set_logging_level

Applicability

Product	Supported
Atlas 350 Accelerator Card	x
Atlas A3 training product/Atlas A3 inference product	x
Atlas A2 training product/Atlas A2 inference product	x
Atlas 200I/500 A2 inference product	x
Atlas inference product	x
Atlas training product	√

Description

Sets the log levels of the messages printed to the screen and those saved in the amct_log/amct_tensorflow.log file.

Prototype

set_logging_level(print_level='info', save_level='info')

Parameters

Parameter	Input/Output	Description
print_level	Input	Sets the level of the information to be printed. debug: DEBUG, INFO, WARNING, and ERROR logs. info: INFO, WARNING, and ERROR logs. warning: WARNING and ERROR logs. error: ERROR logs. Default: info A string.
save_level	Input	Sets the level of information saved in the quant_info.log file. debug: DEBUG, INFO, WARNING, and ERROR logs. info: INFO, WARNING, and ERROR logs. warning: WARNING and ERROR logs. error: ERROR logs. Default: info A string.

Parameter

Input/Output

Description

print_level

Input

Sets the level of the information to be printed.

debug: DEBUG, INFO, WARNING, and ERROR logs.
info: INFO, WARNING, and ERROR logs.
warning: WARNING and ERROR logs.
error: ERROR logs.

Default: info

A string.

save_level

Input

Sets the level of information saved in the quant_info.log file.

debug: DEBUG, INFO, WARNING, and ERROR logs.
info: INFO, WARNING, and ERROR logs.
warning: WARNING and ERROR logs.
error: ERROR logs.

Default: info

A string.

Log Level	Description
DEBUG	Detailed quantization processing information, including the quantization factors (scale and offset) and related debugging information.
INFO	Brief quantization processing information, including the quantized layer names and BN fusion information.
WARNING	Warning messages during quantization.
ERROR	Error messages during quantization.

Log levels are case insensitive. For instance, Info, info, and INFO are all valid values.

Returns

None

Example

import amct_tensorflow as amct
amct.set_logging_level(print_level="info", save_level="info")
amct.quantize_model(
     graph=tf.get_default_graph(),
     config_file="./configs/config.json",
     record_file="./record_scale_offset.txt")
   ...
amct.save_model(pb_model="./user_model.pb",
                outputs=["model/outputs"],
                record_file="./record_scale_offset.txt",
                save_path="./inference/model")

Parent topic: Common API