def main():
arch_pool = utils.generate_arch(args.controller_seed_arch, 5, 5)
valid_arch_pool = utils.generate_arch(100, 5, 5)
train_encoder_input = list(
map(
lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.
parse_arch_to_seq(x[1], 2), arch_pool))
valid_encoder_input = list(
map(
lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.
parse_arch_to_seq(x[1], 2), valid_arch_pool))
train_encoder_target = [
np.random.random() for i in range(args.controller_seed_arch)
]
valid_encoder_target = [np.random.random() for i in range(100)]
nao = NAO(
args.controller_encoder_layers,
args.controller_encoder_vocab_size,
args.controller_encoder_hidden_size,
args.controller_encoder_dropout,
args.controller_encoder_length,
args.controller_source_length,
args.controller_encoder_emb_size,
args.controller_mlp_layers,
args.controller_mlp_hidden_size,
args.controller_mlp_dropout,
args.controller_decoder_layers,
args.controller_decoder_vocab_size,
args.controller_decoder_hidden_size,
args.controller_decoder_dropout,
args.controller_decoder_length,
)
logging.info("param size = %fMB", utils.count_parameters_in_MB(nao))
nao = nao.cuda()
nao_train_dataset = utils.NAODataset(train_encoder_input,
train_encoder_target,
True,
swap=True)
nao_valid_dataset = utils.NAODataset(valid_encoder_input,
valid_encoder_target, False)
nao_train_queue = torch.utils.data.DataLoader(
nao_train_dataset,
batch_size=args.controller_batch_size,
shuffle=True,
pin_memory=True)
nao_valid_queue = torch.utils.data.DataLoader(
nao_valid_dataset,
batch_size=len(nao_valid_dataset),
shuffle=False,
pin_memory=True)
nao_optimizer = torch.optim.Adam(nao.parameters(),
lr=args.controller_lr,
weight_decay=args.controller_l2_reg)
for nao_epoch in range(1, args.controller_epochs + 1):
nao_loss, nao_mse, nao_ce = nao_train(nao_train_queue, nao,
nao_optimizer)
if nao_epoch % 10 == 0:
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f",
nao_epoch, nao_loss, nao_mse, nao_ce)
if nao_epoch % 100 == 0:
pa, hs = nao_valid(nao_valid_queue, nao)
logging.info("Evaluation on training data")
logging.info(
'epoch %04d pairwise accuracy %.6f hamming distance %.6f',
nao_epoch, pa, hs)
new_archs = []
max_step_size = 100
predict_step_size = 0
top100_archs = list(
map(
lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.
parse_arch_to_seq(x[1], 2), arch_pool[:100]))
nao_infer_dataset = utils.NAODataset(top100_archs, None, False)
nao_infer_queue = torch.utils.data.DataLoader(
nao_infer_dataset,
batch_size=len(nao_infer_dataset),
shuffle=False,
pin_memory=True)
while len(new_archs) < args.controller_new_arch:
predict_step_size += 1
logging.info('Generate new architectures with step size %d',
predict_step_size)
new_arch = nao_infer(nao_infer_queue, nao, predict_step_size)
for arch in new_arch:
if arch not in train_encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= args.controller_new_arch:
break
logging.info('%d new archs generated now', len(new_archs))
if predict_step_size > max_step_size:
break
# [[conv, reduc]]
new_archs = list(map(lambda x: utils.parse_seq_to_arch(x, 2),
new_archs)) # [[[conv],[reduc]]]
num_new_archs = len(new_archs)
logging.info("Generate %d new archs", num_new_archs)
new_arch_pool = arch_pool + new_archs + utils.generate_arch(
args.controller_random_arch, 5, 5)
logging.info("Totally %d archs now to train", len(new_arch_pool))
arch_pool = new_arch_pool
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.enabled = True
cudnn.benchmark = False
cudnn.deterministic = True
args.steps = int(np.ceil(
45000 / args.child_batch_size)) * args.child_epochs
logging.info("args = %s", args)
if args.child_arch_pool is not None:
logging.info('Architecture pool is provided, loading')
with open(args.child_arch_pool) as f:
archs = f.read().splitlines()
archs = list(map(utils.build_dag, archs))
child_arch_pool = archs
elif os.path.exists(os.path.join(args.output_dir, 'arch_pool')):
logging.info('Architecture pool is founded, loading')
with open(os.path.join(args.output_dir, 'arch_pool')) as f:
archs = f.read().splitlines()
archs = list(map(utils.build_dag, archs))
child_arch_pool = archs
else:
child_arch_pool = None
child_eval_epochs = eval(args.child_eval_epochs)
build_fn = get_builder(args.dataset)
train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler = build_fn(
ratio=0.9, epoch=-1)
nao = NAO(
args.controller_encoder_layers,
args.controller_encoder_vocab_size,
args.controller_encoder_hidden_size,
args.controller_encoder_dropout,
args.controller_encoder_length,
args.controller_source_length,
args.controller_encoder_emb_size,
args.controller_mlp_layers,
args.controller_mlp_hidden_size,
args.controller_mlp_dropout,
args.controller_decoder_layers,
args.controller_decoder_vocab_size,
args.controller_decoder_hidden_size,
args.controller_decoder_dropout,
args.controller_decoder_length,
)
nao = nao.cuda()
logging.info("Encoder-Predictor-Decoder param size = %fMB",
utils.count_parameters_in_MB(nao))
# Train child model
if child_arch_pool is None:
logging.info(
'Architecture pool is not provided, randomly generating now')
child_arch_pool = utils.generate_arch(args.controller_seed_arch,
args.child_nodes,
5) # [[[conv],[reduc]]]
if args.child_sample_policy == 'params':
child_arch_pool_prob = []
for arch in child_arch_pool:
if args.dataset == 'cifar10':
tmp_model = NASNetworkCIFAR(
args, 10, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob, args.child_use_aux_head,
args.steps, arch)
elif args.dataset == 'cifar100':
tmp_model = NASNetworkCIFAR(
args, 100, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob, args.child_use_aux_head,
args.steps, arch)
else:
tmp_model = NASNetworkImageNet(
args, 1000, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob, args.child_use_aux_head,
args.steps, arch)
child_arch_pool_prob.append(
utils.count_parameters_in_MB(tmp_model))
del tmp_model
else:
child_arch_pool_prob = None
eval_points = utils.generate_eval_points(child_eval_epochs, 0,
args.child_epochs)
step = 0
for epoch in range(1, args.child_epochs + 1):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
# sample an arch to train
train_acc, train_obj, step = child_train(train_queue, model, optimizer,
step, child_arch_pool,
child_arch_pool_prob,
train_criterion)
logging.info('train_acc %f', train_acc)
if epoch not in eval_points:
continue
# Evaluate seed archs
valid_accuracy_list = child_valid(valid_queue, model, child_arch_pool,
eval_criterion)
# Output archs and evaluated error rate
old_archs = child_arch_pool
old_archs_perf = valid_accuracy_list
old_archs_sorted_indices = np.argsort(old_archs_perf)[::-1]
old_archs = [old_archs[i] for i in old_archs_sorted_indices]
old_archs_perf = [old_archs_perf[i] for i in old_archs_sorted_indices]
with open(os.path.join(args.output_dir, 'arch_pool.{}'.format(epoch)),
'w') as fa:
with open(
os.path.join(args.output_dir,
'arch_pool.perf.{}'.format(epoch)),
'w') as fp:
with open(os.path.join(args.output_dir, 'arch_pool'),
'w') as fa_latest:
with open(os.path.join(args.output_dir, 'arch_pool.perf'),
'w') as fp_latest:
for arch, perf in zip(old_archs, old_archs_perf):
arch = ' '.join(map(str, arch[0] + arch[1]))
fa.write('{}\n'.format(arch))
fa_latest.write('{}\n'.format(arch))
fp.write('{}\n'.format(perf))
fp_latest.write('{}\n'.format(perf))
if epoch == args.child_epochs:
break
# Train Encoder-Predictor-Decoder
logging.info('Training Encoder-Predictor-Decoder')
encoder_input = list(
map(
lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.
