Update visualization codes for NATS-Bench
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D-X-Y
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exps/NATS-Bench/draw-correlations.py
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exps/NATS-Bench/draw-correlations.py
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###############################################################
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# NATS-Bench (https://arxiv.org/pdf/2009.00437.pdf) #
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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-correlations.py #
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###############################################################
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import os, gc, sys, time, scipy, 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 compute_kendalltau(vectori, vectorj):
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# indexes = list(range(len(vectori)))
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# rank_1 = sorted(indexes, key=lambda i: vectori[i])
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# rank_2 = sorted(indexes, key=lambda i: vectorj[i])
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# import pdb; pdb.set_trace()
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coef, p = scipy.stats.kendalltau(vectori, vectorj)
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return coef
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def compute_spearmanr(vectori, vectorj):
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coef, p = scipy.stats.spearmanr(vectori, vectorj)
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return coef
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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', 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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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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api = create(None, 'tss', fast_mode=True, verbose=False)
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indexes = list(range(1, 10000, 300))
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scores_1 = []
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scores_2 = []
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for index in indexes:
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valid_acc, test_acc, _ = get_valid_test_acc(api, index, 'cifar10')
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scores_1.append(valid_acc)
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scores_2.append(test_acc)
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correlation = compute_kendalltau(scores_1, scores_2)
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print('The kendall tau correlation of {:} samples : {:}'.format(len(indexes), correlation))
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correlation = compute_spearmanr(scores_1, scores_2)
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print('The spearmanr correlation of {:} samples : {:}'.format(len(indexes), correlation))
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# scores_1 = ['{:.2f}'.format(x) for x in scores_1]
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# scores_2 = ['{:.2f}'.format(x) for x in scores_2]
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# print(', '.join(scores_1))
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# print(', '.join(scores_2))
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dpi, width, height = 250, 1000, 1000
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 14, 14
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fig, ax = plt.subplots(1, 1, figsize=figsize)
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ax.scatter(scores_1, scores_2 , marker='^', s=0.5, c='tab:green', alpha=0.8)
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save_path = '/Users/xuanyidong/Desktop/test-temp-rank.png'
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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plt.close('all')
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exps/NATS-Bench/draw-fig2_5.py
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exps/NATS-Bench/draw-fig2_5.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 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-fig2_5.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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def visualize_sss_info(api, dataset, vis_save_dir):
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vis_save_dir = vis_save_dir.resolve()
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / '{:}-cache-sss-info.pth'.format(dataset)
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if not cache_file_path.exists():
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print ('Do not find cache file : {:}'.format(cache_file_path))
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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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cost_info = api.get_cost_info(index, dataset, hp='90')
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params.append(cost_info['params'])
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flops.append(cost_info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='90', is_random=False)
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train_accs.append(info['train-accuracy'])
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test_accs.append(info['test-accuracy'])
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if dataset == 'cifar10':
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info = api.get_more_info(index, 'cifar10-valid', hp='90', is_random=False)
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valid_accs.append(info['valid-accuracy'])
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else:
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valid_accs.append(info['valid-accuracy'])
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs}
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torch.save(info, cache_file_path)
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else:
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print ('Find cache file : {:}'.format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
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print ('{:} collect data done.'.format(time_string()))
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# pyramid = ['8:16:32:48:64', '8:8:16:32:48', '8:8:16:16:32', '8:8:16:16:48', '8:8:16:16:64', '16:16:32:32:64', '32:32:64:64:64']
