def evaluate(xargs):
# start cudnn
cudnn.enabled = True
# make each conv is the same
cudnn.benchmark = False
# make sure the same seed has the same result
cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
xargs.save_dir += 'eva/'
logger = prepare_logger(xargs)
train_data, valid_data, xshape, class_num = get_dataset(
xargs.dataset, xargs.data_path, -1)
logger.log('Train Config:')
eva_config = load_config(
xargs.eva_config, {
'class_num': class_num,
'xshape': xshape,
'tau_max': xargs.tau_max,
'tau_min': xargs.tau_min
}, logger)
search_loader, train_loader, test_loader = get_nas_search_loaders(
train_data, valid_data, xargs.dataset, 'config/',
eva_config.batch_size, xargs.workers)
logger.log('dataset: {:} Train-Loader-length={:}, batch size={:}'.format(
xargs.dataset, len(train_loader), eva_config.batch_size))
eva_dense_net(search_loader, test_loader, eva_config, logger, 'normal')
def train(xargs):
# start cudnn
cudnn.enabled = True
# make each conv is the same
cudnn.benchmark = False
# make sure the same seed has the same result
cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(xargs)
# get original data(cifar10/cifar100/uci)
train_data, valid_data, xshape, class_num = get_dataset(xargs.dataset, xargs.data_path, -1)
logger.log('{:}Train Config{:}'.format("-" * 50, "-" * 50))
opt_config = load_config(xargs.opt_config, {'class_num': class_num, 'xshape': xshape,
'batch_size': xargs.batch_size, 'epochs': xargs.epochs,
'LR': xargs.opt_learning_rate}, logger)
_, train_loader, _ = get_nas_search_loaders(train_data, valid_data, xargs.dataset,
'config/', opt_config.batch_size, xargs.workers)
logger.log('dataset: {:} Train-Loader-length={:}, batch size={:}'.format(xargs.dataset, len(train_loader),
opt_config.batch_size))
def get_nas_search_loaders(train_data, valid_data, dataset, config_root,
batch_size, workers):
# get search_loader, train_loader, valid_loader
if isinstance(batch_size, (list, tuple)):
batch, test_batch = batch_size
else:
batch, test_batch = batch_size, batch_size
if dataset == 'cifar10':
cifar_split = load_config('{:}/cifar10-split.txt'.format(config_root),
None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
# search over the proposed training and validation set
# To split data
xvalid_data = deepcopy(train_data)
if hasattr(xvalid_data, 'transforms'): # to avoid a print issue
xvalid_data.transforms = valid_data.transform
xvalid_data.transform = deepcopy(valid_data.transform)
search_data = SearchDataset(dataset, train_data, train_split,
valid_split)
# data loader
search_loader = DataLoader(search_data,
batch_size=batch,
shuffle=True,
num_workers=workers,
pin_memory=True)
train_loader = DataLoader(
train_data,
batch_size=batch,
sampler=sampler.SubsetRandomSampler(train_split),
num_workers=workers,
pin_memory=True)
valid_loader = DataLoader(
xvalid_data,
batch_size=test_batch,
sampler=sampler.SubsetRandomSampler(valid_split),
num_workers=workers,
pin_memory=True)
elif dataset == 'cifar100':
cifar100_test_split = load_config(
'{:}/cifar100-split.txt'.format(config_root), None, None)
search_train_data = train_data
search_valid_data = deepcopy(valid_data)
search_valid_data.transform = train_data.transform
search_data = SearchDataset(dataset,
[search_train_data, search_valid_data],
list(range(len(search_train_data))),
cifar100_test_split.xvalid)
search_loader = DataLoader(search_data,
batch_size=batch,
shuffle=True,
num_workers=workers,
pin_memory=True)
train_loader = DataLoader(train_data,
batch_size=batch,
shuffle=True,
num_workers=workers,
pin_memory=True)
valid_loader = DataLoader(valid_data,
batch_size=test_batch,
sampler=sampler.SubsetRandomSampler(
cifar100_test_split.xvalid),
num_workers=workers,
pin_memory=True)
elif dataset == 'HAPT':
HAPT_split = load_config('{:}HAPT-split.txt'.format(config_root), None,
None)
train_split, valid_split = HAPT_split.train, HAPT_split.valid
search_data = GenDataset(dataset, train_data, train_split, valid_split)
search_loader = DataLoader(search_data,
batch_size=batch,
shuffle=True,
num_workers=workers,
pin_memory=True)
train_loader = DataLoader(NormalDataset(dataset, train_data),
batch_size=batch,
shuffle=True,
num_workers=workers,
pin_memory=True)
valid_loader = DataLoader(NormalDataset(dataset, valid_data),
batch_size=batch,
shuffle=False,
num_workers=workers,
pin_memory=True)
else:
raise ValueError('invalid dataset : {:}'.format(dataset))
return search_loader, train_loader, valid_loader
def train(xargs):
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
assert (torch.cuda.is_available(), 'CUDA is not available.')
