From 192f286cfbc1308e779dd19ca806f17edcfeb1f8 Mon Sep 17 00:00:00 2001 From: mhz Date: Fri, 23 Aug 2024 14:22:28 +0200 Subject: [PATCH] add nasbench201 --- NAS-Bench-201/.gitignore | 102 ++++ NAS-Bench-201/LICENSE.md | 21 + NAS-Bench-201/README.md | 185 ++++++ NAS-Bench-201/nas_201_api/__init__.py | 42 ++ NAS-Bench-201/nas_201_api/api_201.py | 274 +++++++++ NAS-Bench-201/nas_201_api/api_utils.py | 750 +++++++++++++++++++++++++ NAS-Bench-201/setup.py | 36 ++ 7 files changed, 1410 insertions(+) create mode 100755 NAS-Bench-201/.gitignore create mode 100644 NAS-Bench-201/LICENSE.md create mode 100644 NAS-Bench-201/README.md create mode 100644 NAS-Bench-201/nas_201_api/__init__.py create mode 100644 NAS-Bench-201/nas_201_api/api_201.py create mode 100644 NAS-Bench-201/nas_201_api/api_utils.py create mode 100644 NAS-Bench-201/setup.py diff --git a/NAS-Bench-201/.gitignore b/NAS-Bench-201/.gitignore new file mode 100755 index 0000000..3802b56 --- /dev/null +++ b/NAS-Bench-201/.gitignore @@ -0,0 +1,102 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# IPython Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# dotenv +.env + +# virtualenv +venv/ +ENV/ + +# Spyder project settings +.spyderproject + +# Rope project settings +.ropeproject + +# Pycharm project +.idea +snapshots +*.pytorch +*.tar.bz +data +.*.swp +*.sh +main_main.py +dist +build +*.egg-info diff --git a/NAS-Bench-201/LICENSE.md b/NAS-Bench-201/LICENSE.md new file mode 100644 index 0000000..57603a6 --- /dev/null +++ b/NAS-Bench-201/LICENSE.md @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) since 2019 Xuanyi Dong (GitHub: https://github.com/D-X-Y) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NAS-Bench-201/README.md b/NAS-Bench-201/README.md new file mode 100644 index 0000000..44970e1 --- /dev/null +++ b/NAS-Bench-201/README.md @@ -0,0 +1,185 @@ +# NAS-BENCH-201 has been extended to [NATS-Bench](https://xuanyidong.com/assets/projects/NATS-Bench) + +**Since our NAS-BENCH-201 has been extended to NATS-Bench, this repo is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/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](https://openreview.net/forum?id=HJxyZkBKDr) + +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](#how-to-use-nas-bench-201) + +For the following two things, please use [AutoDL-Projects](https://github.com/D-X-Y/AutoDL-Projects): +- [Instruction to re-generate NAS-Bench-201](#instruction-to-re-generate-nas-bench-201) +- [10 NAS algorithms evaluated in our paper](#to-reproduce-10-baseline-nas-algorithms-in-nas-bench-201) + +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](https://github.com/D-X-Y/NAS-Bench-201). + +**If you have any questions or issues, please post it at [here](https://github.com/D-X-Y/AutoDL-Projects/issues) or email me.** + +### Preparation and Download + +[deprecated] The **old** benchmark file of NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/file/d/1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs/view?usp=sharing) or [Baidu-Wangpan (code:6u5d)](https://pan.baidu.com/s/1CiaNH6C12zuZf7q-Ilm09w). + +[recommended] The **latest** benchmark file of NAS-Bench-201 (`NAS-Bench-201-v1_1-096897.pth`) can be downloaded from [Google Drive](https://drive.google.com/file/d/16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_/view?usp=sharing). 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`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (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](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (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](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi). +- [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`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (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](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan (code:4fg7)](https://pan.baidu.com/s/1XAzavPKq3zcat1yBA1L2tQ). +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](https://github.com/D-X-Y/AutoDL-Projects/blob/master/exps/NAS-Bench-201/test-nas-api.py)**. + +1. Creating an API instance from a file: +``` +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) +``` + +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.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))) +``` + +4. 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 ) +``` + +5. 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. + +6. `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. + +7. 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 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](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_201_api/api_201.py#L142)): +``` +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) +``` + +# 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} +} +``` diff --git a/NAS-Bench-201/nas_201_api/__init__.py b/NAS-Bench-201/nas_201_api/__init__.py new file mode 100644 index 0000000..15d6940 --- /dev/null +++ b/NAS-Bench-201/nas_201_api/__init__.py @@ -0,0 +1,42 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 # +##################################################### +from .api_utils import ArchResults, ResultsCount +from .api_201 import NASBench201API + +# NAS_BENCH_201_API_VERSION="v1.1" # [2020.02.25] +# NAS_BENCH_201_API_VERSION="v1.2" # [2020.03.09] +# NAS_BENCH_201_API_VERSION="v1.3" # [2020.03.16] +NAS_BENCH_201_API_VERSION="v2.0" # [2020.06.30] + + +def test_api(path): + """This is used to test the API of NAS-Bench-201.""" + api = NASBench201API(path) + num = len(api) + for i, arch_str in enumerate(api): + print ('{:5d}/{:5d} : {:}'.format(i, len(api), arch_str)) + indexes = [1, 2, 11, 301] + for index in indexes: + print('\n--- index={:} ---'.format(index)) + api.show(index) + # show the mean loss and accuracy of an architecture + info = api.query_meta_info_by_index(index) # 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_compute_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(index, '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))) + config = api.get_net_config(index, 'cifar10') + print ('config={:}'.format(config)) + 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) + print('TEST NAS-BENCH-201 DONE.') diff --git a/NAS-Bench-201/nas_201_api/api_201.py b/NAS-Bench-201/nas_201_api/api_201.py new file mode 100644 index 0000000..6801deb --- /dev/null +++ b/NAS-Bench-201/nas_201_api/api_201.py @@ -0,0 +1,274 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 # +############################################################################################ +# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 # +############################################################################################ +# The history of benchmark files: +# [2020.02.25] NAS-Bench-201-v1_0-e61699.pth : 6219 architectures are trained once, 1621 architectures are trained twice, 7785 architectures are trained