Algin the tensorflow graph optimization output log format with quantization feature.
@@ -183,9 +183,11 @@ class AutoMixedPrecisionTuneStrategy(TuneStrategy):
self.baseline = self._evaluate(self.model)
# record the FP32 baseline
self._add_tuning_history()
- logger.info('FP32 baseline is: ' +
- ('[{:.4f}, {:.4f}]'.format(*self.baseline)
- if self.baseline else 'None'))
+ baseline_msg = '[accuracy: {:.4f}, {}: {:.4f}]'.format(self.baseline[0],
+ str(self.objective.measurer),
+ self.baseline[1]) \
+ if self.baseline else 'n/a'
+ logger.info('FP32 baseline is: {}'.format(baseline_msg))
trials_count = 0
for tune_cfg in self.next_tune_cfg():
