MeCo/zero-cost-nas/foresight/pruners/measures/__init__.py
HamsterMimi fd43e67da1 update
2023-05-04 13:23:56 +08:00

64 lines
1.9 KiB
Python

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