102->201 / NAS->autoDL / more configs of TAS / reorganize docs / fix bugs in NAS baselines
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D-X-Y
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exps/NAS-Bench-201/visualize.py
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exps/NAS-Bench-201/visualize.py
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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# python exps/NAS-Bench-201/visualize.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth
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##################################################
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import os, sys, time, argparse, collections
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from tqdm import tqdm
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from collections import OrderedDict
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import numpy as np
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import torch
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from pathlib import Path
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from collections import defaultdict
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import matplotlib
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import seaborn as sns
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from mpl_toolkits.mplot3d import Axes3D
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matplotlib.use('agg')
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import matplotlib.pyplot as plt
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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 log_utils import time_string
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from nas_201_api import NASBench201API as API
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def calculate_correlation(*vectors):
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matrix = []
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for i, vectori in enumerate(vectors):
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x = []
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for j, vectorj in enumerate(vectors):
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x.append( np.corrcoef(vectori, vectorj)[0,1] )
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matrix.append( x )
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return np.array(matrix)
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def visualize_relative_ranking(vis_save_dir):
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print ('\n' + '-'*100)
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cifar010_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('cifar10')
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cifar100_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('cifar100')
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imagenet_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('ImageNet16-120')
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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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# maximum accuracy with ResNet-level params 11472
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x_010_accs = [ cifar010_info['test_accs'][i] if cifar010_info['params'][i] <= cifar010_info['params'][11472] else -1 for i in indexes]
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x_100_accs = [ cifar100_info['test_accs'][i] if cifar100_info['params'][i] <= cifar100_info['params'][11472] else -1 for i in indexes]
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x_img_accs = [ imagenet_info['test_accs'][i] if imagenet_info['params'][i] <= imagenet_info['params'][11472] else -1 for i in indexes]
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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 = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 18
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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(0)
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize, rotation='vertical')
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize)
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#ax.scatter(indexes, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8, label='CIFAR-100')
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#ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8, label='ImageNet-16-120')
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#ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8, label='CIFAR-10')
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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').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').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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# calculate correlation
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sns_size = 15
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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'])
