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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
- from model.utils.image import greyscale
-
- from model.utils.text import load_formulas
- from model.evaluation.text import score_files
-
-
- @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.weights4/test-model.ckpt")
-
- # load dataset
- test_set = DataGenerator(path_formulas=config_data.path_formulas_test,
- dir_images=config_data.dir_images_test,
- max_iter=3000, bucket=config_data.bucket_test,
- path_matching=config_data.path_matching_test,
- max_len=config_data.max_length_formula,
- form_prepro=vocab.form_prepro)
-
- # use model to write predictions in files
- config_eval = Config({"dir_answers": dir_output + "formulas_test/",
- "batch_size": 20})
- files, perplexity = model.write_prediction(config_eval, test_set)
- formula_ref, formula_hyp = files[0], files[1]
-
- # score the ref and prediction files
- scores = score_files(formula_ref, formula_hyp)
- scores["perplexity"] = perplexity
- msg = " - ".join(["{} {:04.2f}".format(k, v) for k, v in scores.items()])
- model.logger.info("- Test Txt: {}".format(msg))
-
-
- if __name__ == "__main__":
- main()
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