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62 righe
2.3 KiB
62 righe
2.3 KiB
import click
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from model.utils.data_generator import DataGenerator
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from model.img2seq import Img2SeqModel
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from model.utils.lr_schedule import LRSchedule
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from model.utils.general import Config
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from model.utils.text import Vocab
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from model.utils.image import greyscale
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@click.command()
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@click.option('--data', default="configs/data.json",
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help='Path to data json config')
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@click.option('--vocab', default="configs/vocab.json",
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help='Path to vocab json config')
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@click.option('--training', default="configs/training.json",
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help='Path to training json config')
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@click.option('--model', default="configs/model.json",
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help='Path to model json config')
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@click.option('--output', default="results/full/",
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help='Dir for results and model weights')
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def main(data, vocab, training, model, output):
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# Load configs
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dir_output = output
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config = Config([data, vocab, training, model])
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config.save(dir_output)
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vocab = Vocab(config)
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# Load datasets
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train_set = DataGenerator(path_formulas=config.path_formulas_train,
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dir_images=config.dir_images_train,
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max_iter=config.max_iter, bucket=config.bucket_train,
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path_matching=config.path_matching_train,
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max_len=config.max_length_formula,
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form_prepro=vocab.form_prepro)
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val_set = DataGenerator(path_formulas=config.path_formulas_val,
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dir_images=config.dir_images_val,
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max_iter=config.max_iter, bucket=config.bucket_val,
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path_matching=config.path_matching_val,
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max_len=config.max_length_formula,
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form_prepro=vocab.form_prepro)
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# Define learning rate schedule
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n_batches_epoch = ((len(train_set) + config.batch_size - 1) //
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config.batch_size)
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lr_schedule = LRSchedule(lr_init=config.lr_init,
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start_decay=config.start_decay*n_batches_epoch,
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end_decay=config.end_decay*n_batches_epoch,
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end_warm=config.end_warm*n_batches_epoch,
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lr_warm=config.lr_warm,
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lr_min=config.lr_min)
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# Build model and train
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model = Img2SeqModel(config, dir_output, vocab)
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model.build_train(config)
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#model.restore_session(dir_output + "model.weights/test-model.ckpt")
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model.train(config, train_set, val_set, lr_schedule)
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if __name__ == "__main__":
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main()
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