parse_arch_to_seq(x[1], 2), old_archs))
# [[conv, reduc]]
min_val = min(old_archs_perf)
max_val = max(old_archs_perf)
encoder_target = [(i - min_val) / (max_val - min_val)
for i in old_archs_perf]
if args.controller_expand is not None:
dataset = list(zip(encoder_input, encoder_target))
n = len(dataset)
ratio = 0.9
split = int(n * ratio)
np.random.shuffle(dataset)
encoder_input, encoder_target = list(zip(*dataset))
train_encoder_input = list(encoder_input[:split])
train_encoder_target = list(encoder_target[:split])
valid_encoder_input = list(encoder_input[split:])
valid_encoder_target = list(encoder_target[split:])
for _ in range(args.controller_expand - 1):
for src, tgt in zip(encoder_input[:split],
encoder_target[:split]):
a = np.random.randint(0, args.child_nodes)
b = np.random.randint(0, args.child_nodes)
src = src[:4 * a] + src[4 * a + 2:4 * a + 4] + \
src[4 * a:4 * a + 2] + src[4 * (a + 1):20 + 4 * b] + \
src[20 + 4 * b + 2:20 + 4 * b + 4] + src[20 + 4 * b:20 + 4 * b + 2] + \
src[20 + 4 * (b + 1):]
train_encoder_input.append(src)
train_encoder_target.append(tgt)
else:
train_encoder_input = encoder_input
train_encoder_target = encoder_target
valid_encoder_input = encoder_input
valid_encoder_target = encoder_target
logging.info('Train data: {}\tValid data: {}'.format(
len(train_encoder_input), len(valid_encoder_input)))
nao_train_dataset = utils.NAODataset(
train_encoder_input,
train_encoder_target,
True,
swap=True if args.controller_expand is None else False)
nao_valid_dataset = utils.NAODataset(valid_encoder_input,
valid_encoder_target, False)
nao_train_queue = torch.utils.data.DataLoader(
nao_train_dataset,
batch_size=args.controller_batch_size,
shuffle=True,
pin_memory=True)
nao_valid_queue = torch.utils.data.DataLoader(
nao_valid_dataset,
batch_size=args.controller_batch_size,
shuffle=False,
pin_memory=True)
nao_optimizer = torch.optim.Adam(nao.parameters(),
lr=args.controller_lr,
weight_decay=args.controller_l2_reg)
for nao_epoch in range(1, args.controller_epochs + 1):
nao_loss, nao_mse, nao_ce = nao_train(nao_train_queue, nao,
nao_optimizer)
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f",
nao_epoch, nao_loss, nao_mse, nao_ce)
if nao_epoch % 100 == 0:
pa, hs = nao_valid(nao_valid_queue, nao)
logging.info("Evaluation on valid data")
logging.info(
'epoch %04d pairwise accuracy %.6f hamming distance %.6f',
epoch, pa, hs)
# Generate new archs
new_archs = []
max_step_size = 50
predict_step_size = 0
top100_archs = list(
map(
lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.
parse_arch_to_seq(x[1], 2), old_archs[:100]))
nao_infer_dataset = utils.NAODataset(top100_archs, None, False)
nao_infer_queue = torch.utils.data.DataLoader(
nao_infer_dataset,
batch_size=len(nao_infer_dataset),
shuffle=False,
pin_memory=True)
while len(new_archs) < args.controller_new_arch:
predict_step_size += 1
logging.info('Generate new architectures with step size %d',
predict_step_size)
new_arch = nao_infer(nao_infer_queue,
nao,
predict_step_size,
direction='+')
for arch in new_arch:
if arch not in encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= args.controller_new_arch:
break
logging.info('%d new archs generated now', len(new_archs))
if predict_step_size > max_step_size:
break
# [[conv, reduc]]
new_archs = list(
map(lambda x: utils.parse_seq_to_arch(x, 2),
new_archs)) # [[[conv],[reduc]]]
num_new_archs = len(new_archs)
logging.info("Generate %d new archs", num_new_archs)
# replace bottom archs
if args.controller_replace:
new_arch_pool = old_archs[:len(old_archs) - (num_new_archs + args.controller_random_arch)] + \
new_archs + utils.generate_arch(args.controller_random_arch, 5, 5)
# discard all archs except top k
elif args.controller_discard:
new_arch_pool = old_archs[:100] + new_archs + utils.generate_arch(
args.controller_random_arch, 5, 5)
# use all
else:
new_arch_pool = old_archs + new_archs + utils.generate_arch(
args.controller_random_arch, 5, 5)
logging.info("Totally %d architectures now to train",
len(new_arch_pool))
child_arch_pool = new_arch_pool
with open(os.path.join(args.output_dir, 'arch_pool'), 'w') as f:
for arch in new_arch_pool:
arch = ' '.join(map(str, arch[0] + arch[1]))
f.write('{}\n'.format(arch))
if args.child_sample_policy == 'params':
child_arch_pool_prob = []
for arch in child_arch_pool:
if args.dataset == 'cifar10':
tmp_model = NASNetworkCIFAR(
args, 10, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob,
args.child_use_aux_head, args.steps, arch)
elif args.dataset == 'cifar100':
tmp_model = NASNetworkCIFAR(
args, 100, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob,
args.child_use_aux_head, args.steps, arch)
else:
tmp_model = NASNetworkImageNet(
args, 1000, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob,
args.child_use_aux_head, args.steps, arch)
child_arch_pool_prob.append(
utils.count_parameters_in_MB(tmp_model))
del tmp_model
else:
child_arch_pool_prob = None
def train():
child_params = get_child_model_params()
controller_params = get_controller_params()
branch_length = controller_params['source_length'] // 2 // 5 // 2
eval_every_epochs = child_params['eval_every_epochs']
child_epoch = 0
while True:
# Train child model
if child_params['arch_pool'] is None:
arch_pool = utils.generate_arch(controller_params['num_seed_arch'],
child_params['num_cells'],
5) #[[[conv],[reduc]]]
child_params['arch_pool'] = arch_pool
child_params['arch_pool_prob'] = None
else:
if child_params['sample_policy'] == 'uniform':
child_params['arch_pool_prob'] = None
elif child_params['sample_policy'] == 'params':
child_params['arch_pool_prob'] = calculate_params(
child_params['arch_pool'])
elif child_params['sample_policy'] == 'valid_performance':
child_params['arch_pool_prob'] = child_valid(child_params)
elif child_params['sample_policy'] == 'predicted_performance':
encoder_input = list(map(lambda x: utils.parse_arch_to_seq(x[0], branch_length) + \
utils.parse_arch_to_seq(x[1], branch_length), child_params['arch_pool']))
predicted_error_rate = controller.test(controller_params,
encoder_input)
child_params['arch_pool_prob'] = [
1 - i[0] for i in predicted_error_rate
]
else:
raise ValueError(
'Child model arch pool sample policy is not provided!')
