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85 lines
3.2 KiB
85 lines
3.2 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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import paddle |
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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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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**3) |
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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**3) |
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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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