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2024-08-25 18:02:31 +02:00
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137
AutoDL-Projects/exps/NAS-Bench-201/check.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
# python exps/NAS-Bench-201/check.py --base_str C16-N5-LESS
#####################################################
import sys, time, argparse, collections
import torch
from pathlib import Path
from collections import defaultdict
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
def check_files(save_dir, meta_file, basestr):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"]
meta_num_archs = meta_infos["total"]
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
num_seeds = defaultdict(lambda: 0)
for index, sub_dir in enumerate(sub_model_dirs):
xcheckpoints = list(sub_dir.glob("arch-*-seed-*.pth"))
# xcheckpoints = list(sub_dir.glob('arch-*-seed-0777.pth')) + list(sub_dir.glob('arch-*-seed-0888.pth')) + list(sub_dir.glob('arch-*-seed-0999.pth'))
arch_indexes = set()
for checkpoint in xcheckpoints:
temp_names = checkpoint.name.split("-")
assert (
len(temp_names) == 4
and temp_names[0] == "arch"
and temp_names[2] == "seed"
), "invalid checkpoint name : {:}".format(checkpoint.name)
arch_indexes.add(temp_names[1])
subdir2archs[sub_dir] = sorted(list(arch_indexes))
num_evaluated_arch += len(arch_indexes)
# count number of seeds for each architecture
for arch_index in arch_indexes:
num_seeds[
len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))
] += 1
print(
"There are {:5d} architectures that have been evaluated ({:} in total, {:} ckps in total).".format(
num_evaluated_arch, meta_num_archs, sum(k * v for k, v in num_seeds.items())
)
)
for key in sorted(list(num_seeds.keys())):
print(
"There are {:5d} architectures that are evaluated {:} times.".format(
num_seeds[key], key
)
)
dir2ckps, dir2ckp_exists = dict(), dict()
start_time, epoch_time = time.time(), AverageMeter()
for IDX, (sub_dir, arch_indexes) in enumerate(subdir2archs.items()):
if basestr == "C16-N5":
seeds = [777, 888, 999]
elif basestr == "C16-N5-LESS":
seeds = [111, 777]
else:
raise ValueError("Invalid base str : {:}".format(basestr))
numrs = defaultdict(lambda: 0)
all_checkpoints, all_ckp_exists = [], []
for arch_index in arch_indexes:
checkpoints = [
"arch-{:}-seed-{:04d}.pth".format(arch_index, seed) for seed in seeds
]
ckp_exists = [(sub_dir / x).exists() for x in checkpoints]
arch_index = int(arch_index)
assert (
0 <= arch_index < len(meta_archs)
), "invalid arch-index {:} (not found in meta_archs)".format(arch_index)
all_checkpoints += checkpoints
all_ckp_exists += ckp_exists
numrs[sum(ckp_exists)] += 1
dir2ckps[str(sub_dir)] = all_checkpoints
dir2ckp_exists[str(sub_dir)] = all_ckp_exists
# measure time
epoch_time.update(time.time() - start_time)
start_time = time.time()
numrstr = ", ".join(
["{:}: {:03d}".format(x, numrs[x]) for x in sorted(numrs.keys())]
)
print(
"{:} load [{:2d}/{:2d}] [{:03d} archs] [{:04d}->{:04d} ckps] {:} done, need {:}. {:}".format(
time_string(),
IDX + 1,
len(subdir2archs),
len(arch_indexes),
len(all_checkpoints),
sum(all_ckp_exists),
sub_dir,
convert_secs2time(epoch_time.avg * (len(subdir2archs) - IDX - 1), True),
numrstr,
)
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NAS Benchmark 201",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--base_save_dir",
type=str,
default="./output/NAS-BENCH-201-4",
help="The base-name of folder to save checkpoints and log.",
)
parser.add_argument(
"--meta_path",
type=str,
default="./output/NAS-BENCH-201-4/meta-node-4.pth",
help="The meta file path.",
)
parser.add_argument(
"--base_str", type=str, default="C16-N5", help="The basic string."
)
args = parser.parse_args()
save_dir = Path(args.base_save_dir)
meta_path = Path(args.meta_path)
assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir)
assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path)
print("check NAS-Bench-201 in {:}".format(save_dir))
check_files(save_dir, meta_path, args.base_str)

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AutoDL-Projects/exps/NAS-Bench-201/dist-setup.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
# [2020.02.25] Initialize the API as v1.1
# [2020.03.09] Upgrade the API to v1.2
# [2020.03.16] Upgrade the API to v1.3
# [2020.06.30] Upgrade the API to v2.0
import os
from setuptools import setup
def read(fname="README.md"):
with open(
os.path.join(os.path.dirname(__file__), fname), encoding="utf-8"
) as cfile:
return cfile.read()
setup(
name="nas_bench_201",
version="2.0",
author="Xuanyi Dong",
author_email="dongxuanyi888@gmail.com",
description="API for NAS-Bench-201 (a benchmark for neural architecture search).",
license="MIT",
keywords="NAS Dataset API DeepLearning",
url="https://github.com/D-X-Y/NAS-Bench-201",
packages=["nas_201_api"],
long_description=read("README.md"),
long_description_content_type="text/markdown",
classifiers=[
"Programming Language :: Python",
"Topic :: Database",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"License :: OSI Approved :: MIT License",
],
)

182
AutoDL-Projects/exps/NAS-Bench-201/functions.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import time, torch
from procedures import prepare_seed, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from config_utils import dict2config
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net
__all__ = ["evaluate_for_seed", "pure_evaluate"]
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
latencies = []
network.eval()
with torch.no_grad():
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
targets = targets.cuda(non_blocking=True)
inputs = inputs.cuda(non_blocking=True)
data_time.update(time.time() - end)
# forward
features, logits = network(inputs)
loss = criterion(logits, targets)
batch_time.update(time.time() - end)
if batch is None or batch == inputs.size(0):
batch = inputs.size(0)
latencies.append(batch_time.val - data_time.val)
# record loss and accuracy
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
end = time.time()
if len(latencies) > 2:
latencies = latencies[1:]
return losses.avg, top1.avg, top5.avg, latencies
def procedure(xloader, network, criterion, scheduler, optimizer, mode):
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
if mode == "train":
network.train()
elif mode == "valid":
network.eval()
else:
raise ValueError("The mode is not right : {:}".format(mode))
data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
for i, (inputs, targets) in enumerate(xloader):
if mode == "train":
scheduler.update(None, 1.0 * i / len(xloader))
targets = targets.cuda(non_blocking=True)
if mode == "train":
optimizer.zero_grad()
# forward
features, logits = network(inputs)
loss = criterion(logits, targets)
# backward
if mode == "train":
loss.backward()
optimizer.step()
# record loss and accuracy
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
# count time
batch_time.update(time.time() - end)
end = time.time()
return losses.avg, top1.avg, top5.avg, batch_time.sum
def evaluate_for_seed(
arch_config, config, arch, train_loader, valid_loaders, seed, logger
):
prepare_seed(seed) # random seed
net = get_cell_based_tiny_net(
dict2config(
{
"name": "infer.tiny",
"C": arch_config["channel"],
"N": arch_config["num_cells"],
"genotype": arch,
"num_classes": config.class_num,
},
None,
)
)
# net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
flop, param = get_model_infos(net, config.xshape)
logger.log("Network : {:}".format(net.get_message()), False)
logger.log(
"{:} Seed-------------------------- {:} --------------------------".format(
time_string(), seed
)
)
logger.log("FLOP = {:} MB, Param = {:} MB".format(flop, param))
# train and valid
optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), config)
network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
# start training
start_time, epoch_time, total_epoch = (
time.time(),
AverageMeter(),
config.epochs + config.warmup,
)
(
train_losses,
train_acc1es,
train_acc5es,
valid_losses,
valid_acc1es,
valid_acc5es,
) = ({}, {}, {}, {}, {}, {})
train_times, valid_times = {}, {}
for epoch in range(total_epoch):
scheduler.update(epoch, 0.0)
train_loss, train_acc1, train_acc5, train_tm = procedure(
train_loader, network, criterion, scheduler, optimizer, "train"
)
train_losses[epoch] = train_loss
train_acc1es[epoch] = train_acc1
train_acc5es[epoch] = train_acc5
