181 lines
8.1 KiB
Python
181 lines
8.1 KiB
Python
###############################################################
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# NATS-Bench (https://arxiv.org/pdf/2009.00437.pdf) #
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# The code to draw Figure 7 in our paper. #
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###############################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
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###############################################################
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# Usage: python exps/NATS-Bench/draw-fig7.py #
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###############################################################
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import os, gc, sys, time, torch, argparse
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import numpy as np
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from typing import List, Text, Dict, Any
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from shutil import copyfile
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from collections import defaultdict, OrderedDict
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from copy import deepcopy
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from pathlib import Path
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import matplotlib
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import seaborn as sns
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matplotlib.use('agg')
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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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 nats_bench import create
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from log_utils import time_string
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def get_valid_test_acc(api, arch, dataset):
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is_size_space = api.search_space_name == 'size'
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if dataset == 'cifar10':
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xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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test_acc = xinfo['test-accuracy']
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xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False)
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valid_acc = xinfo['valid-accuracy']
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else:
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xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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valid_acc = xinfo['valid-accuracy']
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test_acc = xinfo['test-accuracy']
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return valid_acc, test_acc, 'validation = {:.2f}, test = {:.2f}\n'.format(valid_acc, test_acc)
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def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-WARM0.3'):
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ss_dir = '{:}-{:}'.format(root_dir, search_space)
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alg2name, alg2path = OrderedDict(), OrderedDict()
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seeds = [777, 888, 999]
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print('\n[fetch data] from {:} on {:}'.format(search_space, dataset))
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if search_space == 'tss':
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alg2name['GDAS'] = 'gdas-affine0_BN0-None'
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alg2name['RSPS'] = 'random-affine0_BN0-None'
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alg2name['DARTS (1st)'] = 'darts-v1-affine0_BN0-None'
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alg2name['DARTS (2nd)'] = 'darts-v2-affine0_BN0-None'
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alg2name['ENAS'] = 'enas-affine0_BN0-None'
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alg2name['SETN'] = 'setn-affine0_BN0-None'
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else:
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alg2name['channel-wise interpolation'] = 'tas-affine0_BN0-AWD0.001{:}'.format(suffix)
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alg2name['masking + Gumbel-Softmax'] = 'mask_gumbel-affine0_BN0-AWD0.001{:}'.format(suffix)
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alg2name['masking + sampling'] = 'mask_rl-affine0_BN0-AWD0.0{:}'.format(suffix)
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for alg, name in alg2name.items():
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alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
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alg2data = OrderedDict()
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for alg, path in alg2path.items():
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alg2data[alg], ok_num = [], 0
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for seed in seeds:
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xpath = path.format(seed)
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if os.path.isfile(xpath):
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ok_num += 1
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else:
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print('This is an invalid path : {:}'.format(xpath))
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continue
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data = torch.load(xpath, map_location=torch.device('cpu'))
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try:
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data = torch.load(data['last_checkpoint'], map_location=torch.device('cpu'))
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except:
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xpath = str(data['last_checkpoint']).split('E100-')
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if len(xpath) == 2 and os.path.isfile(xpath[0] + xpath[1]):
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xpath = xpath[0] + xpath[1]
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elif 'fbv2' in str(data['last_checkpoint']):
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xpath = str(data['last_checkpoint']).replace('fbv2', 'mask_gumbel')
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elif 'tunas' in str(data['last_checkpoint']):
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xpath = str(data['last_checkpoint']).replace('tunas', 'mask_rl')
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else:
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raise ValueError('Invalid path: {:}'.format(data['last_checkpoint']))
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data = torch.load(xpath, map_location=torch.device('cpu'))
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alg2data[alg].append(data['genotypes'])
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print('This algorithm : {:} has {:} valid ckps.'.format(alg, ok_num))
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assert ok_num > 0, 'Must have at least 1 valid ckps.'
