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config_parser.py
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config_parser.py
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import argparse
from email.policy import default
def parser_args():
parser = argparse.ArgumentParser()
parser.add_argument('--mode',
type=str,
choices=['train', 'test'],
default='train')
parser.add_argument('--train_1', required=True, help='path to dataset')
parser.add_argument('--train_2', required=False, help='path to dataset')
parser.add_argument('--unl_train_1',
required=False,
help="path to unlabeled dataset")
parser.add_argument('--unl_train_2',
required=False,
help="path to unlabeled dataset")
parser.add_argument('--unl_train_3',
required=False,
help="path to unlabeled dataset")
parser.add_argument('--batchSize',
type=int,
default=384,
help='input batch size')
parser.add_argument('--unl_batchSize',
type=int,
default=288,
help='input batch size')
parser.add_argument('--eval_data',
default='eval_and_val/evaluation/',
help='path to validation dataset')
parser.add_argument('--valid_data',
default='eval_and_val/validation/',
help='path to validation dataset')
parser.add_argument('--workers',
type=int,
default=4,
help='number of data loading workers')
parser.add_argument('--unl_workers',
type=int,
default=4,
help='number of data loading workers')
parser.add_argument('--num_iter',
type=int,
default=250000,
help='number of iterations to train for')
parser.add_argument('--val_interval',
type=int,
default=2000,
help='Interval between each validation')
parser.add_argument('--grad_clip',
type=float,
default=5,
help='gradient clipping value. default=5')
""" Optimizer """
parser.add_argument('--optimizer',
type=str,
default='adam',
help='optimizer |sgd|adadelta|adam|')
parser.add_argument(
'--lr',
type=float,
default=0.001,
help='learning rate, default=1.0 for Adadelta, 0.0005 for Adam')
parser.add_argument('--weight_decay', default=0.0, type=float)
parser.add_argument(
'--schedule',
default='super',
nargs='*',
help=
'(learning rate schedule. default is super for super convergence, 1 for None, [0.6, 0.8] for the same setting with ASTER'
)
parser.add_argument('--model_name',
type=str,
required=True,
help='CRNN|TRBA')
parser.add_argument('--num_fiducial',
type=int,
default=20,
help='number of fiducial points of TPS-STN')
parser.add_argument(
'--input_channel',
type=int,
default=3,
help='the number of input channel of Feature extractor')
parser.add_argument(
'--output_channel',
type=int,
default=512,
help='the number of output channel of Feature extractor')
parser.add_argument('--hidden_size',
type=int,
default=256,
help='the size of the LSTM hidden state')
parser.add_argument('--batch_max_length',
type=int,
default=25,
help='maximum-label-length')
parser.add_argument('--imgH',
type=int,
default=32,
help='the height of the input image')
parser.add_argument('--imgW',
type=int,
default=100,
help='the width of the input image')
parser.add_argument(
'--character',
type=str,
default=
'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~',
help='character label')
parser.add_argument('--NED',
action='store_true',
help='For Normalized edit_distance')
parser.add_argument('--Aug',
type=str,
default='rand',
choices=['rand', 'None', 'weak'])
parser.add_argument('--exp_name', help='Where to store logs and models')
parser.add_argument('--manual_seed',
type=int,
default=111,
help='for random seed setting')
parser.add_argument('--saved_model',
default='',
help="path to model to continue training")
parser.add_argument('--displayInterval', default=100, type=int)
parser.add_argument('--checkpoint_dir', default="try", type=str)
""" Semi-supervised learning """
parser.add_argument(
'--semi',
type=str,
default='None',
help='whether to use semi-supervised learning |None|KLDiv|CrossEntropy|'
)
parser.add_argument('--Aug_semi',
type=str,
default='rand',
choices=['rand', 'None', 'weak'])
parser.add_argument('--ema_alpha',
type=float,
default=0.999,
help='EMA decay')
# for semi supervised
parser.add_argument('--lambda_cons',
type=float,
default=1,
help='Mean Teacher consistency weight')
parser.add_argument('--lambda_mmd', default=0.01, type=float)
parser.add_argument('--confident_threshold', default=0.5, type=float)
parser.add_argument('--l_confident_threshold', default=0.6, type=float)
parser.add_argument('--uda_softmax_temp', default=0.4, type=float)
parser.add_argument('--eval_type', default="simple")
parser.add_argument('--projection_type',
type=str,
choices=['pff', 'linear'],
default='pff')
opt = parser.parse_args()
if opt.model_name == 'CRNN': # CRNN = NVBC
opt.Transformation = 'None'
opt.FeatureExtraction = 'VGG'
opt.SequenceModeling = 'BiLSTM'
opt.Prediction = 'CTC'
elif opt.model_name == 'TRBA': # TRBA
opt.Transformation = 'TPS'
opt.FeatureExtraction = 'ResNet'
opt.SequenceModeling = 'BiLSTM'
opt.Prediction = 'Attn'
elif opt.model_name == 'RBA': # RBA
opt.Transformation = 'None'
opt.FeatureExtraction = 'ResNet'
opt.SequenceModeling = 'BiLSTM'
opt.Prediction = 'Attn'
opt.run_code_root = "saved_models"
opt.checkpoint_root = "saved_models"
return opt