def data_consumers(args, q, save_dir, i, search_space):
set_random_seed(int(str(time.time()).split('.')[0][::-1][:9]))
file_name = 'log_%s_%d' % ('gpus', i)
logger = setup_logger(file_name,
save_dir,
i,
log_level='DEBUG',
filename='%s.txt' % file_name)
while True:
msg = q.get()
if msg == 'done':
logger.info('thread %d end' % i)
break
iterations = msg['iterate']
run_experiments_bananas_paradigm(args, save_dir, i, iterations, logger,
search_space)
def main(args):
set_random_seed(args.seed)
encoding_sample_pipeline1, x_idx = meta_neural_net_experiment_pipeline1(
samples=args.sample_nums)
encoding_sample_pipeline2, _ = meta_neural_net_experiment_pipeline2(
samples=args.sample_nums)
encoding_full = meta_neural_net_experiment_full()
# with open(args.save_dir, 'wb') as f:
# pickle.dump(encoding_sample_pipeline1, f)
# pickle.dump(encoding_sample_pipeline2, f)
# pickle.dump(encoding_full, f)
# pickle.dump(x_idx, f)
print(encoding_sample_pipeline1)
print(encoding_sample_pipeline2)
print(encoding_full)
plot_combine_graph(encoding_1=encoding_sample_pipeline1,
encoding_2=encoding_sample_pipeline2,
idx=x_idx,
encoding_all=encoding_full)
plot_combine_graph2_log_scale(encoding_1=encoding_sample_pipeline1,
encoding_2=encoding_sample_pipeline2,
idx=x_idx,
encoding_all=encoding_full)
print('kl divergence ################')
normal_cell = genotype.normal
reduce_cell = genotype.reduce
normal_cell_new = [(cell[1], ops_list.index(cell[0]))
for cell in normal_cell]
reduce_cell_new = [(cell[1], ops_list.index(cell[0]))
for cell in reduce_cell]
genotype_new = Genotype(normal=normal_cell_new,
reduce=reduce_cell_new,
normal_concat=genotype.normal_concat,
reduce_concat=genotype.reduce_concat)
return genotype_new
if __name__ == '__main__':
# Running this code have to modify the predictor_list, search_space, trails, seq_len, gpu, load_dir, save_dir
parser = argparse.ArgumentParser(
description='Predictor comparison parameters!')
parser.add_argument('--nums', type=int, default=10000)
parser.add_argument('--seed', type=int, default=112)
parser.add_argument(
'--save_dir',
type=str,
default=
'/home/aurora/data_disk_new/train_output_2021/darts_save_path/darts.pkl'
)
args = parser.parse_args()
set_random_seed(args.seed)
total_archs = gen_arch_wo_key_lists(args.nums, OPS, args.save_dir)
def main(args):
file_name = 'log_%s_%d' % ('gpus', args.gpu)
logger = setup_logger(file_name,
args.save_dir,
args.gpu,
log_level='DEBUG',
filename='%s.txt' % file_name)
logger.info(args)
if args.search_space == 'nasbench_101':
with open(nas_bench_101_all_data, 'rb') as fpkl:
all_data = pickle.load(fpkl)
else:
raise NotImplementedError(
f'The search space {args.search_space} does not support now!')
for k in range(args.trails):
seed = random_id_int(4)
set_random_seed(seed)
s_results_dict = defaultdict(list)
k_results_dict = defaultdict(list)
logger.info(
f'====================== Trails {k} Begin Setting Seed to {seed} ==========================='
)
for budget in args.search_budget:
train_data, test_data = data.dataset_split_idx(all_data, budget)
print(
f'budget: {budget}, train data size: {len(train_data)}, test data size: {len(test_data)}'
)
for epochs in args.train_iterations:
if args.compare_supervised == 'T':
logger.info(
f'==== predictor type: SUPERVISED, load pretrain model False, '
f'search budget is {budget}. Training epoch is {epochs} ===='
)
spearman_corr, kendalltau_corr, duration = predictor_retrain_compare(
args,
'SS_RL',
train_data,
test_data,
flag=False,
train_epochs=epochs,
logger=logger)
if math.isnan(spearman_corr):
spearman_corr = 0
if math.isnan(kendalltau_corr):
kendalltau_corr = 0
s_results_dict[f'supervised#{budget}#{epochs}'].append(
spearman_corr)
k_results_dict[f'supervised#{budget}#{epochs}'].append(
kendalltau_corr)
for predictor_type, dir in zip(args.predictor_list,
args.load_dir):
logger.info(
f'==== predictor type: {predictor_type}, load pretrain model True. '
f'Search budget is {budget}. Training epoch is {epochs}. '
f'The model save dir is {dir.split("/")[-1][:-3]} ===='
)
spearman_corr, kendalltau_corr, duration = predictor_retrain_compare(
args,
predictor_type,
train_data,
test_data,
flag=True,
load_dir=dir,
train_epochs=epochs,
logger=logger)
if math.isnan(spearman_corr):
spearman_corr = 0
if math.isnan(kendalltau_corr):
kendalltau_corr = 0
s_results_dict[predictor_type + '#' + str(budget) + '#' +
str(epochs)].append(spearman_corr)
k_results_dict[predictor_type + '#' + str(budget) + '#' +
str(epochs)].append(kendalltau_corr)
file_id = random_id(6)
save_path = os.path.join(
args.save_dir,
f'{file_id}_{args.predictor_list[0]}_{args.search_space.split("_")[-1]}_{args.gpu}_{k}.pkl'
)
with open(save_path, 'wb') as fp:
pickle.dump(s_results_dict, fp)
pickle.dump(k_results_dict, fp)
