Update visualization codees for WS.

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 --learning_rate 0.001 
+# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.01 
-# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.001 
+# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.01 
-# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.001 
+# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.01 
-# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.001 
+# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.01 
-# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.001 
+# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.01 
-# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.001 
+# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.01 
 #####################################################################################################
 import os, sys, time, glob, random, argparse
 import numpy as np, collections

exps/algos-v2/run-all.sh

@@ -11,7 +11,7 @@ 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/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.01
     python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
     python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
     python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3

exps/algos-v2/search-cell.py

@@ -399,6 +399,9 @@ def main(xargs):
     logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
 
     network.set_drop_path(float(epoch+1) / total_epoch, xargs.drop_path_rate)
+    if xargs.algo == 'gdas':
+      network.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
+      logger.log('[Reset tau as : {:}'.format(network.tau))
     search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
                 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, xargs.algo, logger)
     search_time.update(time.time() - start_time)
@@ -480,6 +483,9 @@ 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,   default='tss', choices=['tss'], help='The search space name.')
   parser.add_argument('--algo'        ,       type=str,   choices=['darts-v1', 'darts-v2', 'gdas', 'setn', 'random', 'enas'], help='The search space name.')
+  # FOR GDAS
+  parser.add_argument('--tau_min',            type=float, default=0.1,  help='The minimum tau for Gumbel Softmax.')
+  parser.add_argument('--tau_max',            type=float, default=10,   help='The maximum tau for Gumbel Softmax.')
   # channels and number-of-cells
   parser.add_argument('--max_nodes'   ,       type=int,   default=4,  help='The maximum number of nodes.')
   parser.add_argument('--channel'     ,       type=int,   default=16, help='The number of channels.')

exps/experimental/vis-bench-algos.py

@@ -30,7 +30,7 @@ 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'
+  alg2name['REINFORCE'] = 'REINFORCE-0.01'
   alg2name['RANDOM'] = 'RANDOM'
   alg2name['BOHB'] = 'BOHB'
   for alg, name in alg2name.items():

exps/experimental/vis-bench-ws.py

@@ -0,0 +1,126 @@
+###############################################################
+# 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-ws.py --search_space tss
+# Usage: python exps/experimental/vis-bench-ws.py --search_space sss
+###############################################################
+import os, gc, 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()
+  seeds = [777]
+  alg2name['GDAS'] = 'gdas-affine1_BN0-None'
+  """
+  alg2name['DARTS (1st)'] = 'darts-v1-affine1_BN0-None'
+  alg2name['DARTS (2nd)'] = 'darts-v2-affine1_BN0-None'
+  alg2name['SETN'] = 'setn-affine1_BN0-None'
+  alg2name['RSPS'] = 'random-affine1_BN0-None'
+  """
+  for alg, name in alg2name.items():
+    alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
+  alg2data = OrderedDict()
+  for alg, path in alg2path.items():
+    alg2data[alg] = []
+    for seed in seeds:
+      xpath = path.format(seed)
+      assert os.path.isfile(xpath), 'invalid path : {:}'.format(xpath)
+      data = torch.load(xpath, map_location=torch.device('cpu'))
+      data = torch.load(data['last_checkpoint'], map_location=torch.device('cpu'))
+      alg2data[alg].append(data['genotypes'])
+  return alg2data
+
+
+y_min_s = {('cifar10', 'tss'): 90,
+           ('cifar10', 'sss'): 92,
+           ('cifar100', 'tss'): 65,
+           ('cifar100', 'sss'): 65,
+           ('ImageNet16-120', 'tss'): 36,
+           ('ImageNet16-120', 'sss'): 40}
+
+y_max_s = {('cifar10', 'tss'): 94.5,
+           ('cifar10', 'sss'): 93.3,
+           ('cifar100', 'tss'): 72,
+           ('cifar100', 'sss'): 70,
+           ('ImageNet16-120', 'tss'): 44,
+           ('ImageNet16-120', 'sss'): 46}
+
+def visualize_curve(api, vis_save_dir, search_space):
+  vis_save_dir = vis_save_dir.resolve()
+  vis_save_dir.mkdir(parents=True, exist_ok=True)
+
+  dpi, width, height = 250, 5200, 1400
+  figsize = width / float(dpi), height / float(dpi)
+  LabelSize, LegendFontsize = 16, 16
+
+  def sub_plot_fn(ax, dataset):
+    alg2data = fetch_data(search_space=search_space, dataset=dataset)
+    alg2accuracies = OrderedDict()
+    epochs = 20
+    colors = ['b', 'g', 'c', 'm', 'y']
+    ax.set_xlim(0, epochs)
+    # ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
+    for idx, (alg, data) in enumerate(alg2data.items()):
+      print('plot alg : {:}'.format(alg))
+      xs, accuracies = [], []
+      for iepoch in range(epochs+1):
+        structures, accs = [_[iepoch-1] for _ in data], []
+        for structure in structures:
+          info = api.get_more_info(structure, dataset=dataset, hp=90 if isinstance(api, NASBench301API) else 200, is_random=False)
+          accs.append(info['test-accuracy'])
+        accuracies.append(sum(accs)/len(accs))
+        xs.append(iepoch)
+      alg2accuracies[alg] = accuracies
+      ax.plot(xs, accuracies, c=colors[idx], label='{:}'.format(alg))
+      ax.set_xlabel('The searching epoch', fontsize=LabelSize)
+      ax.set_ylabel('Test accuracy on {:}'.format(dataset), fontsize=LabelSize)
+      ax.set_title('Searching results on {:}'.format(dataset), fontsize=LabelSize+4)
+    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 / '{:}-ws-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('--search_space', type=str,   default='tss', choices=['tss', 'sss'], help='Choose the search space.')
+  args = parser.parse_args()
+
+  save_dir = Path(args.save_dir)
+  alg2data = fetch_data(search_space='tss', dataset='cifar10')
+
+  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))
+  visualize_curve(api, save_dir, args.search_space)