def main(_):
config_dic = flags.FLAGS.flag_values_dict()
config_dic["out_dir"] = os.path.join(flags.FLAGS.out_base_dir,
flags.FLAGS.model_name,
str(flags.FLAGS.run_id).zfill(2))
config = AttrDict(config_dic)
m(config)
def main(_):
config = flags.FLAGS
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
str(config.run_id).zfill(2))
m(config)
def main():
config = get_args()
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
str(config.run_id).zfill(2))
print("In main.....")
m(config)
def main(_):
config = flags.FLAGS
if config.nmn_cfg:
cfg_path = os.path.join("snmn/cfgs", config.run_id+'.yaml')
merge_cfg_from_file(cfg_path)
config.data_dir = os.path.join('data', config.dataset)
if config.mode == 'test':
config.input_keep_prob = 1.0
config.highway_keep_prob = 1.0
config.out_dir = os.path.join(config.out_base_dir, config.dataset, config.model_name, str(config.run_id).zfill(2))
if config.dataset == 'hotpotqa':
if config.emb_dim == 300:
config.data_dir = join(config.data_dir, '840b300d')
elif config.emb_dim == 100:
config.data_dir = join(config.data_dir, '6b100d')
else:
raise NotImplementedError
#if config.supervise_bridge_entity:
config.data_dir += '-bridge'
m(config)
def main(_):
config = flags.FLAGS
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
str(config.run_id).zfill(2))
try:
m(config)
except Exception as e:
exstr = traceback.format_exc()
print(repr(e))
with open('error.txt', 'w') as f:
f.write(exstr)
else:
print('c')
def main(_):
from basic.main import main as m
config = flags.FLAGS
config.model_name = "basic-class"
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
str(config.run_id).zfill(2))
print(config.out_dir)
evaluator = m(config)
"""Generating metrics for the squad model"""
metrics = {
"metrics": [
{
"name": "accuracy-score",
"numberValue": str(evaluator.acc),
"format": "RAW",
},
{
"name": "loss",
"numberValue": str(evaluator.loss),
"format": "RAW",
},
]
}
import json
with open(mlpipeline_metrics_path, "w") as f:
json.dump(metrics, f)
def main(_):
config = flags.FLAGS
if 'train' == config.mode:
# get logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('bidaf')
logger.setLevel(logging.INFO)
# saving path
subfolder_name = strftime("%Y-%m-%d___%H-%M-%S", gmtime())
config.out_base_dir = os.path.join(config.out_base_dir, subfolder_name)
if not os.path.exists(config.out_base_dir):
os.mkdir(config.out_base_dir)
else:
raise IOError('%s exist!' % config.out_base_dir)
log_file = os.path.join(config.out_base_dir, 'output.log')
logger.addHandler(logging.FileHandler(log_file))
logger.info('configurations in file:\n %s \n', vars(config))
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
str(config.run_id).zfill(2))
m(config)
def main(_):
config = flags.FLAGS
config.data_dir = os.path.join('data', config.dataset)
if config.mode == 'test':
config.input_keep_prob = 1.0
config.highway_keep_prob = 1.0
if config.read_topk_docs > 0:
config.use_ranked_docs = True
assert config.mac_prediction == 'candidates' or config.mac_prediction == 'span-single' \
or config.mac_prediction == 'span-dual'
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
config.dataset,
str(config.run_id).zfill(2))
if config.hierarchical_attn:
config.get_query_subject = True
if config.medhop:
config.data_dir = join(config.data_dir, "medhop")
config.num_steps = 3000
config.save_period = 100
config.log_period = 10
config.eval_period = 6000
config.val_num_batches = 0
if config.oracle == 'extra':
assert config.use_assembler
if config.split_supports is True:
config.data_dir = join(config.data_dir, 'split-supports')
if config.select_top_n_doc > 0 or config.use_ranked_docs:
if config.filter_by_annotations == 'single':
if config.emb_dim == 300:
config.data_dir = join(
config.data_dir,
'candi-2layer-tfidf-truncated500-300d840b-followsingle'
)
else:
config.data_dir = join(config.data_dir,
'candi-2layer-tfidf-followsingle')
elif config.filter_by_annotations == 'multiple':
if config.emb_dim == 300:
config.data_dir = join(
config.data_dir,
'candi-2layer-tfidf-truncated500-300d840b-followmultiple'
)
else:
config.data_dir = join(
config.data_dir, 'candi-2layer-tfidf-followmultiple')
elif config.filter_by_annotations == 'follow':
if config.emb_dim == 300:
config.data_dir = join(
config.data_dir,
'candi-2layer-tfidf-truncated500-300d840b-follow')
else:
config.data_dir = join(config.data_dir,
'candi-2layer-tfidf-follow')
else:
if config.emb_dim == 100:
config.data_dir = join(config.data_dir,
'candi-2layer-tfidf')
elif config.emb_dim == 300:
print('300')
if config.truncate_at == 500:
config.data_dir = join(
config.data_dir,
'candi-2layer-tfidf-truncated500-300d840b')
elif config.truncate_at == 300:
config.data_dir = join(
config.data_dir,
'candi-2layer-tfidf-truncated300-300d840b')
else:
assert False, ("Large model must uses truncated data.")
else:
raise NotImplementedError
else:
if config.filter_by_annotations == 'follow':
config.data_dir = join(config.data_dir, 'w-candi-follow')
elif config.filter_by_annotations == 'single':
config.data_dir = join(config.data_dir, 'w-candi-followsingle')
elif config.filter_by_annotations == 'multiple':
config.data_dir = join(config.data_dir,
'w-candi-followmultiple')
else:
if config.emb_dim == 100:
if config.use_doc_selector:
config.data_dir = join(config.data_dir, 'w-candi')
else:
config.data_dir = join(config.data_dir,
'candi-2layer-tfidf')
elif config.emb_dim == 300:
print('300')
if config.truncate_at == 500:
config.data_dir = join(
config.data_dir, 'w-candi-truncated500-300d840b')
elif config.truncate_at == 300:
config.data_dir = join(
config.data_dir, 'w-candi-truncated300-300d840b')
else:
assert False, ("Large model must uses truncated data.")
else:
raise NotImplementedError
else:
config.data_dir = join(config.data_dir, 'concat-supports')
m(config)
def main(_):
config = flags.FLAGS
config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2))
m(config)