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pyramid = ['8:16:24:32:40', '8:16:32:48:64', '32:40:48:56:64']
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pyramid_indexes = [api.query_index_by_arch(x) for x in pyramid]
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largest_indexes = [api.query_index_by_arch('64:64:64:64:64')]
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indexes = list(range(len(params)))
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dpi, width, height = 250, 8500, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 24, 24
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# resnet_scale, resnet_alpha = 120, 0.5
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xscale, xalpha = 120, 0.8
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fig, axs = plt.subplots(1, 4, figsize=figsize)
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# ax1, ax2, ax3, ax4, ax5 = axs
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for ax in axs:
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax1, ax2, ax3, ax4 = axs
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ax1.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax1.scatter([params[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax1.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax1.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax1.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax1.legend(loc=4, fontsize=LegendFontsize)
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ax2.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
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ax2.scatter([flops[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax2.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax2.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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# ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax2.legend(loc=4, fontsize=LegendFontsize)
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ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
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ax3.scatter([params[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax3.legend(loc=4, fontsize=LegendFontsize)
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ax4.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
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ax4.scatter([flops[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax4.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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# ax4.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax4.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / 'sss-{:}.png'.format(dataset.lower())
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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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def visualize_tss_info(api, dataset, vis_save_dir):
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vis_save_dir = vis_save_dir.resolve()
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / '{:}-cache-tss-info.pth'.format(dataset)
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if not cache_file_path.exists():
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print ('Do not find cache file : {:}'.format(cache_file_path))
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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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cost_info = api.get_cost_info(index, dataset, hp='12')
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params.append(cost_info['params'])
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flops.append(cost_info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='200', is_random=False)
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train_accs.append(info['train-accuracy'])
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test_accs.append(info['test-accuracy'])
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if dataset == 'cifar10':
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info = api.get_more_info(index, 'cifar10-valid', hp='200', is_random=False)
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valid_accs.append(info['valid-accuracy'])
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else:
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valid_accs.append(info['valid-accuracy'])
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print('')
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs}
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torch.save(info, cache_file_path)
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else:
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print ('Find cache file : {:}'.format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
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print ('{:} collect data done.'.format(time_string()))
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resnet = ['|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|']
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resnet_indexes = [api.query_index_by_arch(x) for x in resnet]
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largest_indexes = [api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|')]
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indexes = list(range(len(params)))
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dpi, width, height = 250, 8500, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 24, 24
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# resnet_scale, resnet_alpha = 120, 0.5
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xscale, xalpha = 120, 0.8
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fig, axs = plt.subplots(1, 4, figsize=figsize)
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for ax in axs:
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax1, ax2, ax3, ax4 = axs
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ax1.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax1.scatter([params[x] for x in resnet_indexes] , [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax1.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax1.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax1.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax1.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax2.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax2.scatter([flops[x] for x in resnet_indexes], [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax2.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax2.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
# ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax2.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax3.scatter([params[x] for x in resnet_indexes] , [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax3.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax4.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax4.scatter([flops[x] for x in resnet_indexes], [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax4.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
# ax4.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
save_path = vis_save_dir / 'tss-{:}.png'.format(dataset.lower())
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
def visualize_rank_info(api, vis_save_dir, indicator):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
# print ('{:} start to visualize {:} information'.format(time_string(), api))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
|
||||
cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
|
||||
imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
|
||||
cifar010_info = torch.load(cifar010_cache_path)
|
||||
cifar100_info = torch.load(cifar100_cache_path)
|
||||
imagenet_info = torch.load(imagenet_cache_path)
|
||||
indexes = list(range(len(cifar010_info['params'])))
|
||||
|
||||
print ('{:} start to visualize relative ranking'.format(time_string()))
|
||||
|
||||
dpi, width, height = 250, 3800, 1200
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 14, 14
|
||||
|
||||
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
||||
ax1, ax2, ax3 = axs
|
||||
|
||||
def get_labels(info):
|
||||
ord_test_indexes = sorted(indexes, key=lambda i: info['test_accs'][i])
|
||||
ord_valid_indexes = sorted(indexes, key=lambda i: info['valid_accs'][i])
|
||||
labels = []
|
||||
for idx in ord_test_indexes:
|
||||
labels.append(ord_valid_indexes.index(idx))
|
||||
return labels
|
||||
|
||||
def plot_ax(labels, ax, name):
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
tick.label.set_rotation(90)
|
||||
ax.set_xlim(min(indexes), max(indexes))
|
||||
ax.set_ylim(min(indexes), max(indexes))
|
||||
ax.yaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//3))
|
||||
ax.xaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//5))
|
||||
ax.scatter(indexes, labels , marker='^', s=0.5, c='tab:green', alpha=0.8)
|
||||
ax.scatter(indexes, indexes, marker='o', s=0.5, c='tab:blue' , alpha=0.8)
|
||||
ax.scatter([-1], [-1], marker='^', s=100, c='tab:green' , label='{:} test'.format(name))
|
||||
ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='{:} validation'.format(name))
|
||||
ax.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax.set_xlabel('ranking on the {:} validation'.format(name), fontsize=LabelSize)
|
||||
ax.set_ylabel('architecture ranking', fontsize=LabelSize)
|
||||
labels = get_labels(cifar010_info)
|
||||
plot_ax(labels, ax1, 'CIFAR-10')
|
||||
labels = get_labels(cifar100_info)
|
||||
plot_ax(labels, ax2, 'CIFAR-100')
|
||||
labels = get_labels(imagenet_info)
|
||||
plot_ax(labels, ax3, 'ImageNet-16-120')
|
||||
|
||||
save_path = (vis_save_dir / '{:}-same-relative-rank.pdf'.format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
save_path = (vis_save_dir / '{:}-same-relative-rank.png'.format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
def compute_kendalltau(vectori, vectorj):
|
||||
# indexes = list(range(len(vectori)))
|
||||
# rank_1 = sorted(indexes, key=lambda i: vectori[i])
|
||||
# rank_2 = sorted(indexes, key=lambda i: vectorj[i])
|
||||
return scipy.stats.kendalltau(vectori, vectorj).correlation
|
||||
|
||||
|
||||
def calculate_correlation(*vectors):
|
||||
matrix = []
|
||||
for i, vectori in enumerate(vectors):
|
||||
x = []
|
||||
for j, vectorj in enumerate(vectors):
|
||||
# x.append(np.corrcoef(vectori, vectorj)[0,1])
|
||||
x.append(compute_kendalltau(vectori, vectorj))
|
||||
matrix.append( x )
|
||||
return np.array(matrix)
|
||||
|
||||
|
||||
def visualize_all_rank_info(api, vis_save_dir, indicator):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
# print ('{:} start to visualize {:} information'.format(time_string(), api))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
|
||||
cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
|
||||
imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
|
||||
cifar010_info = torch.load(cifar010_cache_path)
|
||||
cifar100_info = torch.load(cifar100_cache_path)
|
||||
imagenet_info = torch.load(imagenet_cache_path)
|
||||
indexes = list(range(len(cifar010_info['params'])))
|
||||
|
||||
print ('{:} start to visualize relative ranking'.format(time_string()))
|
||||
|
||||
|
||||
dpi, width, height = 250, 3200, 1400
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 14, 14
|
||||
|
||||
fig, axs = plt.subplots(1, 2, figsize=figsize)
|
||||
ax1, ax2 = axs
|
||||
|
||||
sns_size, xformat = 15, '.2f'
|
||||
CoRelMatrix = calculate_correlation(cifar010_info['valid_accs'], cifar010_info['test_accs'], cifar100_info['valid_accs'], cifar100_info['test_accs'], imagenet_info['valid_accs'], imagenet_info['test_accs'])
|
||||
|
||||
sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt=xformat, linewidths=0.5, ax=ax1,
|
||||
xticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'],
|
||||
yticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'])
|
||||
|
||||
selected_indexes, acc_bar = [], 92
|
||||
for i, acc in enumerate(cifar010_info['test_accs']):
|
||||
if acc > acc_bar: selected_indexes.append( i )
|
||||
cifar010_valid_accs = np.array(cifar010_info['valid_accs'])[ selected_indexes ]
|
||||
cifar010_test_accs = np.array(cifar010_info['test_accs']) [ selected_indexes ]
|
||||
cifar100_valid_accs = np.array(cifar100_info['valid_accs'])[ selected_indexes ]
|
||||
cifar100_test_accs = np.array(cifar100_info['test_accs']) [ selected_indexes ]
|
||||
imagenet_valid_accs = np.array(imagenet_info['valid_accs'])[ selected_indexes ]
|
||||
imagenet_test_accs = np.array(imagenet_info['test_accs']) [ selected_indexes ]
|
||||
CoRelMatrix = calculate_correlation(cifar010_valid_accs, cifar010_test_accs, cifar100_valid_accs, cifar100_test_accs, imagenet_valid_accs, imagenet_test_accs)
|
||||
|
||||
sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt=xformat, linewidths=0.5, ax=ax2,
|
||||
xticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'],
|
||||
yticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'])
|
||||
ax1.set_title('Correlation coefficient over ALL candidates')
|
||||
ax2.set_title('Correlation coefficient over candidates with accuracy > {:}%'.format(acc_bar))
|
||||
save_path = (vis_save_dir / '{:}-all-relative-rank.png'.format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench', help='Folder to save checkpoints and log.')