if isinstance(eval_every_epochs, int):
child_params['eval_every_epochs'] = eval_every_epochs
else:
for index, e in enumerate(eval_every_epochs):
if child_epoch < e:
child_params['eval_every_epochs'] = e
break
child_epoch = child_train(child_params)
# Evaluate seed archs
valid_accuracy_list = child_valid(child_params)
# Output archs and evaluated error rate
old_archs = child_params['arch_pool']
old_archs_perf = [1 - i for i in valid_accuracy_list]
old_archs_sorted_indices = np.argsort(old_archs_perf)
old_archs = np.array(old_archs)[old_archs_sorted_indices].tolist()
old_archs_perf = np.array(
old_archs_perf)[old_archs_sorted_indices].tolist()
with open(
os.path.join(child_params['model_dir'],
'arch_pool.{}'.format(child_epoch)), 'w') as fa:
with open(
os.path.join(child_params['model_dir'],
'arch_pool.perf.{}'.format(child_epoch)),
'w') as fp:
with open(os.path.join(child_params['model_dir'], 'arch_pool'),
'w') as fa_latest:
with open(
os.path.join(child_params['model_dir'],
'arch_pool.perf'), 'w') as fp_latest:
for arch, perf in zip(old_archs, old_archs_perf):
arch = ' '.join(map(str, arch[0] + arch[1]))
fa.write('{}\n'.format(arch))
fa_latest.write('{}\n'.format(arch))
fp.write('{}\n'.format(perf))
fp_latest.write('{}\n'.format(perf))
if child_epoch >= child_params['num_epochs']:
break
# Train Encoder-Predictor-Decoder
encoder_input = list(map(lambda x : utils.parse_arch_to_seq(x[0], branch_length) + \
utils.parse_arch_to_seq(x[1], branch_length), old_archs))
#[[conv, reduc]]
min_val = min(old_archs_perf)
max_val = max(old_archs_perf)
encoder_target = [(i - min_val) / (max_val - min_val)
for i in old_archs_perf]
decoder_target = copy.copy(encoder_input)
controller_params['batches_per_epoch'] = math.ceil(
len(encoder_input) / controller_params['batch_size'])
#if clean controller model
controller.train(controller_params, encoder_input, encoder_target,
decoder_target)
# Generate new archs
#old_archs = old_archs[:450]
new_archs = []
max_step_size = 100
controller_params['predict_lambda'] = 0
top100_archs = list(map(lambda x : utils.parse_arch_to_seq(x[0], branch_length) + \
utils.parse_arch_to_seq(x[1], branch_length), old_archs[:100]))
while len(new_archs) < 500:
controller_params['predict_lambda'] += 1
new_arch = controller.predict(controller_params, top100_archs)
for arch in new_arch:
if arch not in encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= 500:
break
tf.logging.info('{} new archs generated now'.format(
len(new_archs)))
if controller_params['predict_lambda'] > max_step_size:
break
#[[conv, reduc]]
new_archs = list(
map(lambda x: utils.parse_seq_to_arch(x, branch_length),
new_archs)) #[[[conv],[reduc]]]
num_new_archs = len(new_archs)
tf.logging.info("Generate {} new archs".format(num_new_archs))
new_arch_pool = old_archs[:len(old_archs) - (
num_new_archs + 50)] + new_archs + utils.generate_arch(50, 5, 5)
tf.logging.info("Totally {} archs now to train".format(
len(new_arch_pool)))
child_params['arch_pool'] = new_arch_pool
with open(os.path.join(child_params['model_dir'], 'arch_pool'),
'w') as f:
for arch in new_arch_pool:
arch = ' '.join(map(str, arch[0] + arch[1]))
f.write('{}\n'.format(arch))
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.enabled = True
cudnn.benchmark = True
cudnn.deterministic = True
if args.dataset == 'cifar10':
args.num_class = 10
elif args.dataset == 'cifar100':
args.num_class = 100
else:
args.num_class = 10
if args.search_space == 'small':
OPERATIONS = OPERATIONS_search_small
elif args.search_space == 'middle':
OPERATIONS = OPERATIONS_search_middle
args.child_num_ops = len(OPERATIONS)
args.controller_encoder_vocab_size = 1 + (args.child_nodes + 2 -
1) + args.child_num_ops
args.controller_decoder_vocab_size = args.controller_encoder_vocab_size
args.steps = int(np.ceil(
45000 / args.child_batch_size)) * args.child_epochs
logging.info("args = %s", args)
if args.child_arch_pool is not None:
logging.info('Architecture pool is provided, loading')
with open(args.child_arch_pool) as f:
archs = f.read().splitlines()
archs = list(map(utils.build_dag, archs))
child_arch_pool = archs
elif os.path.exists(os.path.join(args.output_dir, 'arch_pool')):
logging.info('Architecture pool is founded, loading')
with open(os.path.join(args.output_dir, 'arch_pool')) as f:
archs = f.read().splitlines()
archs = list(map(utils.build_dag, archs))
child_arch_pool = archs
else:
child_arch_pool = None
build_fn = get_builder(args.dataset)
train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler = build_fn(
ratio=0.9, epoch=-1)
nao = NAO(
args.controller_encoder_layers,
args.controller_encoder_vocab_size,
args.controller_encoder_hidden_size,
args.controller_encoder_dropout,
args.controller_encoder_length,
args.controller_source_length,
args.controller_encoder_emb_size,
args.controller_mlp_layers,
args.controller_mlp_hidden_size,
args.controller_mlp_dropout,
args.controller_decoder_layers,
args.controller_decoder_vocab_size,
args.controller_decoder_hidden_size,
args.controller_decoder_dropout,
args.controller_decoder_length,
)
nao = nao.cuda()
logging.info("Encoder-Predictor-Decoder param size = %fMB",
utils.count_parameters_in_MB(nao))
if child_arch_pool is None:
logging.info(
'Architecture pool is not provided, randomly generating now')
child_arch_pool = utils.generate_arch(
args.controller_seed_arch, args.child_nodes,
args.child_num_ops) # [[[conv],[reduc]]]
arch_pool = []
arch_pool_valid_acc = []
for i in range(4):
logging.info('Iteration %d', i)
child_arch_pool_prob = []
for arch in child_arch_pool:
if args.dataset == 'cifar10':
tmp_model = NASNetworkCIFAR(
args, args.num_class, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob, args.child_use_aux_head,
args.steps, arch)
elif args.dataset == 'cifar100':
tmp_model = NASNetworkCIFAR(
args, args.num_class, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob, args.child_use_aux_head,
args.steps, arch)
else:
tmp_model = NASNetworkImageNet(
args, args.num_class, args.child_layers, args.child_nodes,
args.child_channels, args.child_keep_prob,
args.child_drop_path_keep_prob, args.child_use_aux_head,
args.steps, arch)
child_arch_pool_prob.append(
utils.count_parameters_in_MB(tmp_model))
del tmp_model
step = 0
scheduler = get_scheduler(optimizer, args.dataset)
for epoch in range(1, args.child_epochs + 1):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
# sample an arch to train
train_acc, train_obj, step = child_train(train_queue, model,
optimizer, step,
child_arch_pool,
child_arch_pool_prob,
train_criterion)
logging.info('train_acc %f', train_acc)
logging.info("Evaluate seed archs")
arch_pool += child_arch_pool
arch_pool_valid_acc = child_valid(valid_queue, model, arch_pool,
eval_criterion)
arch_pool_valid_acc_sorted_indices = np.argsort(
arch_pool_valid_acc)[::-1]
arch_pool = [arch_pool[i] for i in arch_pool_valid_acc_sorted_indices]
arch_pool_valid_acc = [
arch_pool_valid_acc[i] for i in arch_pool_valid_acc_sorted_indices
]
with open(os.path.join(args.output_dir, 'arch_pool.{}'.format(i)),
'w') as fa:
with open(
os.path.join(args.output_dir,
'arch_pool.perf.{}'.format(i)), 'w') as fp:
for arch, perf in zip(arch_pool, arch_pool_valid_acc):
arch = ' '.join(map(str, arch[0] + arch[1]))
fa.write('{}\n'.format(arch))
fp.write('{}\n'.format(perf))
if i == 3:
break
# Train Encoder-Predictor-Decoder
logging.info('Train Encoder-Predictor-Decoder')
encoder_input = list(
map(
lambda x: utils.parse_arch_to_seq(x[0]) + utils.
