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update configs

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D-X-Y
2019-12-09 16:15:08 +11:00
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README.md
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# Nueral Architecture Search (NAS)
This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org). More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS).
This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org).
More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS).
- Network Pruning via Transformable Architecture Search, NeurIPS 2019
- One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
- Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019
- several typical classification models, e.g., ResNet and DenseNet (see [BASELINE.md](https://github.com/D-X-Y/NAS-Projects/blob/master/BASELINE.md))
- 10 NAS algorithms for the neural topology in `exps/algos`
- Several typical classification models, e.g., ResNet and DenseNet (see [BASELINE.md](https://github.com/D-X-Y/NAS-Projects/blob/master/BASELINE.md))
## Requirements and Preparation
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The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
### usefull tools
### Usefull tools
1. Compute the number of parameters and FLOPs of a model:
```
from utils import get_model_infos
@@ -52,13 +54,18 @@ CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-width-gumbel.sh cifar10 Re
Search for both depth and width configuration of ResNet:
```
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-cifar.sh cifar10 ResNet56 CIFARX 0.47 -1
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-shape-cifar.sh cifar10 ResNet56 CIFARX 0.47 -1
```
args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel name, `CIFARX` indicates the searching hyper-parameters, `0.47/0.57` indicates the expected FLOP ratio, `-1` indicates the random seed.
### Model Configuration
The searched shapes for ResNet-20/32/56/110/164 in Table 3 in the original paper are listed in [`configs/NeurIPS-2019`](https://github.com/D-X-Y/NAS-Projects/tree/master/configs/NeurIPS-2019).
If you want to directly train a model with searched configuration of TAS, try these:
```
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/tas-infer-train.sh cifar10 C010-ResNet32 -1
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/tas-infer-train.sh cifar100 C100-ResNet32 -1
```
## [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)