|
||||
# use for train the model
|
||||
args = parser.parse_args()
|
||||
|
||||
to_save_dir = Path(args.save_dir)
|
||||
|
||||
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
|
||||
# Figure 3 (a-c)
|
||||
api_tss = create(None, 'tss', verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_tss_info(api_tss, xdata, to_save_dir)
|
||||
# Figure 3 (d-f)
|
||||
api_sss = create(None, 'size', verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_sss_info(api_sss, xdata, to_save_dir)
|
||||
|
||||
# Figure 2
|
||||
visualize_relative_info(None, to_save_dir, 'tss')
|
||||
visualize_relative_info(None, to_save_dir, 'sss')
|
||||
|
||||
# Figure 4
|
||||
visualize_rank_info(None, to_save_dir, 'tss')
|
||||
visualize_rank_info(None, to_save_dir, 'sss')
|
||||
|
||||
# Figure 5
|
||||
visualize_all_rank_info(None, to_save_dir, 'tss')
|
||||
visualize_all_rank_info(None, to_save_dir, 'sss')
|
||||
@ -33,7 +33,7 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
|
||||
alg2name['REA'] = 'R-EA-SS3'
|
||||
alg2name['REINFORCE'] = 'REINFORCE-0.01'
|
||||
alg2name['RANDOM'] = 'RANDOM'
|
||||
# alg2name['BOHB'] = 'BOHB'
|
||||
alg2name['BOHB'] = 'BOHB'
|
||||
for alg, name in alg2name.items():
|
||||
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
|
||||
assert os.path.isfile(alg2path[alg]), 'invalid path : {:}'.format(alg2path[alg])
|
||||
@ -59,7 +59,26 @@ def query_performance(api, data, dataset, ticket):
|
||||
accuracy_a, accuracy_b = info_a['test-accuracy'], info_b['test-accuracy']
|
||||
interplate = (time_b-ticket) / (time_b-time_a) * accuracy_a + (ticket-time_a) / (time_b-time_a) * accuracy_b
|
||||
results.append(interplate)
|
||||
return sum(results) / len(results)
|
||||
# return sum(results) / len(results)
|
||||
return np.mean(results), np.std(results)
|
||||
|
||||
|
||||
def show_valid_test(api, data, dataset):
|
||||
valid_accs, test_accs, is_size_space = [], [], api.search_space_name == 'size'
|
||||
for i, info in data.items():
|
||||
time, arch = info['time_w_arch'][-1]
|
||||
if dataset == 'cifar10':
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
test_accs.append(xinfo['test-accuracy'])
|
||||
xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_accs.append(xinfo['valid-accuracy'])
|
||||
else:
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_accs.append(xinfo['valid-accuracy'])
|
||||
test_accs.append(xinfo['test-accuracy'])
|
||||
valid_str = '{:.2f}$\pm${:.2f}'.format(np.mean(valid_accs), np.std(valid_accs))
|
||||
test_str = '{:.2f}$\pm${:.2f}'.format(np.mean(test_accs), np.std(test_accs))
|
||||
return valid_str, test_str
|
||||
|
||||
|
||||
y_min_s = {('cifar10', 'tss'): 90,
|
||||
@ -69,11 +88,11 @@ y_min_s = {('cifar10', 'tss'): 90,
|
||||
('ImageNet16-120', 'tss'): 36,
|
||||
('ImageNet16-120', 'sss'): 40}
|
||||
|
||||
y_max_s = {('cifar10', 'tss'): 94.5,
|
||||