train_times[epoch] = train_tm
with torch.no_grad():
for key, xloder in valid_loaders.items():
valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(
xloder, network, criterion, None, None, "valid"
)
valid_losses["{:}@{:}".format(key, epoch)] = valid_loss
valid_acc1es["{:}@{:}".format(key, epoch)] = valid_acc1
valid_acc5es["{:}@{:}".format(key, epoch)] = valid_acc5
valid_times["{:}@{:}".format(key, epoch)] = valid_tm
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True)
)
logger.log(
"{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%]".format(
time_string(),
need_time,
epoch,
total_epoch,
train_loss,
train_acc1,
train_acc5,
valid_loss,
valid_acc1,
valid_acc5,
)
)
info_seed = {
"flop": flop,
"param": param,
"channel": arch_config["channel"],
"num_cells": arch_config["num_cells"],
"config": config._asdict(),
"total_epoch": total_epoch,
"train_losses": train_losses,
"train_acc1es": train_acc1es,
"train_acc5es": train_acc5es,
"train_times": train_times,
"valid_losses": valid_losses,
"valid_acc1es": valid_acc1es,
"valid_acc5es": valid_acc5es,
"valid_times": valid_times,
"net_state_dict": net.state_dict(),
"net_string": "{:}".format(net),
"finish-train": True,
}
return info_seed

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AutoDL-Projects/exps/NAS-Bench-201/main.py Normal file
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###############################################################
# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
###############################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
###############################################################
import os, sys, time, torch, random, argparse
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy import deepcopy
from pathlib import Path
from xautodl.config_utils import load_config
from xautodl.procedures import save_checkpoint, copy_checkpoint
from xautodl.procedures import get_machine_info
from xautodl.datasets import get_datasets
from xautodl.log_utils import Logger, AverageMeter, time_string, convert_secs2time
from xautodl.models import CellStructure, CellArchitectures, get_search_spaces
from xautodl.functions import evaluate_for_seed
def evaluate_all_datasets(
arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
):
machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
all_infos = {"info": machine_info}
all_dataset_keys = []
# look all the datasets
for dataset, xpath, split in zip(datasets, xpaths, splits):
# train valid data
train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
# load the configuration
if dataset == "cifar10" or dataset == "cifar100":
if use_less:
config_path = "configs/nas-benchmark/LESS.config"
else:
config_path = "configs/nas-benchmark/CIFAR.config"
split_info = load_config(
"configs/nas-benchmark/cifar-split.txt", None, None
)
elif dataset.startswith("ImageNet16"):
if use_less:
config_path = "configs/nas-benchmark/LESS.config"
else:
config_path = "configs/nas-benchmark/ImageNet-16.config"
split_info = load_config(
"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
)
else:
raise ValueError("invalid dataset : {:}".format(dataset))
config = load_config(
config_path, {"class_num": class_num, "xshape": xshape}, logger
)
# check whether use splited validation set
if bool(split):
assert dataset == "cifar10"
ValLoaders = {
"ori-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
}
assert len(train_data) == len(split_info.train) + len(
split_info.valid
), "invalid length : {:} vs {:} + {:}".format(
len(train_data), len(split_info.train), len(split_info.valid)
)
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
# data loader
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
num_workers=workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
num_workers=workers,
pin_memory=True,
)
ValLoaders["x-valid"] = valid_loader
else:
# data loader
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
if dataset == "cifar10":
ValLoaders = {"ori-test": valid_loader}
elif dataset == "cifar100":
cifar100_splits = load_config(
"configs/nas-benchmark/cifar100-test-split.txt", None, None
)
ValLoaders = {
"ori-test": valid_loader,
"x-valid": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
cifar100_splits.xvalid
),
num_workers=workers,
pin_memory=True,
),
"x-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
cifar100_splits.xtest
),
num_workers=workers,
pin_memory=True,
),
}
elif dataset == "ImageNet16-120":
imagenet16_splits = load_config(
"configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None
)
ValLoaders = {
"ori-test": valid_loader,
"x-valid": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
imagenet16_splits.xvalid
),
num_workers=workers,
pin_memory=True,
),
"x-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
imagenet16_splits.xtest
),
num_workers=workers,
pin_memory=True,
),
}
else:
raise ValueError("invalid dataset : {:}".format(dataset))
dataset_key = "{:}".format(dataset)
if bool(split):
dataset_key = dataset_key + "-valid"
logger.log(
"Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
dataset_key,
len(train_data),
len(valid_data),
len(train_loader),
len(valid_loader),
config.batch_size,
)
)
logger.log(
"Evaluate ||||||| {:10s} ||||||| Config={:}".format(dataset_key, config)
)
for key, value in ValLoaders.items():
logger.log(
"Evaluate ---->>>> {:10s} with {:} batchs".format(key, len(value))
)
results = evaluate_for_seed(
arch_config, config, arch, train_loader, ValLoaders, seed, logger
)
all_infos[dataset_key] = results
all_dataset_keys.append(dataset_key)
all_infos["all_dataset_keys"] = all_dataset_keys
return all_infos
def main(
save_dir,
workers,
datasets,
xpaths,
splits,
use_less,
srange,
arch_index,
seeds,
cover_mode,
meta_info,
arch_config,
):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
# torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
torch.set_num_threads(workers)
assert (
len(srange) == 2 and 0 <= srange[0] <= srange[1]
), "invalid srange : {:}".format(srange)
if use_less:
sub_dir = Path(save_dir) / "{:06d}-{:06d}-C{:}-N{:}-LESS".format(
srange[0], srange[1], arch_config["channel"], arch_config["num_cells"]
)
else:
sub_dir = Path(save_dir) / "{:06d}-{:06d}-C{:}-N{:}".format(
srange[0], srange[1], arch_config["channel"], arch_config["num_cells"]
)
logger = Logger(str(sub_dir), 0, False)
all_archs = meta_info["archs"]
assert srange[1] < meta_info["total"], "invalid range : {:}-{:} vs. {:}".format(
srange[0], srange[1], meta_info["total"]
)
assert (
arch_index == -1 or srange[0] <= arch_index <= srange[1]
), "invalid range : {:} vs. {:} vs. {:}".format(srange[0], arch_index, srange[1])
if arch_index == -1:
to_evaluate_indexes = list(range(srange[0], srange[1] + 1))
else:
to_evaluate_indexes = [arch_index]
logger.log("xargs : seeds = {:}".format(seeds))
logger.log("xargs : arch_index = {:}".format(arch_index))
logger.log("xargs : cover_mode = {:}".format(cover_mode))
logger.log("-" * 100)
logger.log(
"Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}".format(
srange[0], arch_index, srange[1], meta_info["total"], cover_mode
)
)
for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
logger.log(
"--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}".format(
i, len(datasets), dataset, xpath, split
)
)
logger.log("--->>> architecture config : {:}".format(arch_config))
start_time, epoch_time = time.time(), AverageMeter()
for i, index in enumerate(to_evaluate_indexes):
arch = all_archs[index]
logger.log(
"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}".format(
"-" * 15,
i,
len(to_evaluate_indexes),
index,
meta_info["total"],
seeds,
"-" * 15,
)
)
# logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
logger.log("{:} {:} {:}".format("-" * 15, arch, "-" * 15))
# test this arch on different datasets with different seeds
has_continue = False
for seed in seeds:
to_save_name = sub_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
if to_save_name.exists():
if cover_mode:
logger.log(
"Find existing file : {:}, remove it before evaluation".format(
to_save_name
)
)
os.remove(str(to_save_name))
else:
logger.log(
"Find existing file : {:}, skip this evaluation".format(
to_save_name
)
)
has_continue = True
continue
results = evaluate_all_datasets(
CellStructure.str2structure(arch),
datasets,
xpaths,
splits,
use_less,
seed,
arch_config,
workers,
logger,
)
torch.save(results, to_save_name)
logger.log(
