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.
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:
For the following two things, please use AutoDL-Projects:
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.
[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.
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.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.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 $TORCH_HOME (~/.torch/ by default). If you want to generate NAS-Bench-201 or similar NAS datasets or training models by yourself, you need these data.
More usage can be found in our test codes.
from nas_201_api import NASBench201API as API
api = API('$path_to_meta_nas_bench_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['TORCH_HOME'], 'NAS-Bench-201-v1_1-096897.pth')
api = API(None)
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))
# show all information for a specific architecture
api.show(1)
api.show(2)
# show the mean loss and accuracy of an architecture
info = api.query_meta_info_by_index(1) # This is an instance of `ArchResults`
res_metrics = info.get_metrics('cifar10', 'train') # This is a dict with metric names as keys
cost_metrics = info.get_comput_costs('cifar100') # This is a dict with metric names as keys, e.g., flops, params, latency
# get the detailed information
results = api.query_by_index(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.get_latency()))
print ('Train Info : {:}'.format(result.get_train()))
print ('Valid Info : {:}'.format(result.get_eval('x-valid')))
print ('Test Info : {:}'.format(result.get_eval('x-test')))
# for the metric after a specific epoch
print ('Train Info [10-th epoch] : {:}'.format(result.get_train(10)))
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 )
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.
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 get_more_info function.
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.
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 nas_201_api import ArchResults
xdata = torch.load('000157-FULL.pth')
archRes = ArchResults.create_from_state_dict(xdata['less']) # load trials trained with 12 epochs
archRes = ArchResults.create_from_state_dict(xdata['full']) # load trials trained with 200 epochs
print(archRes.arch_idx_str()) # print the index of this architecture
print(archRes.get_dataset_names()) # print the supported training data
print(archRes.get_compute_costs('cifar10-valid')) # print all computational info when training on cifar10-valid
print(archRes.get_metrics('cifar10-valid', 'x-valid', None, False)) # print the average loss/accuracy/time on all trials
print(archRes.get_metrics('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 nas_201_api import NASBench201API as API
api = API('NAS-Bench-201-v1_1-096897.pth') # This will load all the information of NAS-Bench-201 except the trained weights
api = API('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_1-096897.pth')) # The same as the above line while I usually save NAS-Bench-201-v1_1-096897.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.get_net_param(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.get_more_info(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.get_more_info(112, 'ImageNet16-120', None, hp='200', is_random=True)
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}
}
FAQs
API for NAS-Bench-201 (a benchmark for neural architecture search).
We found that nas-bench-201 demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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