Create NATS

exps/experimental/vis-nats-bench-ws.py

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+###############################################################
+# 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-nats-bench-ws.py --search_space tss
+# Usage: python exps/experimental/vis-nats-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 nats_bench import create
+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, 888, 999]
+  print('\n[fetch data] from {:} on {:}'.format(search_space, dataset))
+  if search_space == 'tss':
+    alg2name['GDAS'] = 'gdas-affine0_BN0-None'
+    alg2name['RSPS'] = 'random-affine0_BN0-None'
+    alg2name['DARTS (1st)'] = 'darts-v1-affine0_BN0-None'
+    alg2name['DARTS (2nd)'] = 'darts-v2-affine0_BN0-None'
+    alg2name['ENAS'] = 'enas-affine0_BN0-None'
+    alg2name['SETN'] = 'setn-affine0_BN0-None'
+  else:
+    alg2name['TAS'] = 'tas-affine0_BN0'
+    alg2name['FBNetV2'] = 'fbv2-affine0_BN0'
+    alg2name['TuNAS'] = 'tunas-affine0_BN0'
+  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], ok_num = [], 0
+    for seed in seeds:
+      xpath = path.format(seed)
+      if os.path.isfile(xpath):
+        ok_num += 1
+      else:
+        print('This is an invalid path : {:}'.format(xpath))
+        continue
+      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'])
+    print('This algorithm : {:} has {:} valid ckps.'.format(alg, ok_num))
+    assert ok_num > 0, 'Must have at least 1 valid ckps.'
+  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}
+
+name2label = {'cifar10': 'CIFAR-10',
+              'cifar100': 'CIFAR-100',
+              'ImageNet16-120': 'ImageNet-16-120'}
+
+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 = 100
+    colors = ['b', 'g', 'c', 'm', 'y', 'r']
+    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 api.search_space_name == 'size' 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(name2label[dataset]), fontsize=LabelSize)
+      ax.set_title('Searching results on {:}'.format(name2label[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)
+
+  api = create(None, args.search_space, verbose=False)
+  visualize_curve(api, save_dir, args.search_space)