three times. `LESS` only supports CIFAR10-VALID. +# [2020.03.16] NAS-Bench-201-v1_1-096897.pth : 2225 architectures are trained once, 5439 archiitectures are trained twice, 7961 architectures are trained three times on all training sets. For the hyper-parameters with the total epochs of 12, each model is trained on CIFAR-10, CIFAR-100, ImageNet16-120 once, and is trained on CIFAR-10-VALID twice. +# +# I'm still actively enhancing this benchmark, while it is now maintained at https://github.com/D-X-Y/NATS-Bench +# +import os, copy, random, torch, numpy as np +from pathlib import Path +from typing import List, Text, Union, Dict, Optional +from collections import OrderedDict, defaultdict + +from .api_utils import ArchResults +from .api_utils import NASBenchMetaAPI +from .api_utils import remap_dataset_set_names + + +ALL_BENCHMARK_FILES = ['NAS-Bench-201-v1_0-e61699.pth', 'NAS-Bench-201-v1_1-096897.pth'] +ALL_ARCHIVE_DIRS = ['NAS-Bench-201-v1_1-archive'] + + +def print_information(information, extra_info=None, show=False): + dataset_names = information.get_dataset_names() + strings = [information.arch_str, 'datasets : {:}, extra-info : {:}'.format(dataset_names, extra_info)] + def metric2str(loss, acc): + return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc) + + for ida, dataset in enumerate(dataset_names): + metric = information.get_compute_costs(dataset) + flop, param, latency = metric['flops'], metric['params'], metric['latency'] + str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency is not None and latency > 0 else None) + train_info = information.get_metrics(dataset, 'train') + if dataset == 'cifar10-valid': + valid_info = information.get_metrics(dataset, 'x-valid') + str2 = '{:14s} train : [{:}], valid : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy'])) + elif dataset == 'cifar10': + test__info = information.get_metrics(dataset, 'ori-test') + str2 = '{:14s} train : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy'])) + else: + valid_info = information.get_metrics(dataset, 'x-valid') + test__info = information.get_metrics(dataset, 'x-test') + str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy'])) + strings += [str1, str2] + if show: print('\n'.join(strings)) + return strings + + +""" +This is the class for the API of NAS-Bench-201. +""" +class NASBench201API(NASBenchMetaAPI): + + """ The initialization function that takes the dataset file path (or a dict loaded from that path) as input. """ + def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None, + verbose: bool=True): + self.filename = None + self.reset_time() + if file_path_or_dict is None: + file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], ALL_BENCHMARK_FILES[-1]) + print ('Try to use the default NAS-Bench-201 path from {:}.'.format(file_path_or_dict)) + if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path): + file_path_or_dict = str(file_path_or_dict) + if verbose: print('try to create the NAS-Bench-201 api from {:}'.format(file_path_or_dict)) + assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict) + self.filename = Path(file_path_or_dict).name + file_path_or_dict = torch.load(file_path_or_dict, map_location='cpu') + elif isinstance(file_path_or_dict, dict): + file_path_or_dict = copy.deepcopy(file_path_or_dict) + else: raise ValueError('invalid type : {:} not in [str, dict]'.format(type(file_path_or_dict))) + assert isinstance(file_path_or_dict, dict), 'It should be a dict instead of {:}'.format(type(file_path_or_dict)) + self.verbose = verbose # [TODO] a flag indicating whether to print more logs + keys = ('meta_archs', 'arch2infos', 'evaluated_indexes') + for key in keys: assert key in file_path_or_dict, 'Can not find key[{:}] in the dict'.format(key) + self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] ) + # This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults + self.arch2infos_dict = OrderedDict() + self._avaliable_hps = set(['12', '200']) + for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())): + all_info = file_path_or_dict['arch2infos'][xkey] + hp2archres = OrderedDict() + # self.arch2infos_less[xkey] = ArchResults.create_from_state_dict( all_info['less'] ) + # self.arch2infos_full[xkey] = ArchResults.create_from_state_dict( all_info['full'] ) + hp2archres['12'] = ArchResults.create_from_state_dict(all_info['less']) + hp2archres['200'] = ArchResults.create_from_state_dict(all_info['full']) + self.arch2infos_dict[xkey] = hp2archres + self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes'])) + self.archstr2index = {} + for idx, arch in enumerate(self.meta_archs): + assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch]) + self.archstr2index[ arch ] = idx + + def reload(self, archive_root: Text = None, index: int = None): + """Overwrite all information of the 'index'-th architecture in the search space. + It will load its data from 'archive_root'. + """ + if archive_root is None: + archive_root = os.path.join(os.environ['TORCH_HOME'], ALL_ARCHIVE_DIRS[-1]) + assert os.path.isdir(archive_root), 'invalid directory : {:}'.format(archive_root) + if index is None: + indexes = list(range(len(self))) + else: + indexes = [index] + for idx in indexes: + assert 0 <= idx < len(self.meta_archs), 'invalid index of {:}'.format(idx) + xfile_path = os.path.join(archive_root, '{:06d}-FULL.pth'.format(idx)) + assert os.path.isfile(xfile_path), 'invalid data path : {:}'.format(xfile_path) + xdata = torch.load(xfile_path, map_location='cpu') + assert isinstance(xdata, dict) and 'full' in xdata and 'less' in xdata, 'invalid format of data in {:}'.format(xfile_path) + hp2archres = OrderedDict() + hp2archres['12'] = ArchResults.create_from_state_dict(xdata['less']) + hp2archres['200'] = ArchResults.create_from_state_dict(xdata['full']) + self.arch2infos_dict[idx] = hp2archres + + def query_info_str_by_arch(self, arch, hp: Text='12'): + """ This function is used to query the information of a specific architecture + 'arch' can be an architecture index or an architecture string + When hp=12, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/12E.config' + When hp=200, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/200E.config' + The difference between these three configurations are the number of training epochs. + """ + if self.verbose: + print('Call query_info_str_by_arch with arch={:} and hp={:}'.format(arch, hp)) + return self._query_info_str_by_arch(arch, hp, print_information) + + # obtain the metric for the `index`-th architecture + # `dataset` indicates the dataset: + # 'cifar10-valid' : using the proposed train set of CIFAR-10 as the training set + # 'cifar10' : using the proposed train+valid set of CIFAR-10 as the training set + # 'cifar100' : using the proposed train set of CIFAR-100 as the training set + # 'ImageNet16-120' : using the proposed train set of ImageNet-16-120 as the training set + # `iepoch` indicates the index of training epochs from 0 to 11/199. + # When iepoch=None, it will return the metric for the last training epoch + # When iepoch=11, it will return the metric for the 11-th training epoch (starting from 0) + # `use_12epochs_result` indicates different hyper-parameters for training + # When use_12epochs_result=True, it trains the network with 12 epochs and the LR decayed from 0.1 to 0 within 12 epochs + # When use_12epochs_result=False, it trains the network with 200 epochs and the LR decayed from 0.1 to 0 within 200 epochs + # `is_random` + # When is_random=True, the performance of a random architecture will be returned + # When is_random=False, the performanceo of all trials will be averaged. + def get_more_info(self, index, dataset, iepoch=None, hp='12', is_random=True): + if self.verbose: + print('Call the get_more_info function with index={:}, dataset={:}, iepoch={:}, hp={:}, and is_random={:}.'