226 lines
8.4 KiB
Python
226 lines
8.4 KiB
Python
###############################################################
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# NATS-Bench (arxiv.org/pdf/2009.00437.pdf), IEEE TPAMI 2021 #
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# The code to draw Figure 6 in our paper. #
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###############################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
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###############################################################
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# Usage: python exps/NATS-Bench/draw-fig6.py --search_space tss
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# Usage: python exps/NATS-Bench/draw-fig6.py --search_space sss
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###############################################################
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import os, gc, sys, time, torch, argparse
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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 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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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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from xautodl.config_utils import dict2config, load_config
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from xautodl.log_utils import time_string
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from nats_bench import create
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def fetch_data(root_dir="./output/search", search_space="tss", dataset=None):
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ss_dir = "{:}-{:}".format(root_dir, search_space)
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alg2name, alg2path = OrderedDict(), OrderedDict()
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alg2name["REA"] = "R-EA-SS3"
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alg2name["REINFORCE"] = "REINFORCE-0.01"
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alg2name["RANDOM"] = "RANDOM"
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alg2name["BOHB"] = "BOHB"
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for alg, name in alg2name.items():
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alg2path[alg] = os.path.join(ss_dir, dataset, name, "results.pth")
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assert os.path.isfile(alg2path[alg]), "invalid path : {:}".format(alg2path[alg])
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alg2data = OrderedDict()
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for alg, path in alg2path.items():
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data = torch.load(path)
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for index, info in data.items():
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info["time_w_arch"] = [
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(x, y) for x, y in zip(info["all_total_times"], info["all_archs"])
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]
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for j, arch in enumerate(info["all_archs"]):
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assert arch != -1, "invalid arch from {:} {:} {:} ({:}, {:})".format(
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alg, search_space, dataset, index, j
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)
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alg2data[alg] = data
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return alg2data
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def query_performance(api, data, dataset, ticket):
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results, is_size_space = [], api.search_space_name == "size"
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for i, info in data.items():
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time_w_arch = sorted(info["time_w_arch"], key=lambda x: abs(x[0] - ticket))
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time_a, arch_a = time_w_arch[0]
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time_b, arch_b = time_w_arch[1]
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info_a = api.get_more_info(
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arch_a, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
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)
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info_b = api.get_more_info(
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arch_b, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
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)
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accuracy_a, accuracy_b = info_a["test-accuracy"], info_b["test-accuracy"]
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interplate = (time_b - ticket) / (time_b - time_a) * accuracy_a + (
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ticket - time_a
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) / (time_b - time_a) * accuracy_b
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results.append(interplate)
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# return sum(results) / len(results)
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return np.mean(results), np.std(results)
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def show_valid_test(api, data, dataset):
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valid_accs, test_accs, is_size_space = [], [], api.search_space_name == "size"
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for i, info in data.items():
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time, arch = info["time_w_arch"][-1]
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if dataset == "cifar10":
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xinfo = api.get_more_info(
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arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
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)
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test_accs.append(xinfo["test-accuracy"])
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xinfo = api.get_more_info(
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arch,
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dataset="cifar10-valid",
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hp=90 if is_size_space else 200,
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is_random=False,
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)
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valid_accs.append(xinfo["valid-accuracy"])
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else:
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xinfo = api.get_more_info(
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arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
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)
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valid_accs.append(xinfo["valid-accuracy"])
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test_accs.append(xinfo["test-accuracy"])
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valid_str = "{:.2f}$\pm${:.2f}".format(np.mean(valid_accs), np.std(valid_accs))
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test_str = "{:.2f}$\pm${:.2f}".format(np.mean(test_accs), np.std(test_accs))
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return valid_str, test_str
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y_min_s = {
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("cifar10", "tss"): 90,
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("cifar10", "sss"): 92,
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("cifar100", "tss"): 65,
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("cifar100", "sss"): 65,
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("ImageNet16-120", "tss"): 36,
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("ImageNet16-120", "sss"): 40,
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}
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y_max_s = {
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("cifar10", "tss"): 94.3,
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("cifar10", "sss"): 93.3,
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("cifar100", "tss"): 72.5,
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("cifar100", "sss"): 70.5,
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("ImageNet16-120", "tss"): 46,
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("ImageNet16-120", "sss"): 46,
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}
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x_axis_s = {
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("cifar10", "tss"): 200,
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("cifar10", "sss"): 200,
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("cifar100", "tss"): 400,
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("cifar100", "sss"): 400,
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("ImageNet16-120", "tss"): 1200,
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("ImageNet16-120", "sss"): 600,
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}
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name2label = {
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"cifar10": "CIFAR-10",
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"cifar100": "CIFAR-100",
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"ImageNet16-120": "ImageNet-16-120",
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}
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def visualize_curve(api, vis_save_dir, search_space):
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vis_save_dir = vis_save_dir.resolve()
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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dpi, width, height = 250, 5200, 1400
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 16, 16
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def sub_plot_fn(ax, dataset):
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xdataset, max_time = dataset.split("-T")
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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total_tickets = 150
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time_tickets = [
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float(i) / total_tickets * int(max_time) for i in range(total_tickets)
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]
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colors = ["b", "g", "c", "m", "y"]
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ax.set_xlim(0, x_axis_s[(xdataset, search_space)])
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ax.set_ylim(
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y_min_s[(xdataset, search_space)], y_max_s[(xdataset, search_space)]
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)
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for idx, (alg, data) in enumerate(alg2data.items()):
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accuracies = []
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for ticket in time_tickets:
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accuracy, accuracy_std = query_performance(api, data, xdataset, ticket)
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accuracies.append(accuracy)
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valid_str, test_str = show_valid_test(api, data, xdataset)
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# print('{:} plot alg : {:10s}, final accuracy = {:.2f}$\pm${:.2f}'.format(time_string(), alg, accuracy, accuracy_std))
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print(
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"{:} plot alg : {:10s} | validation = {:} | test = {:}".format(
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time_string(), alg, valid_str, test_str
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)
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)
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alg2accuracies[alg] = accuracies
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ax.plot(
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[x / 100 for x in time_tickets],
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accuracies,
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c=colors[idx],
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label="{:}".format(alg),
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)
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ax.set_xlabel("Estimated wall-clock time (1e2 seconds)", fontsize=LabelSize)
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ax.set_ylabel(
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"Test accuracy on {:}".format(name2label[xdataset]), fontsize=LabelSize
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)
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ax.set_title(
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"Searching results on {:}".format(name2label[xdataset]),
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fontsize=LabelSize + 4,
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)
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ax.legend(loc=4, fontsize=LegendFontsize)
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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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if search_space == "tss":
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datasets = ["cifar10-T20000", "cifar100-T40000", "ImageNet16-120-T120000"]
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elif search_space == "sss":
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datasets = ["cifar10-T20000", "cifar100-T40000", "ImageNet16-120-T60000"]
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else:
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raise ValueError("Unknown search space: {:}".format(search_space))
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for dataset, ax in zip(datasets, axs):
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sub_plot_fn(ax, dataset)
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print("sub-plot {:} on {:} done.".format(dataset, search_space))
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save_path = (vis_save_dir / "{:}-curve.png".format(search_space)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("{:} save into {:}".format(time_string(), save_path))
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plt.close("all")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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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/nas-algos",
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help="Folder to save checkpoints and log.",
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)
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parser.add_argument(
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"--search_space",
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type=str,
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choices=["tss", "sss"],
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help="Choose the search space.",
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)
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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api = create(None, args.search_space, fast_mode=True, verbose=False)
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visualize_curve(api, save_dir, args.search_space)
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