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')