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fig = plt.figure(figsize=figsize)
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plt.axis('off')
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h = sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5)
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save_path = (vis_save_dir / 'co-relation-all.pdf').resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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print ('{:} save into {:}'.format(time_string(), save_path))
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# calculate correlation
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acc_bars = [92, 93]
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for acc_bar in acc_bars:
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selected_indexes = []
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for i, acc in enumerate(cifar010_info['test_accs']):
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if acc > acc_bar: selected_indexes.append( i )
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print ('select {:} architectures'.format(len(selected_indexes)))
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cifar010_valid_accs = np.array(cifar010_info['valid_accs'])[ selected_indexes ]
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cifar010_test_accs = np.array(cifar010_info['test_accs']) [ selected_indexes ]
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cifar100_valid_accs = np.array(cifar100_info['valid_accs'])[ selected_indexes ]
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cifar100_test_accs = np.array(cifar100_info['test_accs']) [ selected_indexes ]
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imagenet_valid_accs = np.array(imagenet_info['valid_accs'])[ selected_indexes ]
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imagenet_test_accs = np.array(imagenet_info['test_accs']) [ selected_indexes ]
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CoRelMatrix = calculate_correlation(cifar010_valid_accs, cifar010_test_accs, cifar100_valid_accs, cifar100_test_accs, imagenet_valid_accs, imagenet_test_accs)
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fig = plt.figure(figsize=figsize)
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plt.axis('off')
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h = sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5)
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save_path = (vis_save_dir / 'co-relation-top-{:}.pdf'.format(len(selected_indexes))).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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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_info(meta_file, dataset, vis_save_dir):
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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cache_file_path = vis_save_dir / '{:}-cache-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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nas_bench = API(str(meta_file))
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params, flops, train_accs, valid_accs, test_accs, otest_accs = [], [], [], [], [], []
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for index in range( len(nas_bench) ):
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info = nas_bench.query_by_index(index, use_12epochs_result=False)
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resx = info.get_comput_costs(dataset) ; flop, param = resx['flops'], resx['params']
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if dataset == 'cifar10':
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res = info.get_metrics('cifar10', 'train') ; train_acc = res['accuracy']
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res = info.get_metrics('cifar10-valid', 'x-valid') ; valid_acc = res['accuracy']
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res = info.get_metrics('cifar10', 'ori-test') ; test_acc = res['accuracy']
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res = info.get_metrics('cifar10', 'ori-test') ; otest_acc = res['accuracy']
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else:
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res = info.get_metrics(dataset, 'train') ; train_acc = res['accuracy']
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res = info.get_metrics(dataset, 'x-valid') ; valid_acc = res['accuracy']
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res = info.get_metrics(dataset, 'x-test') ; test_acc = res['accuracy']
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res = info.get_metrics(dataset, 'ori-test') ; otest_acc = res['accuracy']
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if index == 11472: # resnet
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resnet = {'params':param, 'flops': flop, 'index': 11472, 'train_acc': train_acc, 'valid_acc': valid_acc, 'test_acc': test_acc, 'otest_acc': otest_acc}
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flops.append( flop )
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params.append( param )