parse_arch_to_seq(x[1]), arch_pool))
# [[conv, reduc]]
min_val = min(arch_pool_valid_acc)
max_val = max(arch_pool_valid_acc)
encoder_target = [(i - min_val) / (max_val - min_val)
for i in arch_pool_valid_acc]
if args.controller_expand:
dataset = list(zip(encoder_input, encoder_target))
n = len(dataset)
ratio = 0.9
split = int(n * ratio)
np.random.shuffle(dataset)
encoder_input, encoder_target = list(zip(*dataset))
train_encoder_input = list(encoder_input[:split])
train_encoder_target = list(encoder_target[:split])
valid_encoder_input = list(encoder_input[split:])
valid_encoder_target = list(encoder_target[split:])
for _ in range(args.controller_expand - 1):
for src, tgt in zip(encoder_input[:split],
encoder_target[:split]):
a = np.random.randint(0, args.child_nodes)
b = np.random.randint(0, args.child_nodes)
src = src[:4 * a] + src[4 * a + 2:4 * a + 4] + \
src[4 * a:4 * a + 2] + src[4 * (a + 1):20 + 4 * b] + \
src[20 + 4 * b + 2:20 + 4 * b + 4] + src[20 + 4 * b:20 + 4 * b + 2] + \
src[20 + 4 * (b + 1):]
train_encoder_input.append(src)
train_encoder_target.append(tgt)
else:
train_encoder_input = encoder_input
train_encoder_target = encoder_target
valid_encoder_input = encoder_input
valid_encoder_target = encoder_target
logging.info('Train data: {}\tValid data: {}'.format(
len(train_encoder_input), len(valid_encoder_input)))
nao_train_dataset = utils.NAODataset(
train_encoder_input,
train_encoder_target,
True,
swap=True if args.controller_expand is None else False)
nao_valid_dataset = utils.NAODataset(valid_encoder_input,
valid_encoder_target, False)
nao_train_queue = torch.utils.data.DataLoader(
nao_train_dataset,
batch_size=args.controller_batch_size,
shuffle=True,
pin_memory=True)
nao_valid_queue = torch.utils.data.DataLoader(
nao_valid_dataset,
batch_size=args.controller_batch_size,
shuffle=False,
pin_memory=True)
nao_optimizer = torch.optim.Adam(nao.parameters(),
lr=args.controller_lr,
weight_decay=args.controller_l2_reg)
for nao_epoch in range(1, args.controller_epochs + 1):
nao_loss, nao_mse, nao_ce = nao_train(nao_train_queue, nao,
nao_optimizer)
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f",
nao_epoch, nao_loss, nao_mse, nao_ce)
if nao_epoch % 100 == 0:
pa, hs = nao_valid(nao_valid_queue, nao)
logging.info("Evaluation on valid data")
logging.info(
'epoch %04d pairwise accuracy %.6f hamming distance %.6f',
nao_epoch, pa, hs)
# Generate new archs
new_archs = []
max_step_size = 50
predict_step_size = 0
top100_archs = list(
map(
lambda x: utils.parse_arch_to_seq(x[0]) + utils.