"{:} --evaluate-- {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}".format(
"-" * 15,
i,
len(to_evaluate_indexes),
index,
meta_info["total"],
seed,
to_save_name,
)
)
# measure elapsed time
if not has_continue:
epoch_time.update(time.time() - start_time)
start_time = time.time()
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True)
)
logger.log(
"This arch costs : {:}".format(convert_secs2time(epoch_time.val, True))
)
logger.log("{:}".format("*" * 100))
logger.log(
"{:} {:74s} {:}".format(
"*" * 10,
"{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}".format(
i, len(to_evaluate_indexes), index, meta_info["total"], need_time
),
"*" * 10,
)
)
logger.log("{:}".format("*" * 100))
logger.close()
def train_single_model(
save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config
):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = True
torch.set_num_threads(workers)
save_dir = (
Path(save_dir)
/ "specifics"
/ "{:}-{:}-{:}-{:}".format(
"LESS" if use_less else "FULL",
model_str,
arch_config["channel"],
arch_config["num_cells"],
)
)
logger = Logger(str(save_dir), 0, False)
if model_str in CellArchitectures:
arch = CellArchitectures[model_str]
logger.log(
"The model string is found in pre-defined architecture dict : {:}".format(
model_str
)
)
else:
try:
arch = CellStructure.str2structure(model_str)
except:
raise ValueError(
"Invalid model string : {:}. It can not be found or parsed.".format(
model_str
)
)
assert arch.check_valid_op(
get_search_spaces("cell", "full")
), "{:} has the invalid op.".format(arch)
logger.log("Start train-evaluate {:}".format(arch.tostr()))
logger.log("arch_config : {:}".format(arch_config))
start_time, seed_time = time.time(), AverageMeter()
for _is, seed in enumerate(seeds):
logger.log(
"\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------".format(
_is, len(seeds), seed
)
)
to_save_name = save_dir / "seed-{:04d}.pth".format(seed)
if to_save_name.exists():
logger.log(
"Find the existing file {:}, directly load!".format(to_save_name)
)
checkpoint = torch.load(to_save_name)
else:
logger.log(
"Does not find the existing file {:}, train and evaluate!".format(
to_save_name
)
)
checkpoint = evaluate_all_datasets(
arch,
datasets,
xpaths,
splits,
use_less,
seed,
arch_config,
workers,
logger,
)
torch.save(checkpoint, to_save_name)
# log information
logger.log("{:}".format(checkpoint["info"]))
all_dataset_keys = checkpoint["all_dataset_keys"]
for dataset_key in all_dataset_keys:
logger.log(
"\n{:} dataset : {:} {:}".format("-" * 15, dataset_key, "-" * 15)
)
dataset_info = checkpoint[dataset_key]
# logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
logger.log(
"Flops = {:} MB, Params = {:} MB".format(
dataset_info["flop"], dataset_info["param"]
)
)
logger.log("config : {:}".format(dataset_info["config"]))
logger.log(
"Training State (finish) = {:}".format(dataset_info["finish-train"])
)
last_epoch = dataset_info["total_epoch"] - 1
train_acc1es, train_acc5es = (
dataset_info["train_acc1es"],
dataset_info["train_acc5es"],
)
valid_acc1es, valid_acc5es = (
dataset_info["valid_acc1es"],
dataset_info["valid_acc5es"],
)
logger.log(
"Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%".format(
train_acc1es[last_epoch],
train_acc5es[last_epoch],
100 - train_acc1es[last_epoch],
valid_acc1es[last_epoch],
valid_acc5es[last_epoch],
100 - valid_acc1es[last_epoch],
)
)
# measure elapsed time
seed_time.update(time.time() - start_time)
start_time = time.time()
need_time = "Time Left: {:}".format(
convert_secs2time(seed_time.avg * (len(seeds) - _is - 1), True)
)
logger.log(
"\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}".format(
_is, len(seeds), seed, need_time
)
)
logger.close()
def generate_meta_info(save_dir, max_node, divide=40):
aa_nas_bench_ss = get_search_spaces("cell", "nas-bench-201")
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
print(
"There are {:} archs vs {:}.".format(
len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)
)
)
random.seed(88) # please do not change this line for reproducibility
random.shuffle(archs)
# to test fixed-random shuffle
# print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
# print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
assert (
archs[0].tostr()
== "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
), "please check the 0-th architecture : {:}".format(archs[0])
assert (
archs[9].tostr()
== "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
), "please check the 9-th architecture : {:}".format(archs[9])
assert (
archs[123].tostr()
== "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
), "please check the 123-th architecture : {:}".format(archs[123])
total_arch = len(archs)
num = 50000
indexes_5W = list(range(num))
random.seed(1021)
random.shuffle(indexes_5W)
train_split = sorted(list(set(indexes_5W[: num // 2])))
valid_split = sorted(list(set(indexes_5W[num // 2 :])))
assert len(train_split) + len(valid_split) == num
assert (
train_split[0] == 0
and train_split[10] == 26
and train_split[111] == 203
and valid_split[0] == 1
and valid_split[10] == 18
and valid_split[111] == 242
), "{:} {:} {:} - {:} {:} {:}".format(
train_split[0],
train_split[10],
train_split[111],
valid_split[0],
valid_split[10],
valid_split[111],
)
splits = {num: {"train": train_split, "valid": valid_split}}
info = {
"archs": [x.tostr() for x in archs],
"total": total_arch,
"max_node": max_node,
"splits": splits,
}
save_dir = Path(save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
save_name = save_dir / "meta-node-{:}.pth".format(max_node)
assert not save_name.exists(), "{:} already exist".format(save_name)
torch.save(info, save_name)
print("save the meta file into {:}".format(save_name))
script_name_full = save_dir / "BENCH-201-N{:}.opt-full.script".format(max_node)
script_name_less = save_dir / "BENCH-201-N{:}.opt-less.script".format(max_node)
full_file = open(str(script_name_full), "w")
less_file = open(str(script_name_less), "w")
gaps = total_arch // divide
for start in range(0, total_arch, gaps):
xend = min(start + gaps, total_arch)
full_file.write(
"bash ./scripts-search/NAS-Bench-201/train-models.sh 0 {:5d} {:5d} -1 '777 888 999'\n".format(
start, xend - 1
)
)
less_file.write(
"bash ./scripts-search/NAS-Bench-201/train-models.sh 1 {:5d} {:5d} -1 '777 888 999'\n".format(
start, xend - 1
)
)
print(
"save the training script into {:} and {:}".format(
script_name_full, script_name_less
)
)
full_file.close()
less_file.close()
script_name = save_dir / "meta-node-{:}.cal-script.txt".format(max_node)
macro = "OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0"
with open(str(script_name), "w") as cfile:
for start in range(0, total_arch, gaps):
xend = min(start + gaps, total_arch)
cfile.write(
"{:} python exps/NAS-Bench-201/statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n".format(
macro, start, xend - 1
)
)
print("save the post-processing script into {:}".format(script_name))
if __name__ == "__main__":
# mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
# parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = argparse.ArgumentParser(
description="NAS-Bench-201",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--mode", type=str, required=True, help="The script mode.")
parser.add_argument(
"--save_dir", type=str, help="Folder to save checkpoints and log."
)
parser.add_argument("--max_node", type=int, help="The maximum node in a cell.")
# use for train the model
parser.add_argument(
"--workers",
type=int,
default=8,
help="number of data loading workers (default: 2)",
)
parser.add_argument(
"--srange", type=int, nargs="+", help="The range of models to be evaluated"
)
parser.add_argument(
"--arch_index",
type=int,
default=-1,
help="The architecture index to be evaluated (cover mode).",
)
parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
parser.add_argument(
"--xpaths", type=str, nargs="+", help="The root path for this dataset."
)
parser.add_argument(
"--splits", type=int, nargs="+", help="The root path for this dataset."
)
parser.add_argument(
"--use_less",
type=int,
default=0,
choices=[0, 1],
help="Using the less-training-epoch config.",
)
parser.add_argument(
"--seeds", type=int, nargs="+", help="The range of models to be evaluated"
)
parser.add_argument("--channel", type=int, help="The number of channels.")
parser.add_argument(
"--num_cells", type=int, help="The number of cells in one stage."
