forked from PulseFocusPlatform/PulseFocusPlatform
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
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# Copyright (c) 2020 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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import paddle
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from paddle.utils import try_import
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from ppdet.core.workspace import register, serializable
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from ppdet.utils.logger import setup_logger
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logger = setup_logger(__name__)
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def print_prune_params(model):
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model_dict = model.state_dict()
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for key in model_dict.keys():
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weight_name = model_dict[key].name
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logger.info('Parameter name: {}, shape: {}'.format(
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weight_name, model_dict[key].shape))
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@register
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@serializable
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class Pruner(object):
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def __init__(self,
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criterion,
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pruned_params,
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pruned_ratios,
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print_params=False):
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super(Pruner, self).__init__()
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assert criterion in ['l1_norm', 'fpgm'], \
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"unsupported prune criterion: {}".format(criterion)
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self.criterion = criterion
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self.pruned_params = pruned_params
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self.pruned_ratios = pruned_ratios
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self.print_params = print_params
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def __call__(self, model):
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# FIXME: adapt to network graph when Training and inference are
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# inconsistent, now only supports prune inference network graph.
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model.eval()
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paddleslim = try_import('paddleslim')
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from paddleslim.analysis import dygraph_flops as flops
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input_spec = [{
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"image": paddle.ones(
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shape=[1, 3, 640, 640], dtype='float32'),
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"im_shape": paddle.full(
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[1, 2], 640, dtype='float32'),
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"scale_factor": paddle.ones(
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shape=[1, 2], dtype='float32')
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}]
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if self.print_params:
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print_prune_params(model)
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ori_flops = flops(model, input_spec) / 1000
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logger.info("FLOPs before pruning: {}GFLOPs".format(ori_flops))
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if self.criterion == 'fpgm':
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pruner = paddleslim.dygraph.FPGMFilterPruner(model, input_spec)
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elif self.criterion == 'l1_norm':
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pruner = paddleslim.dygraph.L1NormFilterPruner(model, input_spec)
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logger.info("pruned params: {}".format(self.pruned_params))
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pruned_ratios = [float(n) for n in self.pruned_ratios]
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ratios = {}
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for i, param in enumerate(self.pruned_params):
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ratios[param] = pruned_ratios[i]
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pruner.prune_vars(ratios, [0])
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pruned_flops = flops(model, input_spec) / 1000
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logger.info("FLOPs after pruning: {}GFLOPs; pruned ratio: {}".format(
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pruned_flops, (ori_flops - pruned_flops) / ori_flops))
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return model
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