Examples Reference

November 8, 2021 · View on GitHub

Vega provides guidance on how to use algorithms and tasks, and provides guidance on algorithm development for developers, such as expanding the search space and search algorithm and building datasets applicable to Vega.

1. Example List

In the following example, the following directory is generated after the package is decompressed:

Contents	Description
compression	compression algorithm usage example, including Quant-EA、 Prune-EA.
data augmentation	Example of using the data augmentation algorithm, including the PBA, CycleSR
hpo	hyperparameter optimization algorithm use examples, including ASHA, BO, TPE, BOHB, BOSS, Random, Random Pareto.
nas	Examples of network architecture search, including CARS, SP-NAS, auto-lane, SR-EA, ESR-EA, Adelaide-EA, NAGO, BackboneNas, DartsCNN, FIS, GDAS, MFKD, SegmentationEA, SGAS, ModuleNas, DNet-NAS
fully train	Samples related to fully train, including the EfficientNet B0/B4 model training example and FMD operator example.
classification	An example of using NAS + HPO + FullyTrain to complete an image classification task
features	Cluster, custom dataset, model evaluation, quota samples.

2. Run Example

2.1 Run PyTorch Example

Generally, an algorithm example contains a configuration file, some algorithms, and some matching code.

You can execute the following commands in the examples directory:

vega <algorithm config file>

For example, the command of CARS example is as follows:

vega ./nas/cars/cars.yml

All information is stored in configuration files. Configuration items are classified into public configuration items and algorithm configuration items. For details about public configuration items, see the configuration reference. For details about algorithm configuration, see the reference documents of each algorithm.Error! Hyperlink reference not valid.

Before running an example, you need to configure the directory where the dataset is located in the algorithm configuration file. The root directory of the default dataset is /cache/datasets/, for example, the directory of the Cifar10 is /cache/datasets/cifar10/.

Before running the example, you need to download the dataset to the default data configuration directory. Before running the example, you need to create the directory /cache/datasets/, then download each dataset to the directory and unzip it. The default directory configuration of each dataset is as follows:

example	Pre-trained Model	Default Path	Model Source
adelaide_ea	mobilenet_v2-b0353104.pth	/cache/models/mobilenet_v2-b0353104.pth	download
BackboneNas (mindspore)	resnet50-19c8e357.pth	/cache/models/resnet50-19c8e357.pth	download
BackboneNas (tensorflow), classification, prune_ea(tensorflow)	resnet_imagenet_v1_fp32_20181001	/cache/models/resnet_imagenet_v1_fp32_20181001/ keep only these files: checkpoint, graph.pbtxt, model.ckpt-225207.data-00000-of-00002, model.ckpt-225207.data-00001-of-00002, model.ckpt-225207.index, model.ckpt-225207.meta	download
dnet_nas	031-_64_12-1111-11211112-2.pth	/cache/models/031-_64_12-1111-11211112-2.pth	download
prune_ea(pytorch)	resnet20.pth	/cache/models/resnet20.pth	download
prune_ea(mindspore)	resnet20.ckpt	/cache/models/resnet20.ckpt	download
sp_nas	fasterrcnn_resnet50_fpn_coco-258fb6c6.pth	/cache/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth	download

In the configuration file of each example, the platform description (PyTorch, TensorFlow, and MindSpore) applicable to the example is provided in general/backend.

For example, the following configuration indicates that the sample can run on three platforms:

general:
    backend: pytorch # pytorch | tensorflow | mindspore

The following configurations can run only in TensorFlow:

general:
    backend: tensorflow

2.2 Run TensorFlow Example

Command line (GPU):

vega <algorithm config file> -b t

for example:

vega ./nas/backbone_nas/backbone_nas.yml -b t

Command line (Atlas 900):

vega <algorithm config file> -b t -d NPU

for example:

vega ./nas/backbone_nas/backbone_nas.yml -b t -d NPU

2.3 Run MindSpore Example

Command line (Atlas 900):

vega <algorithm config file> -b m -d NPU

for example:

vega ./nas/backbone_nas/backbone_nas.yml -b m -d NPU

3. Examples' Input and Output

3.1 Model Compression

Prune-EA

Stage	Option	Content
nas	Input	Config File: compression/prune-ea/prune.yml Pre-Trained Model: /cache/models/resnet20.pth Dataset: /cache/datasets/cifar10
nas	Output	Network Description File: tasks/<task id>/output/nas/model_desc_<id>.json
nas	approximate running time	(random_samples + num_generation * num_individual) * epochs / Number of GPUs * Training time per epoch
fully train	Input	Config File: compression/prune-ea/prune.yml Network Description File: tasks/<task id>/output/nas/model_desc_<id>.json Dataset: /cache/datasets/cifar10
fully train	Output	Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