)
args = parser.parse_args()
assert args.mode in ["meta", "new", "cover"] or args.mode.startswith(
"specific-"
), "invalid mode : {:}".format(args.mode)
if args.mode == "meta":
generate_meta_info(args.save_dir, args.max_node)
elif args.mode.startswith("specific"):
assert len(args.mode.split("-")) == 2, "invalid mode : {:}".format(args.mode)
model_str = args.mode.split("-")[1]
train_single_model(
args.save_dir,
args.workers,
args.datasets,
args.xpaths,
args.splits,
args.use_less > 0,
tuple(args.seeds),
model_str,
{"channel": args.channel, "num_cells": args.num_cells},
)
else:
meta_path = Path(args.save_dir) / "meta-node-{:}.pth".format(args.max_node)
assert meta_path.exists(), "{:} does not exist.".format(meta_path)
meta_info = torch.load(meta_path)
# check whether args is ok
assert (
len(args.srange) == 2 and args.srange[0] <= args.srange[1]
), "invalid length of srange args: {:}".format(args.srange)
assert len(args.seeds) > 0, "invalid length of seeds args: {:}".format(
args.seeds
)
assert (
len(args.datasets) == len(args.xpaths) == len(args.splits)
), "invalid infos : {:} vs {:} vs {:}".format(
len(args.datasets), len(args.xpaths), len(args.splits)
)
assert args.workers > 0, "invalid number of workers : {:}".format(args.workers)
main(
args.save_dir,
args.workers,
args.datasets,
args.xpaths,
args.splits,
args.use_less > 0,
tuple(args.srange),
args.arch_index,
tuple(args.seeds),
args.mode == "cover",
meta_info,
{"channel": args.channel, "num_cells": args.num_cells},
)

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
################################################################################################
# python exps/NAS-Bench-201/show-best.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth #
################################################################################################
import argparse
from pathlib import Path
from nas_201_api import NASBench201API as API
if __name__ == "__main__":
parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
parser.add_argument(
"--api_path",
type=str,
default=None,
help="The path to the NAS-Bench-201 benchmark file.",
)
args = parser.parse_args()
meta_file = Path(args.api_path)
assert meta_file.exists(), "invalid path for api : {:}".format(meta_file)
api = API(str(meta_file))
# This will show the results of the best architecture based on the validation set of each dataset.
arch_index, accuracy = api.find_best("cifar10-valid", "x-valid", None, None, False)
print("FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::")
print("arch-index={:5d}, arch={:}".format(arch_index, api.arch(arch_index)))
api.show(arch_index)
print("")
arch_index, accuracy = api.find_best("cifar100", "x-valid", None, None, False)
print("FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::")
print("arch-index={:5d}, arch={:}".format(arch_index, api.arch(arch_index)))
api.show(arch_index)
print("")
arch_index, accuracy = api.find_best("ImageNet16-120", "x-valid", None, None, False)
print("FOR ImageNet16-120, using the hyper-parameters with 200 training epochs :::")
print("arch-index={:5d}, arch={:}".format(arch_index, api.arch(arch_index)))
api.show(arch_index)
print("")

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import os, sys, time, argparse, collections
import numpy as np
import torch
from pathlib import Path
from collections import defaultdict, OrderedDict
from typing import Dict, Any, Text, List
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.config_utils import dict2config
# NAS-Bench-201 related module or function
from xautodl.models import CellStructure, get_cell_based_tiny_net
from xautodl.procedures import (
bench_pure_evaluate as pure_evaluate,
get_nas_bench_loaders,
)
from nas_201_api import NASBench201API, ArchResults, ResultsCount
api = NASBench201API(
"{:}/.torch/NAS-Bench-201-v1_0-e61699.pth".format(os.environ["HOME"])
)
def create_result_count(
used_seed: int,
dataset: Text,
arch_config: Dict[Text, Any],
results: Dict[Text, Any],
dataloader_dict: Dict[Text, Any],
) -> ResultsCount:
xresult = ResultsCount(
dataset,
results["net_state_dict"],
results["train_acc1es"],
results["train_losses"],
results["param"],
results["flop"],
arch_config,
used_seed,
results["total_epoch"],
None,
)
net_config = dict2config(
{
"name": "infer.tiny",
"C": arch_config["channel"],
"N": arch_config["num_cells"],
"genotype": CellStructure.str2structure(arch_config["arch_str"]),
"num_classes": arch_config["class_num"],
},
None,
)
network = get_cell_based_tiny_net(net_config)
network.load_state_dict(xresult.get_net_param())
if "train_times" in results: # new version
xresult.update_train_info(
results["train_acc1es"],
results["train_acc5es"],
results["train_losses"],
results["train_times"],
)
xresult.update_eval(
results["valid_acc1es"], results["valid_losses"], results["valid_times"]
)
else:
if dataset == "cifar10-valid":
xresult.update_OLD_eval(
"x-valid", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()
)
xresult.update_OLD_eval(
"ori-test",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
xresult.update_latency(latencies)
elif dataset == "cifar10":
xresult.update_OLD_eval(
"ori-test", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
)
xresult.update_latency(latencies)
elif dataset == "cifar100" or dataset == "ImageNet16-120":
xresult.update_OLD_eval(
"ori-test", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()
)
xresult.update_OLD_eval(
"x-valid",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
)
xresult.update_OLD_eval(
"x-test",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
xresult.update_latency(latencies)
else:
raise ValueError("invalid dataset name : {:}".format(dataset))
return xresult
def account_one_arch(
arch_index: int,
arch_str: Text,
checkpoints: List[Text],
datasets: List[Text],
dataloader_dict: Dict[Text, Any],
) -> ArchResults:
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
checkpoint = torch.load(checkpoint_path, map_location="cpu")
used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
ok_dataset = 0
for dataset in datasets:
if dataset not in checkpoint:
print(
"Can not find {:} in arch-{:} from {:}".format(
dataset, arch_index, checkpoint_path
)
)
continue
else:
ok_dataset += 1
results = checkpoint[dataset]
assert results[
"finish-train"
], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
arch_index, used_seed, dataset, checkpoint_path
)
arch_config = {
"channel": results["channel"],
"num_cells": results["num_cells"],
"arch_str": arch_str,
"class_num": results["config"]["class_num"],
}
xresult = create_result_count(
used_seed, dataset, arch_config, results, dataloader_dict
)
information.update(dataset, int(used_seed), xresult)
if ok_dataset == 0:
raise ValueError("{:} does not find any data".format(checkpoint_path))
return information
def correct_time_related_info(
arch_index: int, arch_info_full: ArchResults, arch_info_less: ArchResults
):
# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
cifar010_latency = (
api.get_latency(arch_index, "cifar10-valid", hp="200")
+ api.get_latency(arch_index, "cifar10", hp="200")
) / 2
arch_info_full.reset_latency("cifar10-valid", None, cifar010_latency)
arch_info_full.reset_latency("cifar10", None, cifar010_latency)
arch_info_less.reset_latency("cifar10-valid", None, cifar010_latency)
arch_info_less.reset_latency("cifar10", None, cifar010_latency)
cifar100_latency = api.get_latency(arch_index, "cifar100", hp="200")
arch_info_full.reset_latency("cifar100", None, cifar100_latency)
arch_info_less.reset_latency("cifar100", None, cifar100_latency)
image_latency = api.get_latency(arch_index, "ImageNet16-120", hp="200")
arch_info_full.reset_latency("ImageNet16-120", None, image_latency)
arch_info_less.reset_latency("ImageNet16-120", None, image_latency)
train_per_epoch_time = list(
arch_info_less.query("cifar10-valid", 777).train_times.values()
)
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time = [], []
for key, value in arch_info_less.query("cifar10-valid", 777).eval_times.items():
if key.startswith("ori-test@"):
eval_ori_test_time.append(value)
elif key.startswith("x-valid@"):
eval_x_valid_time.append(value)
else:
raise ValueError("-- {:} --".format(key))
eval_ori_test_time, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(
np.mean(eval_x_valid_time)
)
nums = {
"ImageNet16-120-train": 151700,
"ImageNet16-120-valid": 3000,
"ImageNet16-120-test": 6000,
"cifar10-valid-train": 25000,
"cifar10-valid-valid": 25000,
"cifar10-train": 50000,
"cifar10-test": 10000,
"cifar100-train": 50000,
"cifar100-test": 10000,
"cifar100-valid": 5000,
}
eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (
nums["cifar10-valid-valid"] + nums["cifar10-test"]
)
for arch_info in [arch_info_less, arch_info_full]:
arch_info.reset_pseudo_train_times(
"cifar10-valid",
None,
train_per_epoch_time
/ nums["cifar10-valid-train"]
* nums["cifar10-valid-train"],
)
arch_info.reset_pseudo_train_times(
"cifar10",
None,
train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-train"],
)
arch_info.reset_pseudo_train_times(
"cifar100",
None,
train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar100-train"],
)
arch_info.reset_pseudo_train_times(
"ImageNet16-120",
None,
train_per_epoch_time
/ nums["cifar10-valid-train"]
* nums["ImageNet16-120-train"],
)
arch_info.reset_pseudo_eval_times(
"cifar10-valid",
None,
"x-valid",
eval_per_sample * nums["cifar10-valid-valid"],
)
arch_info.reset_pseudo_eval_times(
"cifar10-valid", None, "ori-test", eval_per_sample * nums["cifar10-test"]
)
arch_info.reset_pseudo_eval_times(
"cifar10", None, "ori-test", eval_per_sample * nums["cifar10-test"]
)
arch_info.reset_pseudo_eval_times(
"cifar100", None, "x-valid", eval_per_sample * nums["cifar100-valid"]
)
arch_info.reset_pseudo_eval_times(
"cifar100", None, "x-test", eval_per_sample * nums["cifar100-valid"]
)
arch_info.reset_pseudo_eval_times(
"cifar100", None, "ori-test", eval_per_sample * nums["cifar100-test"]
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120",
None,
"x-valid",
eval_per_sample * nums["ImageNet16-120-valid"],
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120",
None,
"x-test",
eval_per_sample * nums["ImageNet16-120-valid"],
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120",
None,
"ori-test",
eval_per_sample * nums["ImageNet16-120-test"],
)
# arch_info_full.debug_test()
# arch_info_less.debug_test()
return arch_info_full, arch_info_less
def simplify(save_dir, meta_file, basestr, target_dir):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"] # a list of architecture strings
meta_num_archs = meta_infos["total"]
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
num_seeds = defaultdict(lambda: 0)
for index, sub_dir in enumerate(sub_model_dirs):
xcheckpoints = list(sub_dir.glob("arch-*-seed-*.pth"))
arch_indexes = set()
for checkpoint in xcheckpoints:
temp_names = checkpoint.name.split("-")
assert (
len(temp_names) == 4
and temp_names[0] == "arch"
and temp_names[2] == "seed"
), "invalid checkpoint name : {:}".format(checkpoint.name)
arch_indexes.add(temp_names[1])
subdir2archs[sub_dir] = sorted(list(arch_indexes))
num_evaluated_arch += len(arch_indexes)
# count number of seeds for each architecture
for arch_index in arch_indexes:
num_seeds[
len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))
] += 1
print(
"{:} There are {:5d} architectures that have been evaluated ({:} in total).".format(
time_string(), num_evaluated_arch, meta_num_archs
)
)
for key in sorted(list(num_seeds.keys())):
print(
"{:} There are {:5d} architectures that are evaluated {:} times.".format(
time_string(), num_seeds[key], key
)
)
dataloader_dict = get_nas_bench_loaders(6)
to_save_simply = save_dir / "simplifies"
to_save_allarc = save_dir / "simplifies" / "architectures"
if not to_save_simply.exists():
to_save_simply.mkdir(parents=True, exist_ok=True)
if not to_save_allarc.exists():
to_save_allarc.mkdir(parents=True, exist_ok=True)
assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format(
target_dir
)
arch2infos, datasets = {}, (
"cifar10-valid",
"cifar10",
"cifar100",
"ImageNet16-120",
)
evaluated_indexes = set()
target_full_dir = save_dir / target_dir
target_less_dir = save_dir / "{:}-LESS".format(target_dir)
arch_indexes = subdir2archs[target_full_dir]
num_seeds = defaultdict(lambda: 0)
end_time = time.time()
arch_time = AverageMeter()
for idx, arch_index in enumerate(arch_indexes):
checkpoints = list(
target_full_dir.glob("arch-{:}-seed-*.pth".format(arch_index))
)
ckps_less = list(target_less_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))
# create the arch info for each architecture
try:
arch_info_full = account_one_arch(
arch_index,
meta_archs[int(arch_index)],
checkpoints,
datasets,
dataloader_dict,
)
arch_info_less = account_one_arch(
arch_index,
meta_archs[int(arch_index)],
ckps_less,
datasets,
dataloader_dict,
)
num_seeds[len(checkpoints)] += 1
except:
print("Loading {:} failed, : {:}".format(arch_index, checkpoints))
continue
assert (
int(arch_index) not in evaluated_indexes
), "conflict arch-index : {:}".format(arch_index)
assert (
0 <= int(arch_index) < len(meta_archs)
), "invalid arch-index {:} (not found in meta_archs)".format(arch_index)
arch_info = {"full": arch_info_full, "less": arch_info_less}
evaluated_indexes.add(int(arch_index))
arch2infos[int(arch_index)] = arch_info
# to correct the latency and training_time info.
arch_info_full, arch_info_less = correct_time_related_info(
int(arch_index), arch_info_full, arch_info_less
)
to_save_data = OrderedDict(
full=arch_info_full.state_dict(), less=arch_info_less.state_dict()
)
torch.save(to_save_data, to_save_allarc / "{:}-FULL.pth".format(arch_index))
arch_info["full"].clear_params()
arch_info["less"].clear_params()
torch.save(to_save_data, to_save_allarc / "{:}-SIMPLE.pth".format(arch_index))
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = "{:}".format(
convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True)
)
print(
"{:} {:} [{:03d}/{:03d}] : {:} still need {:}".format(
time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time
)
)
# measure time
xstrs = [
"{:}:{:03d}".format(key, num_seeds[key])
for key in sorted(list(num_seeds.keys()))
]
print("{:} {:} done : {:}".format(time_string(), target_dir, xstrs))
final_infos = {
"meta_archs": meta_archs,
"total_archs": meta_num_archs,
"basestr": basestr,
"arch2infos": arch2infos,
"evaluated_indexes": evaluated_indexes,
}
save_file_name = to_save_simply / "{:}.pth".format(target_dir)
torch.save(final_infos, save_file_name)
print(
"Save {:} / {:} architecture results into {:}.".format(
len(evaluated_indexes), meta_num_archs, save_file_name
)
)
def merge_all(save_dir, meta_file, basestr):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"]
meta_num_archs = meta_infos["total"]
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
for index, sub_dir in enumerate(sub_model_dirs):
arch_info_files = sorted(list(sub_dir.glob("arch-*-seed-*.pth")))
print(
"The {:02d}/{:02d}-th directory : {:} : {:} runs.".format(
index, len(sub_model_dirs), sub_dir, len(arch_info_files)
)
)
arch2infos, evaluated_indexes = dict(), set()
for IDX, sub_dir in enumerate(sub_model_dirs):
ckp_path = sub_dir.parent / "simplifies" / "{:}.pth".format(sub_dir.name)
if ckp_path.exists():
sub_ckps = torch.load(ckp_path, map_location="cpu")
assert (
sub_ckps["total_archs"] == meta_num_archs
and sub_ckps["basestr"] == basestr
)
xarch2infos = sub_ckps["arch2infos"]
xevalindexs = sub_ckps["evaluated_indexes"]
for eval_index in xevalindexs:
assert (
eval_index not in evaluated_indexes and eval_index not in arch2infos
)
# arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
arch2infos[eval_index] = {
"full": xarch2infos[eval_index]["full"].state_dict(),
"less": xarch2infos[eval_index]["less"].state_dict(),
}
evaluated_indexes.add(eval_index)
print(
"{:} [{:03d}/{:03d}] merge data from {:} with {:} models.".format(
time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)
)
)
else:
raise ValueError("Can not find {:}".format(ckp_path))
# print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
evaluated_indexes = sorted(list(evaluated_indexes))
print(
"Finally, there are {:} architectures that have been trained and evaluated.".format(
len(evaluated_indexes)
)
)
to_save_simply = save_dir / "simplifies"
if not to_save_simply.exists():
to_save_simply.mkdir(parents=True, exist_ok=True)
final_infos = {
"meta_archs": meta_archs,
"total_archs": meta_num_archs,
"arch2infos": arch2infos,
"evaluated_indexes": evaluated_indexes,
}
save_file_name = to_save_simply / "{:}-final-infos.pth".format(basestr)
torch.save(final_infos, save_file_name)
print(
"Save {:} / {:} architecture results into {:}.".format(
len(evaluated_indexes), meta_num_archs, save_file_name
)
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NAS-BENCH-201",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--mode",
type=str,
choices=["cal", "merge"],
help="The running mode for this script.",
)
parser.add_argument(
"--base_save_dir",
type=str,
default="./output/NAS-BENCH-201-4",
help="The base-name of folder to save checkpoints and log.",
)
parser.add_argument("--target_dir", type=str, help="The target directory.")
parser.add_argument(
"--max_node", type=int, default=4, help="The maximum node in a cell."
