Update the test codes for NAS-Bench-API
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D-X-Y
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1906454a73
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@ -40,6 +40,8 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default).
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## How to Use NAS-Bench-201
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**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/master/exps/NAS-Bench-201/test-nas-api.py)**.
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1. Creating an API instance from a file:
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```
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from nas_201_api import NASBench201API as API
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@ -81,6 +81,244 @@ def visualize_info(api, vis_save_dir, indicator):
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print ('{:} save into {:}'.format(time_string(), save_path))
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def visualize_sss_info(api, dataset, vis_save_dir):
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vis_save_dir = vis_save_dir.resolve()
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / '{:}-cache-sss-info.pth'.format(dataset)
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if not cache_file_path.exists():
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print ('Do not find cache file : {:}'.format(cache_file_path))
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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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info = api.get_cost_info(index, dataset)
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params.append(info['params'])
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flops.append(info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='90')
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train_accs.append(info['train-accuracy'])
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test_accs.append(info['test-accuracy'])
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if dataset == 'cifar10':
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info = api.get_more_info(index, 'cifar10-valid', hp='90')
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valid_accs.append(info['valid-accuracy'])
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else:
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valid_accs.append(info['valid-accuracy'])
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs}
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torch.save(info, cache_file_path)
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else:
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print ('Find cache file : {:}'.format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
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print ('{:} collect data done.'.format(time_string()))
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pyramid = ['8:16:32:48:64', '8:8:16:32:48', '8:8:16:16:32', '8:8:16:16:48', '8:8:16:16:64', '16:16:32:32:64', '32:32:64:64:64']
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pyramid_indexes = [api.query_index_by_arch(x) for x in pyramid]
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largest_indexes = [api.query_index_by_arch('64:64:64:64:64')]
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indexes = list(range(len(params)))
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dpi, width, height = 250, 8500, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 24, 24
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# resnet_scale, resnet_alpha = 120, 0.5
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xscale, xalpha = 120, 0.8
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fig, axs = plt.subplots(1, 4, figsize=figsize)
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# ax1, ax2, ax3, ax4, ax5 = axs
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for ax in axs:
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax2, ax3, ax4, ax5 = axs
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# ax1.xaxis.set_ticks(np.arange(0, max(indexes), max(indexes)//5))
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# ax1.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
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# ax1.set_xlabel('architecture ID', fontsize=LabelSize)
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# ax1.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax2.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax2.scatter([params[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax2.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax2.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax2.legend(loc=4, fontsize=LegendFontsize)
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ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
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ax3.scatter([params[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax3.legend(loc=4, fontsize=LegendFontsize)
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ax4.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
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ax4.scatter([flops[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax4.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax4.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax4.legend(loc=4, fontsize=LegendFontsize)
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ax5.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
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ax5.scatter([flops[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax5.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax5.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax5.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax5.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / 'sss-{:}.png'.format(dataset)
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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def visualize_tss_info(api, dataset, vis_save_dir):
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vis_save_dir = vis_save_dir.resolve()
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / '{:}-cache-tss-info.pth'.format(dataset)
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if not cache_file_path.exists():
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print ('Do not find cache file : {:}'.format(cache_file_path))
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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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info = api.get_cost_info(index, dataset)
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params.append(info['params'])
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flops.append(info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='200')
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train_accs.append(info['train-accuracy'])
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test_accs.append(info['test-accuracy'])
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if dataset == 'cifar10':
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info = api.get_more_info(index, 'cifar10-valid', hp='200')
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valid_accs.append(info['valid-accuracy'])
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else:
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valid_accs.append(info['valid-accuracy'])
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs}
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torch.save(info, cache_file_path)
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else:
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print ('Find cache file : {:}'.format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
