(Optional) Exporting the Quantized Model to ONNX - 3.0 English

The following codes show how to perform post-training quantization and export the quantized model to onnx with vai_q_tensorflow2 API.

model = tf.keras.models.load_model(‘float_model.h5’)
from tensorflow_model_optimization.quantization.keras import vitis_quantize
quantizer = vitis_quantize.VitisQuantizer(model)
quantized_model = quantizer.quantize_model(calib_dataset=calib_dataset, 
                                           output_format='onnx',
                                           onnx_opset_version=11,
                                           output_dir='./quantize_results',
                                           **kwargs) 

output_format

A string object, indicates what format to save the quantized model. Options are: '' for skip saving, 'h5' for saving .h5 file, 'tf' for saving saved_model file, 'onnx' for saving .onnx file. Default to ''.

onnx_opset_version

An int object, the ONNX opset version. Take effect only when output_format is 'onnx'. Default to 11.

output_dir

A string object, indicates the directory to save the quantized model in. Default to './quantize_results'.