.format(index, dataset, iepoch, hp, is_random)) + index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object + if index not in self.arch2infos_dict: + raise ValueError('Did not find {:} from arch2infos_dict.'.format(index)) + archresult = self.arch2infos_dict[index][str(hp)] + # if randomly select one trial, select the seed at first + if isinstance(is_random, bool) and is_random: + seeds = archresult.get_dataset_seeds(dataset) + is_random = random.choice(seeds) + # collect the training information + train_info = archresult.get_metrics(dataset, 'train', iepoch=iepoch, is_random=is_random) + total = train_info['iepoch'] + 1 + xinfo = {'train-loss' : train_info['loss'], + 'train-accuracy': train_info['accuracy'], + 'train-per-time': train_info['all_time'] / total if train_info['all_time'] is not None else None, + 'train-all-time': train_info['all_time']} + # collect the evaluation information + if dataset == 'cifar10-valid': + valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random) + try: + test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random) + except: + test_info = None + valtest_info = None + else: + try: # collect results on the proposed test set + if dataset == 'cifar10': + test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random) + else: + test_info = archresult.get_metrics(dataset, 'x-test', iepoch=iepoch, is_random=is_random) + except: + test_info = None + try: # collect results on the proposed validation set + valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random) + except: + valid_info = None + try: + if dataset != 'cifar10': + valtest_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random) + else: + valtest_info = None + except: + valtest_info = None + if valid_info is not None: + xinfo['valid-loss'] = valid_info['loss'] + xinfo['valid-accuracy'] = valid_info['accuracy'] + xinfo['valid-per-time'] = valid_info['all_time'] / total if valid_info['all_time'] is not None else None + xinfo['valid-all-time'] = valid_info['all_time'] + if test_info is not None: + xinfo['test-loss'] = test_info['loss'] + xinfo['test-accuracy'] = test_info['accuracy'] + xinfo['test-per-time'] = test_info['all_time'] / total if test_info['all_time'] is not None else None + xinfo['test-all-time'] = test_info['all_time'] + if valtest_info is not None: + xinfo['valtest-loss'] = valtest_info['loss'] + xinfo['valtest-accuracy'] = valtest_info['accuracy'] + xinfo['valtest-per-time'] = valtest_info['all_time'] / total if valtest_info['all_time'] is not None else None + xinfo['valtest-all-time'] = valtest_info['all_time'] + return xinfo + + def show(self, index: int = -1) -> None: + """This function will print the information of a specific (or all) architecture(s).""" + self._show(index, print_information) + + @staticmethod + def str2lists(arch_str: Text) -> List[tuple]: + """ + This function shows how to read the string-based architecture encoding. + It is the same as the `str2structure` func in `AutoDL-Projects/lib/models/cell_searchs/genotypes.py` + + :param + arch_str: the input is a string indicates the architecture topology, such as + |nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2| + :return: a list of tuple, contains multiple (op, input_node_index) pairs. + + :usage + arch = api.str2lists( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' ) + print ('there are {:} nodes in this arch'.format(len(arch)+1)) # arch is a list + for i, node in enumerate(arch): + print('the {:}-th node is the sum of these {:} nodes with op: {:}'.format(i+1, len(node), node)) + """ + node_strs = arch_str.split('+') + genotypes = [] + for i, node_str in enumerate(node_strs): + inputs = list(filter(lambda x: x != '', node_str.split('|'))) + for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput) + inputs = ( xi.split('~') for xi in inputs ) + input_infos = tuple( (op, int(IDX)) for (op, IDX) in inputs) + genotypes.append( input_infos ) + return genotypes + + @staticmethod + def str2matrix(arch_str: Text, + search_space: List[Text] = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']) -> np.ndarray: + """ + This func shows how to convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101. + + :param + arch_str: the input is a string indicates the architecture topology, such as + |nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2| + search_space: a list of operation string, the default list is the search space for NAS-Bench-201 + the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24 + :return + the numpy matrix (2-D np.ndarray) representing the DAG of this architecture topology + :usage + matrix = api.str2matrix( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' ) + This matrix is 4-by-4 matrix representing a cell with 4 nodes (only the lower left triangle is useful). + [ [0, 0, 0, 0], # the first line represents the input (0-th) node + [2, 0, 0, 0], # the second line represents the 1-st node, is calculated by 2-th-op( 0-th-node ) + [0, 0, 0, 0], # the third line represents the 2-nd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + [0, 0, 1, 0] ] # the fourth line represents the 3-rd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + 1-th-op( 2-th-node ) + In NAS-Bench-201 search space, 0-th-op is 'none', 1-th-op is 'skip_connect', + 2-th-op is 'nor_conv_1x1', 3-th-op is 'nor_conv_3x3', 4-th-op is 'avg_pool_3x3'. + :(NOTE) + If a node has two input-edges from the same node, this function does not work. One edge will be overlapped. + """ + node_strs = arch_str.split('+') + num_nodes = len(node_strs) + 1 + matrix = np.zeros((num_nodes, num_nodes)) + for i, node_str in enumerate(node_strs): + inputs = list(filter(lambda x: x != '', node_str.split('|'))) + for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput) + for xi in inputs: + op, idx = xi.split('~') + if op not in search_space: raise ValueError('this op ({:}) is not in {:}'.format(op, search_space)) + op_idx, node_idx = search_space.index(op), int(idx) + matrix[i+1, node_idx] = op_idx + return matrix + diff --git a/NAS-Bench-201/nas_201_api/api_utils.py