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return alg2data
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y_min_s = {('cifar10', 'tss'): 90,
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('cifar10', 'sss'): 92,
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('cifar100', 'tss'): 65,
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('cifar100', 'sss'): 65,
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('ImageNet16-120', 'tss'): 36,
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('ImageNet16-120', 'sss'): 40}
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y_max_s = {('cifar10', 'tss'): 94.5,
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('cifar10', 'sss'): 93.3,
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('cifar100', 'tss'): 72,
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('cifar100', 'sss'): 70,
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('ImageNet16-120', 'tss'): 44,
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('ImageNet16-120', 'sss'): 46}
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name2label = {'cifar10': 'CIFAR-10',
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'cifar100': 'CIFAR-100',
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'ImageNet16-120': 'ImageNet-16-120'}
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name2suffix = {('sss', 'warm'): '-WARM0.3',
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('sss', 'none'): '-WARMNone',
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('tss', 'none') : None,
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('tss', None) : None}
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def visualize_curve(api, vis_save_dir, search_space, suffix):
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vis_save_dir = vis_save_dir.resolve()
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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dpi, width, height = 250, 5200, 1400
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 16, 16
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def sub_plot_fn(ax, dataset):
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print('{:} plot {:10s}'.format(time_string(), dataset))
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alg2data = fetch_data(search_space=search_space, dataset=dataset, suffix=name2suffix[(search_space, suffix)])
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alg2accuracies = OrderedDict()
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epochs = 100
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colors = ['b', 'g', 'c', 'm', 'y', 'r']
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ax.set_xlim(0, epochs)
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# ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
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for idx, (alg, data) in enumerate(alg2data.items()):
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xs, accuracies = [], []
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for iepoch in range(epochs + 1):
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try:
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structures, accs = [_[iepoch-1] for _ in data], []
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except:
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raise ValueError('This alg {:} on {:} has invalid checkpoints.'.format(alg, dataset))
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for structure in structures:
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info = api.get_more_info(structure, dataset=dataset, hp=90 if api.search_space_name == 'size' else 200, is_random=False)
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accs.append(info['test-accuracy'])
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accuracies.append(sum(accs)/len(accs))
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xs.append(iepoch)
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alg2accuracies[alg] = accuracies
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ax.plot(xs, accuracies, c=colors[idx], label='{:}'.format(alg))
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ax.set_xlabel('The searching epoch', fontsize=LabelSize)
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ax.set_ylabel('Test accuracy on {:}'.format(name2label[dataset]), fontsize=LabelSize)
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ax.set_title('Searching results on {:}'.format(name2label[dataset]), fontsize=LabelSize+4)
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structures, valid_accs, test_accs = [_[epochs-1] for _ in data], [], []
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print('{:} plot alg : {:} -- final {:} architectures.'.format(time_string(), alg, len(structures)))
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for arch in structures:
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valid_acc, test_acc, _ = get_valid_test_acc(api, arch, dataset)
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test_accs.append(test_acc)
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valid_accs.append(valid_acc)
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print('{:} plot alg : {:} -- validation: {:.2f}$\pm${:.2f} -- test: {:.2f}$\pm${:.2f}'.format(
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time_string(), alg, np.mean(valid_accs), np.std(valid_accs), np.mean(test_accs), np.std(test_accs)))
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ax.legend(loc=4, fontsize=LegendFontsize)
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fig, axs = plt.subplots(1, 3, figsize=figsize)
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datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
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for dataset, ax in zip(datasets, axs):
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sub_plot_fn(ax, dataset)
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print('sub-plot {:} on {:} done.'.format(dataset, search_space))
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save_path = (vis_save_dir / '{:}-ws-{:}-curve.png'.format(search_space, suffix)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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api_tss = create(None, 'tss', fast_mode=True, verbose=False)
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visualize_curve(api_tss, save_dir, 'tss', None)
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api_sss = create(None, 'sss', fast_mode=True, verbose=False)
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visualize_curve(api_sss, save_dir, 'sss', 'warm')
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visualize_curve(api_sss, save_dir, 'sss', 'none')
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