convert_qat_model

Applicable Products

Product	Supported
Ascend 950PR/Ascend 950DT	√
Atlas A3 training products/Atlas A3 inference products	√
Atlas A2 training products/Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	√
Atlas inference products	√
Atlas training products	√

Description

Converts a TensorFlow QAT model to a quantized model that serves for both accuracy simulation on the CPU and GPU and deployment on the AI processor.

Prototype

convert_qat_model(pb_model, outputs, save_path, record_file=None)

Parameters

Parameter	Input/Output	Description
pb_model	Input	Path of the QAT model to be adapted. A string.
outputs	Input	List of output operators in a graph. A list.
record_file	Input	Path of the quantization factor record file (.txt) computed by the user. A string. Default: None
save_path	Input	Model save path. Must include the prefix of the model name, for example, **./quantized_model/model***. A string.

Returns

Returns a list of output operators in a graph.

Restrictions

Only TensorFlow models that contain the FakeQuantWithMinMaxVars and FakeQuantWithMinMaxVarsPerchannel operators are supported. The model format is .pb.

Example

import amct_tensorflow as amct
convert_qat_model(pb_model, outputs, save_path)

Flush file: a .pb model file that can be used for accuracy simulation in the TensorFlow environment or offline inference on the AI processor.

When adaptation is performed again, the preceding files output by the API will be overwritten.

Parent topic: Model Adaptation APIs