Note
Go to the end to download the full example code
Use NAS Benchmarks as Datasets¶
In this tutorial, we show how to use NAS Benchmarks as datasets. For research purposes we sometimes desire to query the benchmarks for architecture accuracies, rather than train them one by one from scratch. NNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.
Prerequisites¶
This tutorial assumes that you have already prepared your NAS benchmarks under cache directory
(by default, ~/.cache/nni/nasbenchmark).
If you haven’t, please follow the data preparation guide in NAS Benchmark.
As a result, the directory should look like:
import os
os.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))
['nds-5745c235.db', 'nasbench201-b2b60732.db', 'nasbench101-209f5694.db']
import pprint
from nni.nas.benchmark.nasbench101 import query_nb101_trial_stats
from nni.nas.benchmark.nasbench201 import query_nb201_trial_stats
from nni.nas.benchmark.nds import query_nds_trial_stats
NAS-Bench-101¶
Use the following architecture as an example:
arch = {
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu',
'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5]
}
for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):
pprint.pprint(t)
{'config': {'arch': {'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5],
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu'},
'hash': '00005c142e6f48ac74fdcf73e3439874',
'id': 4,
'num_epochs': 108,
'num_vertices': 7},
'id': 10,
'intermediates': [{'current_epoch': 54,
'id': 19,
'test_acc': 77.40384340286255,
'train_acc': 82.82251358032227,
'training_time': 883.4580078125,
'valid_acc': 77.76442170143127},
{'current_epoch': 108,
'id': 20,
'test_acc': 92.11738705635071,
'train_acc': 100.0,
'training_time': 1769.1279296875,
'valid_acc': 92.41786599159241}],
'parameters': 8.55553,
'test_acc': 92.11738705635071,
'train_acc': 100.0,
'training_time': 106147.67578125,
'valid_acc': 92.41786599159241}
{'config': {'arch': {'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5],
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu'},
'hash': '00005c142e6f48ac74fdcf73e3439874',
'id': 4,
'num_epochs': 108,
'num_vertices': 7},
'id': 11,
'intermediates': [{'current_epoch': 54,
'id': 21,
'test_acc': 82.04126358032227,
'train_acc': 87.96073794364929,
'training_time': 883.6810302734375,
'valid_acc': 82.91265964508057},
{'current_epoch': 108,
'id': 22,
'test_acc': 91.90705418586731,
'train_acc': 100.0,
'training_time': 1768.2509765625,
'valid_acc': 92.45793223381042}],
'parameters': 8.55553,
'test_acc': 91.90705418586731,
'train_acc': 100.0,
'training_time': 106095.05859375,
'valid_acc': 92.45793223381042}
{'config': {'arch': {'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5],
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu'},
'hash': '00005c142e6f48ac74fdcf73e3439874',
'id': 4,
'num_epochs': 108,
'num_vertices': 7},
'id': 12,
'intermediates': [{'current_epoch': 54,
'id': 23,
'test_acc': 80.58894276618958,
'train_acc': 86.34815812110901,
'training_time': 883.4569702148438,
'valid_acc': 81.1598539352417},
{'current_epoch': 108,
'id': 24,
'test_acc': 92.15745329856873,
'train_acc': 100.0,
'training_time': 1768.9759521484375,
'valid_acc': 93.04887652397156}],
'parameters': 8.55553,
'test_acc': 92.15745329856873,
'train_acc': 100.0,
'training_time': 106138.55712890625,
'valid_acc': 93.04887652397156}
An architecture of NAS-Bench-101 could be trained more than once. Each element of the returned generator is a dict which contains one of the training results of this trial config (architecture + hyper-parameters) including train/valid/test accuracy, training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.
