def decode(self, x, beamsize=5, threshold=1e-3, qscores=False, return_path=False):
if beamsize == 1 or qscores:
seq, path = viterbi_search(x, self.alphabet, qscores, self.qscale, self.qbias)
else:
seq, path = beam_search(x, self.alphabet, beamsize, threshold)
if return_path: return seq, path
return seq
def decode(x, beamsize=5, threshold=1e-3, qscores=False, return_path=False):
"""decode the output of CNN to ACTG
Args:
x:
beamsize:
threshold:
qscores:
return_path:
Returns:
"""
alphabet = ["N", "A", "C", "G", "T"]
if beamsize == 1 or qscores:
qbias = 2.0
qscale = 0.7
seq, path = viterbi_search(x, alphabet, qscores, qscale, qbias)
else:
qbias = 0.0
qscale = 1.0
seq, path = beam_search(x, alphabet, beamsize, threshold)
if return_path:
return seq, path
return seq
def decode(self, x, beamsize=5, threshold=1e-3, qscores=False, return_path=False):
x = x.exp().cpu().numpy().astype(np.float32)
if beamsize == 1 or qscores:
seq, path = viterbi_search(x, self.alphabet, qscores, self.qscale, self.qbias)
else:
seq, path = beam_search(x, self.alphabet, beamsize, threshold)
if return_path: return seq, path
return seq
def process(self, raw, identifiers, frame_meta):
if not self.label_map:
raise ConfigError('Beam Search Decoder requires dataset label map for correct decoding.')
alphabet = list(self.label_map.values())
raw_outputs = self._extract_predictions(raw, frame_meta)
self.select_output_blob(raw_outputs)
result = []
for identifier, out in zip(identifiers, np.exp(raw_outputs[self.output_blob])):
if self.beam_size == 1:
seq, _ = viterbi_search(np.squeeze(out), alphabet, False, 1, 0)
else:
seq, _ = beam_search(np.squeeze(out.astype(np.float32)), alphabet, self.beam_size, self.threshold)
result.append(DNASequencePrediction(identifier, seq))
return result
def decode_ctc_greedy_rust(predictions, labels, *args):
"""
Argmax decoder with collapsing repeats in Rust
"""
seq, path = viterbi_search(predictions, labels)
return seq
