Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Overview

Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search

Pytorch implementation for "Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search".

Curriculum Neural Architecture Search

Requirements

python>=3.7, torch==1.5.0, torchvision==0.6.0, graphviz

Please install all the requirements in requirements.txt.

Datasets

We consider two benchmark classification datsets, including CIFAR-10 and ImageNet.

CIFAR-10 can be automatically downloaded by torchvision.

ImageNet needs to be manually downloaded (preferably to a SSD) following the instructions here.

Training Method

Curriculum search on CIFAR-10

python search.py -o outputs/search

Evaluation Method

  1. Put the searched architectures in cnas/model/genotypes.py as follows.
CNAS = Genotype.from_arch(
    normal_arch=[('dil_conv_3x3', 1, 2),
                 ('sep_conv_3x3', 0, 2),
                 ('sep_conv_3x3', 0, 3),
                 ('skip_connect', 1, 3),
                 ('sep_conv_3x3', 1, 4),
                 ('max_pool_3x3', 3, 4),
                 ('sep_conv_3x3', 2, 5),
                 ('sep_conv_3x3', 4, 5)],
    normal_concat=[2, 3, 4, 5],
    reduced_arch=[('sep_conv_3x3', 0, 2),
                  ('skip_connect', 1, 2),
                  ('dil_conv_5x5', 2, 3),
                  ('skip_connect', 1, 3),
                  ('dil_conv_3x3', 2, 4),
                  ('sep_conv_3x3', 1, 4),
                  ('sep_conv_5x5', 0, 5),
                  ('sep_conv_3x3', 3, 5)],
    reduced_concat=[2, 3, 4, 5])
  1. Evaluate the searched architecture on CIFAR-10 and ImageNet dataset using the following scripts.

Evaluation on CIFAR-10:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \
--nproc_per_node=2 --master_port=23333 \
eval_arch.py \
--arch CNAS --init_channels 36 --layers 20 \
-o outputs/cifar10

Evaluation on ImageNet:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 --master_port=22333 \
eval_arch.py \
--max_epochs 250 --scheduler linear \
--dataset imagenet --data /path/to/imagenet \
--batch_size 64 --no_bias_decay --num_workers 8 \
--arch CNAS --init_channels 48 --layers 14 \
-o outputs/imagenet

Pretrained models

We have released our CNAS ImageNet pretrained model (top-1 accuracy 75.4%, top-5 accuracy 92.6%) on here.

You can use the following codes to load the ptrained models:

from cnas.model.eval import cnas_imagenet
model = cnas_imagenet(pretrained=True)

Citation

If you use any part of our code in your research, please cite our paper:

@InProceedings{guo2020breaking,
  title = {Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search},
  author = {Guo, Yong and Chen, Yaofo and Zheng, Yin and Zhao, Peilin and Chen, Jian and Huang, Junzhou and Tan, Mingkui},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  year = {2020},
  pages = {3822--3831}
}
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Comments
  • Bump numpy from 1.18.2 to 1.22.0

    Bump numpy from 1.18.2 to 1.22.0

    Bumps numpy from 1.18.2 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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