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NAS-Bench-201 API and Instruction

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NAS-BENCH-201 has been extended to NATS-Bench

Since our NAS-BENCH-201 has been extended to NATS-Bench, this repo is deprecated and not maintained. Please use NATS-Bench, which has 5x more architecture information and faster API than NAS-BENCH-201.

NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search

We propose an algorithm-agnostic NAS benchmark (NAS-Bench-201) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms. The design of our search space is inspired by that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph. Each edge here is associated with an operation selected from a predefined operation set. For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-201 includes 4 nodes and 5 associated operation options, which generates 15,625 neural cell candidates in total.

In this Markdown file, we provide: - How to Use NAS-Bench-201

For the following two things, please use AutoDL-Projects: - Instruction to re-generate NAS-Bench-201 - 10 NAS algorithms evaluated in our paper

Note: please use

PyTorch >= 1.2.0
and
Python >= 3.6.0
.

You can simply type

pip install nas-bench-201
to install our api. Please see source codes of
nas-bench-201
module in this repo.

If you have any questions or issues, please post it at here or email me.

Preparation and Download

[deprecated] The old benchmark file of NAS-Bench-201 can be downloaded from Google Drive or Baidu-Wangpan (code:6u5d).

[recommended] The latest benchmark file of NAS-Bench-201 (

NAS-Bench-201-v1_1-096897.pth
) can be downloaded from Google Drive. The files for model weight are too large (431G) and I need some time to upload it. Please be patient, thanks for your understanding.

You can move it to anywhere you want and send its path to our API for initialization. - [2020.02.25] APIv1.0/FILEv1.0:

NAS-Bench-201-v1_0-e61699.pth
(2.2G), where

e61699
is the last six digits for this file. It contains all information except for the trained weights of each trial. - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from NAS-BENCH-201-4-v1.0-archive.tar (about 226GB). This compressed folder has 15625 files containing the the trained weights. - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in Google Drive. - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions - [2020.03.16] APIv1.3/FILEv1.1:
NAS-Bench-201-v1_1-096897.pth
(4.7G), where
096897
is the last six digits for this file. It contains information of more trials compared to
NAS-Bench-201-v1_0-e61699.pth
, especially all models trained by 12 epochs on all datasets are avaliable. - [2020.06.30] APIv2.0: Use abstract class (NASBenchMetaAPI) for APIs of NAS-Bench-x0y. - [2020.06.30] FILEv2.0: coming soon!

We recommend to use

NAS-Bench-201-v1_1-096897.pth

The training and evaluation data used in NAS-Bench-201 can be downloaded from Google Drive or Baidu-Wangpan (code:4fg7). It is recommended to put these data into

$TORCH_HOME
(
~/.torch/
by default). If you want to generate NAS-Bench-201 or similar NAS datasets or training models by yourself, you need these data.

How to Use NAS-Bench-201

More usage can be found in our test codes.

  1. Creating an API instance from a file: ``` from nas201api import NASBench201API as API api = API('$pathtometanasbench_file')

    Create an API without the verbose log

    api = API('NAS-Bench-201-v1_1-096897.pth', verbose=False)

    The default path for benchmark file is '{:}/{:}'.format(os.environ['TORCHHOME'], 'NAS-Bench-201-v11-096897.pth')

    api = API(None) ```

  2. Show the number of architectures

    len(api)
    and each architecture
    api[i]
    :
    num = len(api)
    for i, arch_str in enumerate(api):
    print ('{:5d}/{:5d} : {:}'.format(i, len(api), arch_str))
    
  3. Show the results of all trials for a single architecture: ```

    show all information for a specific architecture

    api.show(1) api.show(2)

show the mean loss and accuracy of an architecture

info = api.querymetainfobyindex(1) # This is an instance of

ArchResults
resmetrics = info.getmetrics('cifar10', 'train') # This is a dict with metric names as keys costmetrics = info.getcomput_costs('cifar100') # This is a dict with metric names as keys, e.g., flops, params, latency

get the detailed information

results = api.querybyindex(1, 'cifar100') # a dict of all trials for 1st net on cifar100, where the key is the seed print ('There are {:} trials for this architecture [{:}] on cifar100'.format(len(results), api[1])) for seed, result in results.items(): print ('Latency : {:}'.format(result.getlatency())) print ('Train Info : {:}'.format(result.gettrain())) print ('Valid Info : {:}'.format(result.geteval('x-valid'))) print ('Test Info : {:}'.format(result.geteval('x-test'))) # for the metric after a specific epoch print ('Train Info [10-th epoch] : {:}'.format(result.get_train(10))) ```

  1. Query the index of an architecture by string

    index = api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|')
    api.show(index)
    
