This is the code and data release for the Are Labels Necessary for Neural Architecture Search? project. It is a superset of the pycls codebase written in PyTorch, by supporting more:
- Tasks: Rotation Prediction (
rot
), Colorization (col
), Solving Jigsaw Puzzles (jig
), Semantic Segmentation (seg
) - Datasets: ImageNet-22K (
imagenet22k
), Cityscapes (cityscapes
) - Backbones: NAS-like (
nas
), NAS-Bench-101 (nas_bench
) - Modes: Differentiable Architecture Search (
nas_search
)
![](https://raw.githubusercontent.com/facebookresearch/unnas/main/docs/fig4_top.png)
![](https://raw.githubusercontent.com/facebookresearch/unnas/main/docs/fig4_bottom.png)
unnas supports a wide range of tasks, datasets, backbones, and modes for neural architecture related research.
In addition to installing PyTorch with CUDA support, run:
cd /path/to/clone/unnas
git clone https://github.com/facebookresearch/unnas
cd unnas
pip install -r requirements.txt
export PYTHONPATH=.
Then please follow DATA.md
to set up the datasets.
All experiments were conducted using (up to 8) Nvidia Quadro GV100, each with 32GB memory. If you are using GPUs with smaller memory, the provided configs may result in out-of-memory error.
To train an architecture used in sample-based experiments:
python tools/train_net.py \
--cfg configs/sample_based/SPACE/DATASET/TASK/ID.yaml \
OUT_DIR tmp
where:
SPACE
is eitherdarts
ornas_bench
DATASET
is eithercifar10
orimagenet
TASK
is eithercls
orrot
orcol
orjig
ID
could be00004
fordarts
, or000907
fornas_bench
To use different random seeds (e.g. 2
), simply append RNG_SEED 2
to the command. To obtain our results of these runs:
wget https://dl.fbaipublicfiles.com/unnas/unnas_summaries.zip
unzip unnas_summaries.zip
rm unnas_summaries.zip
Repeating our analysis is essentially equivalent to generating the figures in the paper. To do so:
python figs/fig234.py
python figs/fig5.py
The generated figures will be under the figs/
folder.
To repeat the search phase:
python tools/train_net.py \
--cfg configs/search_based/search_phase/DATASET/TASK.yaml \
OUT_DIR tmp
where:
DATASET
is eitherimagenet
orimagenet22k
orcityscapes
TASK
is eithercls
/seg
orrot
orcol
orjig
To repeat the evaluation phase:
python tools/train_net.py \
--cfg configs/search_based/eval_phase/TASK/ARCH.yaml \
OUT_DIR tmp
where:
TASK
is eithercls
(ImageNet-1K classification) orseg
(Cityscapes semantic segmentation)ARCH
could bedarts
,imagenet_rot
,imagenet22k_col
,cityscapes_jig
, etc
For either phase, to use different random seeds (e.g. 2
), simply append RNG_SEED 2
to the command.
If you find unnas helpful in your research, please consider citing:
@InProceedings{Liu2020,
title = {Are Labels Necessary for Neural Architecture Search?},
author = {Liu, Chenxi and Doll{\'a}r, Piotr and He, Kaiming and Girshick, Ross and Yuille, Alan and Xie, Saining},
booktitle = {ECCV},
year = {2020}
}
unnas is released under the MIT license. Please see the LICENSE file for more information.