b/NAS-Bench-201/nas_201_api/api_utils.py new file mode 100644 index 0000000..a8383d2 --- /dev/null +++ b/NAS-Bench-201/nas_201_api/api_utils.py @@ -0,0 +1,750 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 # +############################################################################################ +# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 # +############################################################################################ +# In this Python file, we define NASBenchMetaAPI, the abstract class for benchmark APIs. +# We also define the class ArchResults, which contains all information of a single architecture trained by one kind of hyper-parameters on three datasets. +# We also define the class ResultsCount, which contains all information of a single trial for a single architecture. +############################################################################################ +# History: +# [2020.06.30] The first version. +# +import os, abc, copy, random, torch, numpy as np +from pathlib import Path +from typing import List, Text, Union, Dict, Optional +from collections import OrderedDict, defaultdict + + +def remap_dataset_set_names(dataset, metric_on_set, verbose=False): + """re-map the metric_on_set to internal keys""" + if verbose: + print('Call internal function _remap_dataset_set_names with dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set)) + if dataset == 'cifar10' and metric_on_set == 'valid': + dataset, metric_on_set = 'cifar10-valid', 'x-valid' + elif dataset == 'cifar10' and metric_on_set == 'test': + dataset, metric_on_set = 'cifar10', 'ori-test' + elif dataset == 'cifar10' and metric_on_set == 'train': + dataset, metric_on_set = 'cifar10', 'train' + elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'valid': + metric_on_set = 'x-valid' + elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'test': + metric_on_set = 'x-test' + if verbose: + print(' return dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set)) + return dataset, metric_on_set + + +class NASBenchMetaAPI(metaclass=abc.ABCMeta): + + @abc.abstractmethod + def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None, verbose: bool=True): + """The initialization function that takes the dataset file path (or a dict loaded from that path) as input.""" + + def __getitem__(self, index: int): + return copy.deepcopy(self.meta_archs[index]) + + def arch(self, index: int): + """Return the topology structure of the `index`-th architecture.""" + if self.verbose: + print('Call the arch function with index={:}'.format(index)) + assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs)) + return copy.deepcopy(self.meta_archs[index]) + + def __len__(self): + return len(self.meta_archs) + + def __repr__(self): + return ('{name}({num}/{total} architectures, file={filename})'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs), filename=self.filename)) + + @property + def avaliable_hps(self): + return list(copy.deepcopy(self._avaliable_hps)) + + @property + def used_time(self): + return self._used_time + + def reset_time(self): + self._used_time = 0 + + def simulate_train_eval(self, arch, dataset, hp='12', account_time=True): + index = self.query_index_by_arch(arch) + all_names = ('cifar10', 'cifar100', 'ImageNet16-120') + assert dataset in all_names, 'Invalid dataset name : {:} vs {:}'.format(dataset, all_names) + if dataset == 'cifar10': + info = self.get_more_info(index, 'cifar10-valid', iepoch=None, hp=hp, is_random=True) + else: + info = self.get_more_info(index, dataset, iepoch=None, hp=hp, is_random=True) + valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time'] + latency = self.get_latency(index, dataset) + if account_time: + self._used_time += time_cost + return valid_acc, latency, time_cost, self._used_time + + def random(self): + """Return a random index of all architectures.""" + return random.randint(0, len(self.meta_archs)-1) + + def query_index_by_arch(self, arch): + """ This function is used to query the index of an architecture in the search space. + In the topology search space, the input arch can be an architecture string such as '|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|'; + or an instance that has the 'tostr' function that can generate the architecture string; + or it is directly an architecture index, in this case, we will check whether it is valid or not. + This function will return the index. + If return -1, it means this architecture is not in the search space. + Otherwise, it will return an int in [0, the-number-of-candidates-in-the-search-space). + """ + if self.verbose: + print('Call query_index_by_arch with arch={:}'.format(arch)) + if isinstance(arch, int): + if 0 <= arch < len(self): + return arch + else: + raise ValueError('Invalid architecture index {:} vs [{:}, {:}].'.format(arch, 0, len(self))) + elif isinstance(arch, str): + if arch in self.archstr2index: arch_index = self.archstr2index[ arch ] + else : arch_index = -1 + elif hasattr(arch, 'tostr'): + if arch.tostr() in self.archstr2index: arch_index = self.archstr2index[ arch.tostr() ] + else : arch_index = -1 + else: arch_index = -1 + return arch_index + + def query_by_arch(self, arch, hp): + # This is to make the current version be compatible with the old version. + return self.query_info_str_by_arch(arch, hp) + + @abc.abstractmethod + def reload(self, archive_root: Text = None, index: int = None): + """Overwrite all information of the 'index'-th architecture in the search space, where the data will be loaded from 'archive_root'. + If index is None, overwrite all ckps. + """ + + def clear_params(self, index: int, hp: Optional[Text]=None): + """Remove the architecture's weights to save memory. + :arg + index: the index of the target architecture + hp: a flag to controll how to clear the parameters. + -- None: clear all the weights in '01'/'12'/'90', which indicates the number of training epochs. + -- '01' or '12' or '90': clear all the weights in arch2infos_dict[index][hp]. + """ + if self.verbose: + print('Call clear_params with index={:} and hp={:}'.format(index, hp)) + if hp is None: + for key, result in self.arch2infos_dict[index].items(): + result.clear_params() + else: + if str(hp) not in self.arch2infos_dict[index]: + raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[index].keys()), hp)) + self.arch2infos_dict[index][str(hp)].clear_params() + + @abc.abstractmethod + def query_info_str_by_arch(self, arch, hp: Text='12'): + """This function is used to query the information of a specific architecture.""" + + def _query_info_str_by_arch(self, arch, hp: Text='12', print_information=None): + arch_index = self.query_index_by_arch(arch) + if arch_index in self.arch2infos_dict: + if hp not in self.arch2infos_dict[arch_index]: + raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[arch_index].keys()), hp)) + info = self.arch2infos_dict[arch_index][hp] + strings = print_information(info, 'arch-index={:}'.format(arch_index)) + return '\n'.join(strings) + else: + print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index)) + return None + + def query_meta_info_by_index(self, arch_index, hp: Text = '12'): + """Return the ArchResults for the 'arch_index'-th architecture. This function is similar to query_by_index.""" + if self.verbose: + print('Call query_meta_info_by_index with arch_index={:}, hp={:}'.format(arch_index, hp)) + if arch_index in self.arch2infos_dict: + if hp not in self.arch2infos_dict[arch_index]: + raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(arch_index, list(self.arch2infos_dict[arch_index].keys()), hp)) + info = self.arch2infos_dict[arch_index][hp] + else: + raise ValueError('arch_index [{:}] does not in arch2infos'.format(arch_index)) + return copy.deepcopy(info) + + def query_by_index(self, arch_index: int, dataname: Union[None, Text] = None, hp: Text = '12'): + """ This 'query_by_index' function is used to query information with the training of 01 epochs, 12 epochs, 90 epochs, or 200 epochs. + ------ + If hp=01, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/01E.config) + If hp=12, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/12E.config) + If hp=90, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/90E.config) + If hp=200, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/200E.config) + ------ + If dataname is None, return the ArchResults + else, return a dict with all trials on that dataset (the key is the seed) + Options are 'cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'. + -- cifar10-valid : training the model on the CIFAR-10 training set. + -- cifar10 : training the model on the CIFAR-10 training + validation set. + -- cifar100 : training the model on the CIFAR-100 training set. + -- ImageNet16-120 : training the model on the ImageNet16-120 training set. + """ + if self.verbose: + print('Call query_by_index with arch_index={:}, dataname={:}, hp={:}'.format(arch_index, dataname, hp)) + info = self.query_meta_info_by_index(arch_index, hp) + if dataname is None: return info + else: + if dataname not in info.get_dataset_names(): + raise ValueError('invalid dataset-name : {:} vs. {:}'.format(dataname, info.get_dataset_names())) + return info.query(dataname) + + def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None, hp: Text = '12'): + """Find the architecture with the highest accuracy based on some constraints.""" + if self.verbose: + print('Call find_best with dataset={:}, metric_on_set={:}, hp={:} | with #FLOPs < {:} and #Params < {:}'.format(dataset, metric_on_set, hp, FLOP_max, Param_max)) + dataset, metric_on_set = remap_dataset_set_names(dataset, metric_on_set, self.verbose) + best_index, highest_accuracy = -1, None + for i, arch_index in enumerate(self.evaluated_indexes): + arch_info = self.arch2infos_dict[arch_index][hp] + info = arch_info.get_compute_costs(dataset) # the information of costs + flop, param, latency = info['flops'], info['params'], info['latency'] + if FLOP_max is not None and flop > FLOP_max : continue + if Param_max is not None and param > Param_max: continue + xinfo = arch_info.get_metrics(dataset, metric_on_set) # the information of loss and accuracy + loss, accuracy = xinfo['loss'], xinfo['accuracy'] + if best_index == -1: + best_index, highest_accuracy = arch_index, accuracy + elif highest_accuracy < accuracy: + best_index, highest_accuracy = arch_index, accuracy + if self.verbose: + print(' the best architecture : [{:}] {:} with accuracy={:.3f}%'.format(best_index, self.arch(best_index), highest_accuracy)) + return best_index, highest_accuracy + + def get_net_param(self, index, dataset, seed: Optional[int], hp: Text = '12'): + """ + This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed` + Args [seed]: + -- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights. + -- a interger : return the weights of a specific trial, whose seed is this interger. + Args [hp]: + -- 01 : train the model by 01 epochs + -- 12 : train the model by 12 epochs + -- 90 : train the model by 90 epochs + -- 200 : train the model by 200 epochs + """ + if self.verbose: + print('Call the get_net_param function with index={:}, dataset={:}, seed={:}, hp={:}'.format(index, dataset, seed, hp)) + info = self.query_meta_info_by_index(index, hp) + return info.get_net_param(dataset, seed) + + def get_net_config(self, index: int, dataset: Text): + """ + This function is used to obtain the configuration for the `index`-th architecture on `dataset`. + Args [dataset] (4 possible options): + -- cifar10-valid : training the model on the CIFAR-10 training set. + -- cifar10 : training the model on the CIFAR-10 training + validation set. + -- cifar100 : training the model on the CIFAR-100 training set. + -- ImageNet16-120 : training the model on the ImageNet16-120 training set. + This function will return a dict. + ========= Some examlpes for using this function: + config = api.get_net_config(128, 'cifar10') + """ + if self.verbose: + print('Call the get_net_config function with index={:}, dataset={:}.'.format(index, dataset)) + if index in self.arch2infos_dict: + info = self.arch2infos_dict[index] + else: + raise ValueError('The arch_index={:} is not in arch2infos_dict.'.format(arch_index)) + info = next(iter(info.values())) + results = info.query(dataset, None) + results = next(iter(results.values())) + return results.get_config(None) + + def get_cost_info(self, index: int, dataset: Text, hp: Text = '12') -> Dict[Text, float]: + """To obtain the cost metric for the `index`-th architecture on a dataset.""" + if self.verbose: + print('Call the get_cost_info function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp)) + info = self.query_meta_info_by_index(index, hp) + return info.get_compute_costs(dataset) + + def get_latency(self, index: int, dataset: Text, hp: Text = '12') -> float: + """ + To obtain the latency of the network (by default it will return the latency with the batch size of 256). + :param index: the index of the target architecture + :param dataset: the dataset name (cifar10-valid, cifar10, cifar100, ImageNet16-120) + :return: return a float value in seconds + """ + if self.verbose: + print('Call the get_latency function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp)) + cost_dict = self.get_cost_info(index, dataset, hp) + return cost_dict['latency'] + + @abc.abstractmethod + def show(self, index=-1): + """This function will print the information of a specific (or all) architecture(s).""" + + def _show(self, index=-1, print_information=None) -> None: + """ + This function will print the information of a specific (or all) architecture(s). + + :param index: If the index < 0: it will loop for all architectures and print their information one by one. + else: it will print the information of the 'index'-th architecture. + :return: nothing + """ + if index < 0: # show all architectures + print(self) + for i, idx in