def main(args):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
torch.set_num_threads(args.workers)
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
# prepare dataset
train_data, valid_data, xshape, class_num = get_datasets(
args.dataset, args.data_path, args.cutout_length)
# train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
split_file_path = Path(args.split_path)
assert split_file_path.exists(), "{:} does not exist".format(
split_file_path)
split_info = torch.load(split_file_path)
train_split, valid_split = split_info["train"], split_info["valid"]
assert (len(set(train_split).intersection(set(valid_split))) == 0
), "There should be 0 element that belongs to both train and valid"
assert len(train_split) + len(valid_split) == len(
train_data), "{:} + {:} vs {:}".format(len(train_split),
len(valid_split),
len(train_data))
search_dataset = SearchDataset(args.dataset, train_data, train_split,
valid_split)
search_train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
pin_memory=True,
num_workers=args.workers,
)
search_valid_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
pin_memory=True,
num_workers=args.workers,
)
search_loader = torch.utils.data.DataLoader(
search_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
sampler=None,
)
# get configures
model_config = load_config(
args.model_config,
{
"class_num": class_num,
"search_mode": args.search_shape
},
logger,
)
# obtain the model
search_model = obtain_search_model(model_config)
MAX_FLOP, param = get_model_infos(search_model, xshape)
optim_config = load_config(args.optim_config, {
"class_num": class_num,
"FLOP": MAX_FLOP
}, logger)
logger.log("Model Information : {:}".format(search_model.get_message()))
logger.log("MAX_FLOP = {:} M".format(MAX_FLOP))
logger.log("Params = {:} M".format(param))
logger.log("train_data : {:}".format(train_data))
logger.log("search-data: {:}".format(search_dataset))
logger.log("search_train_loader : {:} samples".format(len(train_split)))
logger.log("search_valid_loader : {:} samples".format(len(valid_split)))
base_optimizer, scheduler, criterion = get_optim_scheduler(
search_model.base_parameters(), optim_config)
arch_optimizer = torch.optim.Adam(
search_model.arch_parameters(),
lr=optim_config.arch_LR,
betas=(0.5, 0.999),
weight_decay=optim_config.arch_decay,
)
logger.log("base-optimizer : {:}".format(base_optimizer))
logger.log("arch-optimizer : {:}".format(arch_optimizer))
logger.log("scheduler : {:}".format(scheduler))
logger.log("criterion : {:}".format(criterion))
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()
# load checkpoint
if last_info.exists() or (args.resume is not None and osp.isfile(
args.resume)): # automatically resume from previous checkpoint
if args.resume is not None and osp.isfile(args.resume):
resume_path = Path(args.resume)
elif last_info.exists():
resume_path = last_info
else:
raise ValueError("Something is wrong.")
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
resume_path))
checkpoint = torch.load(resume_path)
if "last_checkpoint" in checkpoint:
last_checkpoint_path = checkpoint["last_checkpoint"]
if not last_checkpoint_path.exists():
logger.log("Does not find {:}, try another path".format(
last_checkpoint_path))
last_checkpoint_path = (resume_path.parent /
last_checkpoint_path.parent.name /
last_checkpoint_path.name)
assert (last_checkpoint_path.exists()
), "can not find the checkpoint from {:}".format(
last_checkpoint_path)
checkpoint = torch.load(last_checkpoint_path)
start_epoch = checkpoint["epoch"] + 1
search_model.load_state_dict(checkpoint["search_model"])
scheduler.load_state_dict(checkpoint["scheduler"])
base_optimizer.load_state_dict(checkpoint["base_optimizer"])
arch_optimizer.load_state_dict(checkpoint["arch_optimizer"])
valid_accuracies = checkpoint["valid_accuracies"]
arch_genotypes = checkpoint["arch_genotypes"]
discrepancies = checkpoint["discrepancies"]
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
.format(resume_path, start_epoch))
else:
logger.log(
"=> do not find the last-info file : {:} or resume : {:}".format(
last_info, args.resume))
start_epoch, valid_accuracies, arch_genotypes, discrepancies = (
0,
{
"best": -1
},
{},
{},
)
# main procedure
train_func, valid_func = get_procedures(args.procedure)
total_epoch = optim_config.epochs + optim_config.warmup
start_time, epoch_time = time.time(), AverageMeter()
for epoch in range(start_epoch, total_epoch):
scheduler.update(epoch, 0.0)
search_model.set_tau(args.gumbel_tau_max, args.gumbel_tau_min,
epoch * 1.0 / total_epoch)
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
LRs = scheduler.get_lr()
find_best = False
