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 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')