def _postprocess(self, cls_outputs, box_outputs, scales, mode='global'):
"""Postprocess class and box predictions."""
if not mode:
return cls_outputs, box_outputs
if mode == 'global':
return postprocess.postprocess_global(self.config.as_dict(),
cls_outputs, box_outputs,
scales)
if mode == 'per_class':
return postprocess.postprocess_per_class(self.config.as_dict(),
cls_outputs, box_outputs,
scales)
if mode == 'combined':
return postprocess.postprocess_combined(self.config.as_dict(),
cls_outputs, box_outputs,
scales)
if mode == 'tflite':
if scales is not None:
# pre_mode should be None for TFLite.
raise ValueError(
'scales not supported for TFLite post-processing')
return postprocess.postprocess_tflite(self.config.as_dict(),
cls_outputs, box_outputs)
raise ValueError('Unsupported postprocess mode {}'.format(mode))
def _postprocess(self, cls_outputs, box_outputs, scales, mode='global'):
"""Postprocess class and box predictions."""
if not mode:
return cls_outputs, box_outputs
# TODO(tanmingxing): remove this cast once FP16 works postprocessing.
cls_outputs = [tf.cast(i, tf.float32) for i in cls_outputs]
box_outputs = [tf.cast(i, tf.float32) for i in box_outputs]
if mode == 'global':
return postprocess.postprocess_global(self.config.as_dict(),
cls_outputs, box_outputs,
scales)
if mode == 'per_class':
return postprocess.postprocess_per_class(self.config.as_dict(),
cls_outputs, box_outputs,
scales)
if mode == 'tflite':
if scales is not None:
# pre_mode should be None for TFLite.
raise ValueError(
'scales not supported for TFLite post-processing')
return postprocess.postprocess_tflite(self.config.as_dict(),
cls_outputs, box_outputs)
raise ValueError('Unsupported postprocess mode {}'.format(mode))