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Update the test codes for NAS-Bench-API

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D-X-Y
2020-07-01 12:29:46 +00:00
parent 1906454a73
commit a45808b8e6
5 changed files with 287 additions and 212 deletions
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2
docs/NAS-Bench-201.md
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@@ -40,6 +40,8 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default).
## How to Use NAS-Bench-201 ## How to Use NAS-Bench-201
**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)**.
1. Creating an API instance from a file: 1. Creating an API instance from a file:
``` ```
from nas_201_api import NASBench201API as API from nas_201_api import NASBench201API as API

254
exps/NAS-Bench-201/test-nas-api-vis.py
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@@ -81,6 +81,244 @@ def visualize_info(api, vis_save_dir, indicator):
print ('{:} save into {:}'.format(time_string(), save_path)) print ('{:} save into {:}'.format(time_string(), save_path))
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 visualize_rank_info(api, vis_save_dir, indicator):
vis_save_dir = vis_save_dir.resolve()
# print ('{:} start to visualize {:} information'.format(time_string(), api))
vis_save_dir.mkdir(parents=True, exist_ok=True)
cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
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()))
dpi, width, height = 250, 3800, 1200
figsize = width / float(dpi), height / float(dpi)
LabelSize, LegendFontsize = 14, 14
fig, axs = plt.subplots(1, 3, figsize=figsize)
ax1, ax2, ax3 = axs
def get_labels(info):
ord_test_indexes = sorted(indexes, key=lambda i: info['test_accs'][i])
ord_valid_indexes = sorted(indexes, key=lambda i: info['valid_accs'][i])
labels = []
for idx in ord_test_indexes:
labels.append(ord_valid_indexes.index(idx))
return labels
def plot_ax(labels, ax, name):
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(LabelSize)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(LabelSize)
tick.label.set_rotation(90)
ax.set_xlim(min(indexes), max(indexes))
ax.set_ylim(min(indexes), max(indexes))
ax.yaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//3))
ax.xaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//5))
ax.scatter(indexes, labels , 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='{:} test'.format(name))
ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='{:} validation'.format(name))
ax.legend(loc=4, fontsize=LegendFontsize)
ax.set_xlabel('ranking on the {:} validation'.format(name), fontsize=LabelSize)
ax.set_ylabel('architecture ranking', fontsize=LabelSize)
labels = get_labels(cifar010_info)
plot_ax(labels, ax1, 'CIFAR-10')
labels = get_labels(cifar100_info)
plot_ax(labels, ax2, 'CIFAR-100')
labels = get_labels(imagenet_info)
plot_ax(labels, ax3, 'ImageNet-16-120')
save_path = (vis_save_dir / '{:}-same-relative-rank.pdf'.format(indicator)).resolve()
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
save_path = (vis_save_dir / '{:}-same-relative-rank.png'.format(indicator)).resolve()
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
print ('{:} save into {:}'.format(time_string(), save_path))
plt.close('all')
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter) 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('--save_dir', type=str, default='output/NAS-BENCH-202', help='Folder to save checkpoints and log.')
