def main(args):
# hard coded values
in_channels = 3 # rgb channels of orignal image fed to rotnet
if args.layer == 1:
in_features = int(96 * 16 * 16)
else:
in_features = int(192 * 8 * 8)
rot_classes = 4
out_classes = 10
lr_decay_rate = 0.2 # lr is multiplied by decay rate after a milestone epoch is reached
mult = 1 # data become mult times
####################
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255, 66.7 / 255))
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255, 66.7 / 255))
])
trainset = datasets.CIFAR10(root='results/',
train=True,
download=True,
transform=train_transform)
testset = datasets.CIFAR10(root='results/',
train=False,
download=True,
transform=test_transform)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0)
rot_network = mdl.RotNet(in_channels=in_channels,
num_nin_blocks=args.nins,
out_classes=rot_classes).to(args.device)
class_network = mdl.NonLinearClassifier(in_channels=in_features,
out_classes=out_classes).to(
args.device)
if args.opt == 'adam':
rot_optimizer = optim.Adam(rot_network.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
class_optimizer = optim.Adam(class_network.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
else:
rot_optimizer = optim.SGD(rot_network.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
class_optimizer = optim.SGD(class_network.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
rot_scheduler = optim.lr_scheduler.MultiStepLR(rot_optimizer,
milestones=args.milestones,
gamma=lr_decay_rate)
class_scheduler = optim.lr_scheduler.MultiStepLR(
class_optimizer, milestones=args.milestones, gamma=lr_decay_rate)
####################################### Saving information
results_dict = {}
# These will store the values for best test accuracy model
results_dict['train_loss'] = -1
results_dict['train_acc'] = -1
results_dict['test_loss'] = -1
results_dict['test_acc'] = -1
results_dict['best_acc_epoch'] = -1
# For storing training history
results_dict['train_loss_hist'] = []
results_dict['train_acc_hist'] = []
results_dict['test_loss_hist'] = []
results_dict['test_acc_hist'] = []
# directories to save models
checkpoint_path = os.path.join(args.results_dir, 'model.pth')
checkpoint_path_best_acc = os.path.join(args.results_dir,
'model_best_acc.pth')
#########
test_acc_max = -math.inf
loop_start_time = time.time()
checkpoint = {}
for epoch in range(args.epochs):
train(args, rot_network, class_network, train_loader, rot_optimizer,
class_optimizer, mult, rot_scheduler, class_scheduler, epoch,
in_features)
train_loss, train_acc = test(args, rot_network, class_network,
train_loader, mult, 'Train', in_features)
results_dict['train_loss_hist'].append(train_loss)
results_dict['train_acc_hist'].append(train_acc)
test_loss, test_acc = test(args, rot_network, class_network,
test_loader, mult, 'Test', in_features)
results_dict['test_loss_hist'].append(test_loss)
results_dict['test_acc_hist'].append(test_acc)
print(
'Epoch {} finished --------------------------------------------------------------------------'
.format(epoch + 1))
checkpoint = {
'class_model_state_dict': class_network.state_dict(),
'class_optimizer_state_dict': class_optimizer.state_dict(),
'rot_model_state_dict': rot_network.state_dict(),
'rot_optimizer_state_dict': rot_optimizer.state_dict(),
'epoch': epoch + 1,
'train_loss': train_loss,
'train_acc': train_acc,
'test_loss': test_loss,
'test_acc': test_acc
}
if test_acc > test_acc_max:
test_acc_max = test_acc
if os.path.isfile(checkpoint_path_best_acc):
os.remove(checkpoint_path_best_acc)
torch.save(checkpoint, checkpoint_path_best_acc)
results_dict['best_acc_epoch'] = epoch + 1
results_dict['train_loss'] = train_loss
results_dict['train_acc'] = train_acc
results_dict['test_loss'] = test_loss
results_dict['test_acc'] = test_acc
torch.save(checkpoint, checkpoint_path)
print('Total time for training loop = ', time.time() - loop_start_time)
return results_dict
def main(args):
# hard coded values
in_channels = 3 # rgb channels of input image
out_classes = out_size # d length
lr_decay_rate = 0.2 # lr is multiplied by decay rate after a milestone epoch is reached
mult = 1 # data become mult times
####################
#train_transform = transforms.Compose([transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor()])
#test_transform = transforms.ToTensor()
trainset = MyTrainset()
testset = MyTestset()
#testset = datasets.CIFAR10(root='.', train=False, download=True, transform=test_transform)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0)
network = mdl.RotNet(in_channels=in_channels,
num_nin_blocks=args.nins,
out_classes=out_classes).to(args.device)
if args.opt == 'adam':
optimizer = optim.Adam(network.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
else:
optimizer = optim.SGD(network.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=args.milestones,
gamma=lr_decay_rate)
####################################### Saving information
results_dict = {}
# These will store the values for best test accuracy model
results_dict['train_loss'] = -1
results_dict['train_acc'] = -1
results_dict['test_loss'] = -1
results_dict['test_acc'] = -1
results_dict['best_acc_epoch'] = -1
# For storing training history
results_dict['train_loss_hist'] = []
results_dict['train_acc_hist'] = []
results_dict['test_loss_hist'] = []
results_dict['test_acc_hist'] = []
# directories to save models
checkpoint_path = os.path.join(args.results_dir, 'model.pth')
checkpoint_path_best_acc = os.path.join(args.results_dir,
'model_best_acc.pth')
test_acc_max = -math.inf
loop_start_time = time.time()
checkpoint = {}
for epoch in range(args.epochs):
train(args, network, train_loader, optimizer, mult, scheduler, epoch)
train_loss, train_acc = test(args, network, train_loader, mult,
'Train')
results_dict['train_loss_hist'].append(train_loss)
results_dict['train_acc_hist'].append(train_acc)
test_loss, test_acc = test(args, network, test_loader, mult, 'Test')
results_dict['test_loss_hist'].append(test_loss)
results_dict['test_acc_hist'].append(test_acc)
print(
'Epoch {} finished --------------------------------------------------------------------------',
epoch + 1)
checkpoint = {
'model_state_dict': network.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch': epoch + 1,
'train_loss': train_loss,
'train_acc': train_acc,
'test_loss': test_loss,
'test_acc': test_acc
}
if test_acc > test_acc_max:
test_acc_max = test_acc
if os.path.isfile(checkpoint_path_best_acc):
os.remove(checkpoint_path_best_acc)
torch.save(checkpoint, checkpoint_path_best_acc)
results_dict['best_acc_epoch'] = epoch + 1
results_dict['train_loss'] = train_loss
results_dict['train_acc'] = train_acc
results_dict['test_loss'] = test_loss
results_dict['test_acc'] = test_acc
if epoch + 1 in args.epochs_to_save:
torch.save(
checkpoint,
os.path.join(args.results_dir,
'model_epoch_' + str(epoch + 1) + '.pth'))
torch.save(checkpoint, checkpoint_path)
print('Total time for training loop = ', time.time() - loop_start_time)
return results_dict
