# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Base Class of Quantizer."""
from abc import ABC, abstractmethod
from enum import Enum
from ..._checkparam import Validator
__all__ = ["OptimizeOption"]
[docs]class OptimizeOption(Enum):
r"""
An enum for the model quantization optimize option, currently only support `QAT` and `LEARNED_SCALE`.
"""
# using quantization aware training
QAT = "QAT"
# using the learned scale quantization
LEARNED_SCALE = "LEARNED_SCALE"
def __str__(self):
return self.value
class Quantizer(ABC):
"""
Base class of Quantizer. You can implement different kind of quantizer to get different quantization result.
Notes:
This class is an abstract class.
Args:
optimize_option (OptimizeOption, list or tuple): Specifies the quant algorithm and options. Default:
OptimizeOption.QAT.
"""
def __init__(self,
optimize_option=OptimizeOption.QAT):
if not isinstance(optimize_option, list) and not isinstance(optimize_option, tuple):
optimize_option = [optimize_option]
for option in optimize_option:
option = Validator.check_isinstance("optimize_option", option, OptimizeOption)
self.optimize_option = optimize_option
@abstractmethod
def quantize(self, network):
"""
Quant API to convert input network to a quantization aware training network
Args:
network (Cell): network to be quantized.
"""