logger.log(
"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}"
.format(
time_string(),
epoch_str,
need_time,
min(LRs),
max(LRs),
scheduler,
search_model.tau,
MAX_FLOP,
))
# train for one epoch
train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(
search_loader,
network,
criterion,
scheduler,
base_optimizer,
arch_optimizer,
optim_config,
{
"epoch-str": epoch_str,
"FLOP-exp": MAX_FLOP * args.FLOP_ratio,
"FLOP-weight": args.FLOP_weight,
"FLOP-tolerant": MAX_FLOP * args.FLOP_tolerant,
},
args.print_freq,
logger,
)
# log the results
logger.log(
"***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}"
.format(
time_string(),
epoch_str,
train_base_loss,
train_arch_loss,
train_acc1,
train_acc5,
))
cur_FLOP, genotype = search_model.get_flop("genotype",
model_config._asdict(),
None)
arch_genotypes[epoch] = genotype
arch_genotypes["last"] = genotype
logger.log("[{:}] genotype : {:}".format(epoch_str, genotype))
arch_info, discrepancy = search_model.get_arch_info()
logger.log(arch_info)
discrepancies[epoch] = discrepancy
logger.log(
"[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}"
.format(
epoch_str,
cur_FLOP,
cur_FLOP / MAX_FLOP,
args.FLOP_ratio,
np.mean(discrepancy),
))
# if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
# init_flop_weight = init_flop_weight * args.FLOP_decay
# else:
# init_flop_weight = init_flop_weight / args.FLOP_decay
# evaluate the performance
if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
logger.log("-" * 150)
valid_loss, valid_acc1, valid_acc5 = valid_func(
search_valid_loader,
network,
criterion,
epoch_str,
args.print_freq_eval,
logger,
)
valid_accuracies[epoch] = valid_acc1
logger.log(
"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}"
.format(
time_string(),
epoch_str,
valid_loss,
valid_acc1,
valid_acc5,
valid_accuracies["best"],
100 - valid_accuracies["best"],
))
if valid_acc1 > valid_accuracies["best"]:
valid_accuracies["best"] = valid_acc1
arch_genotypes["best"] = genotype
find_best = True
logger.log(
"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}."
.format(
epoch,
valid_acc1,
valid_acc5,
100 - valid_acc1,
100 - valid_acc5,
model_best_path,
))
# save checkpoint
save_path = save_checkpoint(
{
"epoch": epoch,
"args": deepcopy(args),
"valid_accuracies": deepcopy(valid_accuracies),
"model-config": model_config._asdict(),
"optim-config": optim_config._asdict(),
"search_model": search_model.state_dict(),
"scheduler": scheduler.state_dict(),
"base_optimizer": base_optimizer.state_dict(),
"arch_optimizer": arch_optimizer.state_dict(),
"arch_genotypes": arch_genotypes,
"discrepancies": discrepancies,
},
model_base_path,
logger,
)
if find_best:
copy_checkpoint(model_base_path, model_best_path, logger)
last_info = save_checkpoint(
{
"epoch": epoch,
"args": deepcopy(args),
"last_checkpoint": save_path,
},
logger.path("info"),
logger,
)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log("")
logger.log("-" * 100)
last_config_path = logger.path("log") / "seed-{:}-last.config".format(
args.rand_seed)
configure2str(arch_genotypes["last"], str(last_config_path))
logger.log("save the last config int {:} :\n{:}".format(
last_config_path, arch_genotypes["last"]))
best_arch, valid_acc = arch_genotypes["best"], valid_accuracies["best"]
for key, config in arch_genotypes.items():
if key == "last":
continue
FLOP_ratio = config["estimated_FLOP"] / MAX_FLOP
if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
if valid_acc < valid_accuracies[key]:
best_arch, valid_acc = config, valid_accuracies[key]
print("Best-Arch : {:}\nRatio={:}, Valid-ACC={:}".format(
best_arch, best_arch["estimated_FLOP"] / MAX_FLOP, valid_acc))
best_config_path = logger.path("log") / "seed-{:}-best.config".format(
args.rand_seed)
configure2str(best_arch, str(best_config_path))
logger.log("save the last config int {:} :\n{:}".format(
best_config_path, best_arch))
logger.log("\n" + "-" * 200)
logger.log(
"Finish training/validation in {:}, and save final checkpoint into {:}"
.format(convert_secs2time(epoch_time.sum, True), logger.path("info")))
logger.close()
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
#torch.backends.cudnn.deterministic = True
torch.set_num_threads(args.workers)
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
# prepare dataset
train_data, valid_data, xshape, class_num = get_datasets(
args.dataset, args.data_path, args.cutout_length)
#train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
split_file_path = Path(args.split_path)
assert split_file_path.exists(), '{:} does not exist'.format(
split_file_path)
split_info = torch.load(split_file_path)
train_split, valid_split = split_info['train'], split_info['valid']
