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Update REA, REINFORCE, RANDOM, and BOHB

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This commit is contained in:
D-X-Y
2020-07-14 11:53:21 +00:00
parent 168b08d9e6
commit 2c861f33c4
8 changed files with 79 additions and 88 deletions
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2
.gitignore vendored
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@@ -123,3 +123,5 @@ scripts-search/l2s-algos
TEMP-L.sh
.nfs00*
*.swo
*/*.swo

102
exps/algos-v2/bohb.py
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@@ -5,9 +5,9 @@
# required to install hpbandster ##################################
# pip install hpbandster ##################################
###################################################################
# python exps/algos-v2/bohb.py --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
# OMP_NUM_THREADS=4 python exps/algos-v2/bohb.py --search_space tss --dataset cifar10 --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 --rand_seed 1
###################################################################
import os, sys, time, random, argparse
import os, sys, time, random, argparse, collections
from copy import deepcopy
from pathlib import Path
import torch
@@ -17,7 +17,7 @@ from config_utils import load_config
from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API as API
from nas_201_api import NASBench201API, NASBench301API
from models import CellStructure, get_search_spaces
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
import ConfigSpace
@@ -63,52 +63,21 @@ def config2topology_func(max_nodes=4):
class MyWorker(Worker):
def __init__(self, *args, convert_func=None, dataname=None, nas_bench=None, time_budget=None, **kwargs):
def __init__(self, *args, convert_func=None, dataset=None, api=None, **kwargs):
super().__init__(*args, **kwargs)
self.convert_func = convert_func
self._dataname = dataname
self._nas_bench = nas_bench
self.time_budget = time_budget
self.seen_archs = []
self.sim_cost_time = 0
self.real_cost_time = 0
self.is_end = False
def get_the_best(self):
assert len(self.seen_archs) > 0
best_index, best_acc = -1, None
for arch_index in self.seen_archs:
info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp='200', is_random=True)
vacc = info['valid-accuracy']
if best_acc is None or best_acc < vacc:
best_acc = vacc
best_index = arch_index
assert best_index != -1
return best_index
self._dataset = dataset
self._api = api
self.total_times = []
self.trajectory = []
def compute(self, config, budget, **kwargs):
start_time = time.time()
structure = self.convert_func( config )
arch_index = self._nas_bench.query_index_by_arch( structure )
info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp='200', is_random=True)
cur_time = info['train-all-time'] + info['valid-per-time']
cur_vacc = info['valid-accuracy']
self.real_cost_time += (time.time() - start_time)
if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
self.sim_cost_time += cur_time
self.seen_archs.append( arch_index )
return ({'loss': 100 - float(cur_vacc),
'info': {'seen-arch' : len(self.seen_archs),
'sim-test-time' : self.sim_cost_time,
'current-arch' : arch_index}
})
else:
self.is_end = True
return ({'loss': 100,
'info': {'seen-arch' : len(self.seen_archs),
'sim-test-time' : self.sim_cost_time,
'current-arch' : None}
})
arch = self.convert_func( config )
accuracy, latency, time_cost, total_time = self._api.simulate_train_eval(arch, self._dataset, iepoch=int(budget)-1, hp='12')
self.trajectory.append((accuracy, arch))
self.total_times.append(total_time)
return ({'loss': 100 - accuracy,
'info': self._api.query_index_by_arch(arch)})
def main(xargs, api):
@@ -117,12 +86,13 @@ def main(xargs, api):
logger = prepare_logger(args)
logger.log('{:} use api : {:}'.format(time_string(), api))
api.reset_time()
search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
if xargs.search_space == 'tss':
cs = get_topology_config_space(xargs.max_nodes, search_space)
config2structure = config2topology_func(xargs.max_nodes)
cs = get_topology_config_space(search_space)
config2structure = config2topology_func()
else:
cs = get_size_config_space(xargs.max_nodes, search_space)
cs = get_size_config_space(search_space)
import pdb; pdb.set_trace()
hb_run_id = '0'
@@ -133,14 +103,13 @@ def main(xargs, api):
workers = []
for i in range(num_workers):
w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, dataname=dataname, nas_bench=nas_bench, time_budget=xargs.time_budget, run_id=hb_run_id, id=i)
w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, dataset=xargs.dataset, api=api, run_id=hb_run_id, id=i)
w.run(background=True)
workers.append(w)
start_time = time.time()
bohb = BOHB(configspace=cs,
run_id=hb_run_id,
eta=3, min_budget=12, max_budget=200,
bohb = BOHB(configspace=cs, run_id=hb_run_id,
eta=3, min_budget=1, max_budget=12,
nameserver=ns_host,
nameserver_port=ns_port,
num_samples=xargs.num_samples,
@@ -152,22 +121,23 @@ def main(xargs, api):
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
real_cost_time = time.time() - start_time
# print('There are {:} runs.'.format(len(results.get_all_runs())))
# workers[0].total_times
# workers[0].trajectory
current_best_index = []
for idx in range(len(workers[0].trajectory)):
trajectory = workers[0].trajectory[:idx+1]
arch = max(trajectory, key=lambda x: x[0])[1]
current_best_index.append(api.query_index_by_arch(arch))
id2config = results.get_id2config_mapping()
incumbent = results.get_incumbent_id()
logger.log('Best found configuration: {:} within {:.3f} s'.format(id2config[incumbent]['config'], real_cost_time))
best_arch = config2structure( id2config[incumbent]['config'] )
info = nas_bench.query_by_arch(best_arch, '200')
if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
else : logger.log('{:}'.format(info))
best_arch = max(workers[0].trajectory, key=lambda x: x[0])[1]
logger.log('Best found configuration: {:} within {:.3f} s'.format(best_arch, workers[0].total_times[-1]))
info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
logger.log('{:}'.format(info))
logger.log('-'*100)
logger.log('workers : {:.1f}s with {:} archs'.format(workers[0].time_budget, len(workers[0].seen_archs)))
logger.close()
return logger.log_dir, nas_bench.query_index_by_arch( best_arch ), real_cost_time
return logger.log_dir, current_best_index, workers[0].total_times
if __name__ == '__main__':
@@ -185,8 +155,8 @@ if __name__ == '__main__':
parser.add_argument('--bandwidth_factor', default=3, type=int, nargs='?', help='factor multiplied to the bandwidth')
parser.add_argument('--n_iters', default=300, type=int, nargs='?', help='number of iterations for optimization method')
# log
parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
parser.add_argument('--rand_seed', type=int, help='manual seed')
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':

