train.py

# Copyright (c) 2020 Sarthak Mittal
#
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# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
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# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
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#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.

import os
import argparse

from invoicenet import FIELDS
from invoicenet.common import trainer
from invoicenet.acp.acp import AttendCopyParse
from invoicenet.acp.data import InvoiceData


def main():
    ap = argparse.ArgumentParser()

    ap.add_argument("--field", type=str,  choices=FIELDS.keys(),
                    help="field to train parser for")
    ap.add_argument("--batch_size", type=int, default=8,
                    help="batch size for training")
    ap.add_argument("--restore", action="store_true",
                    help="restore from checkpoint")
    ap.add_argument("--data_dir", type=str, default='processed_data/',
                    help="path to directory containing prepared data")
    ap.add_argument("--steps", type=int, default=50000,
                    help="maximum number of training steps")
    ap.add_argument("--early_stop_steps", type=int, default=0,
                    help="stop training if validation doesn't improve "
                         "for a given number of steps, disabled when 0 (default)")

    args = ap.parse_args()

    train_data = InvoiceData.create_dataset(field=args.field,
                                            data_dir=os.path.join(args.data_dir, 'train/'),
                                            batch_size=args.batch_size)
    val_data = InvoiceData.create_dataset(field=args.field,
                                          data_dir=os.path.join(args.data_dir, 'val/'),
                                          batch_size=args.batch_size)

    print("Training...")
    trainer.train(
        model=AttendCopyParse(field=args.field, restore=args.restore),
        train_data=train_data,
        val_data=val_data,
        total_steps=args.steps,
        early_stop_steps=args.early_stop_steps
    )


if __name__ == '__main__':
    main()