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

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# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06           #
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# Usage: python exps/experimental/vis-nats-bench-ws.py --search_space tss
# Usage: python exps/experimental/vis-nats-bench-ws.py --search_space sss
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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, suffix='-WARMNone'):
def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-WARM0.3'):
  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{:}'.format(suffix)
    # alg2name['FBNetV2'] = 'fbv2-affine0_BN0{:}'.format(suffix)
    # alg2name['TuNAS'] = 'tunas-affine0_BN0{:}'.format(suffix)
    alg2name['channel-wise interpolation'] = 'tas-affine0_BN0-AWD0.001{:}'.format(suffix)
    alg2name['masking + Gumbel-Softmax'] = 'mask_gumbel-affine0_BN0-AWD0.001{:}'.format(suffix)
    alg2name['masking + sampling'] = 'mask_rl-affine0_BN0-AWD0.0{:}'.format(suffix)
  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):
        try:
          structures, accs = [_[iepoch-1] for _ in data], []
        except:
          raise ValueError('This alg {:} on {:} has invalid checkpoints.'.format(alg, dataset))
        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, fast_mode=True, verbose=False)
  visualize_curve(api, save_dir, args.search_space)