FP-DARTS: Fine-grained Progressive ArchitectureSelection via Gradient-aware Loss PerturbationEstimation

This repository is the official implementation of [FP-DARTS: Fine-grained Progressive ArchitectureSelection via Gradient-aware Loss PerturbationEstimation].

Requirements

To install requirements:

pip install -r requirements.txt

Searching

We utilize first-order optimization of DARTS to alternately update operation parameters and architecture weights.

Refer to scripts/run_search.sh for further introduction.

DARTS's search space

To search the model architectures by FP-DARTS-s1, run this command:

python train_search.py --sal_type task

To search the model architectures by FP-DARTS-s2, run this command:

python train_search.py --sal_type task --sal_second

To search the model architectures by FP-DARTS-s1r, run this command:

python train_search.py --sal_type task --num_compare 5

Larger search space

To search the model architectures by FP-DARTS-s1r on larger search space, run this command:

python train_search.py --sal_type task --num_compare 5 --no_restrict

Evaluation

To evaluate the searched model(s) in the paper, we need to train the model from scratch and then evaluate the ultimate performance.

We follow the configuration of DARTS: use cutout and auxiliary training strategy for CIFAR10 dataset, and use auxiliary training strategy for ImageNet dataset.

Refer to scripts/run_fulltrain.sh for further introduction.

DARTS's search space

To evaluate the searched model on CIFAR10 under DARTS's search space, run this command:

python train.py --mode train --cutout --auxiliary --base_dir <path_to_the_dir_of_model>

To evaluate the searched model on ImageNet, run:

python train_imagenet.py --mode train --auxiliary --base_path <path_to_the_dir_of_model> --genotype_name <the_name_of_genotype_by_model_selection>

Larger search space

To evaluate the searched model on CIFAR10 under larger search space, run this command:

python train.py --mode train --no_restrict --cutout --auxiliary --base_dir <path_to_the_dir_of_model>

To evaluate the searched model on ImageNet, run:

python train_imagenet.py --mode train --no_restrict --auxiliary --base_path <path_to_the_dir_of_model> --genotype_name <the_name_of_genotype_by_model_selection>

Results

DARTS's search space

Our model achieves the following performance on CIFAR10 under DARTS's search space. All results are averaged among three searches with different random seeds.

Model name	Top 1 Acc	Params (M)
FP-DARTS-s1	97.41%	3.3
FP-DARTS-s2	97.45%	3.3
FP-DARTS-s1r	97.55%	3.5

Our model achieves the following performance on ImageNet:

Model name	Top 1 Acc	Params (M)
FP-DARTS-s2	75.0%	4.9
FP-DARTS-s1r	75.4%	5.1

Larger search space

Our model achieves the following performance on CIFAR10 under DARTS's search space:

Model name	Top 1 Acc	Params (M)
FP-DARTS-s1r	97.52%	3.4

Our model achieves the following performance on ImageNet:

Model name	Top 1 Acc	Params (M)
FP-DARTS-s1r	75.3%	5.1