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train_accs.append( train_acc )
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valid_accs.append( valid_acc )
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test_accs.append( test_acc )
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otest_accs.append( otest_acc )
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#resnet = {'params': 0.559, 'flops': 78.56, 'index': 11472, 'train_acc': 99.99, 'valid_acc': 90.84, 'test_acc': 93.97}
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs, 'otest_accs': otest_accs}
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info['resnet'] = resnet
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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, otest_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs'], info['otest_accs']
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resnet = info['resnet']
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print ('{:} collect data done.'.format(time_string()))
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indexes = list(range(len(params)))
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dpi, width, height = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 22, 22
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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.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(params, valid_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['params']], [resnet['valid_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=0.4)
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax.set_ylabel('the validation accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-param-vs-valid.pdf'.format(dataset)).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 / '{:}-param-vs-valid.png'.format(dataset)).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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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['params']], [resnet['test_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax.set_ylabel('the test accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-param-vs-test.pdf'.format(dataset)).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 / '{:}-param-vs-test.png'.format(dataset)).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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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(20, 100)
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plt.yticks(np.arange(20, 101, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(25, 76)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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ax.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['params']], [resnet['train_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax.set_ylabel('the trarining accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-param-vs-train.pdf'.format(dataset)).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 / '{:}-param-vs-train.png'.format(dataset)).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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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(0, max(indexes))
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//5), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['index']], [resnet['test_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('architecture ID', fontsize=LabelSize)
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ax.set_ylabel('the test accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-test-over-ID.pdf'.format(dataset)).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 / '{:}-test-over-ID.png'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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def visualize_rank_over_time(meta_file, vis_save_dir):
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print ('\n' + '-'*150)
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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print ('{:} start to visualize rank-over-time into {:}'.format(time_string(), vis_save_dir))
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cache_file_path = vis_save_dir / 'rank-over-time-cache-info.pth'
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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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nas_bench = API(str(meta_file))
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print ('{:} load nas_bench done'.format(time_string()))
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params, flops, train_accs, valid_accs, test_accs, otest_accs = [], [], defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list)
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#for iepoch in range(200): for index in range( len(nas_bench) ):
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for index in tqdm(range(len(nas_bench))):
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info = nas_bench.query_by_index(index, use_12epochs_result=False)
