Reformulate via black
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D-X-Y
@@ -12,119 +12,132 @@ import numpy as np
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from typing import List, Text, Dict, Any
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from shutil import copyfile
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from collections import defaultdict, OrderedDict
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from copy import deepcopy
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from copy import deepcopy
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from pathlib import Path
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import matplotlib
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import seaborn as sns
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matplotlib.use('agg')
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matplotlib.use("agg")
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import dict2config, load_config
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from log_utils import time_string
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from models import get_cell_based_tiny_net
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from nats_bench import create
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name2label = {'cifar10': 'CIFAR-10',
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'cifar100': 'CIFAR-100',
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'ImageNet16-120': 'ImageNet-16-120'}
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name2label = {"cifar10": "CIFAR-10", "cifar100": "CIFAR-100", "ImageNet16-120": "ImageNet-16-120"}
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def visualize_relative_info(vis_save_dir, search_space, indicator, topk):
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vis_save_dir = vis_save_dir.resolve()
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print ('{:} start to visualize {:} with top-{:} information'.format(time_string(), search_space, topk))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / 'cache-{:}-info.pth'.format(search_space)
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datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
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if not cache_file_path.exists():
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api = create(None, search_space, fast_mode=False, verbose=False)
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all_infos = OrderedDict()
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for index in range(len(api)):
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all_info = OrderedDict()
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for dataset in datasets:
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info_less = api.get_more_info(index, dataset, hp='12', is_random=False)
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info_more = api.get_more_info(index, dataset, hp=api.full_train_epochs, is_random=False)
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all_info[dataset] = dict(less=info_less['test-accuracy'],
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more=info_more['test-accuracy'])
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all_infos[index] = all_info
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torch.save(all_infos, cache_file_path)
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print ('{:} save all cache data into {:}'.format(time_string(), cache_file_path))
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else:
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api = create(None, search_space, fast_mode=True, verbose=False)
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all_infos = torch.load(cache_file_path)
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vis_save_dir = vis_save_dir.resolve()
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print("{:} start to visualize {:} with top-{:} information".format(time_string(), search_space, topk))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / "cache-{:}-info.pth".format(search_space)
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datasets = ["cifar10", "cifar100", "ImageNet16-120"]
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if not cache_file_path.exists():
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api = create(None, search_space, fast_mode=False, verbose=False)
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all_infos = OrderedDict()
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for index in range(len(api)):
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all_info = OrderedDict()
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for dataset in datasets:
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info_less = api.get_more_info(index, dataset, hp="12", is_random=False)
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info_more = api.get_more_info(index, dataset, hp=api.full_train_epochs, is_random=False)
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all_info[dataset] = dict(less=info_less["test-accuracy"], more=info_more["test-accuracy"])
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all_infos[index] = all_info
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torch.save(all_infos, cache_file_path)
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print("{:} save all cache data into {:}".format(time_string(), cache_file_path))
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else:
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api = create(None, search_space, fast_mode=True, verbose=False)
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all_infos = torch.load(cache_file_path)
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dpi, width, height = 250, 5000, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 16, 16
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fig, axs = plt.subplots(1, 3, figsize=figsize)
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datasets = ["cifar10", "cifar100", "ImageNet16-120"]
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def sub_plot_fn(ax, dataset, indicator):
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performances = []
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# pickup top 10% architectures
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for _index in range(len(api)):
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performances.append((all_infos[_index][dataset][indicator], _index))
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performances = sorted(performances, reverse=True)
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performances = performances[: int(len(api) * topk * 0.01)]
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selected_indexes = [x[1] for x in performances]
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print(
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"{:} plot {:10s} with {:}, {:} architectures".format(
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time_string(), dataset, indicator, len(selected_indexes)
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)
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)
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standard_scores = []
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random_scores = []
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for idx in selected_indexes:
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standard_scores.append(
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api.get_more_info(
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idx, dataset, hp=api.full_train_epochs if indicator == "more" else "12", is_random=False
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)["test-accuracy"]
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)
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random_scores.append(
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api.get_more_info(
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idx, dataset, hp=api.full_train_epochs if indicator == "more" else "12", is_random=True
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)["test-accuracy"]
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)
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indexes = list(range(len(selected_indexes)))
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standard_indexes = sorted(indexes, key=lambda i: standard_scores[i])
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random_indexes = sorted(indexes, key=lambda i: random_scores[i])
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random_labels = []
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for idx in standard_indexes:
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random_labels.append(random_indexes.index(idx))
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for tick in ax.get_xticklabels():
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tick.set_fontsize(LabelSize - 3)
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for tick in ax.get_yticklabels():
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tick.set_rotation(25)
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tick.set_fontsize(LabelSize - 3)
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ax.set_xlim(0, len(indexes))
