From 6dc494be080dcfe636d0435bc86cfe78686b659b Mon Sep 17 00:00:00 2001
From: D-X-Y <280835372@qq.com>
Date: Mon, 13 Jul 2020 10:04:52 +0000
Subject: [PATCH] Update REA, REINFORCE, and RANDOM
---
exps/NAS-Bench-201/test-nas-api.py | 8 ++
exps/algos-v2/random_wo_share.py | 91 +++++++++++++++
exps/algos-v2/{REA.py => regularized_ea.py} | 29 ++---
exps/algos-v2/reinforce.py | 33 +++---
exps/algos-v2/run-all.sh | 17 +++
exps/algos/RANDOM.py | 2 +-
exps/experimental/vis-bench-algos.py | 107 ++++++++++++++++++
.../visualize-nas-bench-x.py} | 23 ++--
lib/nas_201_api/api_201.py | 5 +-
lib/nas_201_api/api_301.py | 5 +-
lib/nas_201_api/api_utils.py | 8 +-
lib/utils/nas_utils.py | 2 +
12 files changed, 277 insertions(+), 53 deletions(-)
create mode 100644 exps/algos-v2/random_wo_share.py
rename exps/algos-v2/{REA.py => regularized_ea.py} (87%)
create mode 100644 exps/algos-v2/run-all.sh
create mode 100644 exps/experimental/vis-bench-algos.py
rename exps/{NAS-Bench-201/test-nas-api-vis.py => experimental/visualize-nas-bench-x.py} (96%)
diff --git a/exps/NAS-Bench-201/test-nas-api.py b/exps/NAS-Bench-201/test-nas-api.py
index 9a79f28..62d2bc3 100644
--- a/exps/NAS-Bench-201/test-nas-api.py
+++ b/exps/NAS-Bench-201/test-nas-api.py
@@ -72,6 +72,14 @@ def test_api(api, is_301=True):
print('{:}\n'.format(info))
print('{:} finish testing the api : {:}'.format(time_string(), api))
+ if not is_301:
+ arch_str = '|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|'
+ matrix = api.str2matrix(arch_str)
+ print('Compute the adjacency matrix of {:}'.format(arch_str))
+ print(matrix)
+ info = api.simulate_train_eval(123, 'cifar10')
+ print('simulate_train_eval : {:}'.format(info))
+
def test_issue_81_82(api):
results = api.query_by_index(0, 'cifar10-valid', hp='12')
diff --git a/exps/algos-v2/random_wo_share.py b/exps/algos-v2/random_wo_share.py
new file mode 100644
index 0000000..774dfd4
--- /dev/null
+++ b/exps/algos-v2/random_wo_share.py
@@ -0,0 +1,91 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+##############################################################################
+# Random Search for Hyper-Parameter Optimization, JMLR 2012 ##################
+##############################################################################
+# python ./exps/algos-v2/random_wo_share.py --dataset cifar10 --search_space tss
+##############################################################################
+import os, sys, time, glob, random, argparse
+import numpy as np, collections
+from copy import deepcopy
+import torch
+import torch.nn as nn
+from pathlib import Path
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+from config_utils import load_config, dict2config, configure2str
+from datasets import get_datasets, SearchDataset
+from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
+from utils import get_model_infos, obtain_accuracy
+from log_utils import AverageMeter, time_string, convert_secs2time
+from models import get_search_spaces
+from nas_201_api import NASBench201API, NASBench301API
+from .regularized_ea import random_topology_func, random_size_func
+
+
+def main(xargs, api):
+ torch.set_num_threads(4)
+ prepare_seed(xargs.rand_seed)
+ logger = prepare_logger(args)
+
+ search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
+ if xargs.search_space == 'tss':
+ random_arch = random_topology_func(search_space)
+ else:
+ random_arch = random_size_func(search_space)
+
+ x_start_time = time.time()
+ logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
+ best_arch, best_acc, total_time_cost, history = None, -1, [], []
+ while total_time_cost[-1] < xargs.time_budget:
+ arch = random_arch()
+ accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
+ total_time_cost.append(total_cost)
+ history.append(arch)
+ if best_arch is None or best_acc < accuracy:
+ best_acc, best_arch = accuracy, arch
+ logger.log('[{:03d}] : {:} : accuracy = {:.2f}%'.format(len(history), arch, accuracy))
+ logger.log('{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s (real-cost = {:.3f} s).'.format(time_string(), best_arch, best_acc, len(history), total_time_cost, time.time()-x_start_time))
+
+ info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
+ logger.log('{:}'.format(info))
+ logger.log('-'*100)
+ logger.close()
+ return logger.log_dir, total_time_cost, history
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser("Random NAS")
+ parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
+ parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
+
+ parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).')
+ parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
+ # log
+ parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
+ parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
+ args = parser.parse_args()
+
+ if args.search_space == 'tss':
+ api = NASBench201API(verbose=False)
+ elif args.search_space == 'sss':
+ api = NASBench301API(verbose=False)
+ else:
+ raise ValueError('Invalid search space : {:}'.format(args.search_space))
+
+ args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'RANDOM')
+ print('save-dir : {:}'.format(args.save_dir))
+
+ if args.rand_seed < 0:
+ save_dir, all_info = None, {}
+ for i in range(args.loops_if_rand):
+ print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
+ args.rand_seed = random.randint(1, 100000)
+ save_dir, all_archs, all_total_times = main(args, api)
+ all_info[i] = {'all_archs': all_archs,
+ 'all_total_times': all_total_times}
+ save_path = save_dir / 'results.pth'
+ print('save into {:}'.format(save_path))
+ torch.save(all_info, save_path)
+ else:
+ main(args, api)
diff --git a/exps/algos-v2/REA.py b/exps/algos-v2/regularized_ea.py
similarity index 87%
rename from exps/algos-v2/REA.py
rename to exps/algos-v2/regularized_ea.py
index 7410fa3..4e0a3bd 100644
--- a/exps/algos-v2/REA.py
+++ b/exps/algos-v2/regularized_ea.py
@@ -3,12 +3,12 @@
##################################################################
# Regularized Evolution for Image Classifier Architecture Search #
##################################################################
-# python ./exps/algos-v2/REA.py --dataset cifar10 --search_space tss --time_budget 12000 --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
-# python ./exps/algos-v2/REA.py --dataset cifar100 --search_space tss --time_budget 12000 --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
-# python ./exps/algos-v2/REA.py --dataset ImageNet16-120 --search_space tss --time_budget 12000 --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
-# python ./exps/algos-v2/REA.py --dataset cifar10 --search_space sss --time_budget 12000 --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
-# python ./exps/algos-v2/REA.py --dataset cifar100 --search_space sss --time_budget 12000 --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
-# python ./exps/algos-v2/REA.py --dataset ImageNet16-120 --search_space sss --time_budget 12000 --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
+# python ./exps/algos-v2/regularized_ea.py --dataset cifar10 --search_space tss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
+# python ./exps/algos-v2/regularized_ea.py --dataset cifar100 --search_space tss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
+# python ./exps/algos-v2/regularized_ea.py --dataset ImageNet16-120 --search_space tss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
+# python ./exps/algos-v2/regularized_ea.py --dataset cifar10 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
+# python ./exps/algos-v2/regularized_ea.py --dataset cifar100 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
+# python ./exps/algos-v2/regularized_ea.py --dataset ImageNet16-120 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3 --rand_seed 1
##################################################################
import os, sys, time, glob, random, argparse
import numpy as np, collections
@@ -160,7 +160,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
while len(population) < population_size:
model = Model()
model.arch = random_arch()
- model.accuracy, time_cost, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
+ model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
# Append the info
population.append(model)
history.append(model)
@@ -183,7 +183,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
# Create the child model and store it.
child = Model()
child.arch = mutate_arch(parent.arch)
- child.accuracy, time_cost, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
+ child.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
# Append the info
population.append(child)
history.append(child)
@@ -195,11 +195,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
def main(xargs, api):
- assert torch.cuda.is_available(), 'CUDA is not available.'