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for iepoch in range(200):
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res = info.get_metrics('cifar10' , 'train' , iepoch) ; train_acc = res['accuracy']
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res = info.get_metrics('cifar10-valid', 'x-valid' , iepoch) ; valid_acc = res['accuracy']
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res = info.get_metrics('cifar10' , 'ori-test', iepoch) ; test_acc = res['accuracy']
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res = info.get_metrics('cifar10' , 'ori-test', iepoch) ; otest_acc = res['accuracy']
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train_accs[iepoch].append( train_acc )
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valid_accs[iepoch].append( valid_acc )
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test_accs [iepoch].append( test_acc )
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otest_accs[iepoch].append( otest_acc )
|
||||
if iepoch == 0:
|
||||
res = info.get_comput_costs('cifar10') ; flop, param = res['flops'], res['params']
|
||||
flops.append( flop )
|
||||
params.append( param )
|
||||
info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs, 'otest_accs': otest_accs}
|
||||
torch.save(info, cache_file_path)
|
||||
else:
|
||||
print ('Find cache file : {:}'.format(cache_file_path))
|
||||
info = torch.load(cache_file_path)
|
||||
params, flops, train_accs, valid_accs, test_accs, otest_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs'], info['otest_accs']
|
||||
print ('{:} collect data done.'.format(time_string()))
|
||||
#selected_epochs = [0, 100, 150, 180, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]
|
||||
selected_epochs = list( range(200) )
|
||||
x_xtests = test_accs[199]
|
||||
indexes = list(range(len(x_xtests)))
|
||||
ord_idxs = sorted(indexes, key=lambda i: x_xtests[i])
|
||||
for sepoch in selected_epochs:
|
||||
x_valids = valid_accs[sepoch]
|
||||
valid_ord_idxs = sorted(indexes, key=lambda i: x_valids[i])
|
||||
valid_ord_lbls = []
|
||||
for idx in ord_idxs:
|
||||
valid_ord_lbls.append( valid_ord_idxs.index(idx) )
|
||||
# labeled data
|
||||
dpi, width, height = 300, 2600, 2600
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 18, 18
|
||||
|
||||
fig = plt.figure(figsize=figsize)
|
||||
ax = fig.add_subplot(111)
|
||||
plt.xlim(min(indexes), max(indexes))
|
||||
plt.ylim(min(indexes), max(indexes))
|
||||
plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize, rotation='vertical')
|
||||
plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize)
|
||||
ax.scatter(indexes, valid_ord_lbls, 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='CIFAR-10 validation')
|
||||
ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='CIFAR-10 test')
|
||||
plt.grid(zorder=0)
|
||||
ax.set_axisbelow(True)
|
||||
plt.legend(loc='upper left', fontsize=LegendFontsize)
|
||||
ax.set_xlabel('architecture ranking in the final test accuracy', fontsize=LabelSize)
|
||||
ax.set_ylabel('architecture ranking in the validation set', fontsize=LabelSize)
|
||||
save_path = (vis_save_dir / 'time-{:03d}.pdf'.format(sepoch)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
save_path = (vis_save_dir / 'time-{:03d}.png'.format(sepoch)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
|
||||
def write_video(save_dir):
|
||||
import cv2
|
||||
video_save_path = save_dir / 'time.avi'
|
||||
print ('{:} start create video for {:}'.format(time_string(), video_save_path))
|
||||
images = sorted( list( save_dir.glob('time-*.png') ) )
|
||||
ximage = cv2.imread(str(images[0]))
|
||||
#shape = (ximage.shape[1], ximage.shape[0])
|
||||
shape = (1000, 1000)
|
||||
#writer = cv2.VideoWriter(str(video_save_path), cv2.VideoWriter_fourcc(*"MJPG"), 25, shape)
|
||||
writer = cv2.VideoWriter(str(video_save_path), cv2.VideoWriter_fourcc(*"MJPG"), 5, shape)
|
||||
for idx, image in enumerate(images):
|
||||
ximage = cv2.imread(str(image))
|
||||
_image = cv2.resize(ximage, shape)
|
||||
writer.write(_image)
|
||||
writer.release()
|
||||
print ('write video [{:} frames] into {:}'.format(len(images), video_save_path))
|
||||
|
||||
|
||||
|
||||
def plot_results_nas_v2(api, dataset_xset_a, dataset_xset_b, root, file_name, y_lims):
|
||||
#print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
|
||||
print ('root-path : {:} and {:}'.format(dataset_xset_a, dataset_xset_b))
|
||||
checkpoints = ['./output/search-cell-nas-bench-201/R-EA-cifar10/results.pth',
|
||||
'./output/search-cell-nas-bench-201/REINFORCE-cifar10/results.pth',
|
||||
'./output/search-cell-nas-bench-201/RAND-cifar10/results.pth',
|
||||
'./output/search-cell-nas-bench-201/BOHB-cifar10/results.pth'
|
||||
]
|
||||
legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
|
||||
All_Accs_A, All_Accs_B = OrderedDict(), OrderedDict()
|
||||
for legend, checkpoint in zip(legends, checkpoints):
|
||||
all_indexes = torch.load(checkpoint, map_location='cpu')
|
||||
accuracies_A, accuracies_B = [], []
|
||||
accuracies = []
|
||||
for x in all_indexes:
|
||||
info = api.arch2infos_full[ x ]
|
||||