y_max_s = {('cifar10', 'tss'): 94.3,
|
||||
('cifar10', 'sss'): 93.3,
|
||||
('cifar100', 'tss'): 72,
|
||||
('cifar100', 'sss'): 70,
|
||||
('ImageNet16-120', 'tss'): 44,
|
||||
('cifar100', 'tss'): 72.5,
|
||||
('cifar100', 'sss'): 70.5,
|
||||
('ImageNet16-120', 'tss'): 46,
|
||||
('ImageNet16-120', 'sss'): 46}
|
||||
|
||||
x_axis_s = {('cifar10', 'tss'): 200,
|
||||
@ -87,6 +106,7 @@ name2label = {'cifar10': 'CIFAR-10',
|
||||
'cifar100': 'CIFAR-100',
|
||||
'ImageNet16-120': 'ImageNet-16-120'}
|
||||
|
||||
|
||||
def visualize_curve(api, vis_save_dir, search_space):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
@ -106,11 +126,13 @@ def visualize_curve(api, vis_save_dir, search_space):
|
||||
ax.set_ylim(y_min_s[(xdataset, search_space)],
|
||||
y_max_s[(xdataset, search_space)])
|
||||
for idx, (alg, data) in enumerate(alg2data.items()):
|
||||
print('{:} plot alg : {:}'.format(time_string(), alg))
|
||||
accuracies = []
|
||||
for ticket in time_tickets:
|
||||
accuracy = query_performance(api, data, xdataset, ticket)
|
||||
accuracy, accuracy_std = query_performance(api, data, xdataset, ticket)
|
||||
accuracies.append(accuracy)
|
||||
valid_str, test_str = show_valid_test(api, data, xdataset)
|
||||
# print('{:} plot alg : {:10s}, final accuracy = {:.2f}$\pm${:.2f}'.format(time_string(), alg, accuracy, accuracy_std))
|
||||
print('{:} plot alg : {:10s} | validation = {:} | test = {:}'.format(time_string(), alg, valid_str, test_str))
|
||||
alg2accuracies[alg] = accuracies
|
||||
ax.plot([x/100 for x in time_tickets], accuracies, c=colors[idx], label='{:}'.format(alg))
|
||||
ax.set_xlabel('Estimated wall-clock time (1e2 seconds)', fontsize=LabelSize)
|
||||
|
||||
180
exps/NATS-Bench/draw-fig7.py
Normal file
180
exps/NATS-Bench/draw-fig7.py
Normal file
@ -0,0 +1,180 @@
|
||||
###############################################################
|
||||
# NATS-Bench (https://arxiv.org/pdf/2009.00437.pdf) #
|
||||
# The code to draw Figure 7 in our paper. #
|
||||
###############################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
|
||||
###############################################################
|
||||
# Usage: python exps/NATS-Bench/draw-fig7.py #
|
||||
###############################################################
|
||||
import os, gc, sys, time, torch, argparse
|
||||
import numpy as np
|
||||
from typing import List, Text, Dict, Any
|
||||
from shutil import copyfile
|
||||
from collections import defaultdict, OrderedDict
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
import matplotlib
|
||||
import seaborn as sns
|
||||
matplotlib.use('agg')
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as ticker
|
||||
|
||||
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
|
||||
from config_utils import dict2config, load_config
|
||||
from nats_bench import create
|
||||
from log_utils import time_string
|
||||
|
||||
|
||||
def get_valid_test_acc(api, arch, dataset):
|
||||
is_size_space = api.search_space_name == 'size'
|
||||
if dataset == 'cifar10':
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
test_acc = xinfo['test-accuracy']
|
||||
xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo['valid-accuracy']
|
||||
else:
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo['valid-accuracy']
|
||||
test_acc = xinfo['test-accuracy']
|
||||
return valid_acc, test_acc, 'validation = {:.2f}, test = {:.2f}\n'.format(valid_acc, test_acc)
|
||||
|
||||
|
||||
def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-WARM0.3'):
|
||||
ss_dir = '{:}-{:}'.format(root_dir, search_space)
|
||||
alg2name, alg2path = OrderedDict(), OrderedDict()
|
||||
seeds = [777, 888, 999]
|
||||
print('\n[fetch data] from {:} on {:}'.format(search_space, dataset))
|
||||
if search_space == 'tss':
|
||||
alg2name['GDAS'] = 'gdas-affine0_BN0-None'
|
||||
alg2name['RSPS'] = 'random-affine0_BN0-None'
|
||||
alg2name['DARTS (1st)'] = 'darts-v1-affine0_BN0-None'
|
||||
alg2name['DARTS (2nd)'] = 'darts-v2-affine0_BN0-None'
|
||||
alg2name['ENAS'] = 'enas-affine0_BN0-None'
|
||||
alg2name['SETN'] = 'setn-affine0_BN0-None'
|
||||
else:
|
||||
alg2name['channel-wise interpolation'] = 'tas-affine0_BN0-AWD0.001{:}'.format(suffix)
|
||||
alg2name['masking + Gumbel-Softmax'] = 'mask_gumbel-affine0_BN0-AWD0.001{:}'.format(suffix)
|
||||
alg2name['masking + sampling'] = 'mask_rl-affine0_BN0-AWD0.0{:}'.format(suffix)
|
||||
for alg, name in alg2name.items():
|
||||
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
|
||||
alg2data = OrderedDict()
|
||||
for alg, path in alg2path.items():
|
||||
alg2data[alg], ok_num = [], 0
|
||||
for seed in seeds:
|
||||
xpath = path.format(seed)
|
||||
if os.path.isfile(xpath):
|
||||
ok_num += 1
|
||||
else:
|
||||
print('This is an invalid path : {:}'.format(xpath))
|
||||
continue
|
||||
data = torch.load(xpath, map_location=torch.device('cpu'))
|
||||
try:
|
||||
data = torch.load(data['last_checkpoint'], map_location=torch.device('cpu'))
|
||||
except:
|
||||
xpath = str(data['last_checkpoint']).split('E100-')
|
||||
if len(xpath) == 2 and os.path.isfile(xpath[0] + xpath[1]):
|
||||
xpath = xpath[0] + xpath[1]
|
||||
elif 'fbv2' in str(data['last_checkpoint']):
|
||||
xpath = str(data['last_checkpoint']).replace('fbv2', 'mask_gumbel')
|
||||
elif 'tunas' in str(data['last_checkpoint']):
|
||||
xpath = str(data['last_checkpoint']).replace('tunas', 'mask_rl')
|
||||
else:
|
||||
raise ValueError('Invalid path: {:}'.format(data['last_checkpoint']))
|
||||
data = torch.load(xpath, map_location=torch.device('cpu'))
|
||||
alg2data[alg].append(data['genotypes'])
|
||||
print('This algorithm : {:} has {:} valid ckps.'.format(alg, ok_num))
|
||||
assert ok_num > 0, 'Must have at least 1 valid ckps.'
|
||||
return alg2data
|
||||
|
||||
|
||||
y_min_s = {('cifar10', 'tss'): 90,
|
||||
('cifar10', 'sss'): 92,
|
||||
('cifar100', 'tss'): 65,
|
||||
('cifar100', 'sss'): 65,
|
||||
('ImageNet16-120', 'tss'): 36,
|
||||
('ImageNet16-120', 'sss'): 40}
|
||||
|
||||
y_max_s = {('cifar10', 'tss'): 94.5,
|
||||
('cifar10', 'sss'): 93.3,
|
||||
('cifar100', 'tss'): 72,
|
||||
('cifar100', 'sss'): 70,
|
||||
('ImageNet16-120', 'tss'): 44,
|
||||
('ImageNet16-120', 'sss'): 46}
|
||||
|
||||
name2label = {'cifar10': 'CIFAR-10',
|
||||
'cifar100': 'CIFAR-100',
|
||||
'ImageNet16-120': 'ImageNet-16-120'}
|
||||
|
||||
name2suffix = {('sss', 'warm'): '-WARM0.3',
|
||||
('sss', 'none'): '-WARMNone',
|
||||
('tss', 'none') : None,
|
||||
('tss', None) : None}
|
||||
|
||||
def visualize_curve(api, vis_save_dir, search_space, suffix):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
dpi, width, height = 250, 5200, 1400
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 16, 16
|
||||
|
||||
def sub_plot_fn(ax, dataset):
|
||||
print('{:} plot {:10s}'.format(time_string(), dataset))
|
||||
alg2data = fetch_data(search_space=search_space, dataset=dataset, suffix=name2suffix[(search_space, suffix)])
|
||||
alg2accuracies = OrderedDict()
|
||||
epochs = 100
|
||||
colors = ['b', 'g', 'c', 'm', 'y', 'r']
|
||||
ax.set_xlim(0, epochs)
|
||||
# ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
|
||||
for idx, (alg, data) in enumerate(alg2data.items()):
|
||||
xs, accuracies = [], []
|
||||
for iepoch in range(epochs + 1):
|
||||
try:
|
||||
structures, accs = [_[iepoch-1] for _ in data], []
|
||||
except:
|
||||
raise ValueError('This alg {:} on {:} has invalid checkpoints.'.format(alg, dataset))
|
||||
for structure in structures:
|
||||
info = api.get_more_info(structure, dataset=dataset, hp=90 if api.search_space_name == 'size' else 200, is_random=False)
|
||||
accs.append(info['test-accuracy'])
|
||||
accuracies.append(sum(accs)/len(accs))
|
||||
xs.append(iepoch)
|
||||
alg2accuracies[alg] = accuracies
|
||||
ax.plot(xs, accuracies, c=colors[idx], label='{:}'.format(alg))
|
||||
ax.set_xlabel('The searching epoch', fontsize=LabelSize)
|
||||
ax.set_ylabel('Test accuracy on {:}'.format(name2label[dataset]), fontsize=LabelSize)
|
||||
ax.set_title('Searching results on {:}'.format(name2label[dataset]), fontsize=LabelSize+4)
|
||||
structures, valid_accs, test_accs = [_[epochs-1] for _ in data], [], []
|
||||
print('{:} plot alg : {:} -- final {:} architectures.'.format(time_string(), alg, len(structures)))
|
||||
for arch in structures:
|
||||
valid_acc, test_acc, _ = get_valid_test_acc(api, arch, dataset)
|
||||
test_accs.append(test_acc)
|
||||
valid_accs.append(valid_acc)
|
||||
print('{:} plot alg : {:} -- validation: {:.2f}$\pm${:.2f} -- test: {:.2f}$\pm${:.2f}'.format(
|
||||
time_string(), alg, np.mean(valid_accs), np.std(valid_accs), np.mean(test_accs), np.std(test_accs)))
|
||||
ax.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
||||
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
|
||||
for dataset, ax in zip(datasets, axs):
|
||||
sub_plot_fn(ax, dataset)
|
||||
print('sub-plot {:} on {:} done.'.format(dataset, search_space))
|
||||
save_path = (vis_save_dir / '{:}-ws-{:}-curve.png'.format(search_space, suffix)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path(args.save_dir)
|
||||
|
||||
api_tss = create(None, 'tss', fast_mode=True, verbose=False)
|
||||
visualize_curve(api_tss, save_dir, 'tss', None)
|
||||
|
||||
api_sss = create(None, 'sss', fast_mode=True, verbose=False)
|
||||
visualize_curve(api_sss, save_dir, 'sss', 'warm')
|
||||
visualize_curve(api_sss, save_dir, 'sss', 'none')
|
||||
85
exps/NATS-Bench/draw-table.py
Normal file
85
exps/NATS-Bench/draw-table.py
Normal file
@ -0,0 +1,85 @@
|
||||
###############################################################
|
||||
# NATS-Bench (https://arxiv.org/pdf/2009.00437.pdf) #
|
||||
# The code to draw some results in Table 4 in our paper. #
|
||||
###############################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
|
||||
###############################################################
|
||||
# Usage: python exps/NATS-Bench/draw-table.py #
|
||||
###############################################################
|
||||
import os, gc, sys, time, torch, argparse
|
||||
import numpy as np
|
||||
from typing import List, Text, Dict, Any
|
||||
from shutil import copyfile
|
||||
from collections import defaultdict, OrderedDict
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
import matplotlib
|
||||
import seaborn as sns
|
||||
matplotlib.use('agg')
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as ticker
|
||||
|
||||
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
|
||||
from config_utils import dict2config, load_config
|
||||
from nats_bench import create
|
||||
from log_utils import time_string
|
||||
|
||||
|
||||
def get_valid_test_acc(api, arch, dataset):
|
||||
is_size_space = api.search_space_name == 'size'
|
||||
if dataset == 'cifar10':
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
test_acc = xinfo['test-accuracy']
|
||||
xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo['valid-accuracy']
|
||||
else:
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo['valid-accuracy']
|
||||
test_acc = xinfo['test-accuracy']
|
||||
return valid_acc, test_acc, 'validation = {:.2f}, test = {:.2f}\n'.format(valid_acc, test_acc)
|
||||
|
||||
|
||||
def show_valid_test(api, arch):
|
||||
is_size_space = api.search_space_name == 'size'
|
||||
final_str = ''
|
||||
for dataset in ['cifar10', 'cifar100', 'ImageNet16-120']:
|
||||
valid_acc, test_acc, perf_str = get_valid_test_acc(api, arch, dataset)
|
||||
final_str += '{:} : {:}\n'.format(dataset, perf_str)
|
||||
return final_str
|
||||
|
||||
|
||||
def find_best_valid(api, dataset):
|
||||
all_valid_accs, all_test_accs = [], []
|
||||
for index, arch in enumerate(api):
|
||||
# import pdb; pdb.set_trace()
|
||||
valid_acc, test_acc, perf_str = get_valid_test_acc(api, index, dataset)
|
||||
all_valid_accs.append((index, valid_acc))
|
||||
all_test_accs.append((index, test_acc))
|
||||
best_valid_index = sorted(all_valid_accs, key=lambda x: -x[1])[0][0]
|
||||
best_test_index = sorted(all_test_accs, key=lambda x: -x[1])[0][0]
|
||||
|
||||
print('-' * 50 + '{:10s}'.format(dataset) + '-' * 50)
|
||||
print('Best ({:}) architecture on validation: {:}'.format(best_valid_index, api[best_valid_index]))
|
||||
print('Best ({:}) architecture on test: {:}'.format(best_test_index, api[best_test_index]))
|
||||
_, _, perf_str = get_valid_test_acc(api, best_valid_index, dataset)
|
||||
print('using validation ::: {:}'.format(perf_str))
|
||||
_, _, perf_str = get_valid_test_acc(api, best_test_index, dataset)
|
||||
print('using test ::: {:}'.format(perf_str))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
api_tss = create(None, 'tss', fast_mode=False, verbose=False)
|
||||
resnet = '|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|'
|
||||
resnet_index = api_tss.query_index_by_arch(resnet)
|
||||
print(show_valid_test(api_tss, resnet_index))
|
||||
|
||||
for dataset in ['cifar10', 'cifar100', 'ImageNet16-120']:
|
||||
find_best_valid(api_tss, dataset)
|
||||
|
||||
largest = '64:64:64:64:64'
|
||||
largest_index = api_sss.query_index_by_arch(largest)
|
||||
print(show_valid_test(api_sss, largest_index))
|
||||
for dataset in ['cifar10', 'cifar100', 'ImageNet16-120']:
|
||||
find_best_valid(api_sss, dataset)
|
||||
@ -92,8 +92,8 @@ class NATStopology(NASBenchMetaAPI):
|
||||
file_path_or_dict = os.path.join(
|
||||
os.environ['TORCH_HOME'], '{:}.{:}'.format(
|
||||
ALL_BASE_NAMES[-1], PICKLE_EXT))
|
||||
print('{:} Try to use the default NATS-Bench (topology) path '
|
||||
'from {:}.'.format(time_string(), file_path_or_dict))
|
||||
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))
|
||||
if isinstance(file_path_or_dict, str):
|
||||
file_path_or_dict = str(file_path_or_dict)
|
||||
if verbose:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user