@@ -88,6 +326,20 @@ if __name__ == '__main__':
# use for train the model # use for train the model
args = parser.parse_args() args = parser.parse_args()
visualize_info(None, Path('output/vis-nas-bench/'), 'tss') visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
api201 = NASBench201API(None, verbose=True)
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'))
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'))
visualize_info(None, Path('output/vis-nas-bench/'), 'tss')
visualize_info(None, Path('output/vis-nas-bench/'), 'sss') visualize_info(None, Path('output/vis-nas-bench/'), 'sss')
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')

213
exps/NAS-Bench-201/test-nas-api.py
View File

@@ -48,236 +48,51 @@ def test_api(api, is_301=True):
print('') print('')
params = api.get_net_param(12, 'cifar10', None) params = api.get_net_param(12, 'cifar10', None)
# obtain the config and create the network # Obtain the config and create the network
config = api.get_net_config(12, 'cifar10') config = api.get_net_config(12, 'cifar10')
print('{:}\n'.format(config)) print('{:}\n'.format(config))
network = get_cell_based_tiny_net(config) network = get_cell_based_tiny_net(config)
network.load_state_dict(next(iter(params.values()))) network.load_state_dict(next(iter(params.values())))
# obtain the cost information # Obtain the cost information
info = api.get_cost_info(12, 'cifar10') info = api.get_cost_info(12, 'cifar10')
print('{:}\n'.format(info)) print('{:}\n'.format(info))
info = api.get_latency(12, 'cifar10') info = api.get_latency(12, 'cifar10')
print('{:}\n'.format(info)) print('{:}\n'.format(info))
# count the number of architectures # Count the number of architectures
info = api.statistics('cifar100', '12') info = api.statistics('cifar100', '12')
print('{:}\n'.format(info)) print('{:}\n'.format(info))
# show the information of the 123-th architecture # Show the information of the 123-th architecture
api.show(123) api.show(123)
# obtain both cost and performance information # Obtain both cost and performance information
info = api.get_more_info(1234, 'cifar10') info = api.get_more_info(1234, 'cifar10')
print('{:}\n'.format(info)) print('{:}\n'.format(info))
print('{:} finish testing the api : {:}'.format(time_string(), api)) 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): 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') 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')) 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) result_dict = api.get_more_info(index=0, dataset='cifar10-valid', iepoch=11, hp='200', is_random=False)
if __name__ == '__main__': 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) api201 = NASBench201API(os.path.join(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_0-e61699.pth'), verbose=True)
test_issue_81_82(api201) test_issue_81_82(api201)
test_api(api201, False) # test_api(api201, False)
print ('Test {:} done'.format(api201))
api201 = NASBench201API(None, verbose=True) api201 = NASBench201API(None, verbose=True)
test_issue_81_82(api201) 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) test_api(api201, False)
print ('Test {:} done'.format(api201))
api301 = NASBench301API(None, verbose=True) # api301 = NASBench301API(None, verbose=True)
visualize_sss_info(api301, 'cifar10', Path('output/vis-nas-bench')) # test_api(api301, True)
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)

6
lib/nas_201_api/api_201.py
View File

@@ -184,17 +184,17 @@ class NASBench201API(NASBenchMetaAPI):
if valid_info is not None: if valid_info is not None:
xinfo['valid-loss'] = valid_info['loss'] xinfo['valid-loss'] = valid_info['loss']
xinfo['valid-accuracy'] = valid_info['accuracy'] 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'] xinfo['valid-all-time'] = valid_info['all_time']
if test_info is not None: if test_info is not None:
xinfo['test-loss'] = test_info['loss'] xinfo['test-loss'] = test_info['loss']
xinfo['test-accuracy'] = test_info['accuracy'] 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'] xinfo['test-all-time'] = test_info['all_time']
if valtest_info is not None: if valtest_info is not None:
xinfo['valtest-loss'] = valtest_info['loss'] xinfo['valtest-loss'] = valtest_info['loss']
xinfo['valtest-accuracy'] = valtest_info['accuracy'] 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'] xinfo['valtest-all-time'] = valtest_info['all_time']
return xinfo return xinfo

16
lib/nas_201_api/api_utils.py
View File

@@ -660,15 +660,21 @@ class ResultsCount(object):
"""Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument).""" """Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument)."""
if iepoch is None: iepoch = self.epochs-1 if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs) assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
def _internal_query(xname):
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0: if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
xtime = self.eval_times['{:}@{:}'.format(name,iepoch)] xtime = self.eval_times['{:}@{:}'.format(xname, iepoch)]
atime = sum([self.eval_times['{:}@{:}'.format(name,i)] for i in range(iepoch+1)]) atime = sum([self.eval_times['{:}@{:}'.format(xname, i)] for i in range(iepoch+1)])
else: xtime, atime = None, None else:
xtime, atime = None, None
return {'iepoch' : iepoch, return {'iepoch' : iepoch,
'loss' : self.eval_losses['{:}@{:}'.format(name,iepoch)], 'loss' : self.eval_losses['{:}@{:}'.format(xname, iepoch)],
'accuracy': self.eval_acc1es['{:}@{:}'.format(name,iepoch)], 'accuracy': self.eval_acc1es['{:}@{:}'.format(xname, iepoch)],
'cur_time': xtime, 'cur_time': xtime,
'all_time': atime} 'all_time': atime}
if name == 'valid':
return _internal_query('x-valid')
else:
return _internal_query(name)
def get_net_param(self, clone=False): def get_net_param(self, clone=False):
if clone: return copy.deepcopy(self.net_state_dict) if clone: return copy.deepcopy(self.net_state_dict)