vai_q_tensorflow Quantize Finetuning - 1.2 English

Vitis AI User Guide (UG1414)
Document ID
UG1414
Release Date
2020-07-21
Version
1.2 English

Generally, there is a small accuracy loss after quantization, but for some networks such as Mobilenets, the accuracy loss can be large. In this situation, quantize finetuning can be used to further improve the accuracy of quantized models.

APIs

There are 3 APIs for quantize finetuning in Python package tf.contrib.decent_q.

tf.contrib.decent_q.CreateQuantizeTrainingGraph(config)

Convert the float training graph to quantize training graph, this is done by in-place rewriting on the default graph.

Arguments:

  • config: A tf.contrib.decent_q.QuantizeConfig object, containing the configurations for quantization.
tf.contrib.decent_q.CreateQuantizeEvaluationGraph(config)

Convert the float evaluation graph to quantize evaluation graph, this is done by in-place rewriting on the default graph.

Arguments:

  • config: A tf.contrib.decent_q.QuantizeConfig object, containing the configurations for quantization.
tf.contrib.decent_q.CreateQuantizeDeployGraph(checkpoint, config)

Freeze the checkpoint into the quantize evaluation graph and convert the quantize evaluation graph to deploy graph.

Arguments:

  • checkpoint: A string object, the path to checkpoint folder of file.
  • config: A tf.contrib.decent_q.QuantizeConfig object, containing the configurations for quantization.