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53 lines
1.8 KiB
53 lines
1.8 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.general import Config
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from model.utils.text import Vocab, load_formulas
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from model.utils.image import greyscale, build_images
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from model.evaluation.text import score_files
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from model.evaluation.image import score_dirs
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@click.command()
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@click.option('--results', default="results/full/", help='Dir to results')
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def main(results):
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# restore config and model
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dir_output = results
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config_data = Config(dir_output + "data.json")
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config_vocab = Config(dir_output + "vocab.json")
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config_model = Config(dir_output + "model.json")
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vocab = Vocab(config_vocab)
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model = Img2SeqModel(config_model, dir_output, vocab)
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model.build_pred()
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model.restore_session(dir_output + "model.weights/")
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# load dataset
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test_set = DataGenerator(path_formulas=config_data.path_formulas_test,
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dir_images=config_data.dir_images_test, img_prepro=greyscale,
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max_iter=1, bucket=config_data.bucket_test,
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path_matching=config_data.path_matching_test,
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max_len=config_data.max_length_formula,
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form_prepro=vocab.form_prepro,bucket_size=1)
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# build images from formulas
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formula_ref = dir_output + "formulas_test/ref.txt"
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formula_hyp = dir_output + "formulas_test/hyp_0.txt"
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images_ref = dir_output + "images_test/ref/"
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images_test = dir_output + "images_test/hyp_0/"
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build_images(load_formulas(formula_ref), images_ref)
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build_images(load_formulas(formula_hyp), images_test)
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# score the repositories
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scores = score_dirs(images_ref, images_test, greyscale)
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msg = " - ".join(["{} {:04.2f}".format(k, v) for k, v in scores.items()])
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model.logger.info("- Eval Img: {}".format(msg))
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if __name__ == "__main__":
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main()
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