def apply(self, sample: Sample) -> Sample:
targets: str = sample.targets
outputs: str = sample.outputs
meta = sample.meta
if isinstance(outputs, Prediction):
prediction: Prediction = outputs
prediction.sentence = self._apply_single(prediction.sentence, meta)
return sample
elif isinstance(targets, dict) and "sentence" in targets:
targets["sentence"] = self._apply_single(targets["sentence"], meta)
return sample
elif isinstance(outputs, dict) and "sentence" in outputs:
outputs["sentence"] = self._apply_single(outputs["sentence"], meta)
return sample
else:
if targets:
sample = sample.new_targets(self._apply_single(targets, meta))
if outputs:
sample = sample.new_outputs(self._apply_single(outputs, meta))
return sample
def apply(self, sample: Sample) -> Sample:
if sample.targets and 'gt' in sample.targets:
sample.targets['sentence'] = "".join(
self.data_params.codec.decode(sample.targets['gt']))
if sample.outputs:
def decode(suffix):
outputs = self.ctc_decoder.decode(
sample.outputs['softmax' + suffix].astype(float))
outputs.labels = list(map(int, outputs.labels))
outputs.sentence = "".join(
self.data_params.codec.decode(outputs.labels))
return outputs
outputs = decode("")
outputs.voter_predictions = []
for i in range(self.data_params.ensemble):
outputs.voter_predictions.append(decode(f"_{i}"))
sample = sample.new_outputs(outputs)
return sample