)
parser.add_argument(
"--channel", type=int, default=16, help="The number of channels."
)
parser.add_argument(
"--num_cells", type=int, default=5, help="The number of cells in one stage."
)
args = parser.parse_args()
save_dir = Path(args.base_save_dir)
meta_path = save_dir / "meta-node-{:}.pth".format(args.max_node)
assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir)
assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path)
print(
"start the statistics of our nas-benchmark from {:} using {:}.".format(
save_dir, args.target_dir
)
)
basestr = "C{:}-N{:}".format(args.channel, args.num_cells)
if args.mode == "cal":
simplify(save_dir, meta_path, basestr, args.target_dir)
elif args.mode == "merge":
merge_all(save_dir, meta_path, basestr)
else:
raise ValueError("invalid mode : {:}".format(args.mode))

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import os, sys, time, argparse, collections
from copy import deepcopy
import torch
from pathlib import Path
from collections import defaultdict
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.config_utils import load_config, dict2config
from xautodl.datasets import get_datasets
# NAS-Bench-201 related module or function
from xautodl.models import CellStructure, get_cell_based_tiny_net
from xautodl.procedures import bench_pure_evaluate as pure_evaluate
from nas_201_api import ArchResults, ResultsCount
def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
xresult = ResultsCount(
dataset,
results["net_state_dict"],
results["train_acc1es"],
results["train_losses"],
results["param"],
results["flop"],
arch_config,
used_seed,
results["total_epoch"],
None,
)
net_config = dict2config(
{
"name": "infer.tiny",
"C": arch_config["channel"],
"N": arch_config["num_cells"],
"genotype": CellStructure.str2structure(arch_config["arch_str"]),
"num_classes": arch_config["class_num"],
},
None,
)
network = get_cell_based_tiny_net(net_config)
network.load_state_dict(xresult.get_net_param())
if "train_times" in results: # new version
xresult.update_train_info(
results["train_acc1es"],
results["train_acc5es"],
results["train_losses"],
results["train_times"],
)
xresult.update_eval(
results["valid_acc1es"], results["valid_losses"], results["valid_times"]
)
else:
if dataset == "cifar10-valid":
xresult.update_OLD_eval(
"x-valid", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()
)
xresult.update_OLD_eval(
"ori-test",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
xresult.update_latency(latencies)
elif dataset == "cifar10":
xresult.update_OLD_eval(
"ori-test", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
)
xresult.update_latency(latencies)
elif dataset == "cifar100" or dataset == "ImageNet16-120":
xresult.update_OLD_eval(
"ori-test", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()
)
xresult.update_OLD_eval(
"x-valid",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
)
xresult.update_OLD_eval(
"x-test",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
xresult.update_latency(latencies)
else:
raise ValueError("invalid dataset name : {:}".format(dataset))
return xresult
def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
checkpoint = torch.load(checkpoint_path, map_location="cpu")
used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
for dataset in datasets:
assert (
dataset in checkpoint
), "Can not find {:} in arch-{:} from {:}".format(
dataset, arch_index, checkpoint_path
)
results = checkpoint[dataset]
assert results[
"finish-train"
], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
arch_index, used_seed, dataset, checkpoint_path
)
arch_config = {
"channel": results["channel"],
"num_cells": results["num_cells"],
"arch_str": arch_str,
"class_num": results["config"]["class_num"],
}
xresult = create_result_count(
used_seed, dataset, arch_config, results, dataloader_dict
)
information.update(dataset, int(used_seed), xresult)
return information
def GET_DataLoaders(workers):
torch.set_num_threads(workers)
root_dir = (Path(__file__).parent / ".." / "..").resolve()
torch_dir = Path(os.environ["TORCH_HOME"])
# cifar
cifar_config_path = root_dir / "configs" / "nas-benchmark" / "CIFAR.config"
cifar_config = load_config(cifar_config_path, None, None)
print("{:} Create data-loader for all datasets".format(time_string()))
print("-" * 200)
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets(
"cifar10", str(torch_dir / "cifar.python"), -1
)
print(
"original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
)
)
cifar10_splits = load_config(
root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None
)
assert cifar10_splits.train[:10] == [
0,
5,
7,
11,
13,
15,
16,
17,
20,
24,
] and cifar10_splits.valid[:10] == [
1,
2,
3,
4,
6,
8,
9,
10,
12,
14,
]
temp_dataset = deepcopy(TRAIN_CIFAR10)
temp_dataset.transform = VALID_CIFAR10.transform
# data loader
trainval_cifar10_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR10,
batch_size=cifar_config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
train_cifar10_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR10,
batch_size=cifar_config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train),
num_workers=workers,
pin_memory=True,
)
valid_cifar10_loader = torch.utils.data.DataLoader(
temp_dataset,
batch_size=cifar_config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid),
num_workers=workers,
pin_memory=True,
)
test__cifar10_loader = torch.utils.data.DataLoader(
VALID_CIFAR10,
batch_size=cifar_config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
print(
"CIFAR-10 : trval-loader has {:3d} batch with {:} per batch".format(
len(trainval_cifar10_loader), cifar_config.batch_size
)
)
print(
"CIFAR-10 : train-loader has {:3d} batch with {:} per batch".format(
len(train_cifar10_loader), cifar_config.batch_size
)
)
print(
"CIFAR-10 : valid-loader has {:3d} batch with {:} per batch".format(
len(valid_cifar10_loader), cifar_config.batch_size
)
)
print(
"CIFAR-10 : test--loader has {:3d} batch with {:} per batch".format(
len(test__cifar10_loader), cifar_config.batch_size
)
)
print("-" * 200)
# CIFAR-100
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets(
"cifar100", str(torch_dir / "cifar.python"), -1
)
print(
"original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
)
)
cifar100_splits = load_config(
root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None
)
assert cifar100_splits.xvalid[:10] == [
1,
3,
4,
5,
8,
10,
13,
14,
15,
16,
] and cifar100_splits.xtest[:10] == [
0,
2,
6,
7,
9,
11,
12,
17,
20,
24,
]
train_cifar100_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR100,
batch_size=cifar_config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
valid_cifar100_loader = torch.utils.data.DataLoader(
VALID_CIFAR100,
batch_size=cifar_config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid),
num_workers=workers,
pin_memory=True,
)
test__cifar100_loader = torch.utils.data.DataLoader(
VALID_CIFAR100,
batch_size=cifar_config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest),
num_workers=workers,
pin_memory=True,
)
print(
"CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader))
)
print(
"CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader))
)
print(
"CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader))
)
print("-" * 200)
imagenet16_config_path = "configs/nas-benchmark/ImageNet-16.config"
imagenet16_config = load_config(imagenet16_config_path, None, None)
TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets(
"ImageNet16-120", str(torch_dir / "cifar.python" / "ImageNet16"), -1
)
print(
"original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
)
)
imagenet_splits = load_config(
root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt",
None,
None,
)
assert imagenet_splits.xvalid[:10] == [
1,
2,
3,
6,
7,
8,
9,
12,
16,
18,
] and imagenet_splits.xtest[:10] == [
0,
4,
5,
10,
11,
13,
14,
15,
17,
20,
]
train_imagenet_loader = torch.utils.data.DataLoader(
TRAIN_ImageNet16_120,
batch_size=imagenet16_config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
valid_imagenet_loader = torch.utils.data.DataLoader(
