Update codes for draw
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D-X-Y
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exps/NATS-Bench/draw-fig6.py
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exps/NATS-Bench/draw-fig6.py
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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-fig6.py --search_space tss
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# Usage: python exps/NATS-Bench/draw-fig6.py --search_space sss
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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 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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alg2name, alg2path = OrderedDict(), OrderedDict()
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alg2name['REA'] = 'R-EA-SS3'
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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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for alg, name in alg2name.items():
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alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
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assert os.path.isfile(alg2path[alg]), 'invalid path : {:}'.format(alg2path[alg])
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alg2data = OrderedDict()
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for alg, path in alg2path.items():
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data = torch.load(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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alg2data[alg] = data
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return alg2data
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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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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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def visualize_curve(api, vis_save_dir, search_space, max_time):
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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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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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total_tickets = 150
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time_tickets = [float(i) / total_tickets * max_time for i in range(total_tickets)]
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colors = ['b', 'g', 'c', 'm', 'y']
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ax.set_xlim(0, 200)
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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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print('plot alg : {:}'.format(alg))
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accuracies = []
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for ticket in time_tickets:
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accuracy = query_performance(api, data, dataset, ticket)
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accuracies.append(accuracy)
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alg2accuracies[alg] = accuracies
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ax.plot([x/100 for x in time_tickets], accuracies, c=colors[idx], label='{:}'.format(alg))
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ax.set_xlabel('Estimated wall-clock time (1e2 seconds)', 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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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 / '{:}-curve.png'.format(search_space)).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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parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
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parser.add_argument('--max_time', type=float, default=20000, help='The maximum time budget.')
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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api = create(None, args.search_space, fast_mode=True, verbose=False)
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visualize_curve(api, save_dir, args.search_space, args.max_time)
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@ -1,5 +1,5 @@
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#!/bin/bash
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# bash ./scripts-search/NASNet-space-search-by-GDAS-FRC.sh cifar10 1 -1
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# bash ./scripts-search/NASNet-space-search-by-GDAS-FRC.sh cifar10 0 -1
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echo script name: $0
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echo $# arguments
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if [ "$#" -ne 3 ] ;then
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@ -4,6 +4,11 @@
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##############################################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
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##############################################################################
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# [saturn1] CUDA_VISIBLE_DEVICES=0 bash scripts/NATS-Bench/train-topology.sh 00000-02000 200 "777 888 999"
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# [saturn1] CUDA_VISIBLE_DEVICES=0 bash scripts/NATS-Bench/train-topology.sh 02000-04000 200 "777 888 999"
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# [saturn1] CUDA_VISIBLE_DEVICES=1 bash scripts/NATS-Bench/train-topology.sh 04000-06000 200 "777 888 999"
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# [saturn1] CUDA_VISIBLE_DEVICES=1 bash scripts/NATS-Bench/train-topology.sh 06000-08000 200 "777 888 999"
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#
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# CUDA_VISIBLE_DEVICES=0 bash scripts/NATS-Bench/train-topology.sh 00000-05000 12 777
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# bash ./scripts/NATS-Bench/train-topology.sh 05001-10000 12 777
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# bash ./scripts/NATS-Bench/train-topology.sh 10001-14500 12 777
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