################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ################################################## # python exps/NAS-Bench-201/visualize.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth ################################################## import os, sys, time, argparse, collections from tqdm import tqdm from collections import OrderedDict import numpy as np import torch from pathlib import Path from collections import defaultdict import matplotlib import seaborn as sns from mpl_toolkits.mplot3d import Axes3D matplotlib.use('agg') import matplotlib.pyplot as plt lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) from log_utils import time_string from nas_201_api import NASBench201API as API 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_relative_ranking(vis_save_dir): print ('\n' + '-'*100) cifar010_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('cifar10') cifar100_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('cifar100') imagenet_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('ImageNet16-120') 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())) # maximum accuracy with ResNet-level params 11472 x_010_accs = [ cifar010_info['test_accs'][i] if cifar010_info['params'][i] <= cifar010_info['params'][11472] else -1 for i in indexes] x_100_accs = [ cifar100_info['test_accs'][i] if cifar100_info['params'][i] <= cifar100_info['params'][11472] else -1 for i in indexes] x_img_accs = [ imagenet_info['test_accs'][i] if imagenet_info['params'][i] <= imagenet_info['params'][11472] else -1 for i in indexes] cifar010_ord_indexes = sorted(indexes, key=lambda i: cifar010_info['test_accs'][i]) cifar100_ord_indexes = sorted(indexes, key=lambda i: cifar100_info['test_accs'][i]) imagenet_ord_indexes = sorted(indexes, key=lambda i: imagenet_info['test_accs'][i]) cifar100_labels, imagenet_labels = [], [] for idx in cifar010_ord_indexes: cifar100_labels.append( cifar100_ord_indexes.index(idx) ) imagenet_labels.append( imagenet_ord_indexes.index(idx) ) print ('{:} prepare data done.'.format(time_string())) dpi, width, height = 300, 2600, 2600 figsize = width / float(dpi), height / float(dpi) LabelSize, LegendFontsize = 18, 18 resnet_scale, resnet_alpha = 120, 0.5 fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) plt.xlim(min(indexes), max(indexes)) plt.ylim(min(indexes), max(indexes)) #plt.ylabel('y').set_rotation(0) 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, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8, label='CIFAR-100') #ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8, label='ImageNet-16-120') #ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8, label='CIFAR-10') ax.scatter(indexes, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8) ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8) ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8) ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='CIFAR-10') ax.scatter([-1], [-1], marker='^', s=100, c='tab:green', label='CIFAR-100') ax.scatter([-1], [-1], marker='*', s=100, c='tab:red' , label='ImageNet-16-120') plt.grid(zorder=0) ax.set_axisbelow(True) plt.legend(loc=0, fontsize=LegendFontsize) ax.set_xlabel('architecture ranking in CIFAR-10', fontsize=LabelSize) ax.set_ylabel('architecture ranking', fontsize=LabelSize) save_path = (vis_save_dir / 'relative-rank.pdf').resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') save_path = (vis_save_dir / 'relative-rank.png').resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png') print ('{:} save into {:}'.format(time_string(), save_path)) # calculate correlation 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']) fig = plt.figure(figsize=figsize) plt.axis('off') h = sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5) save_path = (vis_save_dir / 'co-relation-all.pdf').resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') print ('{:} save into {:}'.format(time_string(), save_path)) # calculate correlation acc_bars = [92, 93] for acc_bar in acc_bars: selected_indexes = [] for i, acc in enumerate(cifar010_info['test_accs']): if acc > acc_bar: selected_indexes.append( i ) print ('select {:} architectures'.format(len(selected_indexes))) 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) fig = plt.figure(figsize=figsize) plt.axis('off') h = sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5) save_path = (vis_save_dir / 'co-relation-top-{:}.pdf'.format(len(selected_indexes))).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') print ('{:} save into {:}'.format(time_string(), save_path)) plt.close('all') def visualize_info(meta_file, dataset, vis_save_dir): print ('{:} start to visualize {:} information'.format(time_string(), dataset)) cache_file_path = vis_save_dir / '{:}-cache-info.pth'.format(dataset) if not cache_file_path.exists(): print ('Do not find cache file : {:}'.format(cache_file_path)) nas_bench = API(str(meta_file)) params, flops, train_accs, valid_accs, test_accs, otest_accs = [], [], [], [], [], [] for index in range( len(nas_bench) ): info = nas_bench.query_by_index(index, use_12epochs_result=False) resx = info.get_comput_costs(dataset) ; flop, param = resx['flops'], resx['params'] if dataset == 'cifar10': res = info.get_metrics('cifar10', 'train') ; train_acc = res['accuracy'] res = info.get_metrics('cifar10-valid', 'x-valid') ; valid_acc = res['accuracy'] res = info.get_metrics('cifar10', 'ori-test') ; test_acc = res['accuracy'] res = info.get_metrics('cifar10', 'ori-test') ; otest_acc = res['accuracy'] else: res = info.get_metrics(dataset, 'train') ; train_acc = res['accuracy'] res = info.get_metrics(dataset, 'x-valid') ; valid_acc = res['accuracy'] res = info.get_metrics(dataset, 'x-test') ; test_acc = res['accuracy'] res = info.get_metrics(dataset, 'ori-test') ; otest_acc = res['accuracy'] if index == 11472: # resnet resnet = {'params':param, 'flops': flop, 'index': 11472, 'train_acc': train_acc, 'valid_acc': valid_acc, 'test_acc': test_acc, 'otest_acc': otest_acc} flops.append( flop ) params.append( param ) train_accs.append( train_acc ) valid_accs.append( valid_acc ) test_accs.append( test_acc ) otest_accs.append( otest_acc ) #resnet = {'params': 0.559, 'flops': 78.56, 'index': 11472, 'train_acc': 99.99, 'valid_acc': 90.84, 'test_acc': 93.97} info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs, 'otest_accs': otest_accs} info['resnet'] = resnet 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'] resnet = info['resnet'] print ('{:} collect data done.'