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print ('{:} collect data done.'.format(time_string()))
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resnet = ['|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|']
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resnet_indexes = [api.query_index_by_arch(x) for x in resnet]
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largest_indexes = [api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|')]
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indexes = list(range(len(params)))
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dpi, width, height = 250, 8500, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 24, 24
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# resnet_scale, resnet_alpha = 120, 0.5
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xscale, xalpha = 120, 0.8
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fig, axs = plt.subplots(1, 4, figsize=figsize)
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# ax1, ax2, ax3, ax4, ax5 = axs
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for ax in axs:
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax2, ax3, ax4, ax5 = axs
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# ax1.xaxis.set_ticks(np.arange(0, max(indexes), max(indexes)//5))
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# ax1.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
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# ax1.set_xlabel('architecture ID', fontsize=LabelSize)
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# ax1.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax2.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax2.scatter([params[x] for x in resnet_indexes] , [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
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ax2.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax2.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax2.legend(loc=4, fontsize=LegendFontsize)
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ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
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ax3.scatter([params[x] for x in resnet_indexes] , [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
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ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax3.legend(loc=4, fontsize=LegendFontsize)
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ax4.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
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ax4.scatter([flops[x] for x in resnet_indexes], [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
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ax4.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax4.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax4.legend(loc=4, fontsize=LegendFontsize)
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ax5.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
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ax5.scatter([flops[x] for x in resnet_indexes], [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
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ax5.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax5.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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ax5.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax5.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / 'tss-{:}.png'.format(dataset)
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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def visualize_rank_info(api, vis_save_dir, indicator):
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vis_save_dir = vis_save_dir.resolve()
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# print ('{:} start to visualize {:} information'.format(time_string(), api))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
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cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
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imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
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cifar010_info = torch.load(cifar010_cache_path)
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cifar100_info = torch.load(cifar100_cache_path)
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imagenet_info = torch.load(imagenet_cache_path)
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indexes = list(range(len(cifar010_info['params'])))
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print ('{:} start to visualize relative ranking'.format(time_string()))
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dpi, width, height = 250, 3800, 1200
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 14, 14
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fig, axs = plt.subplots(1, 3, figsize=figsize)
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ax1, ax2, ax3 = axs
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def get_labels(info):
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ord_test_indexes = sorted(indexes, key=lambda i: info['test_accs'][i])
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ord_valid_indexes = sorted(indexes, key=lambda i: info['valid_accs'][i])
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labels = []
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for idx in ord_test_indexes:
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labels.append(ord_valid_indexes.index(idx))
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return labels
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def plot_ax(labels, ax, name):
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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tick.label.set_rotation(90)
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ax.set_xlim(min(indexes), max(indexes))
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ax.set_ylim(min(indexes), max(indexes))
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ax.yaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//3))
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ax.xaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//5))
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ax.scatter(indexes, labels , marker='^', s=0.5, c='tab:green', 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='^', s=100, c='tab:green' , label='{:} test'.format(name))
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ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='{:} validation'.format(name))
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ax.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('ranking on the {:} validation'.format(name), fontsize=LabelSize)
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ax.set_ylabel('architecture ranking', fontsize=LabelSize)
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labels = get_labels(cifar010_info)
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plot_ax(labels, ax1, 'CIFAR-10')
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labels = get_labels(cifar100_info)
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plot_ax(labels, ax2, 'CIFAR-100')
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labels = get_labels(imagenet_info)
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plot_ax(labels, ax3, 'ImageNet-16-120')
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save_path = (vis_save_dir / '{:}-same-relative-rank.pdf'.format(indicator)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-same-relative-rank.png'.format(indicator)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='output/NAS-BENCH-202', help='Folder to save checkpoints and log.')
