def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads( xargs.workers )
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
logger.log('use config from : {:}'.format(xargs.config_path))
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
_, train_loader, valid_loader = get_nas_search_loaders(train_data, test_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
# since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader
valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform)
if hasattr(valid_loader.dataset, 'transforms'):
valid_loader.dataset.transforms = deepcopy(train_loader.dataset.transforms)
# data loader
logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
search_space = get_sub_search_spaces('cell', xargs.search_space_name)
logger.log('search_space={}'.format(search_space))
model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space,
'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
shared_cnn = get_cell_based_tiny_net(model_config)
controller = shared_cnn.create_controller()
w_optimizer, w_scheduler, criterion = get_optim_scheduler(shared_cnn.parameters(), config)
a_optimizer = torch.optim.Adam(controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps)
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(shared_cnn, xshape)
#logger.log('{:}'.format(shared_cnn))
#logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
logger.log('search-space : {:}'.format(search_space))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
shared_cnn, controller, criterion = torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda()
last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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']
baseline = checkpoint['baseline']
valid_accuracies = checkpoint['valid_accuracies']
shared_cnn.load_state_dict( checkpoint['shared_cnn'] )
controller.load_state_dict( checkpoint['controller'] )
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, baseline = 0, {'best': -1}, {}, None
# start training
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)
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), baseline))
cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn(train_loader, shared_cnn, controller, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger)
logger.log('[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, cnn_loss, cnn_top1, cnn_top5))
ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline \
= train_controller(valid_loader, shared_cnn, controller, criterion, a_optimizer, \
dict2config({'baseline': baseline,
'ctl_train_steps': xargs.controller_train_steps, 'ctl_num_aggre': xargs.controller_num_aggregate,
'ctl_entropy_w': xargs.controller_entropy_weight,
'ctl_bl_dec' : xargs.controller_bl_dec}, None), \
epoch_str, xargs.print_freq, logger)
search_time.update(time.time() - start_time)
logger.log('[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}, time-cost={:.1f} s'.format(epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline, search_time.sum))
best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader)
shared_cnn.module.update_arch(best_arch)
_, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion)
genotypes[epoch] = best_arch
# check the best accuracy
valid_accuracies[epoch] = best_valid_acc
if best_valid_acc > valid_accuracies['best']:
valid_accuracies['best'] = best_valid_acc
genotypes['best'] = best_arch
find_best = True
else: find_best = False
logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
# save checkpoint
save_path = save_checkpoint({'epoch' : epoch + 1,
'args' : deepcopy(xargs),
'baseline' : baseline,
'shared_cnn' : shared_cnn.state_dict(),
'controller' : controller.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(args),
'last_checkpoint': save_path,
}, logger.path('info'), logger)
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)
logger.log('During searching, the best architecture is {:}'.format(genotypes['best']))
logger.log('Its accuracy is {:.2f}%'.format(valid_accuracies['best']))
logger.log('Randomly select {:} architectures and select the best.'.format(xargs.controller_num_samples))
start_time = time.time()
final_arch, _ = get_best_arch(controller, shared_cnn, valid_loader, xargs.controller_num_samples)
search_time.update(time.time() - start_time)
shared_cnn.module.update_arch(final_arch)
final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion)
logger.log('The Selected Final Architecture : {:}'.format(final_arch))
logger.log('Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%'.format(final_loss, final_top1, final_top5))
logger.log('ENAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, final_arch))
if api is not None: logger.log('{:}'.format( api.query_by_arch(final_arch) ))
logger.close()
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads( xargs.workers )
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(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, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'DARTS-V2', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, '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))
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('{:}'.format(search_model))
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
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()
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 training
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)
min_LR = min(w_scheduler.get_lr())
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min_LR))
search_w_loss, search_w_top1, search_w_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))
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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(args),
'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('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
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('DARTS-V2 : 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()
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, xargs.data_path, -1)
assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
elif xargs.dataset.startswith('ImageNet16'):
split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(
xargs.dataset)
imagenet16_split = load_config(split_Fpath, None, None)
train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
#config_path = 'configs/nas-benchmark/algos/SETN.config'
config = load_config(xargs.config_path, {
'class_num': class_num,
'xshape': xshape
