229 lines
8.6 KiB
Python
229 lines
8.6 KiB
Python
###############################################################
|
|
# NATS-Bench (arxiv.org/pdf/2009.00437.pdf), IEEE TPAMI 2021 #
|
|
# The code to draw Figure 6 in our paper. #
|
|
###############################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
|
|
###############################################################
|
|
# Usage: python exps/NATS-Bench/draw-fig6.py --search_space tss
|
|
# Usage: python exps/NATS-Bench/draw-fig6.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()
|
|
alg2name["REA"] = "R-EA-SS3"
|
|
alg2name["REINFORCE"] = "REINFORCE-0.01"
|
|
alg2name["RANDOM"] = "RANDOM"
|
|
alg2name["BOHB"] = "BOHB"
|
|
for alg, name in alg2name.items():
|
|
alg2path[alg] = os.path.join(ss_dir, dataset, name, "results.pth")
|
|
assert os.path.isfile(alg2path[alg]), "invalid path : {:}".format(alg2path[alg])
|
|
alg2data = OrderedDict()
|
|
for alg, path in alg2path.items():
|
|
data = torch.load(path)
|
|
for index, info in data.items():
|
|
info["time_w_arch"] = [
|
|
(x, y) for x, y in zip(info["all_total_times"], info["all_archs"])
|
|
]
|
|
for j, arch in enumerate(info["all_archs"]):
|
|
assert arch != -1, "invalid arch from {:} {:} {:} ({:}, {:})".format(
|
|
alg, search_space, dataset, index, j
|
|
)
|
|
alg2data[alg] = data
|
|
return alg2data
|
|
|
|
|
|
def query_performance(api, data, dataset, ticket):
|
|
results, is_size_space = [], api.search_space_name == "size"
|
|
for i, info in data.items():
|
|
time_w_arch = sorted(info["time_w_arch"], key=lambda x: abs(x[0] - ticket))
|
|
time_a, arch_a = time_w_arch[0]
|
|
time_b, arch_b = time_w_arch[1]
|
|
info_a = api.get_more_info(
|
|
arch_a, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
|
|
)
|
|
info_b = api.get_more_info(
|
|
arch_b, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
|
|
)
|
|
accuracy_a, accuracy_b = info_a["test-accuracy"], info_b["test-accuracy"]
|
|
interplate = (time_b - ticket) / (time_b - time_a) * accuracy_a + (
|
|
ticket - time_a
|
|
) / (time_b - time_a) * accuracy_b
|
|
results.append(interplate)
|
|
# return sum(results) / len(results)
|
|
return np.mean(results), np.std(results)
|
|
|
|
|
|
def show_valid_test(api, data, dataset):
|
|
valid_accs, test_accs, is_size_space = [], [], api.search_space_name == "size"
|
|
for i, info in data.items():
|
|
time, arch = info["time_w_arch"][-1]
|
|
if dataset == "cifar10":
|
|
xinfo = api.get_more_info(
|
|
arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
|
|
)
|
|
test_accs.append(xinfo["test-accuracy"])
|
|
xinfo = api.get_more_info(
|
|
arch,
|
|
dataset="cifar10-valid",
|
|
hp=90 if is_size_space else 200,
|
|
is_random=False,
|
|
)
|
|
valid_accs.append(xinfo["valid-accuracy"])
|
|
else:
|
|
xinfo = api.get_more_info(
|
|
arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
|
|
)
|
|
valid_accs.append(xinfo["valid-accuracy"])
|
|
test_accs.append(xinfo["test-accuracy"])
|
|
valid_str = "{:.2f}$\pm${:.2f}".format(np.mean(valid_accs), np.std(valid_accs))
|
|
test_str = "{:.2f}$\pm${:.2f}".format(np.mean(test_accs), np.std(test_accs))
|
|
return valid_str, test_str
|
|
|
|
|
|
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"): 94.3,
|
|
("cifar10", "sss"): 93.3,
|
|
("cifar100", "tss"): 72.5,
|
|
("cifar100", "sss"): 70.5,
|
|
("ImageNet16-120", "tss"): 46,
|
|
("ImageNet16-120", "sss"): 46,
|
|
}
|
|
|
|
x_axis_s = {
|
|
("cifar10", "tss"): 200,
|
|
("cifar10", "sss"): 200,
|
|
("cifar100", "tss"): 400,
|
|
("cifar100", "sss"): 400,
|
|
("ImageNet16-120", "tss"): 1200,
|
|
("ImageNet16-120", "sss"): 600,
|
|
}
|
|
|
|
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):
|
|
xdataset, max_time = dataset.split("-T")
|
|
alg2data = fetch_data(search_space=search_space, dataset=dataset)
|
|
alg2accuracies = OrderedDict()
|
|
total_tickets = 150
|
|
time_tickets = [
|
|
float(i) / total_tickets * int(max_time) for i in range(total_tickets)
|
|
]
|
|
colors = ["b", "g", "c", "m", "y"]
|
|
ax.set_xlim(0, x_axis_s[(xdataset, search_space)])
|
|
ax.set_ylim(
|
|
y_min_s[(xdataset, search_space)], y_max_s[(xdataset, search_space)]
|
|
)
|
|
for idx, (alg, data) in enumerate(alg2data.items()):
|
|
accuracies = []
|
|
for ticket in time_tickets:
|
|
accuracy, accuracy_std = query_performance(api, data, xdataset, ticket)
|
|
accuracies.append(accuracy)
|
|
valid_str, test_str = show_valid_test(api, data, xdataset)
|
|
# print('{:} plot alg : {:10s}, final accuracy = {:.2f}$\pm${:.2f}'.format(time_string(), alg, accuracy, accuracy_std))
|
|
print(
|
|
"{:} plot alg : {:10s} | validation = {:} | test = {:}".format(
|
|
time_string(), alg, valid_str, test_str
|
|
)
|
|
)
|
|
alg2accuracies[alg] = accuracies
|
|
ax.plot(
|
|
[x / 100 for x in time_tickets],
|
|
accuracies,
|
|
c=colors[idx],
|
|
label="{:}".format(alg),
|
|
)
|
|
ax.set_xlabel("Estimated wall-clock time (1e2 seconds)", fontsize=LabelSize)
|
|
ax.set_ylabel(
|
|
"Test accuracy on {:}".format(name2label[xdataset]), fontsize=LabelSize
|
|
)
|
|
ax.set_title(
|
|
"Searching results on {:}".format(name2label[xdataset]),
|
|
fontsize=LabelSize + 4,
|
|
)
|
|
ax.legend(loc=4, fontsize=LegendFontsize)
|
|
|
|
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
|
# datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
|
|
if search_space == "tss":
|
|
datasets = ["cifar10-T20000", "cifar100-T40000", "ImageNet16-120-T120000"]
|
|
elif search_space == "sss":
|
|
datasets = ["cifar10-T20000", "cifar100-T40000", "ImageNet16-120-T60000"]
|
|
else:
|
|
raise ValueError("Unknown search space: {:}".format(search_space))
|
|
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 / "{:}-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="NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size",
|
|
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,
|
|
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)
|