    This string
    |nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|
    means:
    node-0: the input tensor
    node-1: conv-3x3( node-0 )
    node-2: conv-3x3( node-0 ) + avg-pool-3x3( node-1 )
    node-3: skip-connect( node-0 ) + conv-3x3( node-1 ) + skip-connect( node-2 )
    
  2. Create the network from api:

    config = api.get_net_config(123, 'cifar10') # obtain the network configuration for the 123-th architecture on the CIFAR-10 dataset
    from models import get_cell_based_tiny_net # this module is in AutoDL-Projects/lib/models
    network = get_cell_based_tiny_net(config) # create the network from configurration
    print(network) # show the structure of this architecture
    
    If you want to load the trained weights of this created network, you need to use
    api.get_net_param(123, ...)
    to obtain the weights and then load it to the network.
  3. api.get_more_info(...)
    can return the loss / accuracy / time on training / validation / test sets, which is very helpful. For more details, please look at the comments in the getmoreinfo function.
  4. For other usages, please see

    lib/nas_201_api/api.py
    . We provide some usage information in the comments for the corresponding functions. If what you want is not provided, please feel free to open an issue for discussion, and I am happy to answer any questions regarding NAS-Bench-201.

Detailed Instruction

In

nas_201_api
, we define three classes:
NASBench201API
,
ArchResults
,
ResultsCount
.

ResultsCount
maintains all information of a specific trial. One can instantiate ResultsCount and get the info via the following codes (
000157-FULL.pth
saves all information of all trials of 157-th architecture):
from nas_201_api import ResultsCount
xdata  = torch.load('000157-FULL.pth')
odata  = xdata['full']['all_results'][('cifar10-valid', 777)]
result = ResultsCount.create_from_state_dict( odata )
print(result) # print it
print(result.get_train())   # print the final training loss/accuracy/[optional:time-cost-of-a-training-epoch]
print(result.get_train(11)) # print the training info of the 11-th epoch
print(result.get_eval('x-valid'))     # print the final evaluation info on the validation set
print(result.get_eval('x-valid', 11)) # print the info on the validation set of the 11-th epoch
print(result.get_latency())           # print the evaluation latency [in batch]
result.get_net_param()                # the trained parameters of this trial
arch_config = result.get_config(CellStructure.str2structure) # create the network with params
net_config  = dict2config(arch_config, None)
network    = get_cell_based_tiny_net(net_config)
network.load_state_dict(result.get_net_param())

ArchResults
maintains all information of all trials of an architecture. Please see the following usages: ``` from nas201api import ArchResults xdata = torch.load('000157-FULL.pth') archRes = ArchResults.createfromstatedict(xdata['less']) # load trials trained with 12 epochs archRes = ArchResults.createfromstatedict(xdata['full']) # load trials trained with 200 epochs

print(archRes.archidxstr()) # print the index of this architecture print(archRes.getdatasetnames()) # print the supported training data print(archRes.getcomputecosts('cifar10-valid')) # print all computational info when training on cifar10-valid print(archRes.getmetrics('cifar10-valid', 'x-valid', None, False)) # print the average loss/accuracy/time on all trials print(archRes.getmetrics('cifar10-valid', 'x-valid', None, True)) # print loss/accuracy/time of a randomly selected trial ```

NASBench201API
is the topest level api. Please see the following usages: ``` from nas201api import NASBench201API as API api = API('NAS-Bench-201-v11-096897.pth') # This will load all the information of NAS-Bench-201 except the trained weights api = API('{:}/{:}'.format(os.environ['TORCHHOME'], 'NAS-Bench-201-v11-096897.pth')) # The same as the above line while I usually save NAS-Bench-201-v11-096897.pth in ~/.torch/. api.show(-1) # show info of all architectures api.reload('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-BENCH-201-4-v1.0-archive'), 3) # This code will reload the information 3-th architecture with the trained weights

weights = api.getnetparam(3, 'cifar10', None) # Obtaining the weights of all trials for the 3-th architecture on cifar10. It will returns a dict, where the key is the seed and the value is the trained weights. ```

To obtain the training and evaluation information (please see the comments here): ``` api.getmoreinfo(112, 'cifar10', None, hp='200', is_random=True)

Query info of last training epoch for 112-th architecture

using 200-epoch-hyper-parameter and randomly select a trial.

api.getmoreinfo(112, 'ImageNet16-120', None, hp='200', is_random=True) ```

Citation

If you find that NAS-Bench-201 helps your research, please consider citing it:

@inproceedings{dong2020nasbench201,
  title     = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {International Conference on Learning Representations (ICLR)},
  url       = {https://openreview.net/forum?id=HJxyZkBKDr},
  year      = {2020}
}

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