64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
# Copyright 2021 Samsung Electronics Co., Ltd.
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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# =============================================================================
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available_measures = []
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_measure_impls = {}
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def measure(name, bn=True, copy_net=True, force_clean=True, **impl_args):
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def make_impl(func):
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def measure_impl(net_orig, device, *args, **kwargs):
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if copy_net:
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net = net_orig.get_prunable_copy(bn=bn).to(device)
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else:
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net = net_orig
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ret = func(net, *args, **kwargs, **impl_args)
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if copy_net and force_clean:
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import gc
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import torch
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del net
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torch.cuda.empty_cache()
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gc.collect()
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return ret
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global _measure_impls
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if name in _measure_impls:
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raise KeyError(f'Duplicated measure! {name}')
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available_measures.append(name)
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_measure_impls[name] = measure_impl
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return func
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return make_impl
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def calc_measure(name, net, device, *args, **kwargs):
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return _measure_impls[name](net, device, *args, **kwargs)
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def load_all():
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from . import grad_norm
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from . import snip
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from . import grasp
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from . import fisher
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from . import jacob_cov
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from . import plain
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from . import synflow
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from . import meco
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from . import zico
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# TODO: should we do that by default?
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load_all()
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