NAS-Bench-201¶
Use the following architecture as an example:
arch = {
'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'1_2': 'skip_connect',
'0_3': 'conv_1x1',
'1_3': 'skip_connect',
'2_3': 'skip_connect'
}
for t in query_nb201_trial_stats(arch, 200, 'cifar100'):
pprint.pprint(t)
{'config': {'arch': {'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'0_3': 'conv_1x1',
'1_2': 'skip_connect',
'1_3': 'skip_connect',
'2_3': 'skip_connect'},
'dataset': 'cifar100',
'id': 7,
'num_cells': 5,
'num_channels': 16,
'num_epochs': 200},
'flops': 15.65322,
'id': 3,
'latency': 0.013182918230692545,
'ori_test_acc': 53.11,
'ori_test_evaluation_time': 1.0195916947864352,
'ori_test_loss': 1.7307863704681397,
'parameters': 0.135156,
'seed': 999,
'test_acc': 53.07999995727539,
'test_evaluation_time': 0.5097958473932176,
'test_loss': 1.731276072692871,
'train_acc': 57.82,
'train_loss': 1.5116578379058838,
'training_time': 2888.4371995925903,
'valid_acc': 53.14000000610351,
'valid_evaluation_time': 0.5097958473932176,
'valid_loss': 1.7302966793060304}
{'config': {'arch': {'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'0_3': 'conv_1x1',
'1_2': 'skip_connect',
'1_3': 'skip_connect',
'2_3': 'skip_connect'},
'dataset': 'cifar100',
'id': 7,
'num_cells': 5,
'num_channels': 16,
'num_epochs': 200},
'flops': 15.65322,
'id': 7,
'latency': 0.013182918230692545,
'ori_test_acc': 51.93,
'ori_test_evaluation_time': 1.0195916947864352,
'ori_test_loss': 1.7572312774658203,
'parameters': 0.135156,
'seed': 777,
'test_acc': 51.979999938964845,
'test_evaluation_time': 0.5097958473932176,
'test_loss': 1.7429540189743042,
'train_acc': 57.578,
'train_loss': 1.5114233912658692,
'training_time': 2888.4371995925903,
'valid_acc': 51.88,
'valid_evaluation_time': 0.5097958473932176,
'valid_loss': 1.7715086591720581}
{'config': {'arch': {'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'0_3': 'conv_1x1',
'1_2': 'skip_connect',
'1_3': 'skip_connect',
'2_3': 'skip_connect'},
'dataset': 'cifar100',
'id': 7,
'num_cells': 5,
'num_channels': 16,
'num_epochs': 200},
'flops': 15.65322,
'id': 11,
'latency': 0.013182918230692545,
'ori_test_acc': 53.38,
'ori_test_evaluation_time': 1.0195916947864352,
'ori_test_loss': 1.7281623031616211,
'parameters': 0.135156,
'seed': 888,
'test_acc': 53.67999998779297,
'test_evaluation_time': 0.5097958473932176,
'test_loss': 1.7327697801589965,
'train_acc': 57.792,
'train_loss': 1.5091403088760376,
'training_time': 2888.4371995925903,
'valid_acc': 53.08000000610352,
'valid_evaluation_time': 0.5097958473932176,
'valid_loss': 1.7235548280715942}
Intermediate results are also available.
for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):
print(t['config'])
print('Intermediates:', len(t['intermediates']))
{'id': 4, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 12, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 12
{'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 200
{'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 200
{'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 200
NDS¶
Use the following architecture as an example:
Here, bot_muls, ds, num_gs, ss and ws stand for “bottleneck multipliers”,
“depths”, “number of groups”, “strides” and “widths” respectively.
model_spec = {
'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]
}
Use none as a wildcard.