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@ -88,6 +326,20 @@ if __name__ == '__main__':
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# use for train the model
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args = parser.parse_args()
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visualize_info(None, Path('output/vis-nas-bench/'), 'tss')
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visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
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visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
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api201 = NASBench201API(None, verbose=True)
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visualize_tss_info(api201, 'cifar10', Path('output/vis-nas-bench'))
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visualize_tss_info(api201, 'cifar100', Path('output/vis-nas-bench'))
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visualize_tss_info(api201, 'ImageNet16-120', Path('output/vis-nas-bench'))
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api301 = NASBench301API(None, verbose=True)
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visualize_sss_info(api301, 'cifar10', Path('output/vis-nas-bench'))
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visualize_sss_info(api301, 'cifar100', Path('output/vis-nas-bench'))
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visualize_sss_info(api301, 'ImageNet16-120', Path('output/vis-nas-bench'))
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visualize_info(None, Path('output/vis-nas-bench/'), 'tss')
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visualize_info(None, Path('output/vis-nas-bench/'), 'sss')
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visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
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visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
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@ -48,236 +48,51 @@ def test_api(api, is_301=True):
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print('')
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params = api.get_net_param(12, 'cifar10', None)
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# obtain the config and create the network
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# Obtain the config and create the network
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config = api.get_net_config(12, 'cifar10')
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print('{:}\n'.format(config))
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network = get_cell_based_tiny_net(config)
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network.load_state_dict(next(iter(params.values())))
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# obtain the cost information
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# Obtain the cost information
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info = api.get_cost_info(12, 'cifar10')
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print('{:}\n'.format(info))
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info = api.get_latency(12, 'cifar10')
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print('{:}\n'.format(info))
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# count the number of architectures
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# Count the number of architectures
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info = api.statistics('cifar100', '12')
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print('{:}\n'.format(info))
|
||||
|
||||
# show the information of the 123-th architecture
|
||||
# Show the information of the 123-th architecture
|
||||
api.show(123)
|
||||
|
||||
# obtain both cost and performance information
|
||||
# Obtain both cost and performance information
|
||||
info = api.get_more_info(1234, 'cifar10')
|
||||
print('{:}\n'.format(info))
|
||||
print('{:} finish testing the api : {:}'.format(time_string(), api))
|
||||
|
||||
|
||||
def visualize_sss_info(api, dataset, vis_save_dir):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
print ('{:} start to visualize {:} information'.format(time_string(), dataset))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_file_path = vis_save_dir / '{:}-cache-sss-info.pth'.format(dataset)
|
||||
if not cache_file_path.exists():
|
||||
print ('Do not find cache file : {:}'.format(cache_file_path))
|
||||
params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
|
||||
for index in range(len(api)):
|
||||
info = api.get_cost_info(index, dataset)
|
||||
params.append(info['params'])
|
||||
flops.append(info['flops'])
|
||||
# accuracy
|
||||
info = api.get_more_info(index, dataset, hp='90')
|
||||
train_accs.append(info['train-accuracy'])
|
||||
test_accs.append(info['test-accuracy'])
|
||||
if dataset == 'cifar10':
|
||||
info = api.get_more_info(index, 'cifar10-valid', hp='90')
|
||||
valid_accs.append(info['valid-accuracy'])
|
||||
else:
|
||||
valid_accs.append(info['valid-accuracy'])
|
||||
info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_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 = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
|
||||
print ('{:} collect data done.'.format(time_string()))
|
||||
|
||||
pyramid = ['8:16:32:48:64', '8:8:16:32:48', '8:8:16:16:32', '8:8:16:16:48', '8:8:16:16:64', '16:16:32:32:64', '32:32:64:64:64']
|
||||
pyramid_indexes = [api.query_index_by_arch(x) for x in pyramid]
|
||||
largest_indexes = [api.query_index_by_arch('64:64:64:64:64')]
|
||||
|
||||
indexes = list(range(len(params)))
|
||||
dpi, width, height = 250, 8500, 1300
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 24, 24
|
||||
# resnet_scale, resnet_alpha = 120, 0.5
|
||||
xscale, xalpha = 120, 0.8
|
