DropNAS, a grouped operation dropout method for one-level DARTS, with better and more stable performance.
- python-3.5.2
- pytorch-1.0.0
- torchvision-0.2.0
- tensorboardX-2.0
- graphviz-0.14
- Search
# with the default setting presented in paper, but you may need to adjust the batch size to prevent OOM
python3 search.py --name cifar10_example --dataset CIFAR10 --gpus 0
- Augment
# use the genotype we found on CIFAR10
python3 augment.py --name cifar10_example --dataset CIFAR10 --gpus 0 --genotype "Genotype(
normal=[[('sep_conv_3x3', 1), ('skip_connect', 0)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('dil_conv_5x5', 4), ('dil_conv_3x3', 1)]],
normal_concat=range(2, 6),
reduce=[[('max_pool_3x3', 0), ('sep_conv_5x5', 1)], [('dil_conv_5x5', 2), ('sep_conv_5x5', 1)], [('dil_conv_5x5', 3), ('dil_conv_5x5', 2)], [('dil_conv_5x5', 3), ('dil_conv_5x5', 4)]],
reduce_concat=range(2, 6)
)"The following results in CIFAR-10/100 are obtained with the default setting. More results with different arguements and other dataset like ImageNet can be found in the paper.
| Dataset | Avg Acc (%) | Best Acc (%) |
|---|---|---|
| CIFAR-10 | 97.42±0.14 | 97.74 |
| CIFAR-100 | 83.05±0.41 | 83.61 |
The performance of DropNAS and one-level DARTS across different search spaces on CIFAR-10/100.
| Dataset | Search Space | DropNAS Acc (%) | one-level DARTS Acc (%) |
|---|---|---|---|
| CIFAR-10 | 3-skip | 97.32±0.10 | 96.81±0.18 |
| 1-skip | 97.33±0.11 | 97.15±0.12 | |
| original | 97.42±0.14 | 97.10±0.16 | |
| CIFAR-100 | 3-skip | 83.03±0.35 | 82.00±0.34 |
| 1-skip | 83.53±0.19 | 82.27±0.25 | |
| original | 83.05±0.41 | 82.73±0.36 |
The test error of DropNAS on CIFAR-10 when different operation groups are applied with different drop path rates.
| r_p=1e-5 | r_p=3e-5 | r_p=1e-4 | |
|---|---|---|---|
| r_np=1e-5 | 97.40±0.16 | 97.28±0.04 | 97.36±0.12 |
| r_np=3e-5 | 97.36±0.11 | 97.42±0.14 | 97.31±0.05 |
| r_np=1e-4 | 97.35±0.07 | 97.31±0.10 | 97.37±0.16 |
CIFAR-10
CIFAR100
[1] https://github.com/quark0/darts (official implementation of DARTS)
[2] https://github.com/khanrc/pt.darts
[3] https://github.com/susan0199/StacNAS (feature map code used in our paper)