restore_quant_retrain_model

Function Usage

Description: Quantizes a graph based on the given configuration file, inserts quantization-related layers (quantization-aware layers of activations and weights and layers for searching for N), generates a quantization factor record file (record_file), loads the checkpoint weight parameters saved during training, and returns the result model of the torch.nn.Module type.

Prototype

quant_retrain_model = restore_quant_retrain_model (config_file, model, record_file, input_data, pth_file, state_dict_name=None)

Parameters

Option	Input/Return	Meaning	Restriction
config_file	Input	User-defined QAT configuration file, which specifies the configuration of each layer to be quantized.	A string The config.json file passed to this call must be the same as that passed to the create_quant_retrain_model call.
model	Input	Source model, with weights unloaded.	A torch.nn.Module
record_file	Input	Directory of the quantization factor record file, including the file name.	A string
input_data	Input	Input data of the model. A torch.tensor, equivalent to a tuple(torch.tensor).	A tuple.
pth_file	Input	Weight file saved during training.	A string
state_dict_name	Input	Key value corresponding to the weight in the weight file.	Default: None A string
quant_retrain_model	Return	Result model of the torch.nn.Module type for QAT.	Default: None A torch.nn.Module

Returns

Returns the result model of the torch.nn.module type for quantization aware training.

Outputs

None

Example

import amct_pytorch as amct
# Build a graph of the network to be quantized.
model = build_model()
input_data = tuple([torch.randn(input_shape)])

scale_offset_record_file = os.path.join(TMP, 'scale_offset_record.txt')
# Insert the quantization API.
quant_retrain_model = amct.restore_quant_retrain_model(
               config_json_file,
               model,
               scale_offset_record_file,
               input_data,
               pth_file)

Parent topic: QAT APIs