Add get_torch_home func for NATS-Bench
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D-X-Y
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@ -385,7 +385,7 @@ def visualize_all_rank_info(api, vis_save_dir, indicator):
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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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', help='Folder to save checkpoints and log.')
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# use for train the model
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args = parser.parse_args()
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175
exps/NATS-Bench/draw-fig8.py
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175
exps/NATS-Bench/draw-fig8.py
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@ -0,0 +1,175 @@
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###############################################################
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# NATS-Bench (https://arxiv.org/pdf/2009.00437.pdf) #
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# The code to draw Figure 6 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-fig8.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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plt.rcParams.update({
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"text.usetex": True,
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"font.family": "sans-serif",
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"font.sans-serif": ["Helvetica"]})
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## for Palatino and other serif fonts use:
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plt.rcParams.update({
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"text.usetex": True,
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"font.family": "serif",
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"font.serif": ["Palatino"],
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})
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def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
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ss_dir = '{:}-{:}'.format(root_dir, search_space)
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alg2all = OrderedDict()
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# alg2name['REINFORCE'] = 'REINFORCE-0.01'
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# alg2name['RANDOM'] = 'RANDOM'
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# alg2name['BOHB'] = 'BOHB'
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if dataset == 'cifar10':
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suffixes = ['-T200000', '-T200000-FULL']
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elif dataset == 'cifar100':
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suffixes = ['-T40000', '-T40000-FULL']
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elif search_space == 'tss':
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suffixes = ['-T120000', '-T120000-FULL']
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elif search_space == 'sss':
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suffixes = ['-T60000', '-T60000-FULL']
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else:
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raise ValueError('Unkonwn dataset : {:}'.format(dataset))
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if search_space == 'tss':
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hp = '$\mathcal{H}^{1}$'
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elif search_space == 'sss':
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hp = '$\mathcal{H}^{2}$'
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else:
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raise ValueError('Unkonwn search space: {:}'.format(search_space))
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alg2all[r'REA ($\mathcal{H}^{0}$)'] = dict(
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path=os.path.join(ss_dir, dataset + suffixes[0], 'R-EA-SS3', 'results.pth'),
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color='b', linestyle='-')
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alg2all[r'REA ({:})'.format(hp)] = dict(
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path=os.path.join(ss_dir, dataset + suffixes[1], 'R-EA-SS3', 'results.pth'),
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color='b', linestyle='--')
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for alg, xdata in alg2all.items():
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data = torch.load(xdata['path'])
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for index, info in data.items():
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info['time_w_arch'] = [(x, y) for x, y in zip(info['all_total_times'], info['all_archs'])]
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for j, arch in enumerate(info['all_archs']):
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assert arch != -1, 'invalid arch from {:} {:} {:} ({:}, {:})'.format(alg, search_space, dataset, index, j)
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xdata['data'] = data
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return alg2all
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def query_performance(api, data, dataset, ticket):
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results, is_size_space = [], api.search_space_name == 'size'
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for i, info in data.items():
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time_w_arch = sorted(info['time_w_arch'], key=lambda x: abs(x[0]-ticket))
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time_a, arch_a = time_w_arch[0]
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time_b, arch_b = time_w_arch[1]
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info_a = api.get_more_info(arch_a, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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info_b = api.get_more_info(arch_b, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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accuracy_a, accuracy_b = info_a['test-accuracy'], info_b['test-accuracy']
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interplate = (time_b-ticket) / (time_b-time_a) * accuracy_a + (ticket-time_a) / (time_b-time_a) * accuracy_b
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results.append(interplate)
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# return sum(results) / len(results)
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return np.mean(results), np.std(results)
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y_min_s = {('cifar10', 'tss'): 90,
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('cifar10', 'sss'): 90,
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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'): 94.5,
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('cifar100', 'tss'): 72.5,
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('cifar100', 'sss'): 70.5,
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('ImageNet16-120', 'tss'): 46,
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('ImageNet16-120', 'sss'): 46}
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x_axis_s = {('cifar10', 'tss'): 200000,
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('cifar10', 'sss'): 200000,
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('cifar100', 'tss'): 400,
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('cifar100', 'sss'): 400,
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('ImageNet16-120', 'tss'): 1200,
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('ImageNet16-120', 'sss'): 600}
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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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spaces2latex = {'tss': r'$\mathcal{S}_{t}$',
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'sss': r'$\mathcal{S}_{s}$',}
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def visualize_curve(api_dict, vis_save_dir):
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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, 4000, 2400
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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, search_space, dataset):
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max_time = x_axis_s[(dataset, search_space)]
