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85 lines
2.7 KiB
85 lines
2.7 KiB
3 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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3 years ago
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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
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from __future__ import division
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from __future__ import print_function
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from paddle.utils import try_import
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3 years ago
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from paddlers.models.ppdet.core.workspace import register, serializable
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from paddlers.models.ppdet.utils.logger import setup_logger
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3 years ago
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logger = setup_logger(__name__)
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@register
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@serializable
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class QAT(object):
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def __init__(self, quant_config, print_model):
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super(QAT, self).__init__()
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self.quant_config = quant_config
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self.print_model = print_model
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def __call__(self, model):
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paddleslim = try_import('paddleslim')
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self.quanter = paddleslim.dygraph.quant.QAT(config=self.quant_config)
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if self.print_model:
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logger.info("Model before quant:")
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logger.info(model)
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self.quanter.quantize(model)
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if self.print_model:
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logger.info("Quantized model:")
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logger.info(model)
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return model
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def save_quantized_model(self, layer, path, input_spec=None, **config):
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self.quanter.save_quantized_model(
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model=layer, path=path, input_spec=input_spec, **config)
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@register
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@serializable
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class PTQ(object):
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def __init__(self,
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ptq_config,
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quant_batch_num=10,
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output_dir='output_inference',
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fuse=True,
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fuse_list=None):
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super(PTQ, self).__init__()
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self.ptq_config = ptq_config
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self.quant_batch_num = quant_batch_num
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self.output_dir = output_dir
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self.fuse = fuse
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self.fuse_list = fuse_list
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def __call__(self, model):
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paddleslim = try_import('paddleslim')
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self.ptq = paddleslim.PTQ(**self.ptq_config)
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model.eval()
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quant_model = self.ptq.quantize(
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model, fuse=self.fuse, fuse_list=self.fuse_list)
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return quant_model
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def save_quantized_model(self,
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quant_model,
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quantize_model_path,
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input_spec=None):
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self.ptq.save_quantized_model(quant_model, quantize_model_path,
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input_spec)
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