metrics = info.get_metrics(dataset_xset_a[0], dataset_xset_a[1], None, False)
|
||||
accuracies_A.append( metrics['accuracy'] )
|
||||
metrics = info.get_metrics(dataset_xset_b[0], dataset_xset_b[1], None, False)
|
||||
accuracies_B.append( metrics['accuracy'] )
|
||||
accuracies.append( (accuracies_A[-1], accuracies_B[-1]) )
|
||||
if indexes is None: indexes = list(range(len(all_indexes)))
|
||||
accuracies = sorted(accuracies)
|
||||
All_Accs_A[legend] = [x[0] for x in accuracies]
|
||||
All_Accs_B[legend] = [x[1] for x in accuracies]
|
||||
|
||||
color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
|
||||
dpi, width, height = 300, 3400, 2600
|
||||
LabelSize, LegendFontsize = 28, 28
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
fig = plt.figure(figsize=figsize)
|
||||
x_axis = np.arange(0, 600)
|
||||
plt.xlim(0, max(indexes))
|
||||
plt.ylim(y_lims[0], y_lims[1])
|
||||
interval_x, interval_y = 100, y_lims[2]
|
||||
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
||||
plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
|
||||
plt.grid()
|
||||
plt.xlabel('The index of runs', fontsize=LabelSize)
|
||||
plt.ylabel('The accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
for idx, legend in enumerate(legends):
|
||||
plt.plot(indexes, All_Accs_B[legend], color=color_set[idx], linestyle='--', label='{:}'.format(legend), lw=1, alpha=0.5)
|
||||
plt.plot(indexes, All_Accs_A[legend], color=color_set[idx], linestyle='-', lw=1)
|
||||
for All_Accs in [All_Accs_A, All_Accs_B]:
|
||||
print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend]), np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
|
||||
plt.legend(loc=4, fontsize=LegendFontsize)
|
||||
save_path = root / '{:}'.format(file_name)
|
||||
print('save figure into {:}\n'.format(save_path))
|
||||
fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
|
||||
|
||||
|
||||
|
||||
def plot_results_nas(api, dataset, xset, root, file_name, y_lims):
|
||||
print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
|
||||
checkpoints = ['./output/search-cell-nas-bench-201/R-EA-cifar10/results.pth',
|
||||
'./output/search-cell-nas-bench-201/REINFORCE-cifar10/results.pth',
|
||||
'./output/search-cell-nas-bench-201/RAND-cifar10/results.pth',
|
||||
'./output/search-cell-nas-bench-201/BOHB-cifar10/results.pth'
|
||||
]
|
||||
legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
|
||||
All_Accs = OrderedDict()
|
||||
for legend, checkpoint in zip(legends, checkpoints):
|
||||
all_indexes = torch.load(checkpoint, map_location='cpu')
|
||||
accuracies = []
|
||||
for x in all_indexes:
|
||||
info = api.arch2infos_full[ x ]
|
||||
metrics = info.get_metrics(dataset, xset, None, False)
|
||||
accuracies.append( metrics['accuracy'] )
|
||||
if indexes is None: indexes = list(range(len(all_indexes)))
|
||||
All_Accs[legend] = sorted(accuracies)
|
||||
|
||||
color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
|
||||
dpi, width, height = 300, 3400, 2600
|
||||
LabelSize, LegendFontsize = 28, 28
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
fig = plt.figure(figsize=figsize)
|
||||
x_axis = np.arange(0, 600)
|
||||
plt.xlim(0, max(indexes))
|
||||
plt.ylim(y_lims[0], y_lims[1])
|
||||
interval_x, interval_y = 100, y_lims[2]
|
||||
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
||||
plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
|
||||
plt.grid()
|
||||
plt.xlabel('The index of runs', fontsize=LabelSize)
|
||||
plt.ylabel('The accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
for idx, legend in enumerate(legends):
|
||||
plt.plot(indexes, All_Accs[legend], color=color_set[idx], linestyle='-', label='{:}'.format(legend), lw=2)
|
||||
print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend]), np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
|
||||
plt.legend(loc=4, fontsize=LegendFontsize)
|
||||
save_path = root / '{:}-{:}-{:}'.format(dataset, xset, file_name)
|
||||
print('save figure into {:}\n'.format(save_path))
|
||||
fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
|
||||
|
||||
def just_show(api):
|
||||
xtimes = {'RSPS' : [8082.5, 7794.2, 8144.7],
|
||||
'DARTS-V1': [11582.1, 11347.0, 11948.2],
|
||||
'DARTS-V2': [35694.7, 36132.7, 35518.0],
|
||||
'GDAS' : [31334.1, 31478.6, 32016.7],
|
||||
'SETN' : [33528.8, 33831.5, 35058.3],
|
||||
'ENAS' : [14340.2, 13817.3, 14018.9]}
|
||||
for xkey, xlist in xtimes.items():
|
||||
xlist = np.array(xlist)
|
||||
print ('{:4s} : mean-time={:.2f} s'.format(xkey, xlist.mean()))
|
||||
|
||||
xpaths = {'RSPS' : 'output/search-cell-nas-bench-201/RANDOM-NAS-cifar10/checkpoint/',
|
||||
'DARTS-V1': 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/',
|
||||
'DARTS-V2': 'output/search-cell-nas-bench-201/DARTS-V2-cifar10/checkpoint/',
|
||||
'GDAS' : 'output/search-cell-nas-bench-201/GDAS-cifar10/checkpoint/',
|
||||
'SETN' : 'output/search-cell-nas-bench-201/SETN-cifar10/checkpoint/',
|
||||
'ENAS' : 'output/search-cell-nas-bench-201/ENAS-cifar10/checkpoint/',
|
||||
}
|
||||
xseeds = {'RSPS' : [5349, 59613, 5983],
|
||||
'DARTS-V1': [11416, 72873, 81184],
|
||||
'DARTS-V2': [43330, 79405, 79423],