- torch.backends.cudnn.enabled = True
- torch.backends.cudnn.benchmark = False
- torch.backends.cudnn.deterministic = True
- torch.set_num_threads(xargs.workers)
+ torch.set_num_threads(4)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
@@ -235,12 +231,11 @@ if __name__ == '__main__':
parser.add_argument('--ea_cycles', type=int, help='The number of cycles in EA.')
parser.add_argument('--ea_population', type=int, help='The population size in EA.')
parser.add_argument('--ea_sample_size', type=int, help='The sample size in EA.')
- parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
- parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
+ parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).')
+ parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
# log
- parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
- parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
+ parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
diff --git a/exps/algos-v2/reinforce.py b/exps/algos-v2/reinforce.py
index c81708c..400f1ef 100644
--- a/exps/algos-v2/reinforce.py
+++ b/exps/algos-v2/reinforce.py
@@ -3,12 +3,12 @@
#####################################################################################################
# modified from https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py #
#####################################################################################################
-# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space tss --time_budget 12000 --learning_rate 0.001
-# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --time_budget 12000 --learning_rate 0.001
-# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --time_budget 12000 --learning_rate 0.001
-# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --time_budget 12000 --learning_rate 0.001
-# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --time_budget 12000 --learning_rate 0.001
-# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --time_budget 12000 --learning_rate 0.001
+# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.001
+# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.001
+# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.001
+# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.001
+# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.001
+# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.001
#####################################################################################################
import os, sys, time, glob, random, argparse
import numpy as np, collections
@@ -120,15 +120,10 @@ def select_action(policy):
def main(xargs, api):
- assert torch.cuda.is_available(), 'CUDA is not available.'
- torch.backends.cudnn.enabled = True
- torch.backends.cudnn.benchmark = False
- torch.backends.cudnn.deterministic = True
- torch.set_num_threads(xargs.workers)
+ torch.set_num_threads(4)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
-
search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
if xargs.search_space == 'tss':
policy = PolicyTopology(search_space)
@@ -144,6 +139,7 @@ def main(xargs, api):
# nas dataset load
logger.log('{:} use api : {:}'.format(time_string(), api))
+ api.reset_time()
# REINFORCE
x_start_time = time.time()
@@ -153,7 +149,7 @@ def main(xargs, api):
start_time = time.time()
log_prob, action = select_action( policy )
arch = policy.generate_arch( action )
- reward, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
+ reward, _, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
trace.append((reward, arch))
total_costs.append(current_total_cost)
@@ -177,7 +173,7 @@ def main(xargs, api):
logger.log('-'*100)
logger.close()
- return logger.log_dir, [api.query_index_by_arch(x[0]) for x in trace], total_costs
+ return logger.log_dir, [api.query_index_by_arch(x[1]) for x in trace], total_costs
if __name__ == '__main__':
@@ -186,15 +182,14 @@ if __name__ == '__main__':
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
parser.add_argument('--learning_rate', type=float, help='The learning rate for REINFORCE.')
- parser.add_argument('--EMA_momentum', type=float, default=0.9, help='The momentum value for EMA.')
- parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
- parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
+ parser.add_argument('--EMA_momentum', type=float, default=0.9, help='The momentum value for EMA.')
+ parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).')
+ parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
# log
- parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')
parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
- parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
+ parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
diff --git a/exps/algos-v2/run-all.sh b/exps/algos-v2/run-all.sh
new file mode 100644
index 0000000..3f2f01d
--- /dev/null
+++ b/exps/algos-v2/run-all.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# bash ./exps/algos-v2/run-all.sh
+echo script name: $0
+echo $# arguments
+
+datasets="cifar10 cifar100 ImageNet16-120"
+search_spaces="tss sss"
+
+
+for dataset in ${datasets}
+do
+ for search_space in ${search_spaces}
+ do
+ python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
+ python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
+ done
+done
diff --git a/exps/algos/RANDOM.py b/exps/algos/RANDOM.py
index 58af886..e38bf60 100644
--- a/exps/algos/RANDOM.py
+++ b/exps/algos/RANDOM.py
@@ -84,7 +84,7 @@ def main(xargs, nas_bench):
if __name__ == '__main__':
- parser = argparse.ArgumentParser("Regularized Evolution Algorithm")
+ parser = argparse.ArgumentParser("Random NAS")
parser.add_argument('--data_path', type=str, help='Path to dataset')
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
# channels and number-of-cells
diff --git a/exps/experimental/vis-bench-algos.py b/exps/experimental/vis-bench-algos.py
new file mode 100644
index 0000000..f0a4b1b
--- /dev/null
+++ b/exps/experimental/vis-bench-algos.py
@@ -0,0 +1,107 @@
+###############################################################
+# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
+###############################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
+###############################################################
+# Usage: python exps/experimental/vis-bench-algos.py
+###############################################################
+import os, sys, time, torch, argparse
+import numpy as np
+from typing import List, Text, Dict, Any
+from shutil import copyfile
+from collections import defaultdict, OrderedDict
+from copy import deepcopy
+from pathlib import Path
+import matplotlib
+import seaborn as sns
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+from config_utils import dict2config, load_config
+from nas_201_api import NASBench201API, NASBench301API
+from log_utils import time_string
+
+
+def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
+ ss_dir = '{:}-{:}'.format(root_dir, search_space)
+ alg2name, alg2path = OrderedDict(), OrderedDict()
+ alg2name['REA'] = 'R-EA-SS3'
+ alg2name['REINFORCE'] = 'REINFORCE-0.001'
+ for alg, name in alg2name.items():
+ alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
+ assert os.path.isfile(alg2path[alg])
+ alg2data = OrderedDict()
+ for alg, path in alg2path.items():
+ data = torch.load(path)
+ for index, info in data.items():
+ info['time_w_arch'] = [(x, y) for x, y in zip(info['all_total_times'], info['all_archs'])]
+ for j, arch in enumerate(info['all_archs']):
+ assert arch != -1, 'invalid arch from {:} {:} {:} ({:}, {:})'.format(alg, search_space, dataset, index, j)
+ alg2data[alg] = data
+ return alg2data
+
+
+def query_performance(api, data, dataset, ticket):
+ results, is_301 = [], isinstance(api, NASBench301API)
+ for i, info in data.items():
+ time_w_arch = sorted(info['time_w_arch'], key=lambda x: abs(x[0]-ticket))
+ time_a, arch_a = time_w_arch[0]
+ time_b, arch_b = time_w_arch[1]
+ info_a = api.get_more_info(arch_a, dataset=dataset, hp=90 if is_301 else 200, is_random=False)
+ info_b = api.get_more_info(arch_b, dataset=dataset, hp=90 if is_301 else 200, is_random=False)
+ accuracy_a, accuracy_b = info_a['test-accuracy'], info_b['test-accuracy']
+ interplate = (time_b-ticket) / (time_b-time_a) * accuracy_a + (ticket-time_a) / (time_b-time_a) * accuracy_b
+ results.append(interplate)
+ return sum(results) / len(results)
+
+
+def visualize_curve(api, vis_save_dir, search_space, max_time):
+ vis_save_dir = vis_save_dir.resolve()
+ vis_save_dir.mkdir(parents=True, exist_ok=True)
+
+ dpi, width, height = 250, 4700, 1500
+ figsize = width / float(dpi), height / float(dpi)
+ LabelSize, LegendFontsize = 14, 14
+
+ def sub_plot_fn(ax, dataset):
+ alg2data = fetch_data(search_space=search_space, dataset=dataset)
+ alg2accuracies = OrderedDict()
+ time_tickets = [float(i) / 100 * max_time for i in range(100)]
+ colors = ['b', 'g', 'c', 'm', 'y']
+ for idx, (alg, data) in enumerate(alg2data.items()):
+ print('plot alg : {:}'.format(alg))
+ accuracies = []
+ for ticket in time_tickets:
+ accuracy = query_performance(api, data, dataset, ticket)
+ accuracies.append(accuracy)
+ alg2accuracies[alg] = accuracies
+ ax.plot(time_tickets, accuracies, c=colors[idx], label='{:}'.format(alg))
+ ax.legend(loc=4, fontsize=LegendFontsize)
+
+ fig, axs = plt.subplots(1, 3, figsize=figsize)
+ datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
+ for dataset, ax in zip(datasets, axs):
+ sub_plot_fn(ax, dataset)
+ print('sub-plot {:} on {:} done.'.format(dataset, search_space))
+ save_path = (vis_save_dir / '{:}-curve.png'.format(search_space)).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__':
+ parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
+ parser.add_argument('--max_time', type=float, default=20000, help='The maximum time budget.')
+ args = parser.parse_args()
+
+ save_dir = Path(args.save_dir)
+
+ api201 = NASBench201API(verbose=False)
+ visualize_curve(api201, save_dir, 'tss', args.max_time)
+ api301 = NASBench301API(verbose=False)
+ visualize_curve(api301, save_dir, 'sss', args.max_time)
+
diff --git a/exps/NAS-Bench-201/test-nas-api-vis.py b/exps/experimental/visualize-nas-bench-x.py
similarity index 96%
rename from exps/NAS-Bench-201/test-nas-api-vis.py
rename to exps/experimental/visualize-nas-bench-x.py
index 02668fe..e3714a7 100644
--- a/exps/NAS-Bench-201/test-nas-api-vis.py
+++ b/exps/experimental/visualize-nas-bench-x.py
@@ -3,7 +3,7 @@
###############################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
###############################################################
-# Usage: python exps/NAS-Bench-201/test-nas-api-vis.py
+# Usage: python exps/experimental/visualize-nas-bench-x.py
###############################################################
import os, sys, time, torch, argparse
import numpy as np
@@ -384,24 +384,25 @@ def visualize_all_rank_info(api, vis_save_dir, indicator):
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.')
+ parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench', help='Folder to save checkpoints and log.')
# use for train the model
args = parser.parse_args()
+ to_save_dir = Path(args.save_dir)
+
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
api201 = NASBench201API(None, verbose=True)
for xdata in datasets:
- visualize_tss_info(api201, xdata, Path('output/vis-nas-bench'))
+ visualize_tss_info(api201, xdata, to_save_dir)
api301 = NASBench301API(None, verbose=True)
for xdata in datasets:
- visualize_sss_info(api301, xdata, Path('output/vis-nas-bench'))
+ visualize_sss_info(api301, xdata, to_save_dir)
- visualize_info(None, Path('output/vis-nas-bench/'), 'tss')
- 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')
+ visualize_info(None, to_save_dir, 'tss')
+ visualize_info(None, to_save_dir, 'sss')
+ visualize_rank_info(None, to_save_dir, 'tss')
+ visualize_rank_info(None, to_save_dir, 'sss')
- visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
- visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
+ visualize_all_rank_info(None, to_save_dir, 'tss')
+ visualize_all_rank_info(None, to_save_dir, 'sss')
diff --git a/lib/nas_201_api/api_201.py b/lib/nas_201_api/api_201.py
index 454c49a..49d9a68 100644
--- a/lib/nas_201_api/api_201.py
+++ b/lib/nas_201_api/api_201.py
@@ -141,9 +141,12 @@ class NASBench201API(NASBenchMetaAPI):