||||
|
||||
fig, axs = plt.subplots(1, 4, figsize=figsize)
|
||||
# ax1, ax2, ax3, ax4, ax5 = axs
|
||||
for ax in axs:
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax2, ax3, ax4, ax5 = axs
|
||||
# ax1.xaxis.set_ticks(np.arange(0, max(indexes), max(indexes)//5))
|
||||
# ax1.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
# ax1.set_xlabel('architecture ID', fontsize=LabelSize)
|
||||
# ax1.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
ax2.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax2.scatter([params[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
|
||||
ax2.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax2.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax2.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax3.scatter([params[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
|
||||
ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax3.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax4.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax4.scatter([flops[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
|
||||
ax4.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
ax4.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax5.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax5.scatter([flops[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
|
||||
ax5.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax5.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
ax5.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax5.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
save_path = vis_save_dir / 'sss-{:}.png'.format(dataset)
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
def visualize_tss_info(api, dataset, vis_save_dir):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
print ('{:} start to visualize {:} information'.format(time_string(), dataset))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_file_path = vis_save_dir / '{:}-cache-tss-info.pth'.format(dataset)
|
||||
if not cache_file_path.exists():
|
||||
print ('Do not find cache file : {:}'.format(cache_file_path))
|
||||
params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
|
||||
for index in range(len(api)):
|
||||
info = api.get_cost_info(index, dataset)
|
||||
params.append(info['params'])
|
||||
flops.append(info['flops'])
|
||||
# accuracy
|
||||
info = api.get_more_info(index, dataset, hp='200')
|
||||
train_accs.append(info['train-accuracy'])
|
||||
test_accs.append(info['test-accuracy'])
|
||||
if dataset == 'cifar10':
|
||||
info = api.get_more_info(index, 'cifar10-valid', hp='200')
|
||||
valid_accs.append(info['valid-accuracy'])
|
||||
else:
|
||||
valid_accs.append(info['valid-accuracy'])
|
||||
info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_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 = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
|
||||
print ('{:} collect data done.'.format(time_string()))
|
||||
|
||||
resnet = ['|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|']
|
||||
resnet_indexes = [api.query_index_by_arch(x) for x in resnet]
|
||||
largest_indexes = [api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|')]
|
||||
|
||||
indexes = list(range(len(params)))
|
||||
dpi, width, height = 250, 8500, 1300
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 24, 24
|
||||
# resnet_scale, resnet_alpha = 120, 0.5
|
||||
xscale, xalpha = 120, 0.8
|
||||
|
||||
fig, axs = plt.subplots(1, 4, figsize=figsize)
|
||||
# ax1, ax2, ax3, ax4, ax5 = axs
|
||||
for ax in axs:
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax2, ax3, ax4, ax5 = axs
|
||||
# ax1.xaxis.set_ticks(np.arange(0, max(indexes), max(indexes)//5))
|
||||
# ax1.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
# ax1.set_xlabel('architecture ID', fontsize=LabelSize)
|
||||
# ax1.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
|
||||
ax2.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax2.scatter([params[x] for x in resnet_indexes] , [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax2.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax2.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax2.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax3.scatter([params[x] for x in resnet_indexes] , [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax3.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax4.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax4.scatter([flops[x] for x in resnet_indexes], [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax4.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
ax4.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax5.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax5.scatter([flops[x] for x in resnet_indexes], [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax5.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax5.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
ax5.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax5.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
save_path = vis_save_dir / 'tss-{:}.png'.format(dataset)
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
|
||||
|
||||
def test_issue_81_82(api):
|
||||
results = api.query_by_index(0, 'cifar10')
|
||||
results = api.query_by_index(0, 'cifar10-valid', hp='12')
|
||||
results = api.query_by_index(0, 'cifar10-valid', hp='200')
|
||||
print(results.keys())
|
||||
print(list(results.keys()))
|
||||
print(results[888].get_eval('valid'))
|
||||
print(results[888].get_eval('x-valid'))
|
||||
result_dict = api.get_more_info(index=0, dataset='cifar10-valid', iepoch=11, hp='200', is_random=False)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--save_dir', type=str, default='output/NAS-BENCH-202', help='Folder to save checkpoints and log.')
|
||||
parser.add_argument('--check_N', type=int, default=32768, help='For safety.')
