def train_Epoch(args, state_info, Train_loader, Test_loader): # all
start_time = time.time()
best_prec_result = torch.tensor(0, dtype=torch.float32)
mode = args.model
utils.default_model_dir = os.path.join(args.dir, mode)
start_epoch = 0
checkpoint = None
checkpoint = utils.load_checkpoint(utils.default_model_dir)
if not checkpoint:
args.last_epoch = -1
state_info.learning_scheduler_init(args, mode)
else:
print("loading {}/{}".format(utils.default_model_dir,
"checkpoint_best.pth.tar"))
state_info.load_state_dict(checkpoint, mode)
state_info.learning_scheduler_init(args, mode)
utils.default_model_dir = os.path.join(utils.default_model_dir, "cls")
for epoch in range(0, args.epoch):
epoch_result = train(args, state_info, Train_loader, Test_loader,
epoch)
if epoch_result > best_prec_result:
best_prec_result = epoch_result
utils.save_state_checkpoint(state_info, best_prec_result, epoch,
'checkpoint_best.pth.tar',
utils.default_model_dir)
print('save..')
if args.use_switch and epoch % args.iter == args.iter - 1:
utils.switching_learning(state_info.model.module)
print('learning Gate')
epoch_result = train(args, state_info, Train_loader, Test_loader,
epoch)
if epoch_result > best_prec_result:
best_prec_result = epoch_result
utils.save_state_checkpoint(state_info, best_prec_result,
epoch, 'checkpoint_best.pth.tar',
utils.default_model_dir)
print('save..')
utils.switching_learning(state_info.model.module)
print('learning Base')
state_info.lr_model.step()
utils.print_log('')
now = time.gmtime(time.time() - start_time)
utils.print_log('Best Prec : {:.4f}'.format(best_prec_result.item()))
utils.print_log('{} hours {} mins {} secs for training'.format(
now.tm_hour, now.tm_min, now.tm_sec))
def main(model_dir, model, dataset):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
train_loader, test_loader = utils.cifar10_loader()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
for epoch in range(start_epoch, 350):
if epoch < 150:
learning_rate = lr
elif epoch < 250:
learning_rate = lr * 0.1
else:
learning_rate = lr * 0.01
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
train(model, optimizer, criterion, train_loader, epoch, True)
test(model, criterion, test_loader, epoch, True)
utils.switching_learning(model.module)
print('switching_learning to Gate')
train(model, optimizer, criterion, train_loader, epoch, False)
test(model, criterion, test_loader, epoch, False)
utils.switching_learning(model.module)
print('switching_learning to Gate')
if epoch % 5 == 0:
model_filename = 'checkpoint_%03d.pth.tar' % epoch
utils.save_checkpoint({
'epoch': epoch,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_filename, model_dir)
now = time.gmtime(time.time() - start_time)
weight_extract(model, optimizer, criterion, train_loader, epoch)
utils.conv_weight_L1_printing(model.module)
print('{} hours {} mins {} secs for training'.format(now.tm_hour, now.tm_min, now.tm_sec))
def main(model_dir, model, dataset, layer_name, layer_n):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
train_loader, test_loader = utils.cifar10_loader()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
for epoch in range(start_epoch, 165): # change 165
if epoch < 80:
learning_rate = lr
elif epoch < 120:
learning_rate = lr * 0.1
else:
learning_rate = lr * 0.01
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
train(model, optimizer, criterion, train_loader, epoch, True)
test(model, criterion, test_loader, epoch, True)
utils.switching_learning(model.module)
print('switching_learning to Gate')
train(model, optimizer, criterion, train_loader, epoch, False)
test(model, criterion, test_loader, epoch, False)
utils.switching_learning(model.module)
print('switching_learning to Gate')
model_filename = 'checkpoint_%03d.pth.tar' % epoch
utils.save_checkpoint({
'epoch': epoch,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_filename, model_dir)
now = time.gmtime(time.time() - start_time)
weight_extract(model, optimizer, criterion, train_loader, epoch)
class_counter, class_weight_sum, class_average, total_average = utils.load_gate_csv()
_, index = torch.sort(total_average)
layer_name = utils.make_layer_name(layer_n)
for i in index:
# weight delete layer_name[i]
utils.weight_pruning_by_name(model.module, layer_name[i])
test(model, criterion, test_loader, epoch, True)
change index[0] to 'layerN and layerN-M'
# index about (smallest) index[0], index[1], .... (biggest)
# layer name change, layer0 to layer'n-1'
# find 'layer' + str(index[0]) from model.module
# and change self.z to 0
# utils.conv_weight_L1_printing(model.module)
print('{} hours {} mins {} secs for training'.format(now.tm_hour, now.tm_min, now.tm_sec))
def main():
global args, best_prec1
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
args.distributed = args.world_size > 1
# create model
print("=> creating model '{}'".format(args.arch))
model = vision_model.resnet50()
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
# num_workers=args.workers, pin_memory=True, sampler=train_sampler)
train_loader = imagenet_seq.data.Loader(
'train', batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, cuda=True)
# val_loader = torch.utils.data.DataLoader(
# datasets.ImageFolder(valdir, transforms.Compose([
# transforms.Resize(256),
# transforms.CenterCrop(224),
# transforms.ToTensor(),
# normalize,
# ])),
# batch_size=args.batch_size, shuffle=False,
# num_workers=args.workers, pin_memory=True)
val_loader = imagenet_seq.data.Loader(
'val', batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, cuda=True)
if args.evaluate:
validate(val_loader, model, criterion, is_main=True)
return
utils.init_learning(model.module)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, is_main=True)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion, is_main=True)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best)
# if epoch % 3 == 2:
# for i in range(3):
utils.switching_learning(model.module)
train(train_loader, model, criterion, optimizer, epoch, is_main=False)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion, is_main=False)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best)
utils.switching_learning(model.module)