Comments (3)
Thanks for the valuable question.
The gate is actually very sensitive to hyperparameters. To avoid troublesome tuning of the loss balancing factors and other hyperparameters, we use different routing space for different network, e.g. we only use the slimmest few sub-networks to form the DS-Net-S.
This can be find in Appendix.A of the paper
In practice, We first test the MAdds for each sub-networks, then manually choose those sub-networks with MAdds around the target MAdds as the dynamic routing space.
from ds-net.
Yes, the MAdds are averaged over the validation set, e.g.:
DS-Net/dyn_slim/apis/train_slim_gate.py
Line 287 in 2ae9455
from ds-net.
Thank you for your rapid answer. 😊
I think it is hard to know how much MAdds is required before the validation, but you achieved similar MAdds to comparative algorithms.
Is there any rule to achieve specific MAdds?
from ds-net.
Related Issues (19)
- The Approximate Date for Stage II training code HOT 6
- The usage of gumbel softmax in DS-Net HOT 7
- Actual acceleration on Resnet HOT 2
- Dynamic path for DS-mobilenet HOT 1
- 运行问题 HOT 1
- DS-Net for object detection HOT 8
- MAdds of Pretrained Supernet HOT 2
- why not set ensemble_ib to True? HOT 2
- Can we futher improve autoalim without gate? HOT 3
- Softmax twice for SGS loss?
- Commands to perfrom Inference
- Pretrained models HOT 2
- Object Detection HOT 2
- project environment HOT 1
- Why the num_choice in different yml is different? HOT 2
- Some issues about the gradients of slimNet HOT 6
- UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. HOT 3
- Error of change the num_choice in mobilenetv1_bn_uniform_reset_bn.yml HOT 2
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