Merge branch 'master' of github.com:BayesWatch/nas-without-training
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LICENCE
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LICENCE
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MIT License
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Copyright (c) 2020 Anonymous Authors
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@ -12,3 +12,5 @@ conda env create -f environment.yml
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conda activate nas-wot
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conda activate nas-wot
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./reproduce.sh
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./reproduce.sh
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```
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```
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The code is licensed under the MIT licence.
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python search.py --dataset cifar10
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python search.py --dataset cifar10 --data_loc '../datasets/cifar10'
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python search.py --dataset cifar10 --trainval
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python search.py --dataset cifar10 --trainval --data_loc '../datasets/cifar10'
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python search.py --dataset cifar100
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python search.py --dataset cifar100 --data_loc '../datasets/cifar100'
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python search.py --dataset ImageNet16-120
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python search.py --dataset ImageNet16-120 --data_loc '../datasets/ImageNet16'
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@ -55,7 +55,7 @@ def get_batch_jacobian(net, x, target, to, device, args=None):
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return jacob, target.detach()
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return jacob, target.detach()
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def evidenceapprox_eval_score(jacob, labels=None):
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def eval_score(jacob, labels=None):
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corrs = np.corrcoef(jacob)
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corrs = np.corrcoef(jacob)
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v, _ = np.linalg.eig(corrs)
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v, _ = np.linalg.eig(corrs)
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k = 1e-5
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k = 1e-5
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@ -122,7 +122,7 @@ for N in runs:
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jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy()
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jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy()
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try:
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try:
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s = evidenceapprox_eval_score(jacobs, labels)
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s = eval_score(jacobs, labels)
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except Exception as e:
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except Exception as e:
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print(e)
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print(e)
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s = np.nan
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s = np.nan
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