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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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.
- from __future__ import absolute_import, division, print_function
- import paddle
- from paddlex.ppcls.utils import logger
- QUANT_CONFIG = {
- # weight preprocess type, default is None and no preprocessing is performed.
- 'weight_preprocess_type': None,
- # activation preprocess type, default is None and no preprocessing is performed.
- 'activation_preprocess_type': None,
- # weight quantize type, default is 'channel_wise_abs_max'
- 'weight_quantize_type': 'channel_wise_abs_max',
- # activation quantize type, default is 'moving_average_abs_max'
- 'activation_quantize_type': 'moving_average_abs_max',
- # weight quantize bit num, default is 8
- 'weight_bits': 8,
- # activation quantize bit num, default is 8
- 'activation_bits': 8,
- # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
- 'dtype': 'int8',
- # window size for 'range_abs_max' quantization. default is 10000
- 'window_size': 10000,
- # The decay coefficient of moving average, default is 0.9
- 'moving_rate': 0.9,
- # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
- 'quantizable_layer_type': ['Conv2D', 'Linear'],
- }
- def get_quaner(config, model):
- if config.get("Slim", False) and config["Slim"].get("quant", False):
- from paddleslim.dygraph.quant import QAT
- assert config["Slim"]["quant"]["name"].lower(
- ) == 'pact', 'Only PACT quantization method is supported now'
- QUANT_CONFIG["activation_preprocess_type"] = "PACT"
- quanter = QAT(config=QUANT_CONFIG)
- quanter.quantize(model)
- logger.info("QAT model summary:")
- paddle.summary(model, (1, 3, 224, 224))
- else:
- quanter = None
- return quanter
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