Quant-EA

Stage	Option	Content
nas	Input	Config File: compression/quant-ea/quant.yml Dataset: /cache/datasets/cifar10
nas	Output	Network Description File: tasks/<task id>/output/nas/model_desc_<id>.json
nas	approximate running time	(random_samples + num_generation * num_individual) * epochs / Number of GPUs * Training time per epoch
fully train	Input	Config File: compression/quant-ea/quant.yml Network Description File: tasks/<task id>/output/nas/model_desc_<id>.json Dataset: /cache/datasets/cifar10
fully train	Output	Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

3.2 NAS

CARS

Stage	Option	Content
nas	Input	Config File: nas/cars/cars.yml Dataset: /cache/datasets/cifar10
nas	Output	Network Description File: tasks/<task id>/output/nas/model_desc_<id>.json
nas	approximate running time	epochs * Training time per epoch (The training time is affected by num_individual)
fully train	Input	Config File: nas/cars/cars.yml Network Description File: tasks/<task id>/output/nas/model_desc_<id>.json Dataset: /cache/datasets/cifar10
fully train	Output	Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

Adelaide-EA

Stage	Option	Content
random	Input	Config File: nas/adelaide_ea/adelaide_ea.yml Dataset: /cache/datasets/cityscapes
random	Output	Network Description File: tasks/<task id>/output/random/model_desc_<id>.json
random	approximate running time	max_sample * epochs / Number of GPUs * Training time per epoch
mutate	Input	Config File: nas/adelaide_ea/adelaide_ea.yml Dataset: /cache/datasets/cityscapes Network Description File: tasks/<task id>/output/random/model_desc_<id>.json
mutate	Output	Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json
mutate	approximate running time	max_sample * epochs / Number of GPUs * Training time per epoch
fully train	Input	Config File: nas/adelaide_ea/adelaide_ea.yml Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json Dataset: /cache/datasets/cityscapes
fully train	Output	Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

ESR-EA

Stage	Option	Content
nas	Input	Config File: nas/esr_ea/esr_ea.yml Dataset: /cache/datasets/DIV2K
nas	Output	Network Description File: tasks/<task id>/output/nas/selected_arch.npy
nas	approximate running time	num_generation * num_individual * epochs / Number of GPUs * Training time per epoch
fully train	Input	Config File: nas/esr_ea/esr_ea.yml Network Description File: tasks/<task id>/output/nas/selected_arch.npy Dataset: /cache/datasets/DIV2K
fully train	Output	Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

SR-EA

Stage	Option	Content
random	Input	Config File: nas/sr_ea/sr_ea.yml Dataset: /cache/datasets/DIV2K
random	Output	Network Description File: tasks/<task id>/output/random/model_desc_<id>.json
random	approximate running time	num_sample * epochs / Number of GPUs * Training time per epoch
mutate	Input	Config File: nas/sr_ea/sr_ea.yml Dataset: /cache/datasets/DIV2K Network Description File: tasks/<task id>/output/random/model_desc_<id>.json
mutate	Output	Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json
mutate	approximate running time	num_sample * epochs / Number of GPUs * Training time per epoch
fully train	Input	Config File: nas/sr_ea/sr_ea.yml Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json Dataset: /cache/datasets/DIV2K
fully train	Output	Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

SP-NAS

Stage	Option	Content
nas1	Input	Config File: nas/sp_nas/spnas.yml Dataset: /cache/datasets/COCO2017 Pre-Trained Model: /cache/models/resnet50-19c8e357.pth Config File: nas/sp_nas/faster_rcnn_r50_fpn_1x.py
nas1	Output	Network Description File: tasks/<task id>/output/nas1/model_desc_<id>.json Model List: tasks/<task id>/output/total_list_p.csv
nas1	approximate running time	max_sample * epochs / Number of GPUs * Training time per epoch
nas2	Input	Config File: nas/sp_nas/spnas.yml Dataset: /cache/datasets/COCO2017 Network Description File: tasks/<task id>/output/nas1/model_desc_<id>.json Model List: tasks/<task id>/output/total_list_p.csv Config File: nas/sp_nas/faster_rcnn_r50_fpn_1x.py
nas2	Output	Network Description File: tasks/<task id>/output/nas2/model_desc_<id>.json Model List: tasks/<task id>/output/total_list_s.csv
nas2	approximate running time	max_sample * epochs / Number of GPUs * Training time per epoch
fully train	Input	Config File: nas/sp_nas/spnas.yml Dataset: /cache/datasets/COCO2017 Network Description File: tasks/<task id>/output/nas2/model_desc_<id>.json Model List: tasks/<task id>/output/total_list_s.csv Config File: nas/sp_nas/faster_rcnn_r50_fpn_1x.py
fully train	Output	Model: tasks/<task id>/output/fullytrain/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