enumerate(self.evaluated_indexes): + print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10) + print('arch : {:}'.format(self.meta_archs[idx])) + for key, result in self.arch2infos_dict[index].items(): + strings = print_information(result) + print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40) + print('\n'.join(strings)) + print('<' * 40 + '------------' + '<' * 40) + else: + if 0 <= index < len(self.meta_archs): + if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index)) + else: + arch_info = self.arch2infos_dict[index] + for key, result in self.arch2infos_dict[index].items(): + strings = print_information(result) + print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40) + print('\n'.join(strings)) + print('<' * 40 + '------------' + '<' * 40) + else: + print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs))) + + def statistics(self, dataset: Text, hp: Union[Text, int]) -> Dict[int, int]: + """This function will count the number of total trials.""" + if self.verbose: + print('Call the statistics function with dataset={:} and hp={:}.'.format(dataset, hp)) + valid_datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'] + if dataset not in valid_datasets: + raise ValueError('{:} not in {:}'.format(dataset, valid_datasets)) + nums, hp = defaultdict(lambda: 0), str(hp) + for index in range(len(self)): + archInfo = self.arch2infos_dict[index][hp] + dataset_seed = archInfo.dataset_seed + if dataset not in dataset_seed: + nums[0] += 1 + else: + nums[len(dataset_seed[dataset])] += 1 + return dict(nums) + + +class ArchResults(object): + + def __init__(self, arch_index, arch_str): + self.arch_index = int(arch_index) + self.arch_str = copy.deepcopy(arch_str) + self.all_results = dict() + self.dataset_seed = dict() + self.clear_net_done = False + + def get_compute_costs(self, dataset): + x_seeds = self.dataset_seed[dataset] + results = [self.all_results[ (dataset, seed) ] for seed in x_seeds] + + flops = [result.flop for result in results] + params = [result.params for result in results] + latencies = [result.get_latency() for result in results] + latencies = [x for x in latencies if x > 0] + mean_latency = np.mean(latencies) if len(latencies) > 0 else None + time_infos = defaultdict(list) + for result in results: + time_info = result.get_times() + for key, value in time_info.items(): time_infos[key].append( value ) + + info = {'flops' : np.mean(flops), + 'params' : np.mean(params), + 'latency': mean_latency} + for key, value in time_infos.items(): + if len(value) > 0 and value[0] is not None: + info[key] = np.mean(value) + else: info[key] = None + return info + + def get_metrics(self, dataset, setname, iepoch=None, is_random=False): + """ + This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset. + If not specify, each set refer to the proposed split in NAS-Bench-201 paper. + If some args return None or raise error, then it is not avaliable. + ======================================== + Args [dataset] (4 possible options): + -- cifar10-valid : training the model on the CIFAR-10 training set. + -- cifar10 : training the model on the CIFAR-10 training + validation set. + -- cifar100 : training the model on the CIFAR-100 training set. + -- ImageNet16-120 : training the model on the ImageNet16-120 training set. + Args [setname] (each dataset has different setnames): + -- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test' + ------ 'train' : the metric on the training set. + ------ 'x-valid' : the metric on the validation set. + ------ 'ori-test' : the metric on the test set. + -- When dataset = cifar10, you can use 'train', 'ori-test'. + ------ 'train' : the metric on the training + validation set. + ------ 'ori-test' : the metric on the test set. + -- When dataset = cifar100 or ImageNet16-120, you can use 'train', 'ori-test', 'x-valid', 'x-test' + ------ 'train' : the metric on the training set. + ------ 'x-valid' : the metric on the validation set. + ------ 'x-test' : the metric on the test set. + ------ 'ori-test' : the metric on the validation + test set. + Args [iepoch] (None or an integer in [0, the-number-of-total-training-epochs) + ------ None : return the metric after the last training epoch. + ------ an integer i : return the metric after the i-th training epoch. + Args [is_random]: + ------ True : return the metric of a randomly selected trial. + ------ False : return the averaged metric of all avaliable trials. + ------ an integer indicating the 'seed' value : return the metric of a specific trial (whose random seed is 'is_random'). + """ + x_seeds = self.dataset_seed[dataset] + results = [self.all_results[ (dataset, seed) ] for seed in x_seeds] + infos = defaultdict(list) + for result in results: + if setname == 'train': + info = result.get_train(iepoch) + else: + info = result.get_eval(setname, iepoch) + for key, value in info.items(): infos[key].append( value ) + return_info = dict() + if isinstance(is_random, bool) and is_random: # randomly select one + index = random.randint(0, len(results)-1) + for key, value in infos.items(): return_info[key] = value[index] + elif isinstance(is_random, bool) and not is_random: # average + for key, value in infos.items(): + if len(value) > 0 and value[0] is not None: + return_info[key] = np.mean(value) + else: return_info[key] = None + elif isinstance(is_random, int): # specify the seed + if is_random not in x_seeds: raise ValueError('can not find random seed ({:}) from {:}'.format(is_random, x_seeds)) + index = x_seeds.index(is_random) + for key, value in infos.items(): return_info[key] = value[index] + else: + raise ValueError('invalid value for is_random: {:}'.format(is_random)) + return return_info + + def show(self, is_print=False): + return print_information(self, None, is_print) + + def get_dataset_names(self): + return list(self.dataset_seed.keys()) + + def get_dataset_seeds(self, dataset): + return copy.deepcopy( self.dataset_seed[dataset] ) + + def get_net_param(self, dataset: Text, seed: Union[None, int] =None): + """ + This function will return the trained network's weights on the 'dataset'. + :arg + dataset: one of 'cifar10-valid', 'cifar10', 'cifar100', and 'ImageNet16-120'. + seed: an integer indicates the seed value or None that indicates returing all trials. + """ + if seed is None: + x_seeds = self.dataset_seed[dataset] + return {seed: self.all_results[(dataset, seed)].get_net_param() for seed in x_seeds} + else: + xkey = (dataset, seed) + if xkey in self.all_results: + return self.all_results[xkey].get_net_param() + else: + raise ValueError('key={:} not in {:}'.format(xkey, list(self.all_results.keys()))) + + def reset_latency(self, dataset: Text, seed: Union[None, Text], latency: float) -> None: + """This function is used to reset the latency in all corresponding ResultsCount(s).""" + if seed is None: + for seed in self.dataset_seed[dataset]: + self.all_results[(dataset, seed)].update_latency([latency]) + else: + self.all_results[(dataset, seed)].update_latency([latency]) + + def