convert

class torch.ao.quantization.convert(module, mapping=None, inplace=False, remove_qconfig=True, is_reference=False, convert_custom_config_dict=None, use_precomputed_fake_quant=False)

Converts submodules in input module to a different module according to mapping by calling from_float method on the target module class. And remove qconfig at the end if remove_qconfig is set to True.

Parameters

• module – prepared and calibrated module

• mapping – a dictionary that maps from source module type to target module type, can be overwritten to allow swapping user defined Modules

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

• convert_custom_config_dict – custom configuration dictionary for convert function

• use_precomputed_fake_quant – a flag to enable use of precomputed fake quant

# Example of convert_custom_config_dict:
convert_custom_config_dict = {
    # user will manually define the corresponding quantized
    # module class which has a from_observed class method that converts
    # observed custom module to quantized custom module
    "observed_to_quantized_custom_module_class": {
        ObservedCustomModule: QuantizedCustomModule
    }
}