.format(time_string())) indexes = list(range(len(params))) dpi, width, height = 300, 2600, 2600 figsize = width / float(dpi), height / float(dpi) LabelSize, LegendFontsize = 22, 22 resnet_scale, resnet_alpha = 120, 0.5 fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize) if dataset == 'cifar10': plt.ylim(50, 100) plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize) elif dataset == 'cifar100': plt.ylim(25, 75) plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize) else: plt.ylim(0, 50) plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize) ax.scatter(params, valid_accs, marker='o', s=0.5, c='tab:blue') ax.scatter([resnet['params']], [resnet['valid_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=0.4) plt.grid(zorder=0) ax.set_axisbelow(True) plt.legend(loc=4, fontsize=LegendFontsize) ax.set_xlabel('#parameters (MB)', fontsize=LabelSize) ax.set_ylabel('the validation accuracy (%)', fontsize=LabelSize) save_path = (vis_save_dir / '{:}-param-vs-valid.pdf'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') save_path = (vis_save_dir / '{:}-param-vs-valid.png'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png') print ('{:} save into {:}'.format(time_string(), save_path)) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize) if dataset == 'cifar10': plt.ylim(50, 100) plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize) elif dataset == 'cifar100': plt.ylim(25, 75) plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize) else: plt.ylim(0, 50) plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize) ax.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue') ax.scatter([resnet['params']], [resnet['test_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha) plt.grid() ax.set_axisbelow(True) plt.legend(loc=4, fontsize=LegendFontsize) ax.set_xlabel('#parameters (MB)', fontsize=LabelSize) ax.set_ylabel('the test accuracy (%)', fontsize=LabelSize) save_path = (vis_save_dir / '{:}-param-vs-test.pdf'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') save_path = (vis_save_dir / '{:}-param-vs-test.png'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png') print ('{:} save into {:}'.format(time_string(), save_path)) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize) if dataset == 'cifar10': plt.ylim(50, 100) plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize) elif dataset == 'cifar100': plt.ylim(20, 100) plt.yticks(np.arange(20, 101, 10), fontsize=LegendFontsize) else: plt.ylim(25, 76) plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize) ax.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue') ax.scatter([resnet['params']], [resnet['train_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha) plt.grid() ax.set_axisbelow(True) plt.legend(loc=4, fontsize=LegendFontsize) ax.set_xlabel('#parameters (MB)', fontsize=LabelSize) ax.set_ylabel('the trarining accuracy (%)', fontsize=LabelSize) save_path = (vis_save_dir / '{:}-param-vs-train.pdf'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') save_path = (vis_save_dir / '{:}-param-vs-train.png'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png') print ('{:} save into {:}'.format(time_string(), save_path)) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) plt.xlim(0, max(indexes)) plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//5), fontsize=LegendFontsize) if dataset == 'cifar10': plt.ylim(50, 100) plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize) elif dataset == 'cifar100': plt.ylim(25, 75) plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize) else: plt.ylim(0, 50) plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize) ax.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue') ax.scatter([resnet['index']], [resnet['test_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha) plt.grid() ax.set_axisbelow(True) plt.legend(loc=4, fontsize=LegendFontsize) ax.set_xlabel('architecture ID', fontsize=LabelSize) ax.set_ylabel('the test accuracy (%)', fontsize=LabelSize) save_path = (vis_save_dir / '{:}-test-over-ID.pdf'.format(dataset)).resolve() fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf') save_path = (vis_save_dir / '{:}-test-over-ID.png'.format(dataset)).resolve() 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_over_time(meta_file, vis_save_dir): print ('\n' + '-'*150) vis_save_dir.mkdir(parents=True, exist_ok=True) print ('{:} start to visualize rank-over-time into {:}'.format(time_string(), vis_save_dir)) cache_file_path = vis_save_dir / 'rank-over-time-cache-info.pth' if not cache_file_path.exists(): print ('Do not find cache file : {:}'.format(cache_file_path)) nas_bench = API(str(meta_file)) print ('{:} load nas_bench done'.format(time_string())) params, flops, train_accs, valid_accs, test_accs, otest_accs = [], [], defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list) #for iepoch in range(200): for index in range( len(nas_bench) ): for index in tqdm(range(len(nas_bench))): info = nas_bench.query_by_index(index, use_12epochs_result=False) for iepoch in range(200): res = info.get_metrics('cifar10' , 'train' , iepoch) ; train_acc = res['accuracy'] res = info.get_metrics('cifar10-valid', 'x-valid' , iepoch) ; valid_acc = res['accuracy'] res = info.get_metrics('cifar10' , 'ori-test', iepoch) ; test_acc = res['accuracy'] res = info.get_metrics('cifar10' , 'ori-test', iepoch) ; otest_acc = res['accuracy'] train_accs[iepoch].append( train_acc ) valid_accs[iepoch].append( valid_acc ) test_accs [iepoch].append( test_acc ) 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)) """