel, num_classes = 32, 3, 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size,
args.test_size])
elif args.dataset == 'cifar100':
imsize, in_channel, num_classes = 32, 3, 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size,
args.test_size])
elif args.dataset == 'mnist':
imsize, in_channel, num_classes = 28, 1, 10
num_train = 50000
train_loader, val_loader, test_loader = data_loaders.load_mnist(args.batch_size,
subset=[args.train_size, args.val_size, args.test_size],
num_train=num_train, only_split_train=False)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes, num_channels=in_channel)
elif args.model == 'cbr':
cnn = CBRStudent(in_channel, num_classes)
mixup_mat = -1*torch.ones([num_classes,num_classes]).cuda()
mixup_mat.requires_grad = True
checkpoint = None
if args.load_baseline_checkpoint:
checkpoint = torch.load(args.load_baseline_checkpoint)
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
return model, mixup_mat, train_loader, val_loader, test_loader, checkpoint
def load_baseline_model(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'mnist':
args.datasize, args.valsize, args.testsize = 100, 100, 100
num_train = args.datasize
if args.datasize == -1:
num_train = 50000
from data_loaders import load_mnist
train_loader, val_loader, test_loader = load_mnist(args.batch_size,
subset=[args.datasize, args.valsize, args.testsize],
num_train=num_train)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=0.3)
checkpoint = None
if args.load_baseline_checkpoint:
checkpoint = torch.load(args.load_baseline_checkpoint)
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
return model, train_loader, val_loader, test_loader, checkpoint
train_transform.transforms.append(transforms.RandomHorizontalFlip())
train_transform.transforms.append(transforms.ToTensor())
train_transform.transforms.append(normalize)
if args.cutout:
train_transform.transforms.append(
Cutout(n_holes=args.n_holes, length=args.length))
test_transform = transforms.Compose([transforms.ToTensor(), normalize])
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(
args.batch_size, val_split=True, augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(
args.batch_size, val_split=True, augmentation=args.data_augmentation)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28,
num_classes=num_classes,
widen_factor=10,
dropRate=0.3)
cnn = cnn.cuda()
criterion = nn.CrossEntropyLoss().cuda()
if args.optimizer == 'sgdm':
cnn_optimizer = torch.optim.SGD(cnn.parameters(),
lr=args.lr,
def cnn_val_loss(config={}, reporter=None, callback=None, return_all=False):
print("Starting cnn_val_loss...")
###############################################################################
# Arguments
###############################################################################
dataset_options = ['cifar10', 'cifar100', 'fashion']
## Tuning parameters: all of the dropouts
parser = argparse.ArgumentParser(description='CNN')
parser.add_argument('--dataset',
default='cifar10',
choices=dataset_options,
help='Choose a dataset (cifar10, cifar100)')
parser.add_argument(
'--model',
default='resnet32',
choices=['resnet32', 'wideresnet', 'simpleconvnet'],
help='Choose a model (resnet32, wideresnet, simpleconvnet)')
#### Optimization hyperparameters
parser.add_argument('--batch_size',
type=int,
default=128,
help='Input batch size for training (default: 128)')
parser.add_argument('--epochs',
type=int,
default=int(config['epochs']),
help='Number of epochs to train (default: 200)')
parser.add_argument('--lr',
type=float,
default=float(config['lr']),
help='Learning rate')
parser.add_argument('--momentum',
type=float,
default=float(config['momentum']),
help='Nesterov momentum')
parser.add_argument('--lr_decay',
type=float,
default=float(config['lr_decay']),
help='Factor by which to multiply the learning rate.')
