Is it only suitable for VCNN network? Does the residual network fit?

[qq272574497]

No description provided.

[MingLin-home]

It works with residual network. However, only VCNN comes with strong theoretical guarantee. That is, you can apply Zen-score to any network, even non VCNN ones. You loose theoretical guarantee so be careful to verify the results.

[qq272574497]

It works with residual network. However, only VCNN comes with strong theoretical guarantee. That is, you can apply Zen-score to any network, even non VCNN ones. You loose theoretical guarantee so be careful to verify the results.

What are the steps to run my own network structure in your code? For example：

simpleNet(
(layer1): Sequential(
(0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): ReLU()
)
(layer2): Sequential(
(0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): ReLU()
)
(layer3): Sequential(
(0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): ReLU()
)
(dropout): Dropout(p=0.5, inplace=False)
(fc): Linear(in_features=1024, out_features=10, bias=True)
(out): Linear(in_features=10, out_features=10, bias=True)
)

[MingLin-home]

Please check

ZenNAS/ZeroShotProxy/compute_zen_score.py

Line 33 in d1d617e

def compute_nas_score(gpu, model, mixup_gamma, resolution, batch_size, repeat, fp16=False):

for computing zen-score for you model.

[MingLin-home]

Sorry for the late reply. Please refer to line 48-67 in compute_zen_score.py for your resnet model.

…

On Mon, Oct 10, 2022 at 3:06 AM qq272574497 ***@***.***> wrote: Please check https://github.com/idstcv/ZenNAS/blob/d1d617e0352733d39890fb64ea758f9c85b28c1a/ZeroShotProxy/compute_zen_score.py#L33 for computing zen-score for you model. hi, How to run this demo([ZenNAS/ZeroShotProxy/compute_zen_score.py]). i think it need sys.argv and i want to run resnet — Reply to this email directly, view it on GitHub <#25 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFIQVWPMCLTJIKWLVOKLX5DWCPTBTANCNFSM6AAAAAAQ2OOCJI> . You are receiving this because you commented.Message ID: ***@***.***>

[qq272574497]

Excuse me, I want to run the demo (https://github.com/idstcv/ZenNAS/blob/main/ZeroShotProxy/compute_zen_score.py), could you tell me the following parameters are correct? Take myresnet50 and cifir10 as examples

--batch_size 64 --input_image_size 32 --repeat_times 32 --mixup_gamma 1e-2 --arch myresnet50 --num_classes 10

[MingLin-home]

--mixup_gamma 1e-2 : I would suggest 1e-4~1e-6, to approximate gradient. 1e-2 might be too large.

[qq272574497]

It works with residual network. However, only VCNN comes with strong theoretical guarantee. That is, you can apply Zen-score to any network, even non VCNN ones. You loose theoretical guarantee so be careful to verify the results.

in [Zero-Cost Proxies for Lightweight NAS]，reslink was deleted in the search stage, and the residual block was used after the optimal architecture was found. Is that the same with you?

[MingLin-home]

Yes, they are the same.

[qq272574497]

I find that pooling has a great impact on the result. Is there any way to reduce the impact of pooling on the result of zero-cost to 0? The only use of pooling is to reduce calculations

[MingLin-home]

You can use conv with stride=2 to replace pooling. The saving on the computational cost is minor, because there are at most 5 such layers.

[qq272574497]

You can use conv with stride=2 to replace pooling. The saving on the computational cost is minor, because there are at most 5 such layers.

stride=2 does not work;But I agree with you " The saving on the computational cost is minor"