# start cudnn
cudnn.enabled = True
# make each conv is the same
cudnn.benchmark = False
# make sure the same seed has the same result
cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(xargs)
# get original data(cifar10/cifar100/uci)
train_data, valid_data, xshape, class_num = get_dataset(
xargs.dataset, xargs.data_path, -1)
logger.log('{:}Train Config{:}'.format("-" * 50, "-" * 50))
opt_config = load_config(
xargs.opt_config, {
'class_num': class_num,
'xshape': xshape,
'batch_size': xargs.batch_size,
'epochs': xargs.epochs,
'LR': xargs.opt_learning_rate
}, logger)
search_loader, _, valid_loader = get_nas_search_loaders(
train_data, valid_data, xargs.dataset, 'config/',
opt_config.batch_size, xargs.workers)
logger.log('dataset: {:} Search-Loader-length={:}, batch size={:}'.format(
xargs.dataset, len(search_loader), opt_config.batch_size))
logger.log('{:}Arch Config{:}'.format("-" * 50, "-" * 50))
arch_config = load_config(
xargs.arch_config, {
'class_num': class_num,
'space': HAPT_SPACE,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, logger)
if xargs.dataset == 'HAPT':
search_model = DNNModel(config=arch_config, logger=logger)
elif xargs.dataset in ('cifar10', 'cifar100'):
search_model = DNNModel(config=arch_config, logger=logger)
else:
raise NameError(
"dataset must be in \"HAPT\", \"cifar100\", \"cifar100\"")
if xargs.evaluate == 'test':
search_model.load_state_dict(
torch.load(logger.path('best'))['network'])
network = search_model.cuda()
test_loader = valid_loader
test(test_loader, network, arch_config.C_out)
return
# logger.log('search-model :\n{:}'.format(search_model))
logger.log('{:}model-config{:}\n{:}'.format("-" * 50, "-" * 50,
arch_config))
if opt_config.criterion == 'cross_entropy':
criterion = nn.CrossEntropyLoss()
else:
raise NameError('unknown loss function {:}'.format(
opt_config.criterion))
# criterion = nn.MSELoss()
w_optimizer = torch.optim.SGD(params=search_model.get_weights(),
lr=opt_config.LR,
weight_decay=opt_config.w_decay)
w_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer=w_optimizer,
T_max=opt_config.epochs,
eta_min=opt_config.eta_min)
a_optimizer = torch.optim.Adam(params=search_model.get_alphas(),
lr=xargs.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=opt_config.a_decay)
logger.log('{:}w-optimizer{:}\n{:}'.format("-" * 50, "-" * 50,
w_optimizer))
logger.log('{:}a-optimizer{:}\n{:}'.format("-" * 50, "-" * 50,
a_optimizer))
logger.log('{:}w-scheduler{:}\n{:}'.format("-" * 50, "-" * 50,
w_scheduler))
logger.log('{:}criterion{:}\n{:}'.format("-" * 50, "-" * 50, criterion))
flop, param = get_model_infos(search_model, xshape)
logger.log('FLOP = {:.6f} M, Params = {:.6f} MB'.format(flop, param))
logger.log('search-space [{:} ops] : {:}'.format(len(HAPT_SPACE),
HAPT_SPACE))
last_info, model_base_path, model_best_path = logger.path(
'info'), logger.path('model'), logger.path('best')
# network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
network = search_model.cuda()
criterion = criterion.cuda()
if last_info.exists():