# parser.add_argument('--weight_decay', type=float, default=float(config['weight_decay']),
# help='Amount of weight decay to use.')
# parser.add_argument('--dropout', type=float, default=config['dropout'] if 'dropout' in config else 0.0,
# help='Amount of dropout for wideresnet')
# parser.add_argument('--dropout1', type=float, default=config['dropout1'] if 'dropout1' in config else -1,
# help='Amount of dropout for wideresnet')
# parser.add_argument('--dropout2', type=float, default=config['dropout2'] if 'dropout2' in config else -1,
# help='Amount of dropout for wideresnet')
# parser.add_argument('--dropout3', type=float, default=config['dropout3'] if 'dropout3' in config else -1,
# help='Amount of dropout for wideresnet')
parser.add_argument('--dropout_type',
type=str,
default=config['dropout_type'],
help='Type of dropout (bernoulli or gaussian)')
# Data augmentation hyperparameters
parser.add_argument(
'--inscale',
type=float,
default=0 if 'inscale' not in config else config['inscale'],
help='defines input scaling factor')
parser.add_argument('--hue',
type=float,
default=0. if 'hue' not in config else config['hue'],
help='hue jitter rate')
parser.add_argument(
'--brightness',
type=float,
default=0. if 'brightness' not in config else config['brightness'],
help='brightness jitter rate')
parser.add_argument(
'--saturation',
type=float,
default=0. if 'saturation' not in config else config['saturation'],
help='saturation jitter rate')
parser.add_argument(
'--contrast',
type=float,
default=0. if 'contrast' not in config else config['contrast'],
help='contrast jitter rate')
# Weight decay and dropout hyperparameters for each layer
parser.add_argument(
'--weight_decays',
type=str,
default='0.0',
help=
'Amount of weight decay to use for each layer, represented as a comma-separated string of floats.'
)
parser.add_argument(
'--dropouts',
type=str,
default='0.0',
help=
'Dropout rates for each layer, represented as a comma-separated string of floats'
)
parser.add_argument(
'--nonmono',
'-nonm',
type=int,
default=60,
help='how many previous epochs to consider for nonmonotonic criterion')
parser.add_argument(
'--patience',
type=int,
default=75,
help=
'How long to wait for the val loss to improve before early stopping.')
parser.add_argument(
'--data_augmentation',
action='store_true',
default=config['data_augmentation'],
help='Augment data by cropping and horizontal flipping')
parser.add_argument(
'--log_interval',
type=int,
default=10,
help='how many steps before logging stats from training set')
parser.add_argument(
'--valid_log_interval',
type=int,
default=50,
help='how many steps before logging stats from validations set')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='enables CUDA training')
parser.add_argument('--save',
action='store_true',
default=False,
help='whether to save current run')
parser.add_argument('--seed',
type=int,
default=11,
help='random seed (default: 11)')
parser.add_argument(
'--save_dir',
default=config['save_dir'],
help=
'subdirectory of logdir/savedir to save in (default changes to date/time)'
)
args, unknown = parser.parse_known_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
cudnn.benchmark = True # Should make training should go faster for large models
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
print(args)
sys.stdout.flush()
# args.dropout1 = args.dropout1 if args.dropout1 != -1 else args.dropout
# args.dropout2 = args.dropout2 if args.dropout2 != -1 else args.dropout
# args.dropout3 = args.dropout3 if args.dropout3 != -1 else args.dropout
###############################################################################
# Saving
###############################################################################
timestamp = '{:%Y-%m-%d}'.format(datetime.datetime.now())
random_hash = random.getrandbits(16)
exp_name = '{}-dset:{}-model:{}-seed:{}-hash:{}'.format(
timestamp, args.dataset, args.model,
args.seed if args.seed else 'None', random_hash)
dropout_rates = [float(value) for value in args.dropouts.split(',')]
weight_decays = [float(value) for value in args.weight_decays.split(',')]
# Create log folder
BASE_SAVE_DIR = 'experiments'
save_dir = os.path.join(BASE_SAVE_DIR, args.save_dir, exp_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Check whether the result.csv file exists already
if os.path.exists(os.path.join(save_dir, 'result.csv')):
if not args.overwrite:
print(
'The result file {} exists! Run with --overwrite to overwrite this experiment.'