assert len(
set(train_split).intersection(set(valid_split))
) == 0, 'There should be 0 element that belongs to both train and valid'
assert len(train_split) + len(valid_split) == len(
train_data), '{:} + {:} vs {:}'.format(len(train_split),
len(valid_split),
len(train_data))
search_dataset = SearchDataset(args.dataset, train_data, train_split,
valid_split)
search_train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
pin_memory=True,
num_workers=args.workers)
search_valid_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
pin_memory=True,
num_workers=args.workers)
search_loader = torch.utils.data.DataLoader(search_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
sampler=None)
# get configures
if args.ablation_num_select is None or args.ablation_num_select <= 0:
model_config = load_config(args.model_config, {
'class_num': class_num,
'search_mode': 'shape'
}, logger)
else:
model_config = load_config(
args.model_config, {
'class_num': class_num,
'search_mode': 'ablation',
'num_random_select': args.ablation_num_select
}, logger)
# obtain the model
search_model = obtain_search_model(model_config)
MAX_FLOP, param = get_model_infos(search_model, xshape)
optim_config = load_config(args.optim_config, {
'class_num': class_num,
'FLOP': MAX_FLOP
}, logger)
logger.log('Model Information : {:}'.format(search_model.get_message()))
logger.log('MAX_FLOP = {:} M'.format(MAX_FLOP))
logger.log('Params = {:} M'.format(param))
logger.log('train_data : {:}'.format(train_data))
logger.log('search-data: {:}'.format(search_dataset))
logger.log('search_train_loader : {:} samples'.format(len(train_split)))
logger.log('search_valid_loader : {:} samples'.format(len(valid_split)))
base_optimizer, scheduler, criterion = get_optim_scheduler(
search_model.base_parameters(), optim_config)
arch_optimizer = torch.optim.Adam(search_model.arch_parameters(
optim_config.arch_LR),
lr=optim_config.arch_LR,
betas=(0.5, 0.999),
weight_decay=optim_config.arch_decay)
logger.log('base-optimizer : {:}'.format(base_optimizer))
logger.log('arch-optimizer : {:}'.format(arch_optimizer))
logger.log('scheduler : {:}'.format(scheduler))
logger.log('criterion : {:}'.format(criterion))
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()
# load checkpoint
if last_info.exists() or (args.resume is not None and osp.isfile(
args.resume)): # automatically resume from previous checkpoint
if args.resume is not None and osp.isfile(args.resume):
resume_path = Path(args.resume)
elif last_info.exists():
resume_path = last_info
else:
raise ValueError('Something is wrong.')
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
resume_path))
checkpoint = torch.load(resume_path)
if 'last_checkpoint' in checkpoint:
last_checkpoint_path = checkpoint['last_checkpoint']
if not last_checkpoint_path.exists():
logger.log('Does not find {:}, try another path'.format(
last_checkpoint_path))
last_checkpoint_path = resume_path.parent / last_checkpoint_path.parent.name / last_checkpoint_path.name
assert last_checkpoint_path.exists(
), 'can not find the checkpoint from {:}'.format(
last_checkpoint_path)
checkpoint = torch.load(last_checkpoint_path)
start_epoch = checkpoint['epoch'] + 1
#for key, value in checkpoint['search_model'].items():
# print('K {:} = Shape={:}'.format(key, value.shape))
search_model.load_state_dict(checkpoint['search_model'])
scheduler.load_state_dict(checkpoint['scheduler'])
base_optimizer.load_state_dict(checkpoint['base_optimizer'])
arch_optimizer.load_state_dict(checkpoint['arch_optimizer'])
valid_accuracies = checkpoint['valid_accuracies']
arch_genotypes = checkpoint['arch_genotypes']
discrepancies = checkpoint['discrepancies']
max_bytes = checkpoint['max_bytes']
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
.format(resume_path, start_epoch))
else:
logger.log(
"=> do not find the last-info file : {:} or resume : {:}".format(
last_info, args.resume))
start_epoch, valid_accuracies, arch_genotypes, discrepancies, max_bytes = 0, {
'best': -1
}, {}, {}, {}
# main procedure
train_func, valid_func = get_procedures(args.procedure)
total_epoch = optim_config.epochs + optim_config.warmup
start_time, epoch_time = time.time(), AverageMeter()
for epoch in range(start_epoch, total_epoch):
scheduler.update(epoch, 0.0)
search_model.set_tau(args.gumbel_tau_max, args.gumbel_tau_min,
epoch * 1.0 / total_epoch)
need_time = 'Time Left: {:}'.format(
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
epoch_str = 'epoch={:03d}/{:03d}'.format(epoch, total_epoch)
LRs = scheduler.get_lr()
find_best = False
logger.log(
'\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}'
