prepare#

classtorch.ao.quantization.prepare(model,inplace=False,allow_list=None,observer_non_leaf_module_list=None,prepare_custom_config_dict=None)[source]#

Prepares a copy of the model for quantization calibration or quantization-aware training.

Quantization configuration should be assigned preemptivelyto individual submodules in.qconfig attribute.

The model will be attached with observer or fake quant modules, and qconfigwill be propagated.

Parameters

• model – input model to be modified in-place

• inplace – carry out model transformations in-place, the original module is mutated

• allow_list – list of quantizable modules

• observer_non_leaf_module_list – list of non-leaf modules we want to add observer

• prepare_custom_config_dict – customization configuration dictionary for prepare function

# Example of prepare_custom_config_dict:prepare_custom_config_dict={# user will manually define the corresponding observed# module class which has a from_float class method that converts# float custom module to observed custom module"float_to_observed_custom_module_class":{CustomModule:ObservedCustomModule}}