.format(os.path.join(save_dir, 'result.csv')))
sys.exit(0)
# Save command-line arguments
with open(os.path.join(save_dir, 'args.yaml'), 'w') as f:
yaml.dump(vars(args), f)
epoch_csv_logger = CSVLogger(
fieldnames=['epoch', 'train_loss', 'train_acc', 'val_loss', 'val_acc'],
filename=os.path.join(save_dir, 'epoch_log.csv'))
###############################################################################
# Data Loading/Model/Optimizer
###############################################################################
if args.dataset == 'cifar10':
train_loader, valid_loader, test_loader = data_loaders.load_cifar10(
args,
args.batch_size,
val_split=True,
augmentation=args.data_augmentation)
num_classes = 10
elif args.dataset == 'cifar100':
train_loader, valid_loader, test_loader = data_loaders.load_cifar100(
args,
args.batch_size,
val_split=True,
augmentation=args.data_augmentation)
num_classes = 100
elif args.dataset == 'fashion':
train_loader, valid_loader, test_loader = data_loaders.load_fashion_mnist(
args.batch_size, val_split=True)
num_classes = 10
if args.model == 'resnet32':
cnn = resnet_cifar.resnet32(dropRates=dropout_rates)
elif args.model == 'wideresnet':
cnn = wide_resnet.WideResNet(depth=16,
num_classes=num_classes,
widen_factor=8,
dropRates=dropout_rates,
dropType=args.dropout_type)
# cnn = wide_resnet.WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=args.dropout)
elif args.model == 'simpleconvnet':
cnn = models.SimpleConvNet(dropType=args.dropout_type,
conv_drop1=args.dropout1,
conv_drop2=args.dropout2,
fc_drop=args.dropout3)
def optim_parameters(model):
module_list = [
m for m in model.modules()
if type(m) == nn.Linear or type(m) == nn.Conv2d
]
weight_decays = [1e-4] * len(module_list)
return [{
'params': layer.parameters(),
'weight_decay': wdecay
} for (layer, wdecay) in zip(module_list, weight_decays)]
cnn = cnn.to(device)
criterion = nn.CrossEntropyLoss()
# cnn_optimizer = torch.optim.SGD(cnn.parameters(),
# lr=args.lr,
# momentum=args.momentum,
# nesterov=True,
# weight_decay=args.weight_decay)
cnn_optimizer = torch.optim.SGD(optim_parameters(cnn),
lr=args.lr,
momentum=args.momentum,
nesterov=True)
###############################################################################
# Training/Evaluation
###############################################################################
def evaluate(loader):
"""Returns the loss and accuracy on the entire validation/test set."""
cnn.eval()
correct = total = loss = 0.
with torch.no_grad():
for images, labels in loader:
images, labels = images.to(device), labels.to(device)
pred = cnn(images)
loss += F.cross_entropy(pred, labels, reduction='sum').item()
hard_pred = torch.max(pred, 1)[1]
total += labels.size(0)
correct += (hard_pred == labels).sum().item()
accuracy = correct / total
mean_loss = loss / total
cnn.train()
return mean_loss, accuracy
epoch = 1
global_step = 0
patience_elapsed = 0
stored_loss = 1e8
best_val_loss = []
start_time = time.time()
# This is based on the schedule used for WideResNets. The gamma (decay factor) can also be 0.2 (= 5x decay)
# Right now we're not using the scheduler because we use nonmonotonic lr decay (based on validation performance)
# scheduler = MultiStepLR(cnn_optimizer, milestones=[60,120,160], gamma=args.lr_decay)
while epoch < args.epochs + 1 and patience_elapsed < args.patience:
running_xentropy = correct = total = 0.
