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51 regels
1.7 KiB
51 regels
1.7 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
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from model.utils.image import greyscale
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from model.utils.text import load_formulas
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from model.evaluation.text import score_files
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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.weights4/test-model.ckpt")
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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,
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max_iter=3000, 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)
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# use model to write predictions in files
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config_eval = Config({"dir_answers": dir_output + "formulas_test/",
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"batch_size": 20})
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files, perplexity = model.write_prediction(config_eval, test_set)
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formula_ref, formula_hyp = files[0], files[1]
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# score the ref and prediction files
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scores = score_files(formula_ref, formula_hyp)
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scores["perplexity"] = perplexity
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msg = " - ".join(["{} {:04.2f}".format(k, v) for k, v in scores.items()])
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model.logger.info("- Test Txt: {}".format(msg))
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if __name__ == "__main__":
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main()
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