parsing/src/test.py

import argparse
from benepar import nkutil

def make_hparams():
    return nkutil.HParams(
        # Data processing
        max_len_train=0,  # no length limit
        max_len_dev=0,  # no length limit
        # Optimization
        batch_size=32,
        learning_rate=0.00005,
        learning_rate_warmup_steps=160,
        clip_grad_norm=0.0,  # no clipping
        checks_per_epoch=4,
        step_decay_factor=0.5,
        step_decay_patience=5,
        max_consecutive_decays=3,  # establishes a termination criterion
        # CharLSTM
        use_chars_lstm=False,
        d_char_emb=64,
        char_lstm_input_dropout=0.2,
        # BERT and other pre-trained models
        use_pretrained=False,
        pretrained_model="bert-base-uncased",
        # Partitioned transformer encoder
        use_encoder=False,
        d_model=1024,
        num_layers=8,
        num_heads=8,
        d_kv=64,
        d_ff=2048,
        encoder_max_len=512,
        # Dropout
        morpho_emb_dropout=0.2,
        attention_dropout=0.2,
        relu_dropout=0.1,
        residual_dropout=0.2,
        # Output heads and losses
        force_root_constituent="auto",
        predict_tags=False,
        d_label_hidden=256,
        d_tag_hidden=256,
        tag_loss_scale=5.0,
    )

def run_train(args, hparams):
    print("Train:")
    print("dimension of attention key value: {}".format(hparams.d_kv))
    print("use pretrained: {}".format(args.use_pretrained))
    print("text processing mode: {}".format(args.text_processing))

def run_test(args):
    print("Test:")
    print("text processing mode: {}".format(args.text_processing))

def main():
    print("running...")
    parser = argparse.ArgumentParser()
    subparsers = parser.add_subparsers()

    hparams = make_hparams()
    subparser = subparsers.add_parser('train')
    subparser.set_defaults(callback=lambda args: run_train(args, hparams))

    hparams.populate_arguments(subparser)
    subparser.add_argument("--text-processing", default='default')

    subparser = subparsers.add_parser("test")
    subparser.set_defaults(callback=run_test)
    subparser.add_argument("--test", required=True)
    subparser.add_argument("--text-processing", default='default')

    args = parser.parse_args()
    print(args.__dict__)
    args.callback(args)

if __name__ == '__main__':
    main()