|
||||
'GDAS' : [19677, 884, 95950],
|
||||
'SETN' : [20518, 61817, 89144],
|
||||
'ENAS' : [3231, 34238, 96929],
|
||||
}
|
||||
|
||||
def get_accs(xdata, index=-1):
|
||||
if index == -1:
|
||||
epochs = xdata['epoch']
|
||||
genotype = xdata['genotypes'][epochs-1]
|
||||
index = api.query_index_by_arch(genotype)
|
||||
pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
|
||||
xresults = []
|
||||
for dataset, xset in pairs:
|
||||
metrics = api.arch2infos_full[index].get_metrics(dataset, xset, None, False)
|
||||
xresults.append( metrics['accuracy'] )
|
||||
return xresults
|
||||
|
||||
for xkey in xpaths.keys():
|
||||
all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
|
||||
all_datas = [torch.load(xpath) for xpath in all_paths]
|
||||
accyss = [get_accs(xdatas) for xdatas in all_datas]
|
||||
accyss = np.array( accyss )
|
||||
print('\nxkey = {:}'.format(xkey))
|
||||
for i in range(accyss.shape[1]): print('---->>>> {:.2f}$\\pm${:.2f}'.format(accyss[:,i].mean(), accyss[:,i].std()))
|
||||
|
||||
print('\n{:}'.format(get_accs(None, 11472))) # resnet
|
||||
pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
|
||||
for dataset, metric_on_set in pairs:
|
||||
arch_index, highest_acc = api.find_best(dataset, metric_on_set)
|
||||
print ('[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}'.format(dataset, metric_on_set, arch_index, highest_acc))
|
||||
|
||||
|
||||
def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims, x_maxs):
|
||||
color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
|
||||
dpi, width, height = 300, 3400, 2600
|
||||
LabelSize, LegendFontsize = 28, 28
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
fig = plt.figure(figsize=figsize)
|
||||
#x_maxs = 250
|
||||
plt.xlim(0, x_maxs+1)
|
||||
plt.ylim(y_lims[0], y_lims[1])
|
||||
interval_x, interval_y = x_maxs // 5, y_lims[2]
|
||||
plt.xticks(np.arange(0, x_maxs+1, interval_x), fontsize=LegendFontsize)
|
||||
plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
|
||||
plt.grid()
|
||||
plt.xlabel('The searching epoch', fontsize=LabelSize)
|
||||
plt.ylabel('The accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
xpaths = {'RSPS' : 'output/search-cell-nas-bench-201/RANDOM-NAS-cifar10/checkpoint/',
|
||||
'DARTS-V1': 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/',
|
||||
'DARTS-V2': 'output/search-cell-nas-bench-201/DARTS-V2-cifar10/checkpoint/',
|
||||
'GDAS' : 'output/search-cell-nas-bench-201/GDAS-cifar10/checkpoint/',
|
||||
'SETN' : 'output/search-cell-nas-bench-201/SETN-cifar10/checkpoint/',
|
||||
'ENAS' : 'output/search-cell-nas-bench-201/ENAS-cifar10/checkpoint/',
|
||||
}
|
||||
xseeds = {'RSPS' : [5349, 59613, 5983],
|
||||
'DARTS-V1': [11416, 72873, 81184, 28640],
|
||||
'DARTS-V2': [43330, 79405, 79423],
|
||||
'GDAS' : [19677, 884, 95950],
|
||||
'SETN' : [20518, 61817, 89144],
|
||||
'ENAS' : [3231, 34238, 96929],
|
||||
}
|
||||
|
||||
def get_accs(xdata):
|
||||
epochs, xresults = xdata['epoch'], []
|
||||
if -1 in xdata['genotypes']:
|
||||
metrics = api.arch2infos_full[ api.query_index_by_arch(xdata['genotypes'][-1]) ].get_metrics(dataset, subset, None, False)
|
||||
else:
|
||||
metrics = api.arch2infos_full[ api.random() ].get_metrics(dataset, subset, None, False)
|
||||
xresults.append( metrics['accuracy'] )
|
||||
for iepoch in range(epochs):
|
||||
genotype = xdata['genotypes'][iepoch]
|
||||
index = api.query_index_by_arch(genotype)
|
||||
metrics = api.arch2infos_full[index].get_metrics(dataset, subset, None, False)
|
||||
xresults.append( metrics['accuracy'] )
|
||||
return xresults
|
||||
|
||||
if x_maxs == 50:
|
||||
xox, xxxstrs = 'v2', ['DARTS-V1', 'DARTS-V2']
|
||||
elif x_maxs == 250:
|
||||
xox, xxxstrs = 'v1', ['RSPS', 'GDAS', 'SETN', 'ENAS']
|
||||
else: raise ValueError('invalid x_maxs={:}'.format(x_maxs))
|
||||
|
||||
for idx, method in enumerate(xxxstrs):
|
||||
xkey = method
|
||||
all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
|
||||
all_datas = [torch.load(xpath, map_location='cpu') for xpath in all_paths]
|
||||
accyss = [get_accs(xdatas) for xdatas in all_datas]
|
||||
accyss = np.array( accyss )
|
||||
epochs = list(range(accyss.shape[1]))
|
||||
plt.plot(epochs, [accyss[:,i].mean() for i in epochs], color=color_set[idx], linestyle='-', label='{:}'.format(method), lw=2)
|
||||
plt.fill_between(epochs, [accyss[:,i].mean()-accyss[:,i].std() for i in epochs], [accyss[:,i].mean()+accyss[:,i].std() for i in epochs], alpha=0.2, color=color_set[idx])
|
||||
#plt.legend(loc=4, fontsize=LegendFontsize)
|
||||
plt.legend(loc=0, fontsize=LegendFontsize)
|
||||
save_path = vis_save_dir / '{:}-{:}-{:}-{:}'.format(xox, dataset, subset, file_name)
|
||||
print('save figure into {:}\n'.format(save_path))
|
||||
fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
|
||||
|
||||
def show_nas_sharing_w_v2(api, data_sub_a, data_sub_b, vis_save_dir, file_name, y_lims, x_maxs):
|
||||
color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
|
||||
dpi, width, height = 300, 3400, 2600
|
||||
LabelSize, LegendFontsize = 28, 28
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
