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- 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/full/", 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=1, bucket=config_data.bucket_test,
- path_matching=config_data.path_matching_test,
- max_len=config_data.max_length_formula,
- form_prepro=vocab.form_prepro,bucket_size=1)
-
-
- # 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(["{} {:04.2f}".format(k, v) for k, v in scores.items()])
- model.logger.info("- Eval Img: {}".format(msg))
-
-
- if __name__ == "__main__":
- main()
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