for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):
pprint.pprint(t)
{'best_test_acc': 90.48,
'best_train_acc': 96.356,
'best_train_loss': 0.116,
'config': {'base_lr': 0.1,
'cell_spec': {},
'dataset': 'cifar10',
'generator': 'random',
'id': 45505,
'model_family': 'residual_bottleneck',
'model_spec': {'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]},
'num_epochs': 100,
'proposer': 'resnext-a',
'weight_decay': 0.0005},
'final_test_acc': 90.39,
'final_train_acc': 96.298,
'final_train_loss': 0.116,
'flops': 69.890986,
'id': 45505,
'iter_time': 0.065,
'parameters': 0.083002,
'seed': 1}
model_spec = {
'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]
}
for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):
pprint.pprint(t['intermediates'][:10])
[{'current_epoch': 1,
'id': 4494501,
'test_acc': 41.76,
'train_acc': 30.421000000000006,
'train_loss': 1.793},
{'current_epoch': 2,
'id': 4494502,
'test_acc': 54.66,
'train_acc': 47.24,
'train_loss': 1.415},
{'current_epoch': 3,
'id': 4494503,
'test_acc': 59.97,
'train_acc': 56.983,
'train_loss': 1.179},
{'current_epoch': 4,
'id': 4494504,
'test_acc': 62.91,
'train_acc': 61.955,
'train_loss': 1.048},
{'current_epoch': 5,
'id': 4494505,
'test_acc': 66.16,
'train_acc': 64.493,
'train_loss': 0.983},
{'current_epoch': 6,
'id': 4494506,
'test_acc': 66.5,
'train_acc': 66.274,
'train_loss': 0.937},
{'current_epoch': 7,
'id': 4494507,
'test_acc': 67.55,
'train_acc': 67.426,
'train_loss': 0.907},
{'current_epoch': 8,
'id': 4494508,
'test_acc': 69.45,
'train_acc': 68.45400000000001,
'train_loss': 0.878},
{'current_epoch': 9,
'id': 4494509,
'test_acc': 70.14,
'train_acc': 69.295,
'train_loss': 0.857},
{'current_epoch': 10,
'id': 4494510,
'test_acc': 69.47,
'train_acc': 70.304,
'train_loss': 0.832}]
model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}
for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):
pprint.pprint(t)
{'best_test_acc': 93.58,
'best_train_acc': 99.772,
'best_train_loss': 0.011,
'config': {'base_lr': 0.1,
'cell_spec': {},
'dataset': 'cifar10',
'generator': 'random',
'id': 108998,
'model_family': 'residual_basic',
'model_spec': {'ds': [1, 12, 12, 12],
'ss': [1, 1, 2, 2],
'ws': [16, 24, 24, 40]},
'num_epochs': 100,
'proposer': 'resnet',
'weight_decay': 0.0005},
'final_test_acc': 93.49,
'final_train_acc': 99.772,
'final_train_loss': 0.011,
'flops': 184.519578,
'id': 108998,
'iter_time': 0.059,
'parameters': 0.594138,
'seed': 1}
Get the first one.
pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))
{'best_test_acc': 84.5,
'best_train_acc': 89.66499999999999,
'best_train_loss': 0.302,
'config': {'base_lr': 0.1,
'cell_spec': {},
'dataset': 'cifar10',
'generator': 'random',
'id': 139492,
'model_family': 'vanilla',
'model_spec': {'ds': [1, 12, 12, 12],
'ss': [1, 1, 2, 2],
'ws': [16, 24, 32, 40]},
'num_epochs': 100,
'proposer': 'vanilla',
'weight_decay': 0.0005},
'final_test_acc': 84.35,
'final_train_acc': 89.633,
'final_train_loss': 0.303,
'flops': 208.36393,
'id': 154692,
'iter_time': 0.058,
'parameters': 0.68977,
'seed': 1}
Count number.
