pcdarts-tf2

:fire: PC-DARTS (PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search, published in ICLR 2020) implemented in Tensorflow 2.0+. This is an unofficial implementation. :fire:

PC-DARTS is a memory efficient differentiable architecture search method, which can be trained with a larger batch size and, consequently, enjoys both faster speed and higher training stability. Experimental results achieve an error rate of 2.57% on CIFAR10 with merely 0.1 GPU-days for architecture search.

Original Paper:   Arxiv   OpenReview

Offical Implementation:   PyTorch

Contents

:bookmark_tabs:

• Installation
• Usage
• Benchmark
• Models
• References

Installation

:pizza:

Create a new python virtual environment by Anaconda or just use pip in your python environment and then clone this repository as following.

Clone this repo

git clone https://github.com/peteryuX/pcdarts-tf2.git
cd pcdarts-tf2

Conda

conda env create -f environment.yml
conda activate pcdarts-tf2

Pip

pip install -r requirements.txt

Usage

:lollipop:

Config File

You can modify your own dataset path or other settings of model in ./configs/*.yaml for training and testing, which would like below.

# general setting
batch_size: 128
input_size: 32
init_channels: 36
layers: 20
num_classes: 10
auxiliary_weight: 0.4
drop_path_prob: 0.3
arch: PCDARTS
sub_name: 'pcdarts_cifar10'
using_normalize: True

# training dataset
dataset_len: 50000  # number of training samples
using_crop: True
using_flip: True
using_cutout: True
cutout_length: 16

# training setting
epoch: 600
init_lr: 0.025
lr_min: 0.0
momentum: 0.9
weights_decay: !!float 3e-4
grad_clip: 5.0

val_steps: 1000
save_steps: 1000

Note:

• The sub_name is the name of outputs directory used in checkpoints and logs folder. (make sure of setting it unique to other models)
• The save_steps is the number interval steps of saving checkpoint file.
• The ./configs/pcdarts_cifar10_search.yaml and ./configs/pcdarts_cifar10.yaml are used by train_search.py and train.py respectively, which have different settings for small proxy model training(architecture searching) and full-size model training. Please make sure you use the correct config file in related script. (The example yaml script above is ./configs/pcdarts_cifar10.yaml.)

Architecture Searching on CIFAR-10 (using small proxy model)

Step1: Search cell architecture on CIFAR-10 using small proxy model.

python train_search.py --cfg_path="./configs/pcdarts_cifar10_search.yaml" --gpu=0

Note:

• The --gpu is used to choose the id of your avaliable GPU devices with CUDA_VISIBLE_DEVICES system varaible.
• You can visualize the training status on tensorboard by running "tensorboard --logdir=./logs/". My logs can be found from search_log and full_train_log.
• You can visualize the learning rate scheduling by running "python ./modules/lr_scheduler.py".
• You can visualize the dataset augmantation by running "python ./dataset_checker.py".

Step2: After the searching completed, you can find the result genotypes in ./logs/{sub_name}/search_arch_genotype.py. Open it and copy the latest genotype into the ./modules/genotypes.py, which will be used for further training later. The genotype like bellow:

TheNameYouWantToCall = Genotype(
    normal=[
        ('sep_conv_3x3', 1),
        ('skip_connect', 0),
        ('sep_conv_3x3', 0),
        ('dil_conv_3x3', 1),
        ('sep_conv_5x5', 0),
        ('sep_conv_3x3', 1),
        ('avg_pool_3x3', 0),
        ('dil_conv_3x3', 1)],
    normal_concat=range(2, 6),
    reduce=[
        ('sep_conv_5x5', 1),
        ('max_pool_3x3', 0),
        ('sep_conv_5x5', 1),
        ('sep_conv_5x5', 2),
        ('sep_conv_3x3', 0),
        ('sep_conv_3x3', 3),
        ('sep_conv_3x3', 1),
        ('sep_conv_3x3', 2)],
    reduce_concat=range(2, 6))

Note:

• You can visualize the genotype by running "python ./visualize_genotype.py TheNameYouWantToCall".

  

Training on CIFAR-10 (using full-sized model)

Step1: Make sure that you already modifed the flag arch in ./configs/pcdarts_cifar10.yaml to match the genotype you want to use in ./modules/genotypes.py.

Note:

• The default flag arch (PCDARTS) is the genotype proposed by official paper. You can train this model by yourself, or use dowload it from BenchmarkModels.

Step2: Train the full-sized model on CIFAR-10 with specific genotype.

python train.py --cfg_path="./configs/pcdarts_cifar10.yaml" --gpu=0

Testing on CIFAR-10 (using full-sized model)

To evaluate the full-sized model with the corresponding cfg file on the testing dataset. You can also download my trained model for testing from Models without training it yourself, which default arch (PCDARTS) is the best cell proposed in paper.

python test.py --cfg_path="./configs/pcdarts_cifar10.yaml" --gpu=0

Benchmark

:coffee:

Results on CIFAR-10

Note:

• Above results are referenced from official repository and orignal paper.
• There still have a slight performance gap between my PC-DARTS TF2 and official version. In both cases, we used Nvidia 1080ti (11G memory). My PC-DARTS TF2 pre-trained model can be found in Models.
• My tensorboard logs can be found from search_log and full_train_log.
• If you get unsatisfactory results with the archecture searched by yourself, you might try to search it more than one time. (see the discussions here)

Models

:doughnut:

Dowload these models bellow, then extract them into ./checkpoints/ for restoring.

Note:

• You can find the training settings of the models in the corresponding ./configs/*.yaml files, and make sure that the arch flag in it is matched with the genotypes name in ./modules/genotypes.py.
• Based on the property of the training dataset, all the pre-trained models can only be used for non-commercial applications.

References

:hamburger:

Thanks for these source codes porviding me with knowledges to complete this repository.

• https://github.com/yuhuixu1993/PC-DARTS (Official)
  • PC-DARTS:Partial Channel Connections for Memory-Efficient Differentiable Architecture Search
• https://github.com/quark0/darts
  • Differentiable architecture search for convolutional and recurrent networks https://arxiv.org/abs/1806.09055
• https://github.com/zzh8829/yolov3-tf2
  • YoloV3 Implemented in TensorFlow 2.0