48 lines
2.4 KiB
Python
48 lines
2.4 KiB
Python
##############################################################################
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# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
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##############################################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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##############################################################################
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# python ./exps/NATS-Bench/main-tss.py --mode meta #
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##############################################################################
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import os, sys, time, torch, random, argparse
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from typing import List, Text, Dict, Any
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import dict2config, load_config
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from datasets import get_datasets
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from nats_bench import create
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def show_imagenet_16_120(dataset_dir=None):
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if dataset_dir is None:
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torch_home_dir = os.environ['TORCH_HOME'] if 'TORCH_HOME' in os.environ else os.path.join(os.environ['HOME'], '.torch')
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dataset_dir = os.path.join(torch_home_dir, 'cifar.python', 'ImageNet16')
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train_data, valid_data, xshape, class_num = get_datasets('ImageNet16-120', dataset_dir, -1)
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split_info = load_config('configs/nas-benchmark/ImageNet16-120-split.txt', None, None)
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print('=' * 10 + ' ImageNet-16-120 ' + '=' * 10)
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print('Training Data: {:}'.format(train_data))
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print('Evaluation Data: {:}'.format(valid_data))
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print('Hold-out training: {:} images.'.format(len(split_info.train)))
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print('Hold-out valid : {:} images.'.format(len(split_info.valid)))
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if __name__ == '__main__':
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# show_imagenet_16_120()
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api_nats_tss = create(None, 'tss', fast_mode=True, verbose=True)
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valid_acc_12e = []
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test_acc_12e = []
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test_acc_200e = []
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for index in range(10000):
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info = api_nats_tss.get_more_info(index, 'ImageNet16-120', hp='12')
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valid_acc_12e.append(info['valid-accuracy']) # the validation accuracy after training the model by 12 epochs
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test_acc_12e.append(info['test-accuracy']) # the test accuracy after training the model by 12 epochs
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info = api_nats_tss.get_more_info(index, 'ImageNet16-120', hp='200')
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test_acc_200e.append(info['test-accuracy']) # the test accuracy after training the model by 200 epochs (which I reported in the paper)
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