evaluate_img.py

import click


from model.utils.data_generator import DataGenerator
from model.img2seq import Img2SeqModel
from model.utils.general import Config
from model.utils.text import Vocab, load_formulas
from model.utils.image import greyscale, build_images

from model.evaluation.text import score_files
from model.evaluation.image import score_dirs


@click.command()
@click.option('--results', default="results/small/", help='Dir to results')
def main(results):
    # restore config and model
    dir_output = results

    config_data = Config(dir_output + "data.json")
    config_vocab = Config(dir_output + "vocab.json")
    config_model = Config(dir_output + "model.json")

    vocab = Vocab(config_vocab)
    model = Img2SeqModel(config_model, dir_output, vocab)
    model.build_pred()
    # model.restore_session(dir_output + "model_weights/")

    # load dataset
    test_set = DataGenerator(path_formulas=config_data.path_formulas_test,
                             dir_images=config_data.dir_images_test,
                             img_prepro=greyscale,
                             max_iter=config_data.max_iter,
                             bucket=config_data.bucket_test,
                             path_matching=config_data.path_matching_test,
                             max_len=config_data.max_length_formula,
                             form_prepro=vocab.form_prepro,)

    # build images from formulas
    formula_ref = dir_output + "formulas_test/ref.txt"
    formula_hyp = dir_output + "formulas_test/hyp_0.txt"
    images_ref = dir_output + "images_test/ref/"
    images_test = dir_output + "images_test/hyp_0/"
    build_images(load_formulas(formula_ref), images_ref)
    build_images(load_formulas(formula_hyp), images_test)

    # score the repositories
    scores = score_dirs(images_ref, images_test, greyscale)
    msg = " || ".join(["{} is {:04.2f}".format(k, v) for k, v in scores.items()])
    model.logger.info("- Eval Img: {}".format(msg))


if __name__ == "__main__":
    main()