# `is_random`
# When is_random=True, the performance of a random architecture will be returned
# When is_random=False, the performanceo of all trials will be averaged.
- def get_more_info(self, index: int, dataset, iepoch=None, hp='12', is_random=True):
+ def get_more_info(self, index, dataset, iepoch=None, hp='12', is_random=True):
if self.verbose:
print('Call the get_more_info function with index={:}, dataset={:}, iepoch={:}, hp={:}, and is_random={:}.'.format(index, dataset, iepoch, hp, is_random))
+ index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
+ if index not in self.arch2infos_dict:
+ raise ValueError('Did not find {:} from arch2infos_dict.'.format(index))
archresult = self.arch2infos_dict[index][str(hp)]
# if randomly select one trial, select the seed at first
if isinstance(is_random, bool) and is_random:
diff --git a/lib/nas_201_api/api_301.py b/lib/nas_201_api/api_301.py
index a349056..8ac77f8 100644
--- a/lib/nas_201_api/api_301.py
+++ b/lib/nas_201_api/api_301.py
@@ -131,7 +131,7 @@ class NASBench301API(NASBenchMetaAPI):
print('Call query_info_str_by_arch with arch={:} and hp={:}'.format(arch, hp))
return self._query_info_str_by_arch(arch, hp, print_information)
- def get_more_info(self, index: int, dataset: Text, iepoch=None, hp='12', is_random=True):
+ def get_more_info(self, index, dataset: Text, iepoch=None, hp='12', is_random=True):
"""This function will return the metric for the `index`-th architecture
`dataset` indicates the dataset:
'cifar10-valid' : using the proposed train set of CIFAR-10 as the training set
@@ -151,6 +151,9 @@ class NASBench301API(NASBenchMetaAPI):
"""
if self.verbose:
print('Call the get_more_info function with index={:}, dataset={:}, iepoch={:}, hp={:}, and is_random={:}.'.format(index, dataset, iepoch, hp, is_random))
+ index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
+ if index not in self.arch2infos_dict:
+ raise ValueError('Did not find {:} from arch2infos_dict.'.format(index))
archresult = self.arch2infos_dict[index][str(hp)]
# if randomly select one trial, select the seed at first
if isinstance(is_random, bool) and is_random:
diff --git a/lib/nas_201_api/api_utils.py b/lib/nas_201_api/api_utils.py
index 53199ae..a8383d2 100644
--- a/lib/nas_201_api/api_utils.py
+++ b/lib/nas_201_api/api_utils.py
@@ -68,7 +68,7 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
def reset_time(self):
self._used_time = 0
- def simulate_train_eval(self, arch, dataset, hp='12'):
+ def simulate_train_eval(self, arch, dataset, hp='12', account_time=True):
index = self.query_index_by_arch(arch)
all_names = ('cifar10', 'cifar100', 'ImageNet16-120')
assert dataset in all_names, 'Invalid dataset name : {:} vs {:}'.format(dataset, all_names)
@@ -77,8 +77,10 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
else:
info = self.get_more_info(index, dataset, iepoch=None, hp=hp, is_random=True)
valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
- self._used_time += time_cost
- return valid_acc, time_cost, self._used_time
+ latency = self.get_latency(index, dataset)
+ if account_time:
+ self._used_time += time_cost
+ return valid_acc, latency, time_cost, self._used_time
def random(self):
"""Return a random index of all architectures."""
diff --git a/lib/utils/nas_utils.py b/lib/utils/nas_utils.py
index c701935..1b1a44d 100644
--- a/lib/utils/nas_utils.py
+++ b/lib/utils/nas_utils.py
@@ -8,7 +8,9 @@ import torch.nn as nn
from models import CellStructure
from log_utils import time_string
+
def evaluate_one_shot(model, xloader, api, cal_mode, seed=111):
+ print ('This is an old version of codes to use NAS-Bench-API, and should be modified to align with the new version. Please contact me for more details if you use this function.')
weights = deepcopy(model.state_dict())
model.train(cal_mode)
with torch.no_grad():