VALID_ImageNet16_120,
batch_size=imagenet16_config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid),
num_workers=workers,
pin_memory=True,
)
test__imagenet_loader = torch.utils.data.DataLoader(
VALID_ImageNet16_120,
batch_size=imagenet16_config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest),
num_workers=workers,
pin_memory=True,
)
print(
"ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch".format(
len(train_imagenet_loader), imagenet16_config.batch_size
)
)
print(
"ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch".format(
len(valid_imagenet_loader), imagenet16_config.batch_size
)
)
print(
"ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch".format(
len(test__imagenet_loader), imagenet16_config.batch_size
)
)
# 'cifar10', 'cifar100', 'ImageNet16-120'
loaders = {
"cifar10@trainval": trainval_cifar10_loader,
"cifar10@train": train_cifar10_loader,
"cifar10@valid": valid_cifar10_loader,
"cifar10@test": test__cifar10_loader,
"cifar100@train": train_cifar100_loader,
"cifar100@valid": valid_cifar100_loader,
"cifar100@test": test__cifar100_loader,
"ImageNet16-120@train": train_imagenet_loader,
"ImageNet16-120@valid": valid_imagenet_loader,
"ImageNet16-120@test": test__imagenet_loader,
}
return loaders
def simplify(save_dir, meta_file, basestr, target_dir):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"] # a list of architecture strings
meta_num_archs = meta_infos["total"]
meta_max_node = meta_infos["max_node"]
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
num_seeds = defaultdict(lambda: 0)
for index, sub_dir in enumerate(sub_model_dirs):
xcheckpoints = list(sub_dir.glob("arch-*-seed-*.pth"))
arch_indexes = set()
for checkpoint in xcheckpoints:
temp_names = checkpoint.name.split("-")
assert (
len(temp_names) == 4
and temp_names[0] == "arch"
and temp_names[2] == "seed"
), "invalid checkpoint name : {:}".format(checkpoint.name)
arch_indexes.add(temp_names[1])
subdir2archs[sub_dir] = sorted(list(arch_indexes))
num_evaluated_arch += len(arch_indexes)
# count number of seeds for each architecture
for arch_index in arch_indexes:
num_seeds[
len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))
] += 1
print(
"{:} There are {:5d} architectures that have been evaluated ({:} in total).".format(
time_string(), num_evaluated_arch, meta_num_archs
)
)
for key in sorted(list(num_seeds.keys())):
print(
"{:} There are {:5d} architectures that are evaluated {:} times.".format(
time_string(), num_seeds[key], key
)
)
dataloader_dict = GET_DataLoaders(6)
to_save_simply = save_dir / "simplifies"
to_save_allarc = save_dir / "simplifies" / "architectures"
if not to_save_simply.exists():
to_save_simply.mkdir(parents=True, exist_ok=True)
if not to_save_allarc.exists():
to_save_allarc.mkdir(parents=True, exist_ok=True)
assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format(
target_dir
)
arch2infos, datasets = {}, (
"cifar10-valid",
"cifar10",
"cifar100",
"ImageNet16-120",
)
evaluated_indexes = set()
target_directory = save_dir / target_dir
target_less_dir = save_dir / "{:}-LESS".format(target_dir)
arch_indexes = subdir2archs[target_directory]
num_seeds = defaultdict(lambda: 0)
end_time = time.time()
arch_time = AverageMeter()
for idx, arch_index in enumerate(arch_indexes):
checkpoints = list(
target_directory.glob("arch-{:}-seed-*.pth".format(arch_index))
)
ckps_less = list(target_less_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))
# create the arch info for each architecture
try:
arch_info_full = account_one_arch(
arch_index,
meta_archs[int(arch_index)],
checkpoints,
datasets,
dataloader_dict,
)
arch_info_less = account_one_arch(
arch_index,
meta_archs[int(arch_index)],
ckps_less,
["cifar10-valid"],
dataloader_dict,
)
num_seeds[len(checkpoints)] += 1
except:
print("Loading {:} failed, : {:}".format(arch_index, checkpoints))
continue
assert (
int(arch_index) not in evaluated_indexes
), "conflict arch-index : {:}".format(arch_index)
assert (
0 <= int(arch_index) < len(meta_archs)
), "invalid arch-index {:} (not found in meta_archs)".format(arch_index)
arch_info = {"full": arch_info_full, "less": arch_info_less}
evaluated_indexes.add(int(arch_index))
arch2infos[int(arch_index)] = arch_info
torch.save(
{"full": arch_info_full.state_dict(), "less": arch_info_less.state_dict()},
to_save_allarc / "{:}-FULL.pth".format(arch_index),
)
arch_info["full"].clear_params()
arch_info["less"].clear_params()
torch.save(
{"full": arch_info_full.state_dict(), "less": arch_info_less.state_dict()},
to_save_allarc / "{:}-SIMPLE.pth".format(arch_index),
)
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = "{:}".format(
convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True)
)
print(
"{:} {:} [{:03d}/{:03d}] : {:} still need {:}".format(
time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time
)
)
# measure time
xstrs = [
"{:}:{:03d}".format(key, num_seeds[key])
for key in sorted(list(num_seeds.keys()))
]
print("{:} {:} done : {:}".format(time_string(), target_dir, xstrs))
final_infos = {
"meta_archs": meta_archs,
"total_archs": meta_num_archs,
"basestr": basestr,
"arch2infos": arch2infos,
"evaluated_indexes": evaluated_indexes,
}
save_file_name = to_save_simply / "{:}.pth".format(target_dir)
torch.save(final_infos, save_file_name)
print(
"Save {:} / {:} architecture results into {:}.".format(
len(evaluated_indexes), meta_num_archs, save_file_name
)
)
def merge_all(save_dir, meta_file, basestr):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"]
meta_num_archs = meta_infos["total"]
meta_max_node = meta_infos["max_node"]
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
for index, sub_dir in enumerate(sub_model_dirs):
arch_info_files = sorted(list(sub_dir.glob("arch-*-seed-*.pth")))
print(
"The {:02d}/{:02d}-th directory : {:} : {:} runs.".format(
index, len(sub_model_dirs), sub_dir, len(arch_info_files)
)
)
arch2infos, evaluated_indexes = dict(), set()
for IDX, sub_dir in enumerate(sub_model_dirs):
ckp_path = sub_dir.parent / "simplifies" / "{:}.pth".format(sub_dir.name)
if ckp_path.exists():
sub_ckps = torch.load(ckp_path, map_location="cpu")
assert (
sub_ckps["total_archs"] == meta_num_archs
and sub_ckps["basestr"] == basestr
)
xarch2infos = sub_ckps["arch2infos"]
xevalindexs = sub_ckps["evaluated_indexes"]
for eval_index in xevalindexs:
assert (
eval_index not in evaluated_indexes and eval_index not in arch2infos
)
# arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
arch2infos[eval_index] = {
"full": xarch2infos[eval_index]["full"].state_dict(),
"less": xarch2infos[eval_index]["less"].state_dict(),
}
evaluated_indexes.add(eval_index)
print(
"{:} [{:03d}/{:03d}] merge data from {:} with {:} models.".format(
time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)
)
)
else:
raise ValueError("Can not find {:}".format(ckp_path))
# print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
evaluated_indexes = sorted(list(evaluated_indexes))
print(
"Finally, there are {:} architectures that have been trained and evaluated.".format(
len(evaluated_indexes)
)
)
to_save_simply = save_dir / "simplifies"
if not to_save_simply.exists():
to_save_simply.mkdir(parents=True, exist_ok=True)
final_infos = {
"meta_archs": meta_archs,
"total_archs": meta_num_archs,
"arch2infos": arch2infos,
"evaluated_indexes": evaluated_indexes,
}
save_file_name = to_save_simply / "{:}-final-infos.pth".format(basestr)
torch.save(final_infos, save_file_name)
print(
"Save {:} / {:} architecture results into {:}.".format(
len(evaluated_indexes), meta_num_archs, save_file_name
)
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NAS-BENCH-201",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--mode",
type=str,
choices=["cal", "merge"],
help="The running mode for this script.",
)
parser.add_argument(
"--base_save_dir",
type=str,
default="./output/NAS-BENCH-201-4",
help="The base-name of folder to save checkpoints and log.",
)
parser.add_argument("--target_dir", type=str, help="The target directory.")
parser.add_argument(
"--max_node", type=int, default=4, help="The maximum node in a cell."