reset_pseudo_train_times(self, dataset: Text, seed: Union[None, Text], estimated_per_epoch_time: float) -> None: + """This function is used to reset the train-times in all corresponding ResultsCount(s).""" + if seed is None: + for seed in self.dataset_seed[dataset]: + self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time) + else: + self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time) + + def reset_pseudo_eval_times(self, dataset: Text, seed: Union[None, Text], eval_name: Text, estimated_per_epoch_time: float) -> None: + """This function is used to reset the eval-times in all corresponding ResultsCount(s).""" + if seed is None: + for seed in self.dataset_seed[dataset]: + self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time) + else: + self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time) + + def get_latency(self, dataset: Text) -> float: + """Get the latency of a model on the target dataset. [Timestamp: 2020.03.09]""" + latencies = [] + for seed in self.dataset_seed[dataset]: + latency = self.all_results[(dataset, seed)].get_latency() + if not isinstance(latency, float) or latency <= 0: + raise ValueError('invalid latency of {:} with seed={:} : {:}'.format(dataset, seed, latency)) + latencies.append(latency) + return sum(latencies) / len(latencies) + + def get_total_epoch(self, dataset=None): + """Return the total number of training epochs.""" + if dataset is None: + epochss = [] + for xdata, x_seeds in self.dataset_seed.items(): + epochss += [self.all_results[(xdata, seed)].get_total_epoch() for seed in x_seeds] + elif isinstance(dataset, str): + x_seeds = self.dataset_seed[dataset] + epochss = [self.all_results[(dataset, seed)].get_total_epoch() for seed in x_seeds] + else: + raise ValueError('invalid dataset={:}'.format(dataset)) + if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss)) + return epochss[-1] + + def query(self, dataset, seed=None): + """Return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'""" + if seed is None: + x_seeds = self.dataset_seed[dataset] + return {seed: self.all_results[(dataset, seed)] for seed in x_seeds} + else: + return self.all_results[(dataset, seed)] + + def arch_idx_str(self): + return '{:06d}'.format(self.arch_index) + + def update(self, dataset_name, seed, result): + if dataset_name not in self.dataset_seed: + self.dataset_seed[dataset_name] = [] + assert seed not in self.dataset_seed[dataset_name], '{:}-th arch alreadly has this seed ({:}) on {:}'.format(self.arch_index, seed, dataset_name) + self.dataset_seed[ dataset_name ].append( seed ) + self.dataset_seed[ dataset_name ] = sorted( self.dataset_seed[ dataset_name ] ) + assert (dataset_name, seed) not in self.all_results + self.all_results[ (dataset_name, seed) ] = result + self.clear_net_done = False + + def state_dict(self): + state_dict = dict() + for key, value in self.__dict__.items(): + if key == 'all_results': # contain the class of ResultsCount + xvalue = dict() + assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value)) + for _k, _v in value.items(): + assert isinstance(_v, ResultsCount), 'invalid type of value for {:}/{:} : {:}'.format(key, _k, type(_v)) + xvalue[_k] = _v.state_dict() + else: + xvalue = value + state_dict[key] = xvalue + return state_dict + + def load_state_dict(self, state_dict): + new_state_dict = dict() + for key, value in state_dict.items(): + if key == 'all_results': # to convert to the class of ResultsCount + xvalue = dict() + assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value)) + for _k, _v in value.items(): + xvalue[_k] = ResultsCount.create_from_state_dict(_v) + else: xvalue = value + new_state_dict[key] = xvalue + self.__dict__.update(new_state_dict) + + @staticmethod + def create_from_state_dict(state_dict_or_file): + x = ArchResults(-1, -1) + if isinstance(state_dict_or_file, str): # a file path + state_dict = torch.load(state_dict_or_file, map_location='cpu') + elif isinstance(state_dict_or_file, dict): + state_dict = state_dict_or_file + else: + raise ValueError('invalid type of state_dict_or_file : {:}'.format(type(state_dict_or_file))) + x.load_state_dict(state_dict) + return x + + # This function is used to clear the weights saved in each 'result' + # This can help reduce the memory footprint. + def clear_params(self): + for key, result in self.all_results.items(): + del result.net_state_dict + result.net_state_dict = None + self.clear_net_done = True + + def debug_test(self): + """This function is used for me to debug and test, which will call most methods.""" + all_dataset = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'] + for dataset in all_dataset: + print('---->>>> {:}'.format(dataset)) + print('The latency on {:} is {:} s'.format(dataset, self.get_latency(dataset))) + for seed in self.dataset_seed[dataset]: + result = self.all_results[(dataset, seed)] + print(' ==>> result = {:}'.format(result)) + print(' ==>> cost = {:}'.format(result.get_times())) + + def __repr__(self): + return ('{name}(arch-index={index}, arch={arch}, {num} runs, clear={clear})'.format(name=self.__class__.__name__, index=self.arch_index, arch=self.arch_str, num=len(self.all_results), clear=self.clear_net_done)) + + +""" +This class (ResultsCount) is used to save the information of one trial for a single architecture. +I did not write much comment for this class, because it is the lowest-level class in NAS-Bench-201 API, which will be rarely called. +If you have any question regarding this class, please open an issue or email me. +""" +class ResultsCount(object): + + def __init__(self, name, state_dict, train_accs, train_losses, params, flop, arch_config, seed, epochs, latency): + self.name = name + self.net_state_dict = state_dict + self.train_acc1es = copy.deepcopy(train_accs) + self.train_acc5es = None + self.train_losses = copy.deepcopy(train_losses) + self.train_times = None + self.arch_config = copy.deepcopy(arch_config) + self.params = params + self.flop = flop + self.seed = seed + self.epochs = epochs + self.latency = latency + # evaluation results + self.reset_eval() + + def update_train_info(self, train_acc1es, train_acc5es, train_losses, train_times) -> None: + self.train_acc1es = train_acc1es + self.train_acc5es = train_acc5es + self.train_losses = train_losses + self.train_times = train_times + + def reset_pseudo_train_times(self, estimated_per_epoch_time: float) -> None: + """Assign the training times.""" + train_times = OrderedDict() + for i in range(self.epochs): + train_times[i] = estimated_per_epoch_time + self.train_times = train_times + + def reset_pseudo_eval_times(self, eval_name: Text, estimated_per_epoch_time: float) -> None: + """Assign the evaluation times.""" + if eval_name not in self.eval_names: raise ValueError('invalid eval name : {:}'.format(eval_name)) + for i in range(self.epochs): + self.eval_times['{:}@{:}'.format(eval_name,i)] = estimated_per_epoch_time + + def