2
exps/algos-v2/random_wo_share.py
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@@ -43,7 +43,7 @@ def main(xargs, api):
current_best_index = []
while len(total_time_cost) == 0 or total_time_cost[-1] < xargs.time_budget:
arch = random_arch()
accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, hp='12')
total_time_cost.append(total_cost)
history.append(arch)
if best_arch is None or best_acc < accuracy:

4
exps/algos-v2/regularized_ea.py
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@@ -160,7 +160,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
while len(population) < population_size:
model = Model()
model.arch = random_arch()
model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, hp='12')
# Append the info
population.append(model)
history.append((model.accuracy, model.arch))
@@ -184,7 +184,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
# Create the child model and store it.
child = Model()
child.arch = mutate_arch(parent.arch)
child.accuracy, _, _, total_cost = api.simulate_train_eval(child.arch, dataset, '12')
child.accuracy, _, _, total_cost = api.simulate_train_eval(child.arch, dataset, hp='12')
# Append the info
population.append(child)
history.append((child.accuracy, child.arch))

2
exps/algos-v2/reinforce.py
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@@ -150,7 +150,7 @@ def main(xargs, api):
start_time = time.time()
log_prob, action = select_action( policy )
arch = policy.generate_arch( action )
reward, _, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
reward, _, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, hp='12')
trace.append((reward, arch))
total_costs.append(current_total_cost)

7
exps/algos-v2/run-all.sh
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@@ -1,18 +1,19 @@
#!/bin/bash
# bash ./exps/algos-v2/run-all.sh
set -e
echo script name: $0
echo $# arguments
datasets="cifar10 cifar100 ImageNet16-120"
search_spaces="tss sss"
for dataset in ${datasets}
do
for search_space in ${search_spaces}
do
# python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
# python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
python exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
done
done

24
exps/experimental/vis-bench-algos.py
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@@ -5,7 +5,7 @@
###############################################################
# Usage: python exps/experimental/vis-bench-algos.py #
###############################################################
import os, sys, time, torch, argparse
import os, gc, sys, time, torch, argparse
import numpy as np
from typing import List, Text, Dict, Any
from shutil import copyfile
@@ -31,6 +31,7 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
alg2name['REA'] = 'R-EA-SS3'
alg2name['REINFORCE'] = 'REINFORCE-0.001'
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])
@@ -58,14 +59,27 @@ def query_performance(api, data, dataset, ticket):
results.append(interplate)
return sum(results) / len(results)
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'): 94.5,
('cifar10', 'sss'): 93.3,
('cifar100', 'tss'): 72,
('cifar100', 'sss'): 70,
('ImageNet16-120', 'tss'): 44,
('ImageNet16-120', 'sss'): 46}
def visualize_curve(api, vis_save_dir, search_space, max_time):
vis_save_dir = vis_save_dir.resolve()
vis_save_dir.mkdir(parents=True, exist_ok=True)
dpi, width, height = 250, 5100, 1500
dpi, width, height = 250, 5200, 1400
figsize = width / float(dpi), height / float(dpi)
LabelSize, LegendFontsize = 14, 14
LabelSize, LegendFontsize = 16, 16
def sub_plot_fn(ax, dataset):
alg2data = fetch_data(search_space=search_space, dataset=dataset)
@@ -73,6 +87,8 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
total_tickets = 150
time_tickets = [float(i) / total_tickets * max_time for i in range(total_tickets)]
colors = ['b', 'g', 'c', 'm', 'y']
ax.set_xlim(0, 200)
ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
for idx, (alg, data) in enumerate(alg2data.items()):
print('plot alg : {:}'.format(alg))
accuracies = []
@@ -107,5 +123,7 @@ if __name__ == '__main__':
api201 = NASBench201API(verbose=False)
visualize_curve(api201, save_dir, 'tss', args.max_time)
del api201
gc.collect()
api301 = NASBench301API(verbose=False)
visualize_curve(api301, save_dir, 'sss', args.max_time)

6
lib/nas_201_api/api_utils.py
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@@ -68,14 +68,14 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
def reset_time(self):
self._used_time = 0
def simulate_train_eval(self, arch, dataset, hp='12', account_time=True):
def simulate_train_eval(self, arch, dataset, iepoch=None, hp='12', account_time=True):
index = self.query_index_by_arch(arch)
all_names = ('cifar10', 'cifar100', 'ImageNet16-120')
assert dataset in all_names, 'Invalid dataset name : {:} vs {:}'.format(dataset, all_names)
if dataset == 'cifar10':
info = self.get_more_info(index, 'cifar10-valid', iepoch=None, hp=hp, is_random=True)
info = self.get_more_info(index, 'cifar10-valid', iepoch=iepoch, hp=hp, is_random=True)
else:
info = self.get_more_info(index, dataset, iepoch=None, hp=hp, is_random=True)
info = self.get_more_info(index, dataset, iepoch=iepoch, hp=hp, is_random=True)
valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
latency = self.get_latency(index, dataset)
if account_time: