Update REA, REINFORCE, and RANDOM
This commit is contained in:
D-X-Y
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###############################################################
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# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
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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/NAS-Bench-201/test-nas-api-vis.py
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###############################################################
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import os, 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
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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 nas_201_api import NASBench201API, NASBench301API
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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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def visualize_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_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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ax2, ax3, ax4, ax5 = axs
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# ax1.xaxis.set_ticks(np.arange(0, max(indexes), max(indexes)//5))
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# ax1.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
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# ax1.set_xlabel('architecture ID', fontsize=LabelSize)
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# ax1.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax2.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax2.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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ax2.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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ax2.set_xlabel('#parameters (MB)', 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, train_accs, marker='o', s=0.5, c='tab:blue')
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ax4.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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ax4.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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ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax4.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax4.legend(loc=4, fontsize=LegendFontsize)
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ax5.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
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ax5.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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ax5.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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ax5.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax5.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax5.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / 'sss-{:}.png'.format(dataset)
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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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# 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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ax2, ax3, ax4, ax5 = axs
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# ax1.xaxis.set_ticks(np.arange(0, max(indexes), max(indexes)//5))
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# ax1.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
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# ax1.set_xlabel('architecture ID', fontsize=LabelSize)
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# ax1.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax2.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax2.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)
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ax2.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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ax2.set_xlabel('#parameters (MB)', 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 resnet_indexes] , [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', 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, train_accs, marker='o', s=0.5, c='tab:blue')
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ax4.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)
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ax4.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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ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax4.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax4.legend(loc=4, fontsize=LegendFontsize)
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ax5.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
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ax5.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)
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ax5.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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ax5.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax5.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax5.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / 'tss-{:}.png'.format(dataset)
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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_rank_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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dpi, width, height = 250, 3800, 1200
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 14, 14
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fig, axs = plt.subplots(1, 3, figsize=figsize)
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ax1, ax2, ax3 = axs
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def get_labels(info):
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ord_test_indexes = sorted(indexes, key=lambda i: info['test_accs'][i])
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ord_valid_indexes = sorted(indexes, key=lambda i: info['valid_accs'][i])
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labels = []
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for idx in ord_test_indexes:
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labels.append(ord_valid_indexes.index(idx))
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return labels
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def plot_ax(labels, ax, name):
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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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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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tick.label.set_rotation(90)
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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 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] )
|
||||
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 = 15
|
||||
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='.3f', 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='.3f', 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/NAS-BENCH-202', help='Folder to save checkpoints and log.')
|
||||
parser.add_argument('--check_N', type=int, default=32768, help='For safety.')
|
||||
# use for train the model
|
||||
args = parser.parse_args()
|
||||
|
||||
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
|
||||
api201 = NASBench201API(None, verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_tss_info(api201, xdata, Path('output/vis-nas-bench'))
|
||||
|
||||
api301 = NASBench301API(None, verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_sss_info(api301, xdata, Path('output/vis-nas-bench'))
|
||||
|
||||
visualize_info(None, Path('output/vis-nas-bench/'), 'tss')
|
||||
visualize_info(None, Path('output/vis-nas-bench/'), 'sss')
|
||||
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
|
||||
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
|
||||
|
||||
visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
|
||||
visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
|
||||
@@ -72,6 +72,14 @@ def test_api(api, is_301=True):
|
||||
print('{:}\n'.format(info))
|
||||
print('{:} finish testing the api : {:}'.format(time_string(), api))
|
||||
|
||||
if not is_301:
|
||||
arch_str = '|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|'
|
||||
matrix = api.str2matrix(arch_str)
|
||||
print('Compute the adjacency matrix of {:}'.format(arch_str))
|
||||
print(matrix)
|
||||
info = api.simulate_train_eval(123, 'cifar10')
|
||||
print('simulate_train_eval : {:}'.format(info))
|
||||
|
||||
|
||||
def test_issue_81_82(api):
|
||||
results = api.query_by_index(0, 'cifar10-valid', hp='12')
|
||||
|
||||
Reference in New Issue
Block a user