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| """Script to evaluate a trained Attention OCR model.
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| A simple usage example:
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| python eval.py
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| """
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| import tensorflow as tf
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| from tensorflow.contrib import slim
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| from tensorflow import app
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| from tensorflow.compat.v1 import flags
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| import data_provider
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| import common_flags
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| FLAGS = flags.FLAGS
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| common_flags.define()
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| flags.DEFINE_integer('num_batches', 100,
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| 'Number of batches to run eval for.')
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| flags.DEFINE_string('eval_log_dir', '/tmp/attention_ocr/eval',
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| 'Directory where the evaluation results are saved to.')
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| flags.DEFINE_integer('eval_interval_secs', 60,
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| 'Frequency in seconds to run evaluations.')
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| flags.DEFINE_integer('number_of_steps', None,
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| 'Number of times to run evaluation.')
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| def main(_):
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| if not tf.io.gfile.exists(FLAGS.eval_log_dir):
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| tf.io.gfile.makedirs(FLAGS.eval_log_dir)
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| dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
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| model = common_flags.create_model(dataset.num_char_classes,
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| dataset.max_sequence_length,
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| dataset.num_of_views, dataset.null_code)
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| data = data_provider.get_data(
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| dataset,
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| FLAGS.batch_size,
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| augment=False,
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| central_crop_size=common_flags.get_crop_size())
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| endpoints = model.create_base(data.images, labels_one_hot=None)
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| model.create_loss(data, endpoints)
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| eval_ops = model.create_summaries(
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| data, endpoints, dataset.charset, is_training=False)
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| slim.get_or_create_global_step()
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| session_config = tf.compat.v1.ConfigProto(device_count={"GPU": 0})
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| slim.evaluation.evaluation_loop(
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| master=FLAGS.master,
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| checkpoint_dir=FLAGS.train_log_dir,
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| logdir=FLAGS.eval_log_dir,
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| eval_op=eval_ops,
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| num_evals=FLAGS.num_batches,
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| eval_interval_secs=FLAGS.eval_interval_secs,
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| max_number_of_evaluations=FLAGS.number_of_steps,
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| session_config=session_config)
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| if __name__ == '__main__':
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| app.run()
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|