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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total_tickets = 200
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time_tickets = [float(i) / total_tickets * int(max_time) for i in range(total_tickets)]
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ax.set_xlim(0, x_axis_s[(dataset, search_space)])
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ax.set_ylim(y_min_s[(dataset, search_space)],
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y_max_s[(dataset, search_space)])
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for idx, (alg, xdata) in enumerate(alg2data.items()):
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accuracies = []
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for ticket in time_tickets:
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# import pdb; pdb.set_trace()
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accuracy, accuracy_std = query_performance(
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api_dict[search_space], xdata['data'], dataset, ticket)
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accuracies.append(accuracy)
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# print('{:} plot alg : {:10s}, final accuracy = {:.2f}$\pm${:.2f}'.format(time_string(), alg, accuracy, accuracy_std))
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print('{:} plot alg : {:10s} on {:}'.format(time_string(), alg, search_space))
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alg2accuracies[alg] = accuracies
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ax.plot(time_tickets, accuracies, c=xdata['color'], linestyle=xdata['linestyle'], label='{:}'.format(alg))
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ax.set_xlabel('Estimated wall-clock time', fontsize=LabelSize)
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ax.set_ylabel('Test accuracy', fontsize=LabelSize)
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ax.set_title(r'Searching results on {:} for {:}'.format(name2label[dataset], spaces2latex[search_space]),
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fontsize=LabelSize+4)
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ax.legend(loc=4, fontsize=LegendFontsize)
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fig, axs = plt.subplots(1, 2, figsize=figsize)
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sub_plot_fn(axs[0], 'tss', 'cifar10')
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sub_plot_fn(axs[1], 'sss', 'cifar10')
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save_path = (vis_save_dir / 'full-curve.png').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: Benchmarking NAS algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos-vs-h', 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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api_sss = create(None, 'sss', fast_mode=True, verbose=False)
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visualize_curve(dict(tss=api_tss, sss=api_sss), save_dir)
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96
exps/NATS-Bench/draw-ranks.py
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96
exps/NATS-Bench/draw-ranks.py
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@ -0,0 +1,96 @@
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###############################################################
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# NATS-Bench (https://arxiv.org/pdf/2009.00437.pdf) #
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# The code to draw Figure 2 / 3 / 4 / 5 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-ranks.py #
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###############################################################
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import os, sys, time, torch, argparse
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import scipy
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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
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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 log_utils import time_string
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from models import get_cell_based_tiny_net
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from nats_bench import create
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def visualize_relative_info(api, vis_save_dir, indicator):
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vis_save_dir = vis_save_dir.resolve()
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# print ('{:} start to visualize {:} information'.format(time_string(), api))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
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cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
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imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
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cifar010_info = torch.load(cifar010_cache_path)
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cifar100_info = torch.load(cifar100_cache_path)
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imagenet_info = torch.load(imagenet_cache_path)
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indexes = list(range(len(cifar010_info['params'])))
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print ('{:} start to visualize relative ranking'.format(time_string()))
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cifar010_ord_indexes = sorted(indexes, key=lambda i: cifar010_info['test_accs'][i])
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cifar100_ord_indexes = sorted(indexes, key=lambda i: cifar100_info['test_accs'][i])
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imagenet_ord_indexes = sorted(indexes, key=lambda i: imagenet_info['test_accs'][i])
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cifar100_labels, imagenet_labels = [], []
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for idx in cifar010_ord_indexes:
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cifar100_labels.append( cifar100_ord_indexes.index(idx) )
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imagenet_labels.append( imagenet_ord_indexes.index(idx) )
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print ('{:} prepare data done.'.format(time_string()))
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dpi, width, height = 200, 1400, 800
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 12
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resnet_scale, resnet_alpha = 120, 0.5
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(min(indexes), max(indexes))
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plt.ylim(min(indexes), max(indexes))
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# plt.ylabel('y').set_rotation(30)
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//3), fontsize=LegendFontsize, rotation='vertical')
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//5), fontsize=LegendFontsize)
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ax.scatter(indexes, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8)
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ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8)
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ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8)
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ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='CIFAR-10')
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ax.scatter([-1], [-1], marker='^', s=100, c='tab:green', label='CIFAR-100')
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ax.scatter([-1], [-1], marker='*', s=100, c='tab:red' , label='ImageNet-16-120')
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=0, fontsize=LegendFontsize)
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ax.set_xlabel('architecture ranking in CIFAR-10', fontsize=LabelSize)
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ax.set_ylabel('architecture ranking', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-relative-rank.pdf'.format(indicator)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-relative-rank.png'.format(indicator)).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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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/rank-stability', help='Folder to save checkpoints and log.')
