64 lines
2.6 KiB
Python
64 lines
2.6 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import, division, print_function
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import paddle
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from ...utils import logger
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QUANT_CONFIG = {
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# weight preprocess type, default is None and no preprocessing is performed.
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'weight_preprocess_type': None,
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# activation preprocess type, default is None and no preprocessing is performed.
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'activation_preprocess_type': None,
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# weight quantize type, default is 'channel_wise_abs_max'
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'weight_quantize_type': 'channel_wise_abs_max',
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# activation quantize type, default is 'moving_average_abs_max'
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'activation_quantize_type': 'moving_average_abs_max',
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# weight quantize bit num, default is 8
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'weight_bits': 8,
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# activation quantize bit num, default is 8
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'activation_bits': 8,
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# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
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'dtype': 'int8',
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# window size for 'range_abs_max' quantization. default is 10000
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'window_size': 10000,
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# The decay coefficient of moving average, default is 0.9
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'moving_rate': 0.9,
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# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
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'quantizable_layer_type': ['Conv2D', 'Linear'],
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}
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def quantize_model(config, model, mode="train"):
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if config.get("Slim", False) and config["Slim"].get("quant", False):
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from paddleslim.dygraph.quant import QAT
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assert config["Slim"]["quant"]["name"].lower(
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) == 'pact', 'Only PACT quantization method is supported now'
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QUANT_CONFIG["activation_preprocess_type"] = "PACT"
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if mode in ["infer", "export"]:
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QUANT_CONFIG['activation_preprocess_type'] = None
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# for rep nets, convert to reparameterized model first
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for layer in model.sublayers():
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if hasattr(layer, "rep"):
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layer.rep()
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model.quanter = QAT(config=QUANT_CONFIG)
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model.quanter.quantize(model)
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logger.info("QAT model summary:")
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paddle.summary(model, (1, 3, 224, 224))
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else:
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model.quanter = None
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return
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