195 lines
7.4 KiB
Python
195 lines
7.4 KiB
Python
###############################################################
|
|
# NATS-Bench (arxiv.org/pdf/2009.00437.pdf), IEEE TPAMI 2021 #
|
|
# The code to draw some results in Table 4 in our paper. #
|
|
###############################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
|
|
###############################################################
|
|
# Usage: python exps/NATS-Bench/draw-table.py #
|
|
###############################################################
|
|
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 get_valid_test_acc(api, arch, dataset):
|
|
is_size_space = api.search_space_name == "size"
|
|
if dataset == "cifar10":
|
|
xinfo = api.get_more_info(
|
|
arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
|
|
)
|
|
test_acc = xinfo["test-accuracy"]
|
|
xinfo = api.get_more_info(
|
|
arch,
|
|
dataset="cifar10-valid",
|
|
hp=90 if is_size_space else 200,
|
|
is_random=False,
|
|
)
|
|
valid_acc = xinfo["valid-accuracy"]
|
|
else:
|
|
xinfo = api.get_more_info(
|
|
arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False
|
|
)
|
|
valid_acc = xinfo["valid-accuracy"]
|
|
test_acc = xinfo["test-accuracy"]
|
|
return (
|
|
valid_acc,
|
|
test_acc,
|
|
"validation = {:.2f}, test = {:.2f}\n".format(valid_acc, test_acc),
|
|
)
|
|
|
|
|
|
def show_valid_test(api, arch):
|
|
is_size_space = api.search_space_name == "size"
|
|
final_str = ""
|
|
for dataset in ["cifar10", "cifar100", "ImageNet16-120"]:
|
|
valid_acc, test_acc, perf_str = get_valid_test_acc(api, arch, dataset)
|
|
final_str += "{:} : {:}\n".format(dataset, perf_str)
|
|
return final_str
|
|
|
|
|
|
def find_best_valid(api, dataset):
|
|
all_valid_accs, all_test_accs = [], []
|
|
for index, arch in enumerate(api):
|
|
valid_acc, test_acc, perf_str = get_valid_test_acc(api, index, dataset)
|
|
all_valid_accs.append((index, valid_acc))
|
|
all_test_accs.append((index, test_acc))
|
|
best_valid_index = sorted(all_valid_accs, key=lambda x: -x[1])[0][0]
|
|
best_test_index = sorted(all_test_accs, key=lambda x: -x[1])[0][0]
|
|
|
|
print("-" * 50 + "{:10s}".format(dataset) + "-" * 50)
|
|
print(
|
|
"Best ({:}) architecture on validation: {:}".format(
|
|
best_valid_index, api[best_valid_index]
|
|
)
|
|
)
|
|
print(
|
|
"Best ({:}) architecture on test: {:}".format(
|
|
best_test_index, api[best_test_index]
|
|
)
|
|
)
|
|
_, _, perf_str = get_valid_test_acc(api, best_valid_index, dataset)
|
|
print("using validation ::: {:}".format(perf_str))
|
|
_, _, perf_str = get_valid_test_acc(api, best_test_index, dataset)
|
|
print("using test ::: {:}".format(perf_str))
|
|
|
|
|
|
def interplate_fn(xpair1, xpair2, x):
|
|
(x1, y1) = xpair1
|
|
(x2, y2) = xpair2
|
|
return (x2 - x) / (x2 - x1) * y1 + (x - x1) / (x2 - x1) * y2
|
|
|
|
|
|
def query_performance(api, info, dataset, ticket):
|
|
info = deepcopy(info)
|
|
results, is_size_space = [], api.search_space_name == "size"
|
|
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]
|
|
|
|
v_acc_a, t_acc_a, _ = get_valid_test_acc(api, arch_a, dataset)
|
|
v_acc_b, t_acc_b, _ = get_valid_test_acc(api, arch_b, dataset)
|
|
v_acc = interplate_fn((time_a, v_acc_a), (time_b, v_acc_b), ticket)
|
|
t_acc = interplate_fn((time_a, t_acc_a), (time_b, t_acc_b), ticket)
|
|
# if True:
|
|
# 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 v_acc, t_acc
|
|
|
|
|
|
def show_multi_trial(search_space):
|
|
api = create(None, search_space, fast_mode=True, verbose=False)
|
|
|
|
def show(dataset):
|
|
print("show {:} on {:} done.".format(dataset, search_space))
|
|
xdataset, max_time = dataset.split("-T")
|
|
alg2data = fetch_data(search_space=search_space, dataset=dataset)
|
|
for idx, (alg, data) in enumerate(alg2data.items()):
|
|
|
|
valid_accs, test_accs = [], []
|
|
for _, x in data.items():
|
|
v_acc, t_acc = query_performance(api, x, xdataset, float(max_time))
|
|
valid_accs.append(v_acc)
|
|
test_accs.append(t_acc)
|
|
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))
|
|
print(
|
|
"{:} plot alg : {:10s} | validation = {:} | test = {:}".format(
|
|
time_string(), alg, valid_str, test_str
|
|
)
|
|
)
|
|
|
|
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 in datasets:
|
|
show(dataset)
|
|
print("{:} complete show multi-trial results.\n".format(time_string()))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
show_multi_trial("tss")
|
|
show_multi_trial("sss")
|
|
|
|
api_tss = create(None, "tss", fast_mode=False, verbose=False)
|
|
resnet = "|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|"
|
|
resnet_index = api_tss.query_index_by_arch(resnet)
|
|
print(show_valid_test(api_tss, resnet_index))
|
|
|
|
for dataset in ["cifar10", "cifar100", "ImageNet16-120"]:
|
|
find_best_valid(api_tss, dataset)
|
|
|
|
largest = "64:64:64:64:64"
|
|
largest_index = api_sss.query_index_by_arch(largest)
|
|
print(show_valid_test(api_sss, largest_index))
|
|
for dataset in ["cifar10", "cifar100", "ImageNet16-120"]:
|
|
find_best_valid(api_sss, dataset)
|