)
parser.add_argument(
"--channel", type=int, default=16, help="The number of channels."
)
parser.add_argument(
"--num_cells", type=int, default=5, help="The number of cells in one stage."
)
args = parser.parse_args()
save_dir = Path(args.base_save_dir)
meta_path = save_dir / "meta-node-{:}.pth".format(args.max_node)
assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir)
assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path)
print(
"start the statistics of our nas-benchmark from {:} using {:}.".format(
save_dir, args.target_dir
)
)
basestr = "C{:}-N{:}".format(args.channel, args.num_cells)
if args.mode == "cal":
simplify(save_dir, meta_path, basestr, args.target_dir)
elif args.mode == "merge":
merge_all(save_dir, meta_path, basestr)
else:
raise ValueError("invalid mode : {:}".format(args.mode))

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
########################################################
# python exps/NAS-Bench-201/test-correlation.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth
########################################################
import sys, argparse
import numpy as np
from copy import deepcopy
from tqdm import tqdm
import torch
from pathlib import Path
from xautodl.log_utils import time_string
from xautodl.models import CellStructure
from nas_201_api import NASBench201API as API
def check_unique_arch(meta_file):
api = API(str(meta_file))
arch_strs = deepcopy(api.meta_archs)
xarchs = [CellStructure.str2structure(x) for x in arch_strs]
def get_unique_matrix(archs, consider_zero):
UniquStrs = [arch.to_unique_str(consider_zero) for arch in archs]
print(
"{:} create unique-string ({:}/{:}) done".format(
time_string(), len(set(UniquStrs)), len(UniquStrs)
)
)
Unique2Index = dict()
for index, xstr in enumerate(UniquStrs):
if xstr not in Unique2Index:
Unique2Index[xstr] = list()
Unique2Index[xstr].append(index)
sm_matrix = torch.eye(len(archs)).bool()
for _, xlist in Unique2Index.items():
for i in xlist:
for j in xlist:
sm_matrix[i, j] = True
unique_ids, unique_num = [-1 for _ in archs], 0
for i in range(len(unique_ids)):
if unique_ids[i] > -1:
continue
neighbours = sm_matrix[i].nonzero().view(-1).tolist()
for nghb in neighbours:
assert unique_ids[nghb] == -1, "impossible"
unique_ids[nghb] = unique_num
unique_num += 1
return sm_matrix, unique_ids, unique_num
print(
"There are {:} valid-archs".format(sum(arch.check_valid() for arch in xarchs))
)
sm_matrix, uniqueIDs, unique_num = get_unique_matrix(xarchs, None)
print(
"{:} There are {:} unique architectures (considering nothing).".format(
time_string(), unique_num
)
)
sm_matrix, uniqueIDs, unique_num = get_unique_matrix(xarchs, False)
print(
"{:} There are {:} unique architectures (not considering zero).".format(
time_string(), unique_num
)
)
sm_matrix, uniqueIDs, unique_num = get_unique_matrix(xarchs, True)
print(
"{:} There are {:} unique architectures (considering zero).".format(
time_string(), unique_num
)
)
def check_cor_for_bandit(
meta_file, test_epoch, use_less_or_not, is_rand=True, need_print=False
):
if isinstance(meta_file, API):
api = meta_file
else:
api = API(str(meta_file))
cifar10_currs = []
cifar10_valid = []
cifar10_test = []
cifar100_valid = []
cifar100_test = []
imagenet_test = []
imagenet_valid = []
for idx, arch in enumerate(api):
results = api.get_more_info(
idx, "cifar10-valid", test_epoch - 1, use_less_or_not, is_rand
)
cifar10_currs.append(results["valid-accuracy"])
# --->>>>>
results = api.get_more_info(idx, "cifar10-valid", None, False, is_rand)
cifar10_valid.append(results["valid-accuracy"])
results = api.get_more_info(idx, "cifar10", None, False, is_rand)
cifar10_test.append(results["test-accuracy"])
results = api.get_more_info(idx, "cifar100", None, False, is_rand)
cifar100_test.append(results["test-accuracy"])
cifar100_valid.append(results["valid-accuracy"])
results = api.get_more_info(idx, "ImageNet16-120", None, False, is_rand)
imagenet_test.append(results["test-accuracy"])
imagenet_valid.append(results["valid-accuracy"])
def get_cor(A, B):
return float(np.corrcoef(A, B)[0, 1])
cors = []
for basestr, xlist in zip(
["C-010-V", "C-010-T", "C-100-V", "C-100-T", "I16-V", "I16-T"],
[
cifar10_valid,
cifar10_test,
cifar100_valid,
cifar100_test,
imagenet_valid,
imagenet_test,
],
):
correlation = get_cor(cifar10_currs, xlist)
if need_print:
print(
"With {:3d}/{:}-epochs-training, the correlation between cifar10-valid and {:} is : {:}".format(
test_epoch,
"012" if use_less_or_not else "200",
basestr,
correlation,
)
)
cors.append(correlation)
# print ('With {:3d}/200-epochs-training, the correlation between cifar10-valid and {:} is : {:}'.format(test_epoch, basestr, get_cor(cifar10_valid_200, xlist)))
# print('-'*200)
# print('*'*230)
return cors
def check_cor_for_bandit_v2(meta_file, test_epoch, use_less_or_not, is_rand):
corrs = []
for i in tqdm(range(100)):
x = check_cor_for_bandit(meta_file, test_epoch, use_less_or_not, is_rand, False)
corrs.append(x)
# xstrs = ['CIFAR-010', 'C-100-V', 'C-100-T', 'I16-V', 'I16-T']
xstrs = ["C-010-V", "C-010-T", "C-100-V", "C-100-T", "I16-V", "I16-T"]
correlations = np.array(corrs)
print(
"------>>>>>>>> {:03d}/{:} >>>>>>>> ------".format(
test_epoch, "012" if use_less_or_not else "200"
)
)
for idx, xstr in enumerate(xstrs):
print(
"{:8s} ::: mean={:.4f}, std={:.4f} :: {:.4f}\\pm{:.4f}".format(
xstr,
correlations[:, idx].mean(),
correlations[:, idx].std(),
correlations[:, idx].mean(),
correlations[:, idx].std(),
)
)
print("")
if __name__ == "__main__":
parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
parser.add_argument(
"--save_dir",
type=str,
default="./output/search-cell-nas-bench-201/visuals",
help="The base-name of folder to save checkpoints and log.",
)
parser.add_argument(
"--api_path",
type=str,
default=None,
help="The path to the NAS-Bench-201 benchmark file.",
)
args = parser.parse_args()
vis_save_dir = Path(args.save_dir)
vis_save_dir.mkdir(parents=True, exist_ok=True)
meta_file = Path(args.api_path)
assert meta_file.exists(), "invalid path for api : {:}".format(meta_file)
# check_unique_arch(meta_file)
api = API(str(meta_file))
# for iepoch in [11, 25, 50, 100, 150, 175, 200]:
# check_cor_for_bandit(api, 6, iepoch)
# check_cor_for_bandit(api, 12, iepoch)
check_cor_for_bandit_v2(api, 6, True, True)
check_cor_for_bandit_v2(api, 12, True, True)
check_cor_for_bandit_v2(api, 12, False, True)
check_cor_for_bandit_v2(api, 24, False, True)
check_cor_for_bandit_v2(api, 100, False, True)
check_cor_for_bandit_v2(api, 150, False, True)
check_cor_for_bandit_v2(api, 175, False, True)
check_cor_for_bandit_v2(api, 200, False, True)
print("----")

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