parse_arch_to_seq(x[1]), arch_pool[:100]))
nao_infer_dataset = utils.NAODataset(top100_archs, None, False)
nao_infer_queue = torch.utils.data.DataLoader(
nao_infer_dataset,
batch_size=len(nao_infer_dataset),
shuffle=False,
pin_memory=True)
while len(new_archs) < args.controller_new_arch:
predict_step_size += 1
logging.info('Generate new architectures with step size %d',
predict_step_size)
new_arch = nao_infer(nao_infer_queue,
nao,
predict_step_size,
direction='+')
for arch in new_arch:
if arch not in encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= args.controller_new_arch:
break
logging.info('%d new archs generated now', len(new_archs))
if predict_step_size > max_step_size:
break
child_arch_pool = list(
map(lambda x: utils.parse_seq_to_arch(x),
new_archs)) # [[[conv],[reduc]]]
logging.info("Generate %d new archs", len(child_arch_pool))
logging.info('Finish Searching')
logging.info('Reranking top 5 architectures')
# reranking top 5
top_archs = arch_pool[:5]
if args.dataset == 'cifar10':
top_archs_perf = train_and_evaluate_top_on_cifar10(
top_archs, train_queue, valid_queue)
elif args.dataset == 'cifar100':
top_archs_perf = train_and_evaluate_top_on_cifar100(
top_archs, train_queue, valid_queue)
else:
top_archs_perf = train_and_evaluate_top_on_imagenet(
top_archs, train_queue, valid_queue)
top_archs_sorted_indices = np.argsort(top_archs_perf)[::-1]
top_archs = [top_archs[i] for i in top_archs_sorted_indices]
top_archs_perf = [top_archs_perf[i] for i in top_archs_sorted_indices]
with open(os.path.join(args.output_dir, 'arch_pool.final'), 'w') as fa:
with open(os.path.join(args.output_dir, 'arch_pool.perf.final'),
'w') as fp:
for arch, perf in zip(top_archs, top_archs_perf):
arch = ' '.join(map(str, arch[0] + arch[1]))
fa.write('{}\n'.format(arch))
fp.write('{}\n'.format(perf))
def train():
child_params = get_child_model_params()
controller_params = get_controller_params()
corpus = data.Corpus(child_params['data_dir'])
eval_batch_size = child_params['eval_batch_size']
best_genotype = None
train_data = batchify(corpus.train, child_params['batch_size'],
child_params['cuda'])
val_data = batchify(corpus.valid, eval_batch_size, child_params['cuda'])
ntokens = len(corpus.dictionary)
if os.path.exists(os.path.join(child_params['model_dir'], 'model.pt')):
print("Found model.pt in {}, automatically continue training.".format(
os.path.join(child_params['model_dir'])))
continue_train_child = True
else:
continue_train_child = False
if continue_train_child:
child_model = torch.load(
os.path.join(child_params['model_dir'], 'model.pt'))
else:
child_model = model_search.RNNModelSearch(
ntokens, child_params['emsize'], child_params['nhid'],
child_params['nhidlast'], child_params['dropout'],
child_params['dropouth'], child_params['dropoutx'],
child_params['dropouti'], child_params['dropoute'],
child_params['drop_path'])
if os.path.exists(os.path.join(controller_params['model_dir'],
'model.pt')):
print("Found model.pt in {}, automatically continue training.".format(
os.path.join(child_params['model_dir'])))
continue_train_controller = True
else:
continue_train_controller = False
if continue_train_controller:
controller_model = torch.load(
os.path.join(controller_params['model_dir'], 'model.pt'))
else:
controller_model = controller.Controller(controller_params)
size = 0
for p in child_model.parameters():
size += p.nelement()
logging.info('child model param size: {}'.format(size))
size = 0
for p in controller_model.parameters():
size += p.nelement()
logging.info('controller model param size: {}'.format(size))
if args.cuda:
if args.single_gpu:
parallel_child_model = child_model.cuda()
parallel_controller_model = controller_model.cuda()
else:
parallel_child_model = nn.DataParallel(child_model, dim=1).cuda()
parallel_controller_model = nn.DataParallel(controller_model,
dim=1).cuda()
else:
parallel_child_model = child_model
parallel_controller_model = controller_model
total_params = sum(x.data.nelement() for x in child_model.parameters())
logging.info('Args: {}'.format(args))
logging.info('Child Model total parameters: {}'.format(total_params))
total_params = sum(x.data.nelement()
for x in controller_model.parameters())
logging.info('Args: {}'.format(args))
logging.info('Controller Model total parameters: {}'.format(total_params))
# Loop over epochs.
if continue_train_child:
optimizer_state = torch.load(
os.path.join(child_params['model_dir'], 'optimizer.pt'))
if 't0' in optimizer_state['param_groups'][0]:
child_optimizer = torch.optim.ASGD(
child_model.parameters(),
lr=child_params['lr'],
t0=0,
lambd=0.,
weight_decay=child_params['wdecay'])
else:
child_optimizer = torch.optim.SGD(
child_model.parameters(),
lr=child_params['lr'],
weight_decay=child_params['wdecay'])
child_optimizer.load_state_dict(optimizer_state)
child_epoch = torch.load(
os.path.join(child_params['model_dir'], 'misc.pt'))['epoch'] - 1
else:
child_optimizer = torch.optim.SGD(child_model.parameters(),
lr=child_params['lr'],
weight_decay=child_params['wdecay'])
child_epoch = 0
if continue_train_controller:
optimizer_state = torch.load(
os.path.join(controller_params['model_dir'], 'optimizer.pt'))
controller_optimizer = torch.optim.Adam(
controller_model.parameters(),
lr=controller_params['lr'],
weight_decay=controller_params['weight_decay'])
controller_optimizer.load_state_dict(optimizer_state)
controller_epoch = torch.load(
os.path.join(controller_params['model_dir'],
'misc.pt'))['epoch'] - 1
else:
controller_optimizer = torch.optim.Adam(
controller_model.parameters(),
lr=controller_params['lr'],
weight_decay=controller_params['weight_decay'])
controller_epoch = 0
eval_every_epochs = child_params['eval_every_epochs']
while True:
# Train child model
if child_params['arch_pool'] is None:
arch_pool = generate_arch(
controller_params['num_seed_arch'],
num_nodes=args.num_intermediate_nodes,
num_operations=args.num_operations) # [[arch]]
child_params['arch_pool'] = arch_pool
child_params['arch'] = None
if isinstance(eval_every_epochs, int):
child_params['eval_every_epochs'] = eval_every_epochs
else:
eval_every_epochs = list(map(int, eval_every_epochs))
for index, e in enumerate(eval_every_epochs):
if child_epoch < e:
child_params['eval_every_epochs'] = e
break
for e in range(child_params['eval_every_epochs']):
child_epoch += 1
model_search.train(train_data, child_model, parallel_child_model,
child_optimizer, child_params, child_epoch)
if child_epoch % child_params['eval_every_epochs'] == 0:
save_checkpoint(child_model, child_optimizer, child_epoch,
child_params['model_dir'])
logging.info('Saving Model!')
if child_epoch >= child_params['train_epochs']:
break
# Evaluate seed archs
valid_accuracy_list = model_search.evaluate(val_data, child_model,
parallel_child_model,
child_params,
eval_batch_size)
# Output archs and evaluated error rate
old_archs = child_params['arch_pool']
old_archs_perf = valid_accuracy_list
old_archs_sorted_indices = np.argsort(old_archs_perf)
old_archs = np.array(old_archs)[old_archs_sorted_indices].tolist()
old_archs_perf = np.array(
old_archs_perf)[old_archs_sorted_indices].tolist()
with open(
os.path.join(child_params['model_dir'],
'arch_pool.{}'.format(child_epoch)), 'w') as fa:
with open(
os.path.join(child_params['model_dir'],
'arch_pool.perf.{}'.format(child_epoch)),
'w') as fp:
with open(os.path.join(child_params['model_dir'], 'arch_pool'),
'w') as fa_latest:
with open(
os.path.join(child_params['model_dir'],
'arch_pool.perf'), 'w') as fp_latest:
for arch, perf in zip(old_archs, old_archs_perf):
genotype = get_genotype(
arch,
num_nodes=args.num_intermediate_nodes,
operations_list=PRIMITIVES)
arch = ' '.join(map(str, arch))
fa.write('{} {}\n'.format(arch, genotype))
fa_latest.write('{} {}\n'.format(arch, genotype))
fp.write('{}\n'.format(perf))
fp_latest.write('{}\n'.format(perf))
best_genotype = get_genotype(old_archs[0],
num_nodes=args.num_intermediate_nodes,
operations_list=PRIMITIVES)
logging.info('BEST ARCH OF EPOCH {} IS: {}'.format(
child_epoch, best_genotype))
logging.info('WITH VALIDATION PPL: {}'.format(old_archs_perf[0]))
if child_epoch >= child_params['train_epochs']:
logging.info('Training finished!')
