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config.py
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import argparse
import pprint
import yaml
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
class Config(object):
def __init__(self, **kwargs):
"""Configuration Class: set kwargs as class attributes with setattr"""
for k, v in kwargs.items():
setattr(self, k, v)
@property
def config_str(self):
return pprint.pformat(self.__dict__)
def __repr__(self):
"""Pretty-print configurations in alphabetical order"""
config_str = 'Configurations\n'
config_str += self.config_str
return config_str
def save(self, path):
with open(path, 'w') as f:
yaml.dump(self.__dict__, f, default_flow_style=False)
@classmethod
def load(cls, path):
with open(path, 'r') as f:
kwargs = yaml.load(f)
return Config(**kwargs)
def read_config(path):
return Config.load(path)
def get_config(parse=True, **optional_kwargs):
"""
Get configurations as attributes of class
1. Parse configurations with argparse.
2. Create Config class initilized with parsed kwargs.
3. Return Config class.
"""
parser = argparse.ArgumentParser()
# Training
parser.add_argument('--epochs', type=int, default=20,
help='num_epochs')
parser.add_argument('--batch_size', type=int, default=64,
help='batch size')
parser.add_argument('--lr', type=float, default=0.001,
help='learning rate')
parser.add_argument('--clip', type=float, default=1.0,
help='gradient clip norm')
parser.add_argument('--max_len', type=int, default=40,
help='gradient clip norm')
parser.add_argument('--optim', type=str, default='adamw',
choices=['adam', 'amsgrad', 'adagrad','adamw'],
help='optimizer')
parser.add_argument('--loss_fn', type=str, default='triplet',
choices=['triplet', 'cosine', 'custom_triplet'],
help='loss function')
parser.add_argument('--dropout', type=float, default=0.2)
parser.add_argument('--embed_dim', type=int, default=100)
parser.add_argument('--freeze', type=bool, default=False)
parser.add_argument('--space_joiner', type=bool, default=True)
parser.add_argument('--seed', type=int, default=2804,
help='Random seed')
parser.add_argument('--PRE_TRAINED_MODEL_NAME', default='allenai/biomed_roberta_base',
help='huggingface model name')
# Data placeholder
parser.add_argument('--train_dir', default='./data/triplet_data.csv')
parser.add_argument('--val_dir', default='./data/test_stroke.csv')
parser.add_argument('--save_dir', default='./ckpt/')
parser.add_argument('--log_dir', default='./log/log.txt')
parser.add_argument('--model_path', default='./ckpt/best_model_state_v1_triplet.bin')
parser.add_argument('--train_threshold', default=0.5)
parser.add_argument('--val_threshold', default=0.5)
parser.add_argument('--print_every', default=30)
parser.add_argument('--use_aux', default=False)
parser.add_argument('--use_aug_data', default=False)
if parse:
kwargs = parser.parse_args()
else:
kwargs = parser.parse_known_args()[0]
# Namespace => Dictionary
kwargs = vars(kwargs)
kwargs.update(optional_kwargs)
return Config(**kwargs)