fig = plt.figure(figsize=figsize)
|
||||
#x_maxs = 250
|
||||
plt.xlim(0, x_maxs+1)
|
||||
plt.ylim(y_lims[0], y_lims[1])
|
||||
interval_x, interval_y = x_maxs // 5, y_lims[2]
|
||||
plt.xticks(np.arange(0, x_maxs+1, interval_x), fontsize=LegendFontsize)
|
||||
plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
|
||||
plt.grid()
|
||||
plt.xlabel('The searching epoch', fontsize=LabelSize)
|
||||
plt.ylabel('The accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
xpaths = {'RSPS' : 'output/search-cell-nas-bench-201/RANDOM-NAS-cifar10/checkpoint/',
|
||||
'DARTS-V1': 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/',
|
||||
'DARTS-V2': 'output/search-cell-nas-bench-201/DARTS-V2-cifar10/checkpoint/',
|
||||
'GDAS' : 'output/search-cell-nas-bench-201/GDAS-cifar10/checkpoint/',
|
||||
'SETN' : 'output/search-cell-nas-bench-201/SETN-cifar10/checkpoint/',
|
||||
'ENAS' : 'output/search-cell-nas-bench-201/ENAS-cifar10/checkpoint/',
|
||||
}
|
||||
xseeds = {'RSPS' : [5349, 59613, 5983],
|
||||
'DARTS-V1': [11416, 72873, 81184, 28640],
|
||||
'DARTS-V2': [43330, 79405, 79423],
|
||||
'GDAS' : [19677, 884, 95950],
|
||||
'SETN' : [20518, 61817, 89144],
|
||||
'ENAS' : [3231, 34238, 96929],
|
||||
}
|
||||
|
||||
def get_accs(xdata, dataset, subset):
|
||||
epochs, xresults = xdata['epoch'], []
|
||||
if -1 in xdata['genotypes']:
|
||||
metrics = api.arch2infos_full[ api.query_index_by_arch(xdata['genotypes'][-1]) ].get_metrics(dataset, subset, None, False)
|
||||
else:
|
||||
metrics = api.arch2infos_full[ api.random() ].get_metrics(dataset, subset, None, False)
|
||||
xresults.append( metrics['accuracy'] )
|
||||
for iepoch in range(epochs):
|
||||
genotype = xdata['genotypes'][iepoch]
|
||||
index = api.query_index_by_arch(genotype)
|
||||
metrics = api.arch2infos_full[index].get_metrics(dataset, subset, None, False)
|
||||
xresults.append( metrics['accuracy'] )
|
||||
return xresults
|
||||
|
||||
if x_maxs == 50:
|
||||
xox, xxxstrs = 'v2', ['DARTS-V1', 'DARTS-V2']
|
||||
elif x_maxs == 250:
|
||||
xox, xxxstrs = 'v1', ['RSPS', 'GDAS', 'SETN', 'ENAS']
|
||||
else: raise ValueError('invalid x_maxs={:}'.format(x_maxs))
|
||||
|
||||
for idx, method in enumerate(xxxstrs):
|
||||
xkey = method
|
||||
all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
|
||||
all_datas = [torch.load(xpath, map_location='cpu') for xpath in all_paths]
|
||||
accyss_A = np.array( [get_accs(xdatas, data_sub_a[0], data_sub_a[1]) for xdatas in all_datas] )
|
||||
accyss_B = np.array( [get_accs(xdatas, data_sub_b[0], data_sub_b[1]) for xdatas in all_datas] )
|
||||
epochs = list(range(accyss_A.shape[1]))
|
||||
for j, accyss in enumerate([accyss_A, accyss_B]):
|
||||
plt.plot(epochs, [accyss[:,i].mean() for i in epochs], color=color_set[idx*2+j], linestyle='-' if j==0 else '--', label='{:} ({:})'.format(method, 'VALID' if j == 0 else 'TEST'), lw=2, alpha=0.9)
|
||||
plt.fill_between(epochs, [accyss[:,i].mean()-accyss[:,i].std() for i in epochs], [accyss[:,i].mean()+accyss[:,i].std() for i in epochs], alpha=0.2, color=color_set[idx*2+j])
|
||||
#plt.legend(loc=4, fontsize=LegendFontsize)
|
||||
plt.legend(loc=0, fontsize=LegendFontsize)
|
||||
save_path = vis_save_dir / '{:}-{:}'.format(xox, file_name)
|
||||
print('save figure into {:}\n'.format(save_path))
|
||||
fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
|
||||
|
||||
def show_reinforce(api, root, dataset, xset, file_name, y_lims):
|
||||
print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
|
||||
LRs = ['0.01', '0.02', '0.1', '0.2', '0.5', '1.0', '1.5', '2.0', '2.5', '3.0']
|
||||
checkpoints = ['./output/search-cell-nas-bench-201/REINFORCE-cifar10-{:}/results.pth'.format(x) for x in LRs]
|
||||
acc_lr_dict, indexes = {}, None
|
||||
for lr, checkpoint in zip(LRs, checkpoints):
|
||||
all_indexes, accuracies = torch.load(checkpoint, map_location='cpu'), []
|
||||
for x in all_indexes:
|
||||
info = api.arch2infos_full[ x ]
|
||||
metrics = info.get_metrics(dataset, xset, None, False)
|
||||
accuracies.append( metrics['accuracy'] )
|
||||
if indexes is None: indexes = list(range(len(accuracies)))
|
||||
acc_lr_dict[lr] = np.array( sorted(accuracies) )
|
||||
print ('LR={:.3f}, mean={:}, std={:}'.format(float(lr), acc_lr_dict[lr].mean(), acc_lr_dict[lr].std()))
|
||||
|
||||
color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
|
||||
dpi, width, height = 300, 3400, 2600
|
||||
LabelSize, LegendFontsize = 28, 22
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
fig = plt.figure(figsize=figsize)
|
||||
x_axis = np.arange(0, 600)
|
||||
plt.xlim(0, max(indexes))
|
||||
plt.ylim(y_lims[0], y_lims[1])
|
||||
interval_x, interval_y = 100, y_lims[2]
|
||||
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
||||
plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
|
||||
plt.grid()
|
||||
plt.xlabel('The index of runs', fontsize=LabelSize)
|
||||
plt.ylabel('The accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
for idx, LR in enumerate(LRs):
|
||||
legend = 'LR={:.2f}'.format(float(LR))
|
||||
color, linestyle = color_set[idx // 2], '-' if idx % 2 == 0 else '-.'