logging.info('BEST ARCH END SEARCH IS: {}'.format(best_genotype))
logging.info('WITH VALIDATION PPL: {}'.format(old_archs_perf[0]))
return best_genotype
# Train Encoder-Predictor-Decoder
# [[arch]]
encoder_input = list(
map(
lambda x: parse_arch_to_seq(
x, num_nodes=args.num_intermediate_nodes), old_archs))
encoder_target = normalize_target(old_archs_perf)
decoder_target = copy.copy(encoder_input)
controller_params['batches_per_epoch'] = math.ceil(
len(encoder_input) / controller_params['batch_size'])
controller_epoch = controller.train(encoder_input, encoder_target,
decoder_target, controller_model,
parallel_controller_model,
controller_optimizer,
controller_params,
controller_epoch)
# Generate new archs
new_archs = []
controller_params['predict_lambda'] = 0
top100_archs = list(
map(lambda x: parse_arch_to_seq(x), old_archs[:100]))
max_step_size = controller_params['max_step_size']
while len(new_archs) < controller_params['max_new_archs']:
controller_params['predict_lambda'] += 1
new_arch = controller.infer(top100_archs, controller_model,
parallel_controller_model,
controller_params)
for arch in new_arch:
if arch not in encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= controller_params['max_new_archs']:
break
logging.info('{} new archs generated now'.format(len(new_archs)))
if controller_params['predict_lambda'] >= max_step_size:
break
# [[arch]]
new_archs = list(
map(
lambda x: parse_seq_to_arch(
x, num_nodes=args.num_intermediate_nodes),
new_archs)) # [[arch]]
num_new_archs = len(new_archs)
logging.info("Generate {} new archs".format(num_new_archs))
random_new_archs = generate_arch(50,
num_nodes=args.num_intermediate_nodes,
num_operations=args.num_operations)
new_arch_pool = old_archs[:len(old_archs) - num_new_archs -
50] + new_archs + random_new_archs
logging.info("Totally {} archs now to train".format(
len(new_arch_pool)))
child_params['arch_pool'] = new_arch_pool
with open(os.path.join(child_params['model_dir'], 'arch_pool'),
'w') as f:
for arch in new_arch_pool:
arch = ' '.join(map(str, arch))
f.write('{}\n'.format(arch))
def main(args):
logging.info('training on {} gpus'.format(torch.cuda.device_count()))
logging.info('max tokens {} per gpu'.format(args.max_tokens))
logging.info('max sentences {} per gpu'.format(
args.max_sentences if args.max_sentences is not None else 'None'))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.enabled = True
cudnn.benchmark = True
cudnn.deterministic = True
args.child_num_ops = len(operations.OPERATIONS_ENCODER)
args.controller_vocab_size = 1 + args.child_num_ops
args.controller_source_length = args.enc_layers + args.dec_layers
args.controller_encoder_length = args.controller_decoder_length = args.controller_source_length
tasks.set_ljspeech_hparams(args)
logging.info("args = {}".format(args))
if args.arch_pool is not None:
logging.info('Architecture pool is provided, loading')
with open(args.arch_pool) as f:
archs = f.read().splitlines()
child_arch_pool = [
list(map(int,
arch.strip().split())) for arch in archs
]
else:
child_arch_pool = None
task = tasks.LJSpeechTask(args)
task.setup_task(ws=True)
logging.info("Model param size = %d", utils.count_parameters(task.model))
controller = nao_controller.NAO(args)
logging.info("Encoder-Predictor-Decoder param size = %d",
utils.count_parameters(controller))
if child_arch_pool is None:
logging.info(
'Architecture pool is not provided, randomly generating now')
child_arch_pool = utils.generate_arch(
args.controller_seed_arch, args.enc_layers + args.dec_layers,
args.child_num_ops)
arch_pool = []
num_updates = 0
r_c = list(map(float, args.reward.strip().split()))
for controller_iteration in range(args.controller_iterations + 1):
logging.info('Iteration %d', controller_iteration + 1)
arch_pool += child_arch_pool
for epoch in range(1, args.max_epochs * len(arch_pool) + 1):
decoder_loss, stop_loss, loss, num_updates = task.train(
epoch=epoch, num_updates=num_updates, arch_pool=arch_pool)
lr = task.scheduler.get_lr()
logging.info(
'epoch %d updates %d lr %.6f decoder loss %.6f stop loss %.6f loss %.6f',
epoch, num_updates, lr, decoder_loss, stop_loss, loss)
frs, pcrs, dfrs, losses = task.valid_for_search(
None, gta=False, arch_pool=arch_pool, layer_norm_training=True)
reward = [
r_c[0] * fr + r_c[1] * pcr + r_c[2] * dfr - r_c[3] * loss
for fr, pcr, dfr, loss in zip(frs, pcrs, dfrs, losses)
]
arch_pool_valid_perf = reward
arch_pool_valid_perf_sorted_indices = np.argsort(
arch_pool_valid_perf)[::-1]
arch_pool = list(
map(lambda x: arch_pool[x], arch_pool_valid_perf_sorted_indices))
arch_pool_valid_perf = list(
map(lambda x: arch_pool_valid_perf[x],
arch_pool_valid_perf_sorted_indices))
os.makedirs(os.path.join(args.output_dir), exist_ok=True)
with open(
os.path.join(args.output_dir,
'arch_pool.{}'.format(controller_iteration)),
'w') as fa:
with open(
os.path.join(
args.output_dir,
'arch_pool.perf.{}'.format(controller_iteration)),
'w') as fp:
for arch, perf in zip(arch_pool, arch_pool_valid_perf):
arch = ' '.join(map(str, arch))
fa.write('{}\n'.format(arch))
fp.write('{}\n'.format(perf))
if controller_iteration == args.controller_iterations:
break
# Train Encoder-Predictor-Decoder
logging.info('Train Encoder-Predictor-Decoder')
inputs = list(map(lambda x: utils.parse_arch_to_seq(x), arch_pool))
min_val = min(arch_pool_valid_perf)
max_val = max(arch_pool_valid_perf)
targets = list(
map(lambda x: (x - min_val) / (max_val - min_val),
arch_pool_valid_perf))
# Pre-train NAO
logging.info('Pre-train EPD')
controller.build_dataset('train', inputs, targets, True)
controller.build_queue('train')
for epoch in range(1, args.controller_pretrain_epochs + 1):
loss, mse, ce = controller.train_epoch('train')
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f", epoch,
loss, mse, ce)
logging.info('Finish pre-training EPD')
# Generate synthetic data
logging.info('Generate synthetic data for EPD')
synthetic_inputs, synthetic_targets = controller.generate_synthetic_data(
inputs, args.controller_random_arch)
if args.controller_up_sample_ratio:
all_inputs = inputs * args.controller_up_sample_ratio + synthetic_inputs
all_targets = targets * args.controller_up_sample_ratio + synthetic_targets
else:
all_inputs = inputs + synthetic_inputs
all_targets = targets + synthetic_targets
# Train NAO
logging.info('Train EPD')
controller.build_dataset('train', all_inputs, all_targets, True)