}, logger)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split,
valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data,
batch_size=config.batch_size,
shuffle=True,
num_workers=xargs.workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.test_batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
num_workers=xargs.workers,
pin_memory=True)
logger.log(
'||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'
.format(xargs.dataset, len(search_loader), len(valid_loader),
config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(
xargs.dataset, config))
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config(
{
'name': 'SETN',
'C': xargs.channel,
'N': xargs.num_cells,
'max_nodes': xargs.max_nodes,
'num_classes': class_num,
'space': search_space,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, None)
logger.log('search space : {:}'.format(search_space))
search_model = get_cell_based_tiny_net(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('{:}'.format(search_model))
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
logger.log('search-space : {:}'.format(search_space))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
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()
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}, {}
# start training
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)
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(
epoch_str, need_time, min(w_scheduler.get_lr())))
search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_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(
'[{:}] search [base] : 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(
'[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
genotype, temp_accuracy = get_best_arch(valid_loader, network,
xargs.select_num)
network.module.set_cal_mode('dynamic', genotype)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion)
logger.log(
'[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'
.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5,
genotype))
#search_model.set_cal_mode('urs')
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
#logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
#search_model.set_cal_mode('joint')
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
#logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
#search_model.set_cal_mode('select')
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
#logger.log('[{:}] Selec-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
genotypes[epoch] = 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(args),
'last_checkpoint': save_path,
}, logger.path('info'), logger)
with torch.no_grad():
logger.log('arch-parameters :\n{:}'.format(
nn.functional.softmax(search_model.arch_parameters,
dim=-1).cpu()))
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()
# the final post procedure : count the time
start_time = time.time()
genotype, temp_accuracy = get_best_arch(valid_loader, network,
xargs.select_num)
search_time.update(time.time() - start_time)
network.module.set_cal_mode('dynamic', genotype)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion)
logger.log(
'Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'
.format(genotype, valid_a_top1))
logger.log('\n' + '-' * 100)
# check the performance from the architecture dataset
logger.log(
'SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(
total_epoch, search_time.sum, genotype))
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype)))
logger.close()
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger4(args)
train_data, valid_data, xshape, class_num = get_datasets(
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, 'configs/nas-benchmark/', \
(config.batch_size, config.test_batch_size), xargs.workers)
logger.log(
'||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'
.format(xargs.dataset, len(search_loader), len(valid_loader),
config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(
xargs.dataset, config))
# search_space = get_search_spaces('cell', xargs.search_space_name)
search_space = get_sub_search_spaces('cell', xargs.search_space_name)
logger.log('search_space={}'.format(search_space))
model_config = dict2config(
{
'name': 'RANDOM',
'C': xargs.channel,
'N': xargs.num_cells,
'max_nodes': xargs.max_nodes,
'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)
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
search_model.parameters(), config)
logger.log('w-optimizer : {:}'.format(w_optimizer))
logger.log('w-scheduler : {:}'.format(w_scheduler))
logger.log('criterion : {:}'.format(criterion))
if xargs.arch_nas_dataset is None: api = None
else: api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
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()
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'])
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}, {}
# start training
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)
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(
epoch_str, need_time, min(w_scheduler.get_lr())))
# selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num)
search_w_loss, search_w_top1, search_w_top5 = search_func(
search_loader, network, criterion, w_scheduler, w_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))
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion)
logger.log(
'[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
cur_arch, cur_valid_acc = search_find_best(valid_loader, network,
xargs.select_num)
logger.log('[{:}] find-the-best : {:}, accuracy@1={:.2f}%'.format(
epoch_str, cur_arch, cur_valid_acc))
genotypes[epoch] = cur_arch
# check the best accuracy
valid_accuracies[epoch] = valid_a_top1
if valid_a_top1 > valid_accuracies['best']:
valid_accuracies['best'] = valid_a_top1
find_best = True
else:
find_best = False
# save checkpoint
save_path = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(xargs),
'search_model': search_model.state_dict(),
'w_optimizer': w_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(args),
'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))
pdb.set_trace()
copy_checkpoint(model_base_path, model_best_path, logger)
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' + '-' * 200)
logger.log('Pre-searching costs {:.1f} s'.format(search_time.sum))
start_time = time.time()
best_arch, best_acc = search_find_best(valid_loader, network,
xargs.select_num)
search_time.update(time.time() - start_time)
logger.log(
'RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.'
