def run(args, myargs):
my_config = getattr(myargs.config, args.command)
config = SearchConfig()
for k, v in args.items():
assert not hasattr(config, k)
setattr(config, k, v)
for k, v in my_config.items():
if not hasattr(config, k):
print('* config does not have %s' % k)
setattr(config, k, v)
device = torch.device("cuda")
writer = myargs.writer
writer.add_text('all_config', config.as_markdown(), 0)
logger = myargs.logger
config.print_params(logger.info_msg)
config.data_path = os.path.expanduser(config.data_path)
config.plot_path = os.path.join(args.outdir, 'plot')
config.path = args.outdir
main(config=config, logger=logger, device=device, myargs=myargs)
def main():
config = SearchConfig(section='fine-tune')
device = torch.device("cuda")
# tensorboard
writer = SummaryWriter(log_dir=os.path.join(config.path, "tb"))
writer.add_text('config', config.as_markdown(), 0)
logger = utils.get_logger(
os.path.join(config.path, "{}_tune.log".format(config.name)))
config.print_params(logger.info)
logger.info("Logger is set - training start")
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
# get data with meta info
input_size, input_channels, n_classes, train_data, valid_data = utils.get_data(
config.dataset, config.data_path, cutout_length=0, validation=True)
logger.debug('loading checkpoint')
best_path = os.path.join(config.path, 'best.pth.tar')
model = torch.load(best_path)
model.prune()
model = model.to(device)
# weights optimizer
w_optim = torch.optim.SGD(model.weights(),
config.w_lr,
momentum=config.w_momentum,
weight_decay=config.w_weight_decay)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
w_optim, config.epochs, eta_min=config.w_lr_min)
architect = Architect(model, config.w_momentum, config.w_weight_decay)
model.print_alphas(logger)
first_top1 = validate(valid_loader, model, -1, 0, device, config, logger,
writer)
os.system('mkdir -p ' + config.fine_tune_path)
# training loop
best_top1 = 0.
for epoch in range(config.epochs):
lr_scheduler.step()
lr = lr_scheduler.get_lr()[0]
model.print_alphas(logger)
# training
train(train_loader, model, architect, w_optim, lr, epoch, writer,
device, config, logger)
# validation
cur_step = (epoch + 1) * len(train_loader)
top1 = validate(valid_loader, model, epoch, cur_step, device, config,
logger, writer)
# save
if best_top1 < top1:
best_top1 = top1
is_best = True
else:
is_best = False
utils.save_checkpoint(model, config.fine_tune_path, is_best)
print("")
logger.info("Initial best Prec@1 = {:.4%}".format(first_top1))
logger.info("Final best Prec@1 = {:.4%}".format(best_top1))
assert error < epsilon
return error
config = SearchConfig()
device = torch.device("cuda")
# tensorboard
writer = SummaryWriter(log_dir=os.path.join(config.path, "tb"))
writer.add_text('config', config.as_markdown(), 0)
logger = utils.get_logger(
os.path.join(config.path, "{}.log".format(config.name)))
config.print_params(logger.info)
def main():
logger.info("Logger is set - training start")
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
def main():
config = SearchConfig()
device = torch.device("cuda")
# tensorboard
tb_path = os.path.join(config.path, "tb")
os.system('rm -r ' + tb_path)
writer = SummaryWriter(log_dir=tb_path)
writer.add_text('config', config.as_markdown(), 0)
logger = utils.get_logger(
os.path.join(config.path, "{}_train.log".format(config.name)))
config.print_params(logger.info)
logger.info("Logger is set - training start")
if int(config.profile) != 0:
logger.info('entering profile mode')
profile = True
config.epochs = 1
max_batches = config.print_freq
else:
profile = False
max_batches = None
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
# get data with meta info
input_size, input_channels, n_classes, train_data = utils.get_data(
config.dataset, config.data_path, cutout_length=0, validation=False)
module_name, class_name = config.controller_class.rsplit('.', 1)
controller_cls = getattr(import_module(module_name), class_name)
model = controller_cls(device, **config.__dict__)
model = model.to(device)
# weights optimizer
w_optim = torch.optim.SGD(model.weights(),
config.w_lr,
momentum=config.w_momentum,
weight_decay=config.w_weight_decay)
# alphas optimizer
alpha_optim = torch.optim.Adam(model.alphas(),
config.alpha_lr,
betas=(0.5, 0.999),
weight_decay=config.alpha_weight_decay)
# split data to train/validation
n_train = len(train_data)
split = int(n_train * config.validate_split)
indices = list(range(n_train))
if split <= 0:
logger.debug('using train as validation')
valid_sampler = train_sampler = torch.utils.data.sampler.SubsetRandomSampler(
indices)
else:
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(
indices[:split])
valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(
indices[split:])
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.batch_size,
sampler=train_sampler,
num_workers=config.workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.batch_size,
sampler=valid_sampler,
num_workers=config.workers,
pin_memory=True)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
w_optim, config.epochs, eta_min=config.w_lr_min)
architect = Architect(model, config.w_momentum, config.w_weight_decay)
# training loop
best = 0
best_genotype = None
for epoch in range(config.epochs):
lr_scheduler.step()
lr = lr_scheduler.get_lr()[0]
model.print_alphas(logger)
# training
if profile:
with torch.autograd.profiler.profile(use_cuda=True) as prof:
train_qual = train(train_loader,
valid_loader,
model,
architect,
w_optim,
alpha_optim,
lr,
epoch,
writer,
device,
config,
logger,
max_batches=max_batches)
print('cpu')
print(prof.key_averages().table(sort_by="cpu_time_total",
row_limit=10))
print(prof.key_averages().table(sort_by="cpu_time", row_limit=10))
print('cuda')
print(prof.key_averages().table(sort_by="cuda_time_total",
row_limit=10))
print(prof.key_averages().table(sort_by="cuda_time", row_limit=10))
break
model.new_epoch(epoch, writer)
train_qual = train(train_loader,
valid_loader,
model,
architect,
w_optim,
alpha_optim,
lr,
epoch,
writer,
device,
config,
logger,
max_batches=max_batches)
# validation
cur_step = (epoch + 1) * len(train_loader)
val_qual = validate(valid_loader, model, epoch, cur_step, device,
config, logger, writer)
# log
# genotype
genotype = model.genotype()
logger.info("genotype = {}".format(genotype))
# genotype as a image
plot_path = os.path.join(config.plot_path,
"EP{:02d}".format(epoch + 1))
caption = "Epoch {}".format(epoch + 1)
model.plot_genotype(plot_path, caption)
#plot(genotype.normal, plot_path + "-normal", caption)
#plot(genotype.reduce, plot_path + "-reduce", caption)
if config.use_train_quality != 0:
cur_qual = train_qual
else:
cur_qual = val_qual
# save
if best < cur_qual:
best = cur_qual
best_genotype = genotype
is_best = True
else:
is_best = False
utils.save_checkpoint(model, config.path, is_best)
logger.info("Quality{}: {} \n\n".format('*' if is_best else '',
cur_qual))
logger.info("Final best = {}".format(best))
logger.info("Best Genotype = {}".format(best_genotype))