# automatically resume from previous checkpoint
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
last_info))
last_info = torch.load(last_info)
start_epoch = last_info['epoch']
checkpoint = torch.load(last_info['last_checkpoint'])
genotypes = checkpoint['genotypes']
valid_accuracies = checkpoint['valid_accuracies']
network.load_state_dict(checkpoint['network'])
w_scheduler.load_state_dict(checkpoint['w_scheduler'])
w_optimizer.load_state_dict(checkpoint['w_optimizer'])
a_optimizer.load_state_dict(checkpoint['a_optimizer'])
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
.format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, \
{-1: network.genotype(xargs.save_dir + xargs.genotype_file)}
start_time, epoch_time = time.time(), AverageMeter()
total_epoch = opt_config.epochs
for epoch in range(start_epoch, total_epoch):
w_scheduler.step(epoch=epoch)
need_time = 'Time Left: {:}'.format(
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
network.set_tau(xargs.tau_max -
(xargs.tau_max - xargs.tau_min) * epoch /
(total_epoch - 1))
logger.log('\n[Search the {:}-th epoch] tau={:.2f} {:}'.format(
epoch_str, network.get_tau(), need_time))
epoch_start = time.time()
base_losses, base_top1, base_top5, arch_losses, arch_top1, arch_top5 = \
search(arch_config, search_loader, network, criterion, w_optimizer, a_optimizer,
xargs.print_frequency, epoch_str, logger)
epoch_time.update(time.time() - epoch_start)
logger.log(
'[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, base_losses, base_top1, base_top5))
logger.log(
'[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, arch_losses, arch_top1, arch_top5))
valid_accuracies[epoch] = arch_top1
if arch_top1 > valid_accuracies['best']:
valid_accuracies['best'] = arch_top1
genotypes['best'] = network.genotype(xargs.save_dir +
xargs.genotype_file)
find_best = True
else:
find_best = False
genotypes[epoch] = network.genotype(xargs.save_dir +
xargs.genotype_file)
logger.log('<<<--->>> The {:}-th epoch : {:}'.format(
epoch_str, genotypes[epoch]))
save_path = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(xargs),
'network': network.state_dict(),
'w_optimizer': w_optimizer.state_dict(),
'a_optimizer': a_optimizer.state_dict(),
'w_scheduler': w_scheduler.state_dict(),
'genotypes': genotypes,
'valid_accuracies': valid_accuracies
}, model_base_path, logger)
last_info = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(xargs),
'last_checkpoint': save_path,
}, logger.path('info'), logger)
if find_best:
logger.log(
'<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'
.format(epoch_str, arch_top1))
copy_checkpoint(model_base_path, model_best_path, logger)
with torch.no_grad():
logger.log('{:}'.format(network.show_alphas()))
logger.log('\n' + '-' * 100)
# check the performance from the architecture dataset
logger.log('GDAS : run {:} epochs, use time : {:}.'.format(
total_epoch, convert_secs2time(time.time() - start_time, True)))
logger.log('best geno is {:}.'.format(genotypes['best']))
def train(xargs):
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
assert (torch.cuda.is_available(), 'CUDA is not available.')