reset_eval(self): + self.eval_names = [] + self.eval_acc1es = {} + self.eval_times = {} + self.eval_losses = {} + + def update_latency(self, latency): + self.latency = copy.deepcopy( latency ) + + def get_latency(self) -> float: + """Return the latency value in seconds. -1 represents not avaliable ; otherwise it should be a float value""" + if self.latency is None: return -1.0 + else: return sum(self.latency) / len(self.latency) + + def update_eval(self, accs, losses, times): # new version + data_names = set([x.split('@')[0] for x in accs.keys()]) + for data_name in data_names: + assert data_name not in self.eval_names, '{:} has already been added into eval-names'.format(data_name) + self.eval_names.append( data_name ) + for iepoch in range(self.epochs): + xkey = '{:}@{:}'.format(data_name, iepoch) + self.eval_acc1es[ xkey ] = accs[ xkey ] + self.eval_losses[ xkey ] = losses[ xkey ] + self.eval_times [ xkey ] = times[ xkey ] + + def update_OLD_eval(self, name, accs, losses): # old version + assert name not in self.eval_names, '{:} has already added'.format(name) + self.eval_names.append( name ) + for iepoch in range(self.epochs): + if iepoch in accs: + self.eval_acc1es['{:}@{:}'.format(name,iepoch)] = accs[iepoch] + self.eval_losses['{:}@{:}'.format(name,iepoch)] = losses[iepoch] + + def __repr__(self): + num_eval = len(self.eval_names) + set_name = '[' + ', '.join(self.eval_names) + ']' + return ('{name}({xname}, arch={arch}, FLOP={flop:.2f}M, Param={param:.3f}MB, seed={seed}, {num_eval} eval-sets: {set_name})'.format(name=self.__class__.__name__, xname=self.name, arch=self.arch_config['arch_str'], flop=self.flop, param=self.params, seed=self.seed, num_eval=num_eval, set_name=set_name)) + + def get_total_epoch(self): + return copy.deepcopy(self.epochs) + + def get_times(self): + """Obtain the information regarding both training and evaluation time.""" + if self.train_times is not None and isinstance(self.train_times, dict): + train_times = list( self.train_times.values() ) + time_info = {'T-train@epoch': np.mean(train_times), 'T-train@total': np.sum(train_times)} + else: + time_info = {'T-train@epoch': None, 'T-train@total': None } + for name in self.eval_names: + try: + xtimes = [self.eval_times['{:}@{:}'.format(name,i)] for i in range(self.epochs)] + time_info['T-{:}@epoch'.format(name)] = np.mean(xtimes) + time_info['T-{:}@total'.format(name)] = np.sum(xtimes) + except: + time_info['T-{:}@epoch'.format(name)] = None + time_info['T-{:}@total'.format(name)] = None + return time_info + + def get_eval_set(self): + return self.eval_names + + # get the training information + def get_train(self, iepoch=None): + if iepoch is None: iepoch = self.epochs-1 + assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs) + if self.train_times is not None: + xtime = self.train_times[iepoch] + atime = sum([self.train_times[i] for i in range(iepoch+1)]) + else: xtime, atime = None, None + return {'iepoch' : iepoch, + 'loss' : self.train_losses[iepoch], + 'accuracy': self.train_acc1es[iepoch], + 'cur_time': xtime, + 'all_time': atime} + + def get_eval(self, name, iepoch=None): + """Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument).""" + if iepoch is None: iepoch = self.epochs-1 + assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs) + def _internal_query(xname): + if isinstance(self.eval_times,dict) and len(self.eval_times) > 0: + xtime = self.eval_times['{:}@{:}'.format(xname, iepoch)] + atime = sum([self.eval_times['{:}@{:}'.format(xname, i)] for i in range(iepoch+1)]) + else: + xtime, atime = None, None + return {'iepoch' : iepoch, + 'loss' : self.eval_losses['{:}@{:}'.format(xname, iepoch)], + 'accuracy': self.eval_acc1es['{:}@{:}'.format(xname, iepoch)], + 'cur_time': xtime, + 'all_time': atime} + if name == 'valid': + return _internal_query('x-valid') + else: + return _internal_query(name) + + def get_net_param(self, clone=False): + if clone: return copy.deepcopy(self.net_state_dict) + else: return self.net_state_dict + + def get_config(self, str2structure): + """This function is used to obtain the config dict for this architecture.""" + if str2structure is None: + # In this case, this is NAS-Bench-301 + if 'name' in self.arch_config and self.arch_config['name'] == 'infer.shape.tiny': + return {'name': 'infer.shape.tiny', 'channels': self.arch_config['channels'], + 'genotype': self.arch_config['genotype'], 'num_classes': self.arch_config['class_num']} + # In this case, this is NAS-Bench-201 + else: + return {'name': 'infer.tiny', 'C': self.arch_config['channel'], + 'N' : self.arch_config['num_cells'], + 'arch_str': self.arch_config['arch_str'], 'num_classes': self.arch_config['class_num']} + else: + # In this case, this is NAS-Bench-301 + if 'name' in self.arch_config and self.arch_config['name'] == 'infer.shape.tiny': + return {'name': 'infer.shape.tiny', 'channels': self.arch_config['channels'], + 'genotype': str2structure(self.arch_config['genotype']), 'num_classes': self.arch_config['class_num']} + # In this case, this is NAS-Bench-201 + else: + return {'name': 'infer.tiny', 'C': self.arch_config['channel'], + 'N' : self.arch_config['num_cells'], + 'genotype': str2structure(self.arch_config['arch_str']), 'num_classes': self.arch_config['class_num']} + + def state_dict(self): + _state_dict = {key: value for key, value in self.__dict__.items()} + return _state_dict + + def load_state_dict(self, state_dict): + self.__dict__.update(state_dict) + + @staticmethod + def create_from_state_dict(state_dict): + x = ResultsCount(None, None, None, None, None, None, None, None, None, None) + x.load_state_dict(state_dict) + return x diff --git a/NAS-Bench-201/setup.py b/NAS-Bench-201/setup.py new file mode 100644 index 0000000..eb9d869 --- /dev/null +++ b/NAS-Bench-201/setup.py @@ -0,0 +1,36 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 # +##################################################### +# [2020.02.25] Initialize the API as v1.1 +# [2020.03.09] Upgrade the API to v1.2 +# [2020.03.16] Upgrade the API to v1.3 +# [2020.06.30] Upgrade the API to v2.0 +# [2020.10.12] Upgrade the API to v2.1 -- deprecate this repo, switch to NATS-Bench. +import os +from setuptools import setup + + +def read(fname='README.md'): + with open(os.path.join(os.path.dirname(__file__), fname), encoding='utf-8') as cfile: + return cfile.read() + + +setup( + name = "nas_bench_201", + version = "2.1", + author = "Xuanyi Dong", + author_email = "dongxuanyi888@gmail.com", + description = "API for NAS-Bench-201 (a benchmark for neural architecture search).", + license = "MIT", + keywords = "NAS Dataset API DeepLearning", + url = "https://github.com/D-X-Y/NAS-Bench-201", + packages=['nas_201_api'], + long_description=read('README.md'), + long_description_content_type='text/markdown', + classifiers=[ + "Programming Language :: Python", + "Topic :: Database", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "License :: OSI Approved :: MIT License", + ], +)