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Merge branch 'master' of github.com:BayesWatch/nas-without-training

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jack-willturner 2020-06-03 15:42:24 +01:00
commit 6f97d1be37
4 changed files with 29 additions and 6 deletions
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21
LICENCE Normal file
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@ -0,0 +1,21 @@
MIT License
Copyright (c) 2020 Anonymous Authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

2
README.md
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@ -12,3 +12,5 @@ conda env create -f environment.yml
conda activate nas-wot
./reproduce.sh
```
The code is licensed under the MIT licence.

8
reproduce.sh
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@ -1,4 +1,4 @@
python search.py --dataset cifar10
python search.py --dataset cifar10 --trainval
python search.py --dataset cifar100
python search.py --dataset ImageNet16-120
python search.py --dataset cifar10 --data_loc '../datasets/cifar10'
python search.py --dataset cifar10 --trainval --data_loc '../datasets/cifar10'
python search.py --dataset cifar100 --data_loc '../datasets/cifar100'
python search.py --dataset ImageNet16-120 --data_loc '../datasets/ImageNet16'

4
search.py
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@ -55,7 +55,7 @@ def get_batch_jacobian(net, x, target, to, device, args=None):
return jacob, target.detach()
def evidenceapprox_eval_score(jacob, labels=None):
def eval_score(jacob, labels=None):
corrs = np.corrcoef(jacob)
v, _ = np.linalg.eig(corrs)
k = 1e-5
@ -122,7 +122,7 @@ for N in runs:
jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy()
try:
s = evidenceapprox_eval_score(jacobs, labels)
s = eval_score(jacobs, labels)
except Exception as e:
print(e)
s = np.nan