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70 lines
2.5 KiB
70 lines
2.5 KiB
from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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import ctc_beam_search_decoder as tested_decoder
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def test_beam_search_decoder():
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max_time_steps = 6
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beam_size = 20
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num_results_per_sample = 20
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input_prob_matrix_0 = np.asarray(
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[
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[0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908],
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[0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581, 0.230517],
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[0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289, 0.238763],
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[0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655],
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[0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878],
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# Random entry added in at time=5
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[0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]
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],
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dtype=np.float32)
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# Add arbitrary offset - this is fine
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input_log_prob_matrix_0 = np.log(input_prob_matrix_0) #+ 2.0
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# len max_time_steps array of batch_size x depth matrices
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inputs = ([
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input_log_prob_matrix_0[t, :][np.newaxis, :]
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for t in range(max_time_steps)
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])
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inputs_t = [ops.convert_to_tensor(x) for x in inputs]
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inputs_t = array_ops.stack(inputs_t)
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# run CTC beam search decoder in tensorflow
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with tf.Session() as sess:
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decoded, log_probabilities = tf.nn.ctc_beam_search_decoder(
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inputs_t, [max_time_steps],
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beam_width=beam_size,
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top_paths=num_results_per_sample,
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merge_repeated=False)
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tf_decoded = sess.run(decoded)
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tf_log_probs = sess.run(log_probabilities)
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# run tested CTC beam search decoder
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beam_result = tested_decoder.ctc_beam_search_decoder(
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input_probs_matrix=input_prob_matrix_0,
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beam_size=beam_size,
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blank_id=5, # default blank_id in tensorflow decoder is (num classes-1)
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space_id=4, # doesn't matter
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max_time_steps=max_time_steps,
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num_results_per_sample=num_results_per_sample)
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# compare decoding result
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print(
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"{tf_decoder log probs} \t {tested_decoder log probs}: {tf_decoder result} {tested_decoder result}"
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)
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for index in range(len(beam_result)):
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print(('%6f\t%6f: ') % (tf_log_probs[0][index], beam_result[index][0]),
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tf_decoded[index].values, ' ', beam_result[index][1])
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if __name__ == '__main__':
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test_beam_search_decoder()
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