|
||||
plt.plot(indexes, acc_lr_dict[LR], color=color, linestyle=linestyle, label=legend, lw=2, alpha=0.8)
|
||||
print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(acc_lr_dict[LR]), np.std(acc_lr_dict[LR]), np.mean(acc_lr_dict[LR]), np.std(acc_lr_dict[LR])))
|
||||
plt.legend(loc=4, fontsize=LegendFontsize)
|
||||
save_path = root / '{:}-{:}-{:}.pdf'.format(dataset, xset, file_name)
|
||||
print('save figure into {:}\n'.format(save_path))
|
||||
fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS-Bench-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--save_dir', type=str, default='./output/search-cell-nas-bench-201/visuals', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--api_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file.')
|
||||
args = parser.parse_args()
|
||||
|
||||
vis_save_dir = Path(args.save_dir)
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
meta_file = Path(args.api_path)
|
||||
assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
|
||||
#visualize_rank_over_time(str(meta_file), vis_save_dir / 'over-time')
|
||||
#write_video(vis_save_dir / 'over-time')
|
||||
#visualize_info(str(meta_file), 'cifar10' , vis_save_dir)
|
||||
#visualize_info(str(meta_file), 'cifar100', vis_save_dir)
|
||||
#visualize_info(str(meta_file), 'ImageNet16-120', vis_save_dir)
|
||||
#visualize_relative_ranking(vis_save_dir)
|
||||
|
||||
api = API(args.api_path)
|
||||
show_reinforce(api, vis_save_dir, 'cifar10-valid' , 'x-valid', 'REINFORCE-CIFAR-10', (75, 95, 5))
|
||||
import pdb; pdb.set_trace()
|
||||
|
||||
for x_maxs in [50, 250]:
|
||||
show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
||||
show_nas_sharing_w(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
||||
show_nas_sharing_w(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
||||
show_nas_sharing_w(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
||||
show_nas_sharing_w(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
||||
show_nas_sharing_w(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
||||
|
||||
show_nas_sharing_w_v2(api, ('cifar10-valid' , 'x-valid'), ('cifar10' , 'ori-test') , vis_save_dir, 'DARTS-CIFAR010.pdf', (0, 100,10), 50)
|
||||
show_nas_sharing_w_v2(api, ('cifar100' , 'x-valid'), ('cifar100' , 'x-test' ) , vis_save_dir, 'DARTS-CIFAR100.pdf', (0, 100,10), 50)
|
||||
show_nas_sharing_w_v2(api, ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test' ) , vis_save_dir, 'DARTS-ImageNet.pdf', (0, 100,10), 50)
|
||||
#just_show(api)
|
||||
"""
|
||||
plot_results_nas(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (85,95, 1))
|
||||
plot_results_nas(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-com.pdf', (85,95, 1))
|
||||
plot_results_nas(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
|
||||
plot_results_nas(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
|
||||
plot_results_nas(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
|
||||
plot_results_nas(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
|
||||
plot_results_nas_v2(api, ('cifar10-valid' , 'x-valid'), ('cifar10' , 'ori-test'), vis_save_dir, 'nas-com-v2-cifar010.pdf', (85,95, 1))
|
||||
plot_results_nas_v2(api, ('cifar100' , 'x-valid'), ('cifar100' , 'x-test' ), vis_save_dir, 'nas-com-v2-cifar100.pdf', (60,75, 3))
|
||||
plot_results_nas_v2(api, ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test' ), vis_save_dir, 'nas-com-v2-imagenet.pdf', (35,48, 2))
|
||||
"""
|
||||
Reference in New Issue
Block a user