/AIML'/im2latex-master/evaluate_img.py

evaluate_img.py

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      "source": "from model.utils.data_generator import DataGenerator\nfrom model.img2seq import Img2SeqModel\nfrom model.utils.general import Config\nfrom model.utils.text import Vocab, load_formulas\nfrom model.utils.image import greyscale, build_images",
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      "source": "from model.evaluation.text import score_files\nfrom model.evaluation.image import score_dirs",
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      "source": "@click.command()\[email protected]('--results', default=\"results/small/\", help='Dir to results')\ndef main(results):\n    # restore config and model\n    dir_output = results\n\n    config_data  = Config(dir_output + \"data.json\")\n    config_vocab = Config(dir_output + \"vocab.json\")\n    config_model = Config(dir_output + \"model.json\")\n\n    vocab = Vocab(config_vocab)\n    model = Img2SeqModel(config_model, dir_output, vocab)\n    model.build_pred()\n    model.restore_session(dir_output + \"model.weights/\")\n\n    # load dataset\n    test_set = DataGenerator(path_formulas=config_data.path_formulas_test,\n            dir_images=config_data.dir_images_test, img_prepro=greyscale,\n            max_iter=config_data.max_iter, bucket=config_data.bucket_test,\n            path_matching=config_data.path_matching_test,\n            max_len=config_data.max_length_formula,\n            form_prepro=vocab.form_prepro,)\n\n\n    # build images from formulas\n    formula_ref = dir_output + \"formulas_test/ref.txt\"\n    formula_hyp = dir_output + \"formulas_test/hyp_0.txt\"\n    images_ref  = dir_output + \"images_test/ref/\"\n    images_test = dir_output + \"images_test/hyp_0/\"\n    build_images(load_formulas(formula_ref), images_ref)\n    build_images(load_formulas(formula_hyp), images_test)\n\n    # score the repositories\n    scores = score_dirs(images_ref, images_test, greyscale)\n    msg = \" - \".join([\"{} {:04.2f}\".format(k, v) for k, v in scores.items()])\n    model.logger.info(\"- Eval Img: {}\".format(msg))",
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