Auto_Lane

Stage	Option	Content
nas	Input	Config File：nas/auto_lane/auto_lane.yml Dataset：/cache/datasets/CULane OR /cache/datasets/CurveLane
nas	Output	Network Description File：tasks/<task id>/output/nas/model_desc_<id>.json
nas	approximate running time	max_sample * epoch / Numbers of GPUs * Training time per epoch
fully train	Input	Config File：nas/sp_nas/auto_lane.yml Dataset：/cache/datasets/CULane OR /cache/datasets/CurveLane Network Description File：tasks/<task id>/output/nas/model_desc_<id>.json
fully train	Output	Model：tasks/<task id>/output/fullytrain/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

AutoGroup

Stage	Option	Content
fully train	Input	Config File nas/fis/autogroup.yml Dataset: /cache/datasets/avazu
fully train	Output	Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json Model: tasks/<task id>/output/fully_train/model_0.pth
fully train	approximate running time	epochs * Training time per epoch

AutoFis

Stage	Option	Content
search	Input	Config File nas/fis/autogate_grda.yml Dataset: /cache/datasets/avazu
search	Output	Model: tasks/<task id>/output/search/0/model.pth
search	approximate running time	epochs * Training time per epoch
retrain	Input	Config File nas/fis/autogate_grda.yml Dataset: /cache/datasets/avazu
retrain	Output	Model: tasks/<task id>/output/retrain/0/model.pth
retrain	approximate running time	epochs * Training time per epoch

3.3 Data Augmentation

PBA

Stage	Option	Content
pba	Input	Config File: data_augmentation/pba/pba.yml Dataset: /cache/datasets/cifar10
pba	Output	Transformer List: tasks/<task id>/output/pba/best_hps.json
pba	approximate running time	total_rungs * each_epochs * config_count / Number of GPUs * Training time per epoch

CycleSR

Stage	Option	Content
fully train	Input	Config File: data_augmentation/cyclesr/cyclesr.yml Dataset: /cache/datasets/DIV2K_unpair
fully train	Output	Model: tasks/<task id>/output/fully_train/model_0.pth
fully train	approximate running time	n_epoch * Training time per epoch

3.4 HPO

ASHA、BOHB、BOSS

Stage Option Content
hpo Input Config File: hpo/asha|bohb|boss/hpo/asha|bohb|boss.yml
Dataset: /cache/datasets/cifar10
hpo Output Hyperparameter file: tasks/<task id>/output/hpo/best_hps.json

Stage	Option	Content
hpo	Input	Config File: hpo/asha\|bohb\|boss/hpo/asha\|bohb\|boss.yml Dataset: /cache/datasets/cifar10
hpo	Output	Hyperparameter file: tasks/<task id>/output/hpo/best_hps.json

3.5 Fully Train

EfficientNet

Stage	Option	Content
fully train	Input	Config File: fully_train/efficientnet/efficientnet_b0.yml Dataset: /cache/datasets/ILSVRC
fully train	Output	Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json Model: tasks/<task id>/output/fully_train/model_<id>.pth
fully train	approximate running time	epochs * Training time per epoch

FMD

Stage	Option	Content
fully train	Input	Config File: fully_train/fmd/fmd.yml Dataset: /cache/datasets/cifar10
fully train	Output	Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json Model: tasks/<task id>/output/fully_train/model_0.pth
fully train	approximate running time	epochs * Training time per epoch

ResNet

Stage	Option	Content
fully train	Input	Config File fully_train/trainer/resnet.yml Dataset: /cache/datasets/ILSVRC
fully train	Output	Network Description File: tasks/<task id>/output/mutate/model_desc_<id>.json Model: tasks/<task id>/output/fully_train/model_0.pth
fully train	approximate running time	epochs * Training time per epoch