.format(time_string(), epoch_str, need_time, min(LRs), max(LRs),
scheduler, search_model.tau, MAX_FLOP))
# train for one epoch
train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(search_loader, network, criterion, scheduler, base_optimizer, arch_optimizer, optim_config, \
{'epoch-str' : epoch_str, 'FLOP-exp': MAX_FLOP * args.FLOP_ratio,
'FLOP-weight': args.FLOP_weight, 'FLOP-tolerant': MAX_FLOP * args.FLOP_tolerant}, args.print_freq, logger)
# log the results
logger.log(
'***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}'
.format(time_string(), epoch_str, train_base_loss, train_arch_loss,
train_acc1, train_acc5))
cur_FLOP, genotype = search_model.get_flop('genotype',
model_config._asdict(),
None)
arch_genotypes[epoch] = genotype
arch_genotypes['last'] = genotype
logger.log('[{:}] genotype : {:}'.format(epoch_str, genotype))
# save the configuration
configure2str(
genotype,
str(
logger.path('log') /
'seed-{:}-temp.config'.format(args.rand_seed)))
arch_info, discrepancy = search_model.get_arch_info()
logger.log(arch_info)
discrepancies[epoch] = discrepancy
logger.log(
'[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}'
.format(epoch_str, cur_FLOP, cur_FLOP / MAX_FLOP, args.FLOP_ratio,
np.mean(discrepancy)))
#if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
# init_flop_weight = init_flop_weight * args.FLOP_decay
#else:
# init_flop_weight = init_flop_weight / args.FLOP_decay
# evaluate the performance
if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
logger.log('-' * 150)
valid_loss, valid_acc1, valid_acc5 = valid_func(
search_valid_loader, network, criterion, epoch_str,
args.print_freq_eval, logger)
valid_accuracies[epoch] = valid_acc1
logger.log(
'***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}'
.format(time_string(), epoch_str, valid_loss, valid_acc1,
valid_acc5, valid_accuracies['best'],
100 - valid_accuracies['best']))
if valid_acc1 > valid_accuracies['best']:
valid_accuracies['best'] = valid_acc1
arch_genotypes['best'] = genotype
find_best = True
logger.log(
'Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.'
.format(epoch, valid_acc1, valid_acc5, 100 - valid_acc1,
100 - valid_acc5, model_best_path))
# log the GPU memory usage
#num_bytes = torch.cuda.max_memory_allocated( next(network.parameters()).device ) * 1.0
num_bytes = torch.cuda.max_memory_cached(
next(network.parameters()).device) * 1.0
logger.log(
'[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]'
.format(
next(network.parameters()).device, int(num_bytes),
num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9))
max_bytes[epoch] = num_bytes
# save checkpoint
save_path = save_checkpoint(
{
'epoch': epoch,
'args': deepcopy(args),
'max_bytes': deepcopy(max_bytes),
'valid_accuracies': deepcopy(valid_accuracies),
'model-config': model_config._asdict(),
'optim-config': optim_config._asdict(),
'search_model': search_model.state_dict(),
'scheduler': scheduler.state_dict(),
'base_optimizer': base_optimizer.state_dict(),
'arch_optimizer': arch_optimizer.state_dict(),
'arch_genotypes': arch_genotypes,
'discrepancies': discrepancies,
}, model_base_path, logger)
if find_best: copy_checkpoint(model_base_path, model_best_path, logger)
last_info = save_checkpoint(
{
'epoch': epoch,
'args': deepcopy(args),
'last_checkpoint': save_path,
}, logger.path('info'), logger)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log('')
logger.log('-' * 100)
last_config_path = logger.path('log') / 'seed-{:}-last.config'.format(
args.rand_seed)
configure2str(arch_genotypes['last'], str(last_config_path))
logger.log('save the last config int {:} :\n{:}'.format(
last_config_path, arch_genotypes['last']))
best_arch, valid_acc = arch_genotypes['best'], valid_accuracies['best']
for key, config in arch_genotypes.items():
if key == 'last': continue
FLOP_ratio = config['estimated_FLOP'] / MAX_FLOP
if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
if valid_acc <= valid_accuracies[key]:
best_arch, valid_acc = config, valid_accuracies[key]
print('Best-Arch : {:}\nRatio={:}, Valid-ACC={:}'.format(
best_arch, best_arch['estimated_FLOP'] / MAX_FLOP, valid_acc))
best_config_path = logger.path('log') / 'seed-{:}-best.config'.format(
args.rand_seed)
configure2str(best_arch, str(best_config_path))
logger.log('save the last config int {:} :\n{:}'.format(
best_config_path, best_arch))
logger.log('\n' + '-' * 200)
logger.log(
'Finish training/validation in {:} with Max-GPU-Memory of {:.2f} GB, and save final checkpoint into {:}'
.format(convert_secs2time(epoch_time.sum, True),
max(v for k, v in max_bytes.items()) / 1e9,
logger.path('info')))
logger.close()