progress_bar = tqdm(train_loader)
for i, (images, labels) in enumerate(progress_bar):
progress_bar.set_description('Epoch ' + str(epoch))
images, labels = images.to(device), labels.to(device)
if args.inscale > 0:
noise = torch.rand(images.size(0), device=device)
scaled_noise = (
(1 + args.inscale) -
(1 / (1 + args.inscale))) * noise + (1 /
(1 + args.inscale))
images = images * scaled_noise[:, None, None, None]
# images = F.dropout(images, p=args.indropout, training=True) # TODO: Incorporate input dropout
cnn.zero_grad()
pred = cnn(images)
xentropy_loss = criterion(pred, labels)
xentropy_loss.backward()
cnn_optimizer.step()
running_xentropy += xentropy_loss.item()
# Calculate running average of accuracy
_, hard_pred = torch.max(pred, 1)
total += labels.size(0)
correct += (hard_pred == labels).sum().item()
accuracy = correct / float(total)
global_step += 1
progress_bar.set_postfix(
xentropy='%.3f' % (running_xentropy / (i + 1)),
acc='%.3f' % accuracy,
lr='%.3e' % cnn_optimizer.param_groups[0]['lr'])
val_loss, val_acc = evaluate(valid_loader)
print('Val loss: {:6.4f} | Val acc: {:6.4f}'.format(val_loss, val_acc))
sys.stdout.flush()
stats = {
'global_step': global_step,
'time': time.time() - start_time,
'loss': val_loss,
'acc': val_acc
}
# logger.write('valid', stats)
if (len(best_val_loss) > args.nonmono
and val_loss > min(best_val_loss[:-args.nonmono])):
cnn_optimizer.param_groups[0]['lr'] *= args.lr_decay
print('Decaying the learning rate to {}'.format(
cnn_optimizer.param_groups[0]['lr']))
sys.stdout.flush()
if val_loss < stored_loss:
with open(os.path.join(save_dir, 'best_checkpoint.pt'), 'wb') as f:
torch.save(cnn.state_dict(), f)
print('Saving model (new best validation)')
sys.stdout.flush()
stored_loss = val_loss
patience_elapsed = 0
else:
patience_elapsed += 1
best_val_loss.append(val_loss)
# scheduler.step(epoch)
avg_xentropy = running_xentropy / (i + 1)
train_acc = correct / float(total)
if callback is not None:
callback(epoch, avg_xentropy, train_acc, val_loss, val_acc, config)
if reporter is not None:
reporter(timesteps_total=epoch, mean_loss=val_loss)
if cnn_optimizer.param_groups[0][
'lr'] < 1e-7: # Another stopping criterion based on decaying the lr
break
epoch += 1
epoch_row = {
'epoch': str(epoch),
'train_loss': avg_xentropy,
'train_acc': str(train_acc),
'val_loss': str(val_loss),
'val_acc': str(val_acc)
}
epoch_csv_logger.writerow(epoch_row)
# Load best model and run on test
with open(os.path.join(save_dir, 'best_checkpoint.pt'), 'rb') as f:
cnn.load_state_dict(torch.load(f))
train_loss = avg_xentropy
train_acc = correct / float(total)
# Run on val and test data.
val_loss, val_acc = evaluate(valid_loader)
test_loss, test_acc = evaluate(test_loader)
print('=' * 89)
print(
'| End of training | trn loss: {:8.5f} | trn acc {:8.5f} | val loss {:8.5f} | val acc {:8.5f} | test loss {:8.5f} | test acc {:8.5f}'
.format(train_loss, train_acc, val_loss, val_acc, test_loss, test_acc))
print('=' * 89)
sys.stdout.flush()
# Save the final val and test performance to a results CSV file
with open(os.path.join(save_dir, 'result_{}.csv'.format(time.time())),
'w') as result_file:
result_writer = csv.DictWriter(result_file,
fieldnames=[
'train_loss', 'train_acc',
'val_loss', 'val_acc', 'test_loss',
'test_acc'
])
result_writer.writeheader()
result_writer.writerow({
'train_loss': train_loss,
'train_acc': train_acc,
'val_loss': val_loss,
'val_acc': val_acc,
'test_loss': test_loss,
'test_acc': test_acc
})
result_file.flush()
if return_all:
print("RETURNING ", train_loss, train_acc, val_loss, val_acc,
test_loss, test_acc)
sys.stdout.flush()
return train_loss, train_acc, val_loss, val_acc, test_loss, test_acc
else:
print("RETURNING ", stored_loss)
sys.stdout.flush()
return stored_loss
def experiment():
parser = argparse.ArgumentParser(description='CNN Hyperparameter Fine-tuning')