# start cudnn
cudnn.enabled = True
# make each conv is the same
cudnn.benchmark = False
# make sure the same seed has the same result
cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(xargs)
# get original data
train_data, valid_data, xshape, class_num = get_dataset(
xargs.dataset, xargs.data_path, -1)
config = load_config(xargs.config_path, {
'class_num': class_num,
'xshape': xshape
}, logger)
search_loader, _, valid_loader = get_nas_search_loaders(
train_data, valid_data, xargs.dataset, 'config/', config.batch_size,
xargs.workers)
logger.log(
'||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(
xargs.dataset, len(search_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(
xargs.dataset, config))
search_space = DARTS_SPACE
model_config = load_config(
xargs.model_config, {
'num_classes': class_num,
'space': search_space,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, None)
search_model = get_cell_based_tiny_net(model_config)
# logger.log('search-model :\n{:}'.format(search_model))
# logger.log('model-config : {:}'.format(model_config))
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
search_model.get_weights(), config)
a_optimizer = torch.optim.Adam(search_model.get_alphas(),
lr=xargs.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=xargs.arch_weight_decay)
# logger.log('w-optimizer : {:}'.format(w_optimizer))
# logger.log('a-optimizer : {:}'.format(a_optimizer))
# logger.log('w-scheduler : {:}'.format(w_scheduler))
# logger.log('criterion : {:}'.format(criterion))
# flop, param = get_model_infos(search_model, xshape)
# logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
logger.log('search-space [{:} ops] : {:}'.format(len(search_space),
search_space))
if xargs.arch_nas_dataset is None:
api = None
else:
pass
# api = API(xargs.arch_nas_dataset)
last_info, model_base_path, model_best_path = logger.path(
'info'), logger.path('model'), logger.path('best')
# network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
network = search_model
if last_info.exists():
# automatically resume from previous checkpoint
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
last_info))
last_info = torch.load(last_info)
start_epoch = last_info['epoch']
checkpoint = torch.load(last_info['last_checkpoint'])
genotypes = checkpoint['genotypes']
valid_accuracies = checkpoint['valid_accuracies']
search_model.load_state_dict(checkpoint['search_model'])
w_scheduler.load_state_dict(checkpoint['w_scheduler'])
w_optimizer.load_state_dict(checkpoint['w_optimizer'])
a_optimizer.load_state_dict(checkpoint['a_optimizer'])
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
.format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {
'best': -1
}, {
-1: search_model.genotype()
}
start_time, search_time, epoch_time, total_epoch = time.time(
), AverageMeter(), AverageMeter(), config.epochs + config.warmup
for epoch in range(start_epoch, total_epoch):
w_scheduler.update(epoch, 0.0)
need_time = 'Time Left: {:}'.format(
convert_secs2time(epoch_time.val * (total_epoch - epoch), True))
epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
search_model.set_tau(xargs.tau_max -
(xargs.tau_max - xargs.tau_min) * epoch /
(total_epoch - 1))
logger.log('\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}'.format(
epoch_str, need_time, search_model.get_tau(),
min(w_scheduler.get_lr())))
search_w_loss, search_w_top1, search_w_top5, valid_a_loss, valid_a_top1, valid_a_top5 \
= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str,
xargs.print_freq, logger)
search_time.update(time.time() - start_time)
logger.log(
'[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'
.format(epoch_str, search_w_loss, search_w_top1, search_w_top5,
search_time.sum))
logger.log(
'[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
# check the best accuracy
valid_accuracies[epoch] = valid_a_top1
if valid_a_top1 > valid_accuracies['best']:
valid_accuracies['best'] = valid_a_top1
genotypes['best'] = search_model.genotype()
find_best = True
else:
find_best = False
genotypes[epoch] = search_model.genotype()
logger.log('<<<--->>> The {:}-th epoch : {:}'.format(
epoch_str, genotypes[epoch]))
# save checkpoint
save_path = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(xargs),
'search_model': search_model.state_dict(),
'w_optimizer': w_optimizer.state_dict(),
'a_optimizer': a_optimizer.state_dict(),
'w_scheduler': w_scheduler.state_dict(),
'genotypes': genotypes,
'valid_accuracies': valid_accuracies
}, model_base_path, logger)
last_info = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(xargs),
'last_checkpoint': save_path,
}, logger.path('info'), logger)
if find_best:
logger.log(
'<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'
.format(epoch_str, valid_a_top1))
copy_checkpoint(model_base_path, model_best_path, logger)
with torch.no_grad():
logger.log('{:}'.format(search_model.show_alphas()))
# if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch])))
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log('\n' + '-' * 100)
# check the performance from the architecture dataset
logger.log(
'GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(
total_epoch, search_time.sum, genotypes[total_epoch - 1]))
# if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[total_epoch - 1])))
logger.close()