model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}
cell_spec = {
'normal_0_op_x': 'avg_pool_3x3',
'normal_0_input_x': 0,
'normal_0_op_y': 'conv_7x1_1x7',
'normal_0_input_y': 1,
'normal_1_op_x': 'sep_conv_3x3',
'normal_1_input_x': 2,
'normal_1_op_y': 'sep_conv_5x5',
'normal_1_input_y': 0,
'normal_2_op_x': 'dil_sep_conv_3x3',
'normal_2_input_x': 2,
'normal_2_op_y': 'dil_sep_conv_3x3',
'normal_2_input_y': 2,
'normal_3_op_x': 'skip_connect',
'normal_3_input_x': 4,
'normal_3_op_y': 'dil_sep_conv_3x3',
'normal_3_input_y': 4,
'normal_4_op_x': 'conv_7x1_1x7',
'normal_4_input_x': 2,
'normal_4_op_y': 'sep_conv_3x3',
'normal_4_input_y': 4,
'normal_concat': [3, 5, 6],
'reduce_0_op_x': 'avg_pool_3x3',
'reduce_0_input_x': 0,
'reduce_0_op_y': 'dil_sep_conv_3x3',
'reduce_0_input_y': 1,
'reduce_1_op_x': 'sep_conv_3x3',
'reduce_1_input_x': 0,
'reduce_1_op_y': 'sep_conv_3x3',
'reduce_1_input_y': 0,
'reduce_2_op_x': 'skip_connect',
'reduce_2_input_x': 2,
'reduce_2_op_y': 'sep_conv_7x7',
'reduce_2_input_y': 0,
'reduce_3_op_x': 'conv_7x1_1x7',
'reduce_3_input_x': 4,
'reduce_3_op_y': 'skip_connect',
'reduce_3_input_y': 4,
'reduce_4_op_x': 'conv_7x1_1x7',
'reduce_4_input_x': 0,
'reduce_4_op_y': 'conv_7x1_1x7',
'reduce_4_input_y': 5,
'reduce_concat': [3, 6]
}
for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):
assert t['config']['model_spec'] == model_spec
assert t['config']['cell_spec'] == cell_spec
pprint.pprint(t)
{'best_test_acc': 93.37,
'best_train_acc': 99.91,
'best_train_loss': 0.006,
'config': {'base_lr': 0.1,
'cell_spec': {'normal_0_input_x': 0,
'normal_0_input_y': 1,
'normal_0_op_x': 'avg_pool_3x3',
'normal_0_op_y': 'conv_7x1_1x7',
'normal_1_input_x': 2,
'normal_1_input_y': 0,
'normal_1_op_x': 'sep_conv_3x3',
'normal_1_op_y': 'sep_conv_5x5',
'normal_2_input_x': 2,
'normal_2_input_y': 2,
'normal_2_op_x': 'dil_sep_conv_3x3',
'normal_2_op_y': 'dil_sep_conv_3x3',
'normal_3_input_x': 4,
'normal_3_input_y': 4,
'normal_3_op_x': 'skip_connect',
'normal_3_op_y': 'dil_sep_conv_3x3',
'normal_4_input_x': 2,
'normal_4_input_y': 4,
'normal_4_op_x': 'conv_7x1_1x7',
'normal_4_op_y': 'sep_conv_3x3',
'normal_concat': [3, 5, 6],
'reduce_0_input_x': 0,
'reduce_0_input_y': 1,
'reduce_0_op_x': 'avg_pool_3x3',
'reduce_0_op_y': 'dil_sep_conv_3x3',
'reduce_1_input_x': 0,
'reduce_1_input_y': 0,
'reduce_1_op_x': 'sep_conv_3x3',
'reduce_1_op_y': 'sep_conv_3x3',
'reduce_2_input_x': 2,
'reduce_2_input_y': 0,
'reduce_2_op_x': 'skip_connect',
'reduce_2_op_y': 'sep_conv_7x7',
'reduce_3_input_x': 4,
'reduce_3_input_y': 4,
'reduce_3_op_x': 'conv_7x1_1x7',
'reduce_3_op_y': 'skip_connect',
'reduce_4_input_x': 0,
'reduce_4_input_y': 5,
'reduce_4_op_x': 'conv_7x1_1x7',
'reduce_4_op_y': 'conv_7x1_1x7',
'reduce_concat': [3, 6]},
'dataset': 'cifar10',
'generator': 'random',
'id': 1,
'model_family': 'nas_cell',
'model_spec': {'aux': False,
'depth': 12,
'drop_prob': 0.0,
'num_nodes_normal': 5,
'num_nodes_reduce': 5,
'width': 32},
'num_epochs': 100,
'proposer': 'amoeba',
'weight_decay': 0.0005},
'final_test_acc': 93.27,
'final_train_acc': 99.91,
'final_train_loss': 0.006,
'flops': 664.400586,
'id': 1,
'iter_time': 0.281,
'parameters': 4.190314,
'seed': 1}
Count number.
print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))
NDS (amoeba) count: 5107
Total running time of the script: ( 1 minutes 51.444 seconds)