parser.add_argument('--dataset', default='cifar10', choices=['cifar10', 'cifar100'],
help='Choose a dataset')
parser.add_argument('--model', default='resnet18', choices=['resnet18', 'wideresnet'],
help='Choose a model')
parser.add_argument('--num_finetune_epochs', type=int, default=200,
help='Number of fine-tuning epochs')
parser.add_argument('--lr', type=float, default=0.1,
help='Learning rate')
parser.add_argument('--optimizer', type=str, default='sgdm',
help='Choose an optimizer')
parser.add_argument('--batch_size', type=int, default=128,
help='Mini-batch size')
parser.add_argument('--data_augmentation', action='store_true', default=True,
help='Whether to use data augmentation')
parser.add_argument('--wdecay', type=float, default=5e-4,
help='Amount of weight decay')
parser.add_argument('--load_checkpoint', type=str,
help='Path to pre-trained checkpoint to load and finetune')
parser.add_argument('--save_dir', type=str, default='finetuned_checkpoints',
help='Save directory for the fine-tuned checkpoint')
args = parser.parse_args()
args.load_checkpoint = '/h/lorraine/PycharmProjects/CG_IFT_test/baseline_checkpoints/cifar10_resnet18_sgdm_lr0.1_wd0.0005_aug0.pt'
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=0.3)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
test_id = '{}_{}_{}_lr{}_wd{}_aug{}'.format(args.dataset, args.model, args.optimizer, args.lr, args.wdecay,
int(args.data_augmentation))
filename = os.path.join(args.save_dir, test_id + '.csv')
csv_logger = CSVLogger(
fieldnames=['epoch', 'train_loss', 'train_acc', 'val_loss', 'val_acc', 'test_loss', 'test_acc'],
filename=filename)
checkpoint = torch.load(args.load_checkpoint)
init_epoch = checkpoint['epoch']
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
args.hyper_train = 'augment' # 'all_weight' # 'weight'
def init_hyper_train(model):
"""
:return:
"""
init_hyper = None
if args.hyper_train == 'weight':
init_hyper = np.sqrt(args.wdecay)
model.weight_decay = Variable(torch.FloatTensor([init_hyper]).cuda(), requires_grad=True)
model.weight_decay = model.weight_decay.cuda()
elif args.hyper_train == 'all_weight':
num_p = sum(p.numel() for p in model.parameters())
weights = np.ones(num_p) * np.sqrt(args.wdecay)
model.weight_decay = Variable(torch.FloatTensor(weights).cuda(), requires_grad=True)
model.weight_decay = model.weight_decay.cuda()
model = model.cuda()
return init_hyper
if args.hyper_train == 'augment': # Dont do inside the prior function, else scope is wrong
augment_net = UNet(in_channels=3,
n_classes=3,
depth=5,
wf=6,
padding=True,
batch_norm=False,
up_mode='upconv') # TODO(PV): Initialize UNet properly
augment_net = augment_net.cuda()
def get_hyper_train():
"""
:return:
"""
if args.hyper_train == 'weight' or args.hyper_train == 'all_weight':
return [model.weight_decay]
if args.hyper_train == 'augment':
return augment_net.parameters()
def get_hyper_train_flat():
return torch.cat([p.view(-1) for p in get_hyper_train()])
# TODO: Check this size
init_hyper_train(model)
if args.hyper_train == 'all_weight':
wdecay = 0.0
else:
wdecay = args.wdecay
optimizer = optim.SGD(model.parameters(), lr=args.lr * 0.2 * 0.2, momentum=0.9, nesterov=True,
weight_decay=wdecay) # args.wdecay)
# print(checkpoint['optimizer_state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
scheduler = MultiStepLR(optimizer, milestones=[60, 120], gamma=0.2) # [60, 120, 160]
hyper_optimizer = torch.optim.Adam(get_hyper_train(), lr=1e-3) # try 0.1 as lr
# Set random regularization hyperparameters
# data_augmentation_hparams = {} # Random values for hue, saturation, brightness, contrast, rotation, etc.
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
def test(loader):
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
correct = 0.
total = 0.
