def test_simple_decode(self):
aa = torch.FloatTensor(np.array([[[1.0, 0.0]], [[1.0, 0.0]], [[0.0, 1.0]], [[1.0, 0.0]], [[1.0, 0.0]]], dtype=np.float32)).log()
seq_len = torch.IntTensor(np.array([5], dtype=np.int32))
labels="A_"
scorer = pytorch_ctc.Scorer()
decoder_merge = pytorch_ctc.CTCBeamDecoder(scorer, labels, blank_index=1, space_index=-1, top_paths=1, beam_width=1, merge_repeated=True)
decoder_nomerge = pytorch_ctc.CTCBeamDecoder(scorer, labels, blank_index=1, space_index=-1, top_paths=1, beam_width=1, merge_repeated=False)
result_merge, _, result_merge_len = decoder_merge.decode(aa, seq_len)
result_nomerge, _, result_nomerge_len = decoder_nomerge.decode(aa, seq_len)
self.assertEqual(result_merge_len[0][0], 1)
self.assertEqual(result_nomerge_len[0][0], 2)
self.assertEqual(result_merge.numpy()[0,0,:result_merge_len[0][0]].tolist(), [0])
self.assertEqual(result_nomerge.numpy()[0,0,:result_nomerge_len[0][0]].tolist(), [0, 0])
def test_ctc_decoder_beam_search(self):
depth = 6
seq_len_0 = 5
input_prob_matrix_0 = np.asarray(
[
[0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908],
[
0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581,
0.230517
],
[
0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289,
0.238763
],
[0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655],
[0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878],
# Random entry added in at time=5
[0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]
],
dtype=np.float32)
# Add arbitrary offset - this is fine
input_log_prob_matrix_0 = np.log(input_prob_matrix_0) + 2.0
# len max_time_steps array of batch_size x depth matrices
inputs = np.array(
[
input_log_prob_matrix_0[t, :][np.newaxis, :]
for t in range(seq_len_0)
] # Pad to max_time_steps = 8
+ 2 * [np.zeros((1, depth), dtype=np.float32)],
dtype=np.float32)
# batch_size length vector of sequence_lengths
seq_lens = np.array([seq_len_0], dtype=np.int32)
th_input = torch.from_numpy(inputs)
th_seq_len = torch.IntTensor(seq_lens)
labels = "ABCDE_"
scorer = pytorch_ctc.Scorer()
decoder = pytorch_ctc.CTCBeamDecoder(scorer,
labels,
blank_index=5,
space_index=-1,
top_paths=2,
beam_width=2)
decode_result, scores, decode_len, alignments = decoder.decode(
th_input, th_seq_len)
self.assertEqual(decode_len[0][0], 2)
self.assertEqual(decode_len[1][0], 3)
self.assertEqual(
decode_result.numpy()[0, 0, :decode_len[0][0]].tolist(), [1, 0])
self.assertEqual(
decode_result.numpy()[1, 0, :decode_len[1][0]].tolist(), [0, 1, 0])
np.testing.assert_almost_equal(scores.numpy(),
np.array([[-0.584855], [-0.389139]]), 5)
def test_simple_decode_different_blank_idx(self):
aa = torch.FloatTensor(
np.array([[[0.0, 1.0]], [[0.0, 1.0]], [[1.0, 0.0]], [[0.0, 1.0]],
[[0.0, 1.0]]],
dtype=np.float32)).log()
seq_len = torch.IntTensor(np.array([5], dtype=np.int32))
labels = "_A"
scorer = pytorch_ctc.Scorer()
decoder_nomerge = pytorch_ctc.CTCBeamDecoder(scorer,
labels,
blank_index=0,
space_index=-1,
top_paths=1,
beam_width=1)
result_nomerge, _, result_nomerge_len, nomerge_alignments = decoder_nomerge.decode(
aa, seq_len)
self.assertEqual(result_nomerge_len[0][0], 2)
self.assertEqual(
result_nomerge.numpy()[0, 0, :result_nomerge_len[0][0]].tolist(),
[1, 1])
def __init__(self,
labels,
scorer,
top_paths=1,
beam_width=20,
output='char',
space_idx=-1):
super(BeamDecoder_test, self).__init__(space_idx)
self.labels = labels
self.output = output
assert top_paths == 1, "Only supports top 1 path in the current version"
try:
import pytorch_ctc
except ImportError:
raise ImportError("BeamCTCDecoder requires pytorch_ctc package.")
self._decoder = pytorch_ctc.CTCBeamDecoder(scorer=scorer,
labels=self.labels,
top_paths=top_paths,
beam_width=beam_width,
blank_index=0,
space_index=self.space_idx,
merge_repeated=False)