Update visualization codes for NATS-Bench
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		| @@ -43,20 +43,14 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None): | ||||
|   # alg2name['REINFORCE'] = 'REINFORCE-0.01' | ||||
|   # alg2name['RANDOM'] = 'RANDOM' | ||||
|   # alg2name['BOHB'] = 'BOHB' | ||||
|   if dataset == 'cifar10': | ||||
|     suffixes = ['-T200000', '-T200000-FULL'] | ||||
|   elif dataset == 'cifar100': | ||||
|     suffixes = ['-T40000', '-T40000-FULL'] | ||||
|   elif search_space == 'tss': | ||||
|     suffixes = ['-T120000', '-T120000-FULL'] | ||||
|   elif search_space == 'sss': | ||||
|     suffixes = ['-T60000', '-T60000-FULL'] | ||||
|   else: | ||||
|     raise ValueError('Unkonwn dataset : {:}'.format(dataset)) | ||||
|   if search_space == 'tss': | ||||
|     hp = '$\mathcal{H}^{1}$' | ||||
|     if dataset == 'cifar10': | ||||
|       suffixes = ['-T800000', '-T800000-FULL'] | ||||
|   elif search_space == 'sss': | ||||
|     hp = '$\mathcal{H}^{2}$' | ||||
|     if dataset == 'cifar10': | ||||
|       suffixes = ['-T200000', '-T200000-FULL'] | ||||
|   else: | ||||
|     raise ValueError('Unkonwn search space: {:}'.format(search_space)) | ||||
|  | ||||
| @@ -92,21 +86,21 @@ def query_performance(api, data, dataset, ticket): | ||||
|   return np.mean(results), np.std(results) | ||||
|  | ||||
|  | ||||
| y_min_s = {('cifar10', 'tss'): 90, | ||||
|            ('cifar10', 'sss'): 90, | ||||
| y_min_s = {('cifar10', 'tss'): 91, | ||||
|            ('cifar10', 'sss'): 91, | ||||
|            ('cifar100', 'tss'): 65, | ||||
|            ('cifar100', 'sss'): 65, | ||||
|            ('ImageNet16-120', 'tss'): 36, | ||||
|            ('ImageNet16-120', 'sss'): 40} | ||||
|  | ||||
| y_max_s = {('cifar10', 'tss'): 94.5, | ||||
|            ('cifar10', 'sss'): 94.5, | ||||
|            ('cifar10', 'sss'): 93.5, | ||||
|            ('cifar100', 'tss'): 72.5, | ||||
|            ('cifar100', 'sss'): 70.5, | ||||
|            ('ImageNet16-120', 'tss'): 46, | ||||
|            ('ImageNet16-120', 'sss'): 46} | ||||
|  | ||||
| x_axis_s = {('cifar10', 'tss'): 200000, | ||||
| x_axis_s = {('cifar10', 'tss'): 800000, | ||||
|             ('cifar10', 'sss'): 200000, | ||||
|             ('cifar100', 'tss'): 400, | ||||
|             ('cifar100', 'sss'): 400, | ||||
| @@ -124,9 +118,9 @@ def visualize_curve(api_dict, vis_save_dir): | ||||
|   vis_save_dir = vis_save_dir.resolve() | ||||
|   vis_save_dir.mkdir(parents=True, exist_ok=True) | ||||
|  | ||||
|   dpi, width, height = 250, 4000, 2400 | ||||
|   dpi, width, height = 250, 5000, 2000 | ||||
|   figsize = width / float(dpi), height / float(dpi) | ||||
|   LabelSize, LegendFontsize = 16, 16 | ||||
|   LabelSize, LegendFontsize = 28, 28 | ||||
|  | ||||
|   def sub_plot_fn(ax, search_space, dataset): | ||||
|     max_time = x_axis_s[(dataset, search_space)] | ||||
| @@ -137,6 +131,11 @@ def visualize_curve(api_dict, vis_save_dir): | ||||
|     ax.set_xlim(0, x_axis_s[(dataset, search_space)]) | ||||
|     ax.set_ylim(y_min_s[(dataset, search_space)], | ||||
|                 y_max_s[(dataset, search_space)]) | ||||
|     for tick in ax.get_xticklabels(): | ||||
|       tick.set_rotation(25) | ||||
|       tick.set_fontsize(LabelSize - 6) | ||||
|     for tick in ax.get_yticklabels(): | ||||
|       tick.set_fontsize(LabelSize - 6) | ||||
|     for idx, (alg, xdata) in enumerate(alg2data.items()): | ||||
|       accuracies = [] | ||||
|       for ticket in time_tickets: | ||||
| @@ -150,8 +149,8 @@ def visualize_curve(api_dict, vis_save_dir): | ||||
|       ax.plot(time_tickets, accuracies, c=xdata['color'], linestyle=xdata['linestyle'], label='{:}'.format(alg)) | ||||
|       ax.set_xlabel('Estimated wall-clock time', fontsize=LabelSize) | ||||
|       ax.set_ylabel('Test accuracy', fontsize=LabelSize) | ||||
|       ax.set_title(r'Searching results on {:} for {:}'.format(name2label[dataset], spaces2latex[search_space]), | ||||
|         fontsize=LabelSize+4) | ||||
|       ax.set_title(r'Results on {:} over {:}'.format(name2label[dataset], spaces2latex[search_space]), | ||||
|         fontsize=LabelSize) | ||||
|     ax.legend(loc=4, fontsize=LegendFontsize) | ||||
|  | ||||
|   fig, axs = plt.subplots(1, 2, figsize=figsize) | ||||
| @@ -165,7 +164,7 @@ def visualize_curve(api_dict, vis_save_dir): | ||||
|  | ||||
| if __name__ == '__main__': | ||||
|   parser = argparse.ArgumentParser(description='NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter) | ||||
|   parser.add_argument('--save_dir',     type=str,   default='output/vis-nas-bench/nas-algos-vs-h', help='Folder to save checkpoints and log.') | ||||
|   parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos-vs-h', help='Folder to save checkpoints and log.') | ||||
|   args = parser.parse_args() | ||||
|  | ||||
|   save_dir = Path(args.save_dir) | ||||
|   | ||||
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