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- # Copyright (c) 2021 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.
- import paddle
- import paddleslim
- FILTER_DIM = paddleslim.dygraph.prune.filter_pruner.FILTER_DIM
- def _pruner_eval_fn(model, eval_dataset, batch_size):
- metric = model.evaluate(eval_dataset, batch_size=batch_size)
- return metric[list(metric.keys())[0]]
- def _pruner_template_input(sample, model_type):
- if model_type == 'detector':
- template_input = [{
- "image": paddle.ones(
- shape=[1, 3] + list(sample["image"].shape[:2]),
- dtype='float32'),
- "im_shape": paddle.full(
- [1, 2], 640, dtype='float32'),
- "scale_factor": paddle.ones(
- shape=[1, 2], dtype='float32')
- }]
- else:
- template_input = [1] + list(sample[0].shape)
- return template_input
- def sensitive_prune(pruner, pruned_flops, skip_vars=[], align=None):
- # skip depthwise convolutions
- for layer in pruner.model.sublayers():
- if isinstance(layer,
- paddle.nn.layer.conv.Conv2D) and layer._groups > 1:
- for param in layer.parameters(include_sublayers=False):
- skip_vars.append(param.name)
- pruner.restore()
- ratios, pruned_flops = pruner.get_ratios_by_sensitivity(
- pruned_flops, align=align, dims=FILTER_DIM, skip_vars=skip_vars)
- pruner.plan = pruner.prune_vars(ratios, FILTER_DIM)
- pruner.plan._pruned_flops = pruned_flops
- return pruner.plan, ratios
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