|
||||
# use for train the model
|
||||
args = parser.parse_args()
|
||||
|
||||
api201 = NASBench201API(os.path.join(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_0-e61699.pth'), verbose=True)
|
||||
test_issue_81_82(api201)
|
||||
test_api(api201, False)
|
||||
# test_api(api201, False)
|
||||
print ('Test {:} done'.format(api201))
|
||||
|
||||
api201 = NASBench201API(None, verbose=True)
|
||||
test_issue_81_82(api201)
|
||||
visualize_tss_info(api201, 'cifar10', Path('output/vis-nas-bench'))
|
||||
visualize_tss_info(api201, 'cifar100', Path('output/vis-nas-bench'))
|
||||
visualize_tss_info(api201, 'ImageNet16-120', Path('output/vis-nas-bench'))
|
||||
test_api(api201, False)
|
||||
print ('Test {:} done'.format(api201))
|
||||
|
||||
api301 = NASBench301API(None, verbose=True)
|
||||
visualize_sss_info(api301, 'cifar10', Path('output/vis-nas-bench'))
|
||||
visualize_sss_info(api301, 'cifar100', Path('output/vis-nas-bench'))
|
||||
visualize_sss_info(api301, 'ImageNet16-120', Path('output/vis-nas-bench'))
|
||||
test_api(api301, True)
|
||||
|
||||
# save_dir = '{:}/visual'.format(args.save_dir)
|
||||
# api301 = NASBench301API(None, verbose=True)
|
||||
# test_api(api301, True)
|
||||
|
||||
@ -184,17 +184,17 @@ class NASBench201API(NASBenchMetaAPI):
|
||||
if valid_info is not None:
|
||||
xinfo['valid-loss'] = valid_info['loss']
|
||||
xinfo['valid-accuracy'] = valid_info['accuracy']
|
||||
xinfo['valid-per-time'] = valid_info['all_time'] / total
|
||||
xinfo['valid-per-time'] = valid_info['all_time'] / total if valid_info['all_time'] is not None else None
|
||||
xinfo['valid-all-time'] = valid_info['all_time']
|
||||
if test_info is not None:
|
||||
xinfo['test-loss'] = test_info['loss']
|
||||
xinfo['test-accuracy'] = test_info['accuracy']
|
||||
xinfo['test-per-time'] = test_info['all_time'] / total
|
||||
xinfo['test-per-time'] = test_info['all_time'] / total if test_info['all_time'] is not None else None
|
||||
xinfo['test-all-time'] = test_info['all_time']
|
||||
if valtest_info is not None:
|
||||
xinfo['valtest-loss'] = valtest_info['loss']
|
||||
xinfo['valtest-accuracy'] = valtest_info['accuracy']
|
||||
xinfo['valtest-per-time'] = valtest_info['all_time'] / total
|
||||
xinfo['valtest-per-time'] = valtest_info['all_time'] / total if valtest_info['all_time'] is not None else None
|
||||
xinfo['valtest-all-time'] = valtest_info['all_time']
|
||||
return xinfo
|
||||
|
||||
|
||||
@ -660,15 +660,21 @@ class ResultsCount(object):
|
||||
"""Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument)."""
|
||||
if iepoch is None: iepoch = self.epochs-1
|
||||
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
|
||||
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
|
||||
xtime = self.eval_times['{:}@{:}'.format(name,iepoch)]
|
||||
atime = sum([self.eval_times['{:}@{:}'.format(name,i)] for i in range(iepoch+1)])
|
||||
else: xtime, atime = None, None
|
||||
return {'iepoch' : iepoch,
|
||||
'loss' : self.eval_losses['{:}@{:}'.format(name,iepoch)],
|
||||
'accuracy': self.eval_acc1es['{:}@{:}'.format(name,iepoch)],
|
||||
'cur_time': xtime,
|
||||
'all_time': atime}
|
||||
def _internal_query(xname):
|
||||
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
|
||||
xtime = self.eval_times['{:}@{:}'.format(xname, iepoch)]
|
||||
atime = sum([self.eval_times['{:}@{:}'.format(xname, i)] for i in range(iepoch+1)])
|
||||
else:
|
||||
xtime, atime = None, None
|
||||
return {'iepoch' : iepoch,
|
||||
'loss' : self.eval_losses['{:}@{:}'.format(xname, iepoch)],
|
||||
'accuracy': self.eval_acc1es['{:}@{:}'.format(xname, iepoch)],
|
||||
'cur_time': xtime,
|
||||
'all_time': atime}
|
||||
if name == 'valid':
|
||||
return _internal_query('x-valid')
|
||||
else:
|
||||
return _internal_query(name)
|
||||
|
||||
def get_net_param(self, clone=False):
|
||||
if clone: return copy.deepcopy(self.net_state_dict)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user