controller.build_queue('train')
for epoch in range(1, args.controller_epochs + 1):
loss, mse, ce = controller.train_epoch('train')
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f", epoch,
loss, mse, ce)
logging.info('Finish training EPD')
# Generate new archs
new_archs = []
max_step_size = 100
predict_step_size = 0
# get top 100 from true data and synthetic data
topk_indices = np.argsort(all_targets)[:100]
topk_archs = list(map(lambda x: all_inputs[x], topk_indices))
controller.build_dataset('infer', topk_archs, None, False)
controller.build_queue('infer',
batch_size=len(topk_archs),
shuffle=False)
while len(new_archs) < args.controller_new_arch:
predict_step_size += 0.1
logging.info('Generate new architectures with step size %.2f',
predict_step_size)
new_arch = controller.infer('infer',
predict_step_size,
direction='+')
for arch in new_arch:
if arch not in inputs and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= args.controller_new_arch:
break
logging.info('%d new archs generated now', len(new_archs))
if predict_step_size > max_step_size:
break
child_arch_pool = list(
map(lambda x: utils.parse_seq_to_arch(x), new_archs))
logging.info("Generate %d new archs", len(child_arch_pool))
logging.info('Finish Searching')
def main():
random.seed(args.seed)
np.random.seed(args.seed)
args.steps = int(np.ceil(45000 / args.child_batch_size)) * args.child_epochs
logging.info("args = %s", args)
if args.child_arch_pool is not None:
logging.info('Architecture pool is provided, loading')
with open(args.child_arch_pool) as f:
archs = f.read().splitlines()
archs = list(map(utils.build_arch, archs))
child_arch_pool = archs
elif os.path.exists(os.path.join(args.output_dir, 'arch_pool')):
logging.info('Architecture pool is founded, loading')
with open(os.path.join(args.output_dir, 'arch_pool')) as f:
archs = f.read().splitlines()
archs = list(map(utils.build_arch, archs))
child_arch_pool = archs
else:
child_arch_pool = None
child_eval_epochs = eval(args.child_eval_epochs)
build_fn = get_builder(args.dataset)
train_queue, valid_queue, model, train_criterion, eval_criterion, optimizer, scheduler = build_fn(ratio=0.9, epoch=-1)
nao = NAO(
args.controller_encoder_layers,
args.controller_encoder_vocab_size,
args.controller_encoder_hidden_size,
args.controller_encoder_dropout,
args.controller_encoder_length,
args.controller_source_length,
args.controller_encoder_emb_size,
args.controller_mlp_layers,
args.controller_mlp_hidden_size,
args.controller_mlp_dropout,
args.controller_decoder_layers,
args.controller_decoder_vocab_size,
args.controller_decoder_hidden_size,
args.controller_decoder_dropout,
args.controller_decoder_length,
)
nao = nao.cuda()
logging.info("Encoder-Predictor-Decoder param size = %fMB", utils.count_parameters_in_MB(nao))
# Train child model
if child_arch_pool is None:
logging.info('Architecture pool is not provided, randomly generating now')
child_arch_pool = utils.generate_arch(args.controller_seed_arch, args.child_nodes, 5) # [[[conv],[reduc]]]
child_arch_pool_prob = None
eval_points = utils.generate_eval_points(child_eval_epochs, 0, args.child_epochs)
step = 0
for epoch in range(1, args.child_epochs + 1):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
# sample an arch to train
train_acc, train_obj, step = child_train(train_queue, model, optimizer, step, child_arch_pool, child_arch_pool_prob, train_criterion)
logging.info('train_acc %f', train_acc)
if epoch not in eval_points:
continue
# Evaluate seed archs
valid_accuracy_list = child_valid(valid_queue, model, child_arch_pool, eval_criterion)
# Output archs and evaluated error rate
old_archs = child_arch_pool
old_archs_perf = valid_accuracy_list
old_archs_sorted_indices = np.argsort(old_archs_perf)[::-1]
old_archs = [old_archs[i] for i in old_archs_sorted_indices]
old_archs_perf = [old_archs_perf[i] for i in old_archs_sorted_indices]
with open(os.path.join(args.output_dir, 'arch_pool.{}'.format(epoch)), 'w') as fa:
with open(os.path.join(args.output_dir, 'arch_pool.perf.{}'.format(epoch)), 'w') as fp:
with open(os.path.join(args.output_dir, 'arch_pool'), 'w') as fa_latest:
with open(os.path.join(args.output_dir, 'arch_pool.perf'), 'w') as fp_latest:
for arch, perf in zip(old_archs, old_archs_perf):
arch = ' '.join(map(str, arch[0] + arch[1]))
fa.write('{}\n'.format(arch))
fa_latest.write('{}\n'.format(arch))
fp.write('{}\n'.format(perf))
fp_latest.write('{}\n'.format(perf))
if epoch == args.child_epochs:
break
# Train Encoder-Predictor-Decoder
logging.info('Training Encoder-Predictor-Decoder')
encoder_input = list \
(map(lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.parse_arch_to_seq(x[1], 2), old_archs))
# [[conv, reduc]]
min_val = min(old_archs_perf)
max_val = max(old_archs_perf)
encoder_target = [(i - min_val) / (max_val - min_val) for i in old_archs_perf]
if args.controller_expand:
dataset = list(zip(encoder_input, encoder_target))
n = len(dataset)
ratio = 0.9
split = int( n *ratio)
np.random.shuffle(dataset)
encoder_input, encoder_target = list(zip(*dataset))
train_encoder_input = list(encoder_input[:split])
train_encoder_target = list(encoder_target[:split])
valid_encoder_input = list(encoder_input[split:])
valid_encoder_target = list(encoder_target[split:])
for _ in range(args.controller_expan d -1):
for src, tgt in zip(encoder_input[:split], encoder_target[:split]):
a = np.random.randint(0, args.child_nodes)
b = np.random.randint(0, args.child_nodes)
src = src[:4 * a] + src[4 * a + 2:4 * a + 4] + \
src[4 * a:4 * a + 2] + src[4 * (a + 1):20 + 4 * b] + \
src[20 + 4 * b + 2:20 + 4 * b + 4] + src[20 + 4 * b:20 + 4 * b + 2] + \
src[20 + 4 * (b + 1):]
train_encoder_input.append(src)
train_encoder_target.append(tgt)
else:
train_encoder_input = encoder_input
train_encoder_target = encoder_target
valid_encoder_input = encoder_input
valid_encoder_target = encoder_target
logging.info('Train data: {}\tValid data: {}'.format(len(train_encoder_input), len(valid_encoder_input)))
nao_train_dataset = utils.NAODataset(train_encoder_input, train_encoder_target, True, swap=True if args.controller_expand is None else False)
nao_valid_dataset = utils.NAODataset(valid_encoder_input, valid_encoder_target, False)
nao_train_queue = torch.utils.data.DataLoader(
nao_train_dataset, batch_size=args.controller_batch_size, shuffle=True, pin_memory=True)
nao_valid_queue = torch.utils.data.DataLoader(
