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This commit is contained in:
HamsterMimi 2023-05-04 13:23:56 +08:00
parent 189df25fd3
commit fd43e67da1
8 changed files with 1 additions and 211 deletions
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14
sota/cnn/init_projection.py
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@ -96,20 +96,6 @@ def project_op(model, input, target, args, cell_type, proj_queue=None, selected_
model.candidate_flags[cell_type][selected_eid] = False
# print(model.get_projected_weights())
if proj_crit == 'comb':
synflow = predictive.find_measures(model,
proj_queue,
('random', 1, n_classes),
torch.device("cuda"),
measure_names=['synflow'])
var = predictive.find_measures(model,
proj_queue,
('random', 1, n_classes),
torch.device("cuda"),
measure_names=['var'])
# print(synflow, var)
comb = np.log(synflow['synflow'] + 1) / (var['var'] + 0.1)
measures = {'comb': comb}
else:
measures = predictive.find_measures(model,
proj_queue,

3
zero-cost-nas/foresight/pruners/measures/__init__.py
View File

@ -55,9 +55,6 @@ def load_all():
from . import jacob_cov
from . import plain
from . import synflow
from . import var
from . import cor
from . import norm
from . import meco
from . import zico

53
zero-cost-nas/foresight/pruners/measures/cor.py
View File

@ -1,53 +0,0 @@
# 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
import time
# 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 numpy as np
import torch
from . import measure
def get_score(net, x, target, device, split_data):
result_list = []
def forward_hook(module, data_input, data_output):
corr = np.mean(np.corrcoef(data_input[0].detach().cpu().numpy()))
result_list.append(corr)
net.classifier.register_forward_hook(forward_hook)
N = x.shape[0]
for sp in range(split_data):
st = sp * N // split_data
en = (sp + 1) * N // split_data
y = net(x[st:en])
cor = result_list[0].item()
result_list.clear()
return cor
@measure('cor', bn=True)
def compute_norm(net, inputs, targets, split_data=1, loss_fn=None):
device = inputs.device
# Compute gradients (but don't apply them)
net.zero_grad()
try:
cor= get_score(net, inputs, targets, device, split_data=split_data)
except Exception as e:
print(e)
cor= np.nan
return cor

55
zero-cost-nas/foresight/pruners/measures/norm.py
View File

@ -1,55 +0,0 @@
# 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
import time
# 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 numpy as np
import torch
from . import measure
def get_score(net, x, target, device, split_data):
result_list = []
def forward_hook(module, data_input, data_output):
norm = torch.norm(data_input[0])
result_list.append(norm)
net.classifier.register_forward_hook(forward_hook)
N = x.shape[0]
for sp in range(split_data):
st = sp * N // split_data
en = (sp + 1) * N // split_data
y = net(x[st:en])
n = result_list[0].item()
result_list.clear()
return n
@measure('norm', bn=True)
def compute_norm(net, inputs, targets, split_data=1, loss_fn=None):
device = inputs.device
# Compute gradients (but don't apply them)
net.zero_grad()
# print('var:', feature.shape)
try:
norm, t = get_score(net, inputs, targets, device, split_data=split_data)
except Exception as e:
print(e)
norm, t = np.nan, None
# print(jc)
# print(f'norm time: {t} s')
return norm, t

16
zero-cost-nas/foresight/pruners/measures/param_count.py
View File

@ -1,16 +0,0 @@
import time
import torch
from . import measure
from ..p_utils import get_layer_metric_array
@measure('param_count', copy_net=False, mode='param')
def get_param_count_array(net, inputs, targets, mode, loss_fn, split_data=1):
s = time.time()
count = get_layer_metric_array(net, lambda l: torch.tensor(sum(p.numel() for p in l.parameters() if p.requires_grad)), mode=mode)
e = time.time()
t = e - s
# print(f'param_count time: {t} s')
return count, t

55
zero-cost-nas/foresight/pruners/measures/var.py
View File

@ -1,55 +0,0 @@
# 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
import time
# 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 numpy as np
import torch
from . import measure
def get_score(net, x, target, device, split_data):
result_list = []
def forward_hook(module, data_input, data_output):
var = torch.var(data_input[0])
result_list.append(var)
net.classifier.register_forward_hook(forward_hook)
N = x.shape[0]
for sp in range(split_data):
st = sp * N // split_data
en = (sp + 1) * N // split_data
y = net(x[st:en])
v = result_list[0].item()
result_list.clear()
return v
@measure('var', bn=True)
def compute_var(net, inputs, targets, split_data=1, loss_fn=None):
device = inputs.device
# Compute gradients (but don't apply them)
net.zero_grad()
# print('var:', feature.shape)
try:
var= get_score(net, inputs, targets, device, split_data=split_data)
except Exception as e:
print(e)
var= np.nan
# print(jc)
# print(f'var time: {t} s')
return var

2
zero-cost-nas/foresight/pruners/predictive.py
View File

@ -108,7 +108,7 @@ def find_measures(net_orig, # neural network
measures = {}
for k,v in measures_arr.items():
if k in ['jacob_cov', 'var', 'cor', 'norm', 'meco', 'zico']:
if k in ['jacob_cov', 'meco', 'zico']:
measures[k] = v
else:
measures[k] = sum_arr(v)

14
zerocostnas/post_validate.py
View File

@ -223,20 +223,6 @@ def main():
else:
#score = score_loop(network, None, train_queue, args.gpu, None, args.proj_crit)
network.requires_feature = False
if args.proj_crit == 'comb':
synflow = predictive.find_measures(network,
train_queue,
('random', 1, n_classes),
torch.device("cuda"),
measure_names=['synflow'])
var = predictive.find_measures(network,
train_queue,
('random', 1, n_classes),
torch.device("cuda"),
measure_names=['var'])
comb = np.log(synflow['synflow'] + 1) / (var['var'] + 0.1)
measures = {'comb': comb}
else:
measures = predictive.find_measures(network,
train_queue,