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ax.set_ylim(0, len(indexes))
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ax.set_yticks(np.arange(min(indexes), max(indexes), max(indexes) // 3))
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ax.set_xticks(np.arange(min(indexes), max(indexes), max(indexes) // 5))
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ax.scatter(indexes, random_labels, marker="^", s=0.5, c="tab:green", alpha=0.8)
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ax.scatter(indexes, indexes, marker="o", s=0.5, c="tab:blue", alpha=0.8)
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ax.scatter([-1], [-1], marker="o", s=100, c="tab:blue", label="Average Over Multi-Trials")
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ax.scatter([-1], [-1], marker="^", s=100, c="tab:green", label="Randomly Selected Trial")
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coef, p = scipy.stats.kendalltau(standard_scores, random_scores)
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ax.set_xlabel("architecture ranking in {:}".format(name2label[dataset]), fontsize=LabelSize)
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if dataset == "cifar10":
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ax.set_ylabel("architecture ranking", fontsize=LabelSize)
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ax.legend(loc=4, fontsize=LegendFontsize)
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return coef
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for dataset, ax in zip(datasets, axs):
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rank_coef = sub_plot_fn(ax, dataset, indicator)
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print("sub-plot {:} on {:} done, the ranking coefficient is {:.4f}.".format(dataset, search_space, rank_coef))
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save_path = (vis_save_dir / "{:}-rank-{:}-top{:}.pdf".format(search_space, indicator, topk)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (vis_save_dir / "{:}-rank-{:}-top{:}.png".format(search_space, indicator, topk)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("Save into {:}".format(save_path))
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dpi, width, height = 250, 5000, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 16, 16
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="NATS-Bench", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument(
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"--save_dir",
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type=str,
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default="output/vis-nas-bench/rank-stability",
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help="Folder to save checkpoints and log.",
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)
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args = parser.parse_args()
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to_save_dir = Path(args.save_dir)
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fig, axs = plt.subplots(1, 3, figsize=figsize)
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datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
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def sub_plot_fn(ax, dataset, indicator):
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performances = []
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# pickup top 10% architectures
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for _index in range(len(api)):
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performances.append((all_infos[_index][dataset][indicator], _index))
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performances = sorted(performances, reverse=True)
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performances = performances[: int(len(api) * topk * 0.01)]
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selected_indexes = [x[1] for x in performances]
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print('{:} plot {:10s} with {:}, {:} architectures'.format(time_string(), dataset, indicator, len(selected_indexes)))
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standard_scores = []
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random_scores = []
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for idx in selected_indexes:
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standard_scores.append(
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api.get_more_info(idx, dataset, hp=api.full_train_epochs if indicator == 'more' else '12', is_random=False)['test-accuracy'])
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random_scores.append(
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api.get_more_info(idx, dataset, hp=api.full_train_epochs if indicator == 'more' else '12', is_random=True)['test-accuracy'])
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indexes = list(range(len(selected_indexes)))
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standard_indexes = sorted(indexes, key=lambda i: standard_scores[i])
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random_indexes = sorted(indexes, key=lambda i: random_scores[i])
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random_labels = []
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for idx in standard_indexes:
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random_labels.append(random_indexes.index(idx))
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for tick in ax.get_xticklabels():
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tick.set_fontsize(LabelSize - 3)
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for tick in ax.get_yticklabels():
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tick.set_rotation(25)
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tick.set_fontsize(LabelSize - 3)
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ax.set_xlim(0, len(indexes))
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ax.set_ylim(0, len(indexes))
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ax.set_yticks(np.arange(min(indexes), max(indexes), max(indexes)//3))
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ax.set_xticks(np.arange(min(indexes), max(indexes), max(indexes)//5))
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ax.scatter(indexes, random_labels, marker='^', s=0.5, c='tab:green', alpha=0.8)
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ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8)
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ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='Average Over Multi-Trials')
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ax.scatter([-1], [-1], marker='^', s=100, c='tab:green', label='Randomly Selected Trial')
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coef, p = scipy.stats.kendalltau(standard_scores, random_scores)
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ax.set_xlabel('architecture ranking in {:}'.format(name2label[dataset]), fontsize=LabelSize)
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if dataset == 'cifar10':
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ax.set_ylabel('architecture ranking', fontsize=LabelSize)
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ax.legend(loc=4, fontsize=LegendFontsize)
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return coef
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for dataset, ax in zip(datasets, axs):
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rank_coef = sub_plot_fn(ax, dataset, indicator)
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print('sub-plot {:} on {:} done, the ranking coefficient is {:.4f}.'.format(dataset, search_space, rank_coef))
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save_path = (vis_save_dir / '{:}-rank-{:}-top{:}.pdf'.format(search_space, indicator, topk)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-rank-{:}-top{:}.png'.format(search_space, indicator, topk)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print('Save into {:}'.format(save_path))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/rank-stability', help='Folder to save checkpoints and log.')
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args = parser.parse_args()
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to_save_dir = Path(args.save_dir)
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for topk in [1, 5, 10, 20]:
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visualize_relative_info(to_save_dir, 'tss', 'more', topk)
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visualize_relative_info(to_save_dir, 'sss', 'less', topk)
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print ('{:} : complete running this file : {:}'.format(time_string(), __file__))
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for topk in [1, 5, 10, 20]:
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visualize_relative_info(to_save_dir, "tss", "more", topk)
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visualize_relative_info(to_save_dir, "sss", "less", topk)
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print("{:} : complete running this file : {:}".format(time_string(), __file__))
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