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# Copyright (c) 2022 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
from __future__ import division
from __future__ import print_function
from paddle.utils import try_import
from paddlers.models.ppdet.core.workspace import register, serializable
from paddlers.models.ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
@register
@serializable
class UnstructuredPruner(object):
def __init__(self,
stable_epochs,
pruning_epochs,
tunning_epochs,
pruning_steps,
ratio,
initial_ratio,
prune_params_type=None):
self.stable_epochs = stable_epochs
self.pruning_epochs = pruning_epochs
self.tunning_epochs = tunning_epochs
self.ratio = ratio
self.prune_params_type = prune_params_type
self.initial_ratio = initial_ratio
self.pruning_steps = pruning_steps
def __call__(self, model, steps_per_epoch, skip_params_func=None):
paddleslim = try_import('paddleslim')
from paddleslim import GMPUnstructuredPruner
configs = {
'pruning_strategy': 'gmp',
'stable_iterations': self.stable_epochs * steps_per_epoch,
'pruning_iterations': self.pruning_epochs * steps_per_epoch,
'tunning_iterations': self.tunning_epochs * steps_per_epoch,
'resume_iteration': 0,
'pruning_steps': self.pruning_steps,
'initial_ratio': self.initial_ratio,
}
pruner = GMPUnstructuredPruner(
model,
ratio=self.ratio,
skip_params_func=skip_params_func,
prune_params_type=self.prune_params_type,
local_sparsity=True,
configs=configs)
return pruner