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# use for train the model
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args = parser.parse_args()
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to_save_dir = Path(args.save_dir)
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# Figure 2
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visualize_relative_info(None, to_save_dir, 'tss')
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visualize_relative_info(None, to_save_dir, 'sss')
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@ -9,6 +9,7 @@
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# python ./exps/NATS-algos/regularized_ea.py --dataset cifar10 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
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# python ./exps/NATS-algos/regularized_ea.py --dataset cifar100 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
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# python ./exps/NATS-algos/regularized_ea.py --dataset ImageNet16-120 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
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# python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --use_proxy 0
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##################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np, collections
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@ -119,10 +120,8 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
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while len(population) < population_size:
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model = Model()
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model.arch = random_arch()
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if use_proxy:
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model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, hp='12')
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else:
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model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, hp=api.full_train_epochs)
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model.accuracy, _, _, total_cost = api.simulate_train_eval(
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model.arch, dataset, hp='12' if use_proxy else api.full_train_epochs)
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# Append the info
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population.append(model)
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history.append((model.accuracy, model.arch))
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@ -146,7 +145,8 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
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# Create the child model and store it.
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child = Model()
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child.arch = mutate_arch(parent.arch)
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child.accuracy, _, _, total_cost = api.simulate_train_eval(child.arch, dataset, hp='12')
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child.accuracy, _, _, total_cost = api.simulate_train_eval(
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child.arch, dataset, hp='12' if use_proxy else api.full_train_epochs)
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# Append the info
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population.append(child)
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history.append((child.accuracy, child.arch))
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@ -17,6 +17,7 @@ from typing import Dict, Optional, Text, Union, Any
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from nats_bench.api_utils import ArchResults
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from nats_bench.api_utils import NASBenchMetaAPI
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from nats_bench.api_utils import get_torch_home
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from nats_bench.api_utils import nats_is_dir
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from nats_bench.api_utils import nats_is_file
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from nats_bench.api_utils import PICKLE_EXT
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@ -88,10 +89,10 @@ class NATSsize(NASBenchMetaAPI):
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if file_path_or_dict is None:
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if self._fast_mode:
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self._archive_dir = os.path.join(
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os.environ['TORCH_HOME'], '{:}-simple'.format(ALL_BASE_NAMES[-1]))
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get_torch_home(), '{:}-simple'.format(ALL_BASE_NAMES[-1]))
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else:
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file_path_or_dict = os.path.join(
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os.environ['TORCH_HOME'], '{:}.{:}'.format(
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get_torch_home(), '{:}.{:}'.format(
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ALL_BASE_NAMES[-1], PICKLE_EXT))
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print('{:} Try to use the default NATS-Bench (size) path from '
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'fast_mode={:} and path={:}.'.format(time_string(), self._fast_mode,
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@ -17,6 +17,7 @@ from typing import Any, Dict, List, Optional, Text, Union
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from nats_bench.api_utils import ArchResults
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from nats_bench.api_utils import NASBenchMetaAPI
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from nats_bench.api_utils import get_torch_home
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from nats_bench.api_utils import nats_is_dir
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||||
from nats_bench.api_utils import nats_is_file
|
||||
from nats_bench.api_utils import PICKLE_EXT
|
||||
@ -88,10 +89,10 @@ class NATStopology(NASBenchMetaAPI):
|
||||
if file_path_or_dict is None:
|
||||
if self._fast_mode:
|
||||
self._archive_dir = os.path.join(
|
||||
os.environ['TORCH_HOME'], '{:}-simple'.format(ALL_BASE_NAMES[-1]))
|
||||
get_torch_home(), '{:}-simple'.format(ALL_BASE_NAMES[-1]))
|
||||
else:
|
||||
file_path_or_dict = os.path.join(
|
||||
os.environ['TORCH_HOME'], '{:}.{:}'.format(
|
||||
get_torch_home(), '{:}.{:}'.format(
|
||||
ALL_BASE_NAMES[-1], PICKLE_EXT))
|
||||
print('{:} Try to use the default NATS-Bench (topology) path from '
|
||||
'fast_mode={:} and path={:}.'.format(time_string(), self._fast_mode, file_path_or_dict))
|
||||
|
||||
@ -45,6 +45,17 @@ def get_file_system():
|
||||
return _FILE_SYSTEM
|
||||
|
||||
|
||||
def get_torch_home():
|
||||
if 'TORCH_HOME' in os.environ:
|
||||
return os.environ['TORCH_HOME']
|
||||
elif 'HOME' in os.environ:
|
||||
return os.path.join(os.environ['HOME'], '.torch')
|
||||
else:
|
||||
raise ValueError('Did not find HOME in os.environ. '
|
||||
'Please at least setup the path of HOME or TORCH_HOME '
|
||||
'in the environment.')
|
||||
|
||||
|
||||
def nats_is_dir(file_path):
|
||||
if _FILE_SYSTEM == 'default':
|
||||
return os.path.isdir(file_path)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user