Update REA, REINFORCE, and RANDOM

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')

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)

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/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/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/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/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/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/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/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/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/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/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 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.set_num_threads(4)
-  torch.backends.cudnn.enabled   = True
-  torch.backends.cudnn.benchmark = False
-  torch.backends.cudnn.deterministic = True
-  torch.set_num_threads(xargs.workers)
   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('--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.')
+  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':

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 cifar10 --search_space tss --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 cifar100 --search_space tss --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 ImageNet16-120 --search_space tss --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 cifar10 --search_space sss --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 cifar100 --search_space sss --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 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.set_num_threads(4)
-  torch.backends.cudnn.enabled   = True
-  torch.backends.cudnn.benchmark = False
-  torch.backends.cudnn.deterministic = True
-  torch.set_num_threads(xargs.workers)
   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('--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('--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.')
+  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':

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

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

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)
+

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('--save_dir',    type=str, default='output/vis-nas-bench', 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()
 
+  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, to_save_dir, 'tss')
-  visualize_info(None, Path('output/vis-nas-bench/'), 'sss')
+  visualize_info(None, to_save_dir, 'sss')
-  visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
+  visualize_rank_info(None, to_save_dir, 'tss')
-  visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
+  visualize_rank_info(None, to_save_dir, 'sss')
 
-  visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
+  visualize_all_rank_info(None, to_save_dir, 'tss')
-  visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
+  visualize_all_rank_info(None, to_save_dir, 'sss')

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:

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:

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
+    latency = self.get_latency(index, dataset)
-    return valid_acc, time_cost, self._used_time
+    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."""

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():