.format(best_arch, best_acc, search_time.sum))
if api is not None: logger.log('{:}'.format(api.query_by_arch(best_arch)))
logger.close()
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, xargs.data_path, -1)
config = load_config(xargs.config_path, {
'class_num': class_num,
'xshape': xshape
}, logger)
print(config)
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
(config.batch_size, config.test_batch_size), xargs.workers)
logger.log(
'||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'
.format(xargs.dataset, len(search_loader), len(valid_loader),
config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(
xargs.dataset, config))
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config(
{
'name': 'SPOS',
'C': xargs.channel,
'N': xargs.num_cells,
'max_nodes': xargs.max_nodes,
'num_classes': class_num,
'space': search_space,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, None)
logger.log('search space : {:}'.format(search_space))
model = get_cell_based_tiny_net(model_config)
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
model.get_weights(), config)
a_optimizer = torch.optim.Adam(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(model, xshape)
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
logger.log('search-space : {:}'.format(search_space))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
last_info, model_base_path, model_best_path = logger.path(
'info'), logger.path('model'), logger.path('best')
network, criterion = torch.nn.DataParallel(model).cuda(), criterion.cuda()
checkpoint_path = 'output/search-cell-nas-bench-102/result-{}/checkpoint/seed-{}_epoch-{}.pth'.format(
xargs.dataset, xargs.rand_seed, xargs.epoch)
if checkpoint_path is not None: # automatically resume from previous checkpoint
logger.log("=> loading checkpoint from {}".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint['search_model'])
# start inference
start_time, search_time, epoch_time, total_epoch = time.time(
), AverageMeter(), AverageMeter(), config.epochs + config.warmup
all_archs = network.module.get_all_archs()
random.shuffle(all_archs)
valid_accuracies = {}
process_start_time = time.time()
for i, genotype in enumerate(all_archs):
network.module.set_cal_mode('dynamic', genotype)
recalculate_bn(network, search_loader)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion)
logger.log(
'[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'
.format(i, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
valid_accuracies[genotype.tostr()] = valid_a_top1
process_end_time = time.time()
logger.log('process time: {}'.format(process_end_time -
process_start_time))
torch.save(valid_accuracies, '{}/result.dat'.format(xargs.save_dir))
logger.log('\n' + '-' * 100)
# check the performance from the architecture dataset
logger.log(
'SPOS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(
total_epoch, search_time.sum, genotype))
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype)))
logger.close()
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads( xargs.workers )
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
#elif xargs.dataset.startswith('ImageNet16'):
# # all_indexes = list(range(len(train_data))) ; random.seed(111) ; random.shuffle(all_indexes)
# # train_split, valid_split = sorted(all_indexes[: len(train_data)//2]), sorted(all_indexes[len(train_data)//2 :])
# # imagenet16_split = dict2config({'train': train_split, 'valid': valid_split}, None)
# # _ = configure2str(imagenet16_split, 'temp.txt')
# split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
# imagenet16_split = load_config(split_Fpath, None, None)
# train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
# logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
logger.log('config : {:}'.format(config))
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.test_batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'RANDOM', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space}, None)
search_model = get_cell_based_tiny_net(model_config)
w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.parameters(), config)
logger.log('w-optimizer : {:}'.format(w_optimizer))
logger.log('w-scheduler : {:}'.format(w_scheduler))
logger.log('criterion : {:}'.format(criterion))
if xargs.arch_nas_dataset is None: api = None
else : api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
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()
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'] )
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}, {}
# start training
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)
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
# selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num)
search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_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))
