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D-DARTS: Nested/Distributed Differentiable Architecture Search

*: This is the official implementation of D-DARTS.

Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. It drastically reduces search cost by resorting to Stochastic Gradient Descent (SGD) and weight-sharing. However, it also dramatically reduces the search space, thus excluding potential promising architectures from being discovered. In this article, we propose D-DARTS, a novel solution that addresses this problem by nesting several neural networks at the cell level instead of using weight-sharing to produce more diversified and specialized architectures. Moreover, we introduce a novel algorithm that can derive deeper architectures from a few trained cells, increasing performance and saving computation time. In addition, we also present an alternative search space (denoted DARTOpti) in which we optimize existing handcrafted architectures such as ResNet rather than starting from scratch. This approach is accompanied by a novel metric that measures the distance between architectures inside our custom search space. Our solution achieves state-of-the-art on CIFAR-10, CIFAR-100, and ImageNet while featuring a search cost significantly lower than previous differentiable NAS approaches.

User Guide

Prerequisites

Python >= 3.7

pip install -r requirements.txt OR conda install -f environment.yml conda activate darts

Datasets

Currently supported datasets are: CIFAR10, CIFAR100, and ImageNet (ILSVRC2012).

To use a specific dataset when searching or training, you must pass the --dataset cifar10/cifar100/imagenet and --data path/to/the/dataset arguments.

Searching for an architecture

python train_search.py --batch_size 96 --pretrain_epochs 0 --init_channels 16 --amp --no_arch_metric

Default batch size is 72 (for low memory GPUs).

Default dataset is CIFAR10.

Logs and results will be saved in the logs/search folder.

Searching for an architecture (DARTOpti)

python train_search.py --batch_size 96 --arch_baseline ResNet18 --amp

Arguments for --arch_baseline can be: ResNet18, ResNet50 or Xception.

New architectures implemented in genotypes.py will automatically be available.

Single model training

python train.py --auxiliary --cutout --amp --auto_aug --arch D-DARTS_threshold_sparse_cifar10_0.85_50 --batch_size 128 --epoch 600 --init_channels 36

The genotype passed with --arch must be a .txt file stored in the genotypes folder.

To use the automatic derivation algorithm presented in the paper, pass --layers x where x is an integer superior to the number of cells in the genotype. /!\ Automatic derivation is not available when training a DARTOpti architecture (i.e., optimized from an existing architecture).

Logs and results will be saved in the logs/eval folder.

Single model evaluation

python evaluate_model.py --arch ResNet18_cifar100_threshold_sparse_0.85 --model_path best_models/DO-2-ResNet18_ImageNet.pth.tar --init_channels 64

Model zoo

Pretrained models can be found in the best_models directory. We currently provide the following pretrained models:

  • DO-2-ResNet18 (trained on ImageNet, 77% top-1-accuracy)
  • DO-2-ResNet50 (trained on ImageNet, 76.3% top-1-accuracy)

Evaluation results on CIFAR-10

Comparison with other state-of-the-art results (CIFAR-10)

Model FLOPs Params Batch size lr DP Performance
DARTS_V2 522M 3.36 96 0.025 0.2 97.00*
PC-DARTS 558M 3.63 96 0.025 0.2 97.43*
PDARTS 532M 3.43 96 0.025 0.2 97.50*
FairDARTS-a 373M 2.83 96 0.025 0.2 97.46*
DD-1 259M 1.68 128 0.025 0.2 97.33
DD-4 948M 6.28 128 0.025 0.2 97.75
DO-ResNet18 1.2G 36.3 128 0.025 0.2 97.39
DO-ResNet50 1.5G 71.2 128 0.025 0.2 97.20

*: Official result, as stated in the corresponding paper.

Comparison with other state-of-the-art results (ImageNet)

Model FLOPs Params Batch size lr DP Performance Searched On
DARTS_V2 574M 4.7 96 0.025 0.2 73.3* CIFAR-100
PC-DARTS 586M 5.3 96 0.025 0.2 74.9* CIFAR-100
PDARTS 577M 5.1 96 0.025 0.2 74.9* CIFAR-100
FairDARTS-D 440M 4.3 96 0.025 0.2 75.6* ImageNet
DD-7 828M 6.4 128 0.025 0.2 75.6 ImageNet
DO-ResNet18 8.6G 56.3 128 0.025 0.2 77.0 CIFAR-100
DO-ResNet18 10.0G 73.2 128 0.025 0.2 76.3 CIFAR-100

*: Official result, as stated in the corresponding paper.

Acknowledgements

This code is based on the implementation of DARTS and FairDARTS.