vitis_quantize.VitisQuantizer.get_qat_model - 1.4.1 English

This function gets the float model for QAT.

vitis_quantize.VitisQuantizer.get_qat_model(
    init_quant=False,
    calib_dataset=None,
    calib_batch_size=None,
    calib_steps=None,
    train_with_bn=False,
    freeze_bn_delay=-1,
    replace_sigmoid=True,
    replace_relu6=True,
    include_cle=True,
    cle_steps=10,
    forced_cle=False)

Arguments

init_quant

A bool object to notify whether or not to run initial quantization before QAT. Running an initial PTQ quantization yields an improved initial state for the quantizer parameters, especially for 8bit_tqt strategy. Otherwise, the training may not converge.

calib_dataset

A tf.data.Dataset, keras.utils.Sequence or np.numpy object, the representative dataset for calibration. Must be set when "init_quant" is set True. You can use full or part of eval_dataset, train_dataset or other datasets as calib_dataset.

calib_steps

An int object, the total number of steps for initial PTQ. Ignored with the default value of None. If "calib_dataset" is a tf.data dataset, generator or keras.utils.Sequence instance and steps is None, calibration will run until the dataset is exhausted. This argument is not supported with array inputs.

calib_batch_size

An int object, the number of samples per batch for initial PTQ. If the "calib_dataset" is in the form of a dataset, generator or keras.utils.Sequence instances, the batch size is controlled by the dataset itself. If the "calib_dataset" is in the form of a numpy.array object, the default batch size is 32.

train_with_bn

A bool object, whether to keep bn layers during QAT.

freeze_bn_delay

An int object, the train steps before freezing the bn parameters. Default value is -1, which means never do bn freezing.

replace_sigmoid

A bool object, whether to replace the Activation(activation='sigmoid') layers into hard sigmoid layers and do quantization. If not, the sigmoid layers will be left unquantized and will be scheduled on CPU.

replace_relu6

A bool object, whether to replace the Relu6 layers with Relu layers.

include_cle

A bool object, whether to do Cross Layer Equalization before quantization.

cle_steps

An int object, the iteration steps to do Cross Layer Equalization.

forced_cle

A bool object, whether to do forced Cross Layer Equalization for relu6 layers.