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
cur_arch, cur_valid_acc = search_find_best(valid_loader, network, xargs.select_num)
logger.log('[{:}] find-the-best : {:}, accuracy@1={:.2f}%'.format(epoch_str, cur_arch, cur_valid_acc))
genotypes[epoch] = cur_arch
# check the best accuracy
valid_accuracies[epoch] = valid_a_top1
if valid_a_top1 > valid_accuracies['best']:
valid_accuracies['best'] = valid_a_top1
find_best = True
else: find_best = False
# save checkpoint
save_path = save_checkpoint({'epoch' : epoch + 1,
'args' : deepcopy(xargs),
'search_model': search_model.state_dict(),
'w_optimizer' : w_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(args),
'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)
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' + '-'*200)
logger.log('Pre-searching costs {:.1f} s'.format(search_time.sum))
start_time = time.time()
best_arch, best_acc = search_find_best(valid_loader, network, xargs.select_num)
search_time.update(time.time() - start_time)
logger.log('RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.'.format(best_arch, best_acc, search_time.sum))
if api is not None: logger.log('{:}'.format( api.query_by_arch(best_arch) ))
logger.close()
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads( xargs.workers )
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
# class_num = 4
# xshape = (1,3,88,88)
# print(xshape)
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = '/home/city/Projects/NAS-Projects/configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
elif xargs.dataset.startswith('ImageNet16'):
split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
imagenet16_split = load_config(split_Fpath, None, None)
train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
config_path = '/home/city/Projects/NAS-Projects/configs/nas-benchmark/algos/DARTS.config'
config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
print('config')
print(config)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
# train_transform = transforms.Compose([
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor()
# # transforms.Normalize(mean=[128, 128, 128], std=[50, 50, 50])
# ])
# val_transform = transforms.Compose([
# # transforms.RandomHorizontalFlip(),
# transforms.ToTensor()
# # transforms.Normalize(mean=[128, 128, 128], std=[50, 50, 50])
# ])
#
# train_data = datasets.ImageFolder(root='/home/city/Projects/build_assessment/data/train',
# transform=train_transform)
# valid_data = datasets.ImageFolder(root='/home/city/Projects/build_assessment/data/val',
# transform=val_transform)
# print(len(train_data))
# print('2333333333333333333333333333333')
# train_split = []
# valid_split = []
#
# for i in range(len(train_data)):
# if i%2==0:
# train_split.append(i)
# else:
# valid_split.append(i)
# search_data = SearchDataset('builds', train_data, train_split, valid_split)
#
# search_loader = torch.utils.data.DataLoader(search_data,
# batch_size=32, shuffle=True,
# num_workers=4, pin_memory=True
# )
# valid_loader = torch.utils.data.DataLoader(valid_data,
# batch_size=32, shuffle=True,
# num_workers=2, pin_memory=True
# )
search_space = get_search_spaces('cell', xargs.search_space_name)
model_config = dict2config({'name': 'DARTS-V2', 'C': xargs.channel, 'N': xargs.num_cells,
'max_nodes': xargs.max_nodes, 'num_classes': class_num,
'space' : search_space}, None)
search_model = get_cell_based_tiny_net(model_config)
logger.log('search-model :\n{:}'.format(search_model))
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('{:}'.format(search_model))
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
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()
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}, {}
# start training
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)
min_LR = min(w_scheduler.get_lr())
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min_LR))
search_w_loss, search_w_top1, search_w_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))
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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()
op_list, _ = genotypes['best'].tolist(remove_str=None)
find_best = True
best_arch_nums = op_list2str(op_list)
torch.save(search_model,'/home/city/disk/log/builds-darts/darts2_%04d_%s_%s_%.2f.pth' %(epoch,time_string_short(),best_arch_nums, valid_a_top1))
print('/home/city/disk/log/builds-darts/darts2_%04d_%s_%s_%.2f.pth' %(epoch,time_string_short(),best_arch_nums, valid_a_top1))
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(args),
'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('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
logger.log('arch :\n{:}'.format(nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu().argmax(dim=-1)))
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('DARTS-V2 : 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()