nao_valid_dataset, batch_size=args.controller_batch_size, shuffle=False, pin_memory=True)
nao_optimizer = torch.optim.Adam(nao.parameters(), lr=args.controller_lr, weight_decay=args.controller_l2_reg)
for nao_epoch in range(1, args.controller_epoch s +1):
nao_loss, nao_mse, nao_ce = nao_train(nao_train_queue, nao, nao_optimizer)
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f", nao_epoch, nao_loss, nao_mse, nao_ce)
if nao_epoch % 100 == 0:
pa, hs = nao_valid(nao_valid_queue, nao)
logging.info("Evaluation on valid data")
logging.info('epoch %04d pairwise accuracy %.6f hamming distance %.6f', epoch, pa, hs)
nao_valid_dataset, batch_size=args.controller_batch_size, shuffle=False, pin_memory=True)
nao_optimizer = torch.optim.Adam(nao.parameters(), lr=args.controller_lr, weight_decay=args.controller_l2_reg)
for nao_epoch in range(1, args.controller_epoch s +1):
nao_loss, nao_mse, nao_ce = nao_train(nao_train_queue, nao, nao_optimizer)
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f", nao_epoch, nao_loss, nao_mse, nao_ce)
if nao_epoch % 100 == 0:
pa, hs = nao_valid(nao_valid_queue, nao)
logging.info("Evaluation on valid data")
logging.info('epoch %04d pairwise accuracy %.6f hamming distance %.6f', epoch, pa, hs)
# Generate new archs
new_archs = []
max_step_size = 50
predict_step_size = 0
top100_archs = list \
(map(lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.parse_arch_to_seq(x[1], 2), old_archs[:100]))
nao_infer_dataset = utils.NAODataset(top100_archs, None, False)
nao_infer_queue = torch.utils.data.DataLoader(
nao_infer_dataset, batch_size=len(nao_infer_dataset), shuffle=False, pin_memory=True)
while len(new_archs) < args.controller_new_arch:
predict_step_size += 1
logging.info('Generate new architectures with step size %d', predict_step_size)
new_arch = nao_infer(nao_infer_queue, nao, predict_step_size, direction='+')
for arch in new_arch:
if arch not in encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= args.controller_new_arch:
break
logging.info('%d new archs generated now', len(new_archs))
if predict_step_size > max_step_size:
break
def main():
random.seed(args.seed)
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
logging.info("Args = %s", args)
nao = NAO(
args.encoder_layers,
args.encoder_vocab_size,
args.encoder_hidden_size,
args.encoder_dropout,
args.encoder_length,
args.source_length,
args.encoder_emb_size,
args.mlp_layers,
args.mlp_hidden_size,
args.mlp_dropout,
args.decoder_layers,
args.decoder_vocab_size,
args.decoder_hidden_size,
args.decoder_dropout,
args.decoder_length,
)
logging.info("param size = %fMB", utils.count_parameters_in_MB(nao))
nao = nao.cuda()
with open(os.path.join(args.output_dir, 'arch_pool.{}'.format(args.iteration))) as f:
arch_pool = f.read().splitlines()
arch_pool = list(map(utils.build_dag, arch_pool))
with open(os.path.join(args.output_dir, 'arch_pool.{}.perf'.format(args.iteration))) as f:
arch_pool_valid_acc = f.read().splitlines()
arch_pool_valid_acc = list(map(float, arch_pool_valid_acc))
logging.info('Training Encoder-Predictor-Decoder')
train_encoder_input = list(
map(lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.parse_arch_to_seq(x[1], 2), arch_pool))
min_val = min(arch_pool_valid_acc)
max_val = max(arch_pool_valid_acc)
train_encoder_target = [(i - min_val) / (max_val - min_val) for i in arch_pool_valid_acc]
if args.expand is not None:
buffer1, buffer2 = [], []
for _ in range(args.expand - 1):
for src, tgt in zip(train_encoder_input, train_encoder_target):
a = np.random.randint(0, 5)
b = np.random.randint(0, 5)
src = src[:4 * a] + src[4 * a + 2:4 * a + 4] + \
src[4 * a:4 * a + 2] + src[4 * (a + 1):20 + 4 * b] + \
src[20 + 4 * b + 2:20 + 4 * b + 4] + src[20 + 4 * b:20 + 4 * b + 2] + \
src[20 + 4 * (b + 1):]
buffer1.append(src)
buffer2.append(tgt)
train_encoder_input += buffer1
train_encoder_target += buffer2
nao_train_dataset = utils.NAODataset(train_encoder_input, train_encoder_target, True, swap=True)
nao_valid_dataset = utils.NAODataset(train_encoder_input, train_encoder_target, False)
nao_train_queue = torch.utils.data.DataLoader(
nao_train_dataset, batch_size=args.batch_size, shuffle=True, pin_memory=True)
nao_valid_queue = torch.utils.data.DataLoader(
nao_valid_dataset, batch_size=len(nao_valid_dataset), shuffle=False, pin_memory=True)
nao_optimizer = torch.optim.Adam(nao.parameters(), lr=args.lr, weight_decay=args.l2_reg)
for nao_epoch in range(1, args.epochs + 1):
nao_loss, nao_mse, nao_ce = nao_train(nao_train_queue, nao, nao_optimizer)
if nao_epoch % 10 == 0 or nao_epoch == 1:
logging.info("epoch %04d train loss %.6f mse %.6f ce %.6f", nao_epoch, nao_loss, nao_mse, nao_ce)
if nao_epoch % 100 == 0 or nao_epoch == 1:
mse, pa, hs = nao_valid(nao_train_queue, nao)
logging.info("Evaluation on train data")
logging.info('epoch %04d mse %.6f pairwise accuracy %.6f hamming distance %.6f', nao_epoch, mse, pa, hs)
mse, pa, hs = nao_valid(nao_valid_queue, nao)
logging.info("Evaluation on valid data")
logging.info('epoch %04d mse %.6f pairwise accuracy %.6f hamming distance %.6f', nao_epoch, mse, pa, hs)
new_archs = []
predict_step_size = 0
top_archs = list(map(lambda x: utils.parse_arch_to_seq(x[0], 2) + utils.parse_arch_to_seq(x[1], 2),
arch_pool[:args.generate_topk]))
nao_infer_dataset = utils.NAODataset(top_archs, None, False)
nao_infer_queue = torch.utils.data.DataLoader(nao_infer_dataset, batch_size=len(nao_infer_dataset), shuffle=False,
pin_memory=True)
while len(new_archs) < args.new_arch:
predict_step_size += 1
logging.info('Generate new architectures with step size %d', predict_step_size)
new_arch = nao_infer(nao_infer_queue, nao, predict_step_size, direction='+')
for arch in new_arch:
if arch not in train_encoder_input and arch not in new_archs:
new_archs.append(arch)
if len(new_archs) >= args.new_arch:
break
logging.info('%d new archs generated now', len(new_archs))
if predict_step_size > args.max_step_size:
break
logging.info("Generate %d new archs", len(new_archs))
new_arch_pool = list(map(lambda x: utils.parse_seq_to_arch(x, 2), new_archs))
new_arch_pool = new_arch_pool + arch_pool[:args.remain_topk]
with open(os.path.join(args.output_dir, 'new_arch_pool.{}'.format(args.iteration)), 'w') as f:
for arch in new_arch_pool:
arch = ' '.join(map(str, arch[0] + arch[1]))
f.write('{}\n'.format(arch))
logging.info('Finish training!')