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66 lines
2.3 KiB
66 lines
2.3 KiB
# Copyright (c) 2022 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 |
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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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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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logger = setup_logger(__name__) |
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@register |
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@serializable |
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class UnstructuredPruner(object): |
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def __init__(self, |
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stable_epochs, |
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pruning_epochs, |
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tunning_epochs, |
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pruning_steps, |
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ratio, |
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initial_ratio, |
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prune_params_type=None): |
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self.stable_epochs = stable_epochs |
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self.pruning_epochs = pruning_epochs |
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self.tunning_epochs = tunning_epochs |
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self.ratio = ratio |
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self.prune_params_type = prune_params_type |
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self.initial_ratio = initial_ratio |
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self.pruning_steps = pruning_steps |
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def __call__(self, model, steps_per_epoch, skip_params_func=None): |
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paddleslim = try_import('paddleslim') |
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from paddleslim import GMPUnstructuredPruner |
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configs = { |
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'pruning_strategy': 'gmp', |
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'stable_iterations': self.stable_epochs * steps_per_epoch, |
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'pruning_iterations': self.pruning_epochs * steps_per_epoch, |
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'tunning_iterations': self.tunning_epochs * steps_per_epoch, |
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'resume_iteration': 0, |
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'pruning_steps': self.pruning_steps, |
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'initial_ratio': self.initial_ratio, |
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} |
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pruner = GMPUnstructuredPruner( |
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model, |
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ratio=self.ratio, |
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skip_params_func=skip_params_func, |
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prune_params_type=self.prune_params_type, |
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local_sparsity=True, |
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configs=configs) |
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return pruner
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