losses = []
for images, labels in loader:
images = images.cuda()
labels = labels.cuda()
with torch.no_grad():
pred = model(images)
xentropy_loss = F.cross_entropy(pred, labels)
losses.append(xentropy_loss.item())
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels).sum().item()
avg_loss = float(np.mean(losses))
acc = correct / total
model.train()
return avg_loss, acc
def prepare_data(x, y):
"""
:param x:
:param y:
:return:
"""
x, y = x.cuda(), y.cuda()
# x, y = Variable(x), Variable(y)
return x, y
def train_loss_func(x, y):
"""
:param x:
:param y:
:return:
"""
x, y = prepare_data(x, y)
reg_loss = 0.0
if args.hyper_train == 'weight':
pred = model(x)
xentropy_loss = F.cross_entropy(pred, y)
# print(f"weight_decay: {torch.exp(model.weight_decay).shape}")
for p in model.parameters():
# print(f"weight_decay: {torch.exp(model.weight_decay).shape}")
# print(f"shape: {p.shape}")
reg_loss = reg_loss + .5 * (model.weight_decay ** 2) * torch.sum(p ** 2)
# print(f"reg_loss: {reg_loss}")
elif args.hyper_train == 'all_weight':
pred = model(x)
xentropy_loss = F.cross_entropy(pred, y)
count = 0
for p in model.parameters():
reg_loss = reg_loss + .5 * torch.sum(
(model.weight_decay[count: count + p.numel()] ** 2) * torch.flatten(p ** 2))
count += p.numel()
elif args.hyper_train == 'augment':
augmented_x = augment_net(x)
pred = model(augmented_x)
xentropy_loss = F.cross_entropy(pred, y)
return xentropy_loss + reg_loss, pred
def val_loss_func(x, y):
"""
:param x:
:param y:
:return:
"""
x, y = prepare_data(x, y)
pred = model(x)
xentropy_loss = F.cross_entropy(pred, y)
return xentropy_loss
for epoch in range(init_epoch, init_epoch + args.num_finetune_epochs):
xentropy_loss_avg = 0.
total_val_loss = 0.
correct = 0.
total = 0.
progress_bar = tqdm(train_loader)
for i, (images, labels) in enumerate(progress_bar):
progress_bar.set_description('Finetune Epoch ' + str(epoch))
# TODO: Take a hyperparameter step here
optimizer.zero_grad(), hyper_optimizer.zero_grad()
val_loss, weight_norm, grad_norm = hyper_step(1, 1, get_hyper_train, get_hyper_train_flat,
model, val_loss_func,
val_loader, train_loss_func, train_loader,
hyper_optimizer)
# del val_loss
# print(f"hyper: {get_hyper_train()}")
images, labels = images.cuda(), labels.cuda()
# pred = model(images)
# xentropy_loss = F.cross_entropy(pred, labels)
xentropy_loss, pred = train_loss_func(images, labels)
optimizer.zero_grad(), hyper_optimizer.zero_grad()
xentropy_loss.backward()
optimizer.step()
xentropy_loss_avg += xentropy_loss.item()
# Calculate running average of accuracy
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels.data).sum().item()
accuracy = correct / total
progress_bar.set_postfix(
train='%.5f' % (xentropy_loss_avg / (i + 1)),
val='%.4f' % (total_val_loss / (i + 1)),
acc='%.4f' % accuracy,
weight='%.2f' % weight_norm,
update='%.3f' % grad_norm)
val_loss, val_acc = test(val_loader)
test_loss, test_acc = test(test_loader)
tqdm.write('val loss: {:6.4f} | val acc: {:6.4f} | test loss: {:6.4f} | test_acc: {:6.4f}'.format(
val_loss, val_acc, test_loss, test_acc))
scheduler.step(epoch)
row = {'epoch': str(epoch),
'train_loss': str(xentropy_loss_avg / (i + 1)), 'train_acc': str(accuracy),
'val_loss': str(val_loss), 'val_acc': str(val_acc),
'test_loss': str(test_loss), 'test_acc': str(test_acc)}
csv_logger.writerow(row)