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update README

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D-X-Y
2019-12-28 15:42:36 +11:00
parent d791622b63
commit 4c144b7437
6 changed files with 59 additions and 28 deletions
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15
NAS-Bench-102.md
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@@ -26,9 +26,10 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default).
1. Creating an API instance from a file:
```
from nas_102_api import NASBench102API
api = NASBench102API('$path_to_meta_nas_bench_file')
api = NASBench102API('NAS-Bench-102-v1_0-e61699.pth')
from nas_102_api import NASBench102API as API
api = API('$path_to_meta_nas_bench_file')
api = API('NAS-Bench-102-v1_0-e61699.pth')
api = API('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-Bench-102-v1_0-e61699.pth'))
```
2. Show the number of architectures `len(api)` and each architecture `api[i]`:
@@ -45,12 +46,12 @@ api.show(1)
api.show(2)
# show the mean loss and accuracy of an architecture
info = api.query_meta_info_by_index(1)
res_metrics = info.get_metrics('cifar10', 'train')
cost_metrics = info.get_comput_costs('cifar100')
info = api.query_meta_info_by_index(1) # This is an instance of `ArchResults`
res_metrics = info.get_metrics('cifar10', 'train') # This is a dict with metric names as keys
cost_metrics = info.get_comput_costs('cifar100') # This is a dict with metric names as keys, e.g., flops, params, latency
# get the detailed information
results = api.query_by_index(1, 'cifar100')
results = api.query_by_index(1, 'cifar100') # a list of all trials on cifar100
print ('There are {:} trials for this architecture [{:}] on cifar100'.format(len(results), api[1]))
print ('Latency : {:}'.format(results[0].get_latency()))
print ('Train Info : {:}'.format(results[0].get_train()))

2
README.md
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@@ -35,6 +35,8 @@ We build a new benchmark for neural architecture search, please see more details
The benchmark data file (v1.0) is `NAS-Bench-102-v1_0-e61699.pth`, which can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs).
## [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/network-pruning-via-transformable/network-pruning-on-cifar-100)](https://paperswithcode.com/sota/network-pruning-on-cifar-100?p=network-pruning-via-transformable)
In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network.
You could see the highlight of our Transformable Architecture Search (TAS) at our [project page](https://xuanyidong.com/assets/projects/NeurIPS-2019-TAS.html).

56
exps/algos/BOHB.py
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@@ -2,6 +2,7 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
# required to install hpbandster #################
# bash ./scripts-search/algos/BOHB.sh -1 #
##################################################
import os, sys, time, glob, random, argparse
import numpy as np, collections
@@ -19,7 +20,6 @@ from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_102_api import NASBench102API as API
from models import CellStructure, get_search_spaces
from R_EA import train_and_eval
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
import ConfigSpace
from hpbandster.optimizers.bohb import BOHB
@@ -53,21 +53,44 @@ def config2structure_func(max_nodes):
class MyWorker(Worker):
def __init__(self, *args, sleep_interval=0, convert_func=None, nas_bench=None, **kwargs):
def __init__(self, *args, convert_func=None, nas_bench=None, time_scale=None, **kwargs):
super().__init__(*args, **kwargs)
self.sleep_interval = sleep_interval
self.convert_func = convert_func
self.nas_bench = nas_bench
self.test_time = 0
self.time_scale = time_scale
self.seen_arch = 0
self.sim_cost_time = 0
self.real_cost_time = 0
def compute(self, config, budget, **kwargs):
structure = self.convert_func( config )
reward, time_cost = train_and_eval(structure, self.nas_bench, None)
import pdb; pdb.set_trace()
self.test_time += 1
start_time = time.time()
structure = self.convert_func( config )
arch_index = self.nas_bench.query_index_by_arch( structure )
iepoch = 0
while iepoch < 12:
info = self.nas_bench.get_more_info(arch_index, 'cifar10-valid', iepoch, True)
cur_time = info['train-all-time'] + info['valid-per-time']
cur_vacc = info['valid-accuracy']
if time.time() - start_time + cur_time / self.time_scale > budget:
break
else:
iepoch += 1
self.sim_cost_time += cur_time
self.seen_arch += 1
remaining_time = cur_time / self.time_scale - (time.time() - start_time)
if remaining_time > 0:
time.sleep(remaining_time)
else:
import pdb; pdb.set_trace()
self.real_cost_time += (time.time() - start_time)
return ({
'loss': float(100-reward),
'info': time_cost})
'loss': 100 - float(cur_vacc),
'info': {'seen-arch' : self.seen_arch,
'sim-test-time' : self.sim_cost_time,
'real-test-time': self.real_cost_time,
'current-arch' : arch_index,
'current-budget': budget}
})
def main(xargs, nas_bench):
@@ -116,26 +139,30 @@ def main(xargs, nas_bench):
#logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
workers = []
for i in range(num_workers):
w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, nas_bench=nas_bench, run_id=hb_run_id, id=i)
w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, nas_bench=nas_bench, time_scale=xargs.time_scale, run_id=hb_run_id, id=i)
w.run(background=True)
workers.append(w)
simulate_time_budge = xargs.time_budget // xargs.time_scale
start_time = time.time()
logger.log('simulate_time_budge : {:} (in seconds).'.format(simulate_time_budge))
bohb = BOHB(configspace=cs,
run_id=hb_run_id,
eta=3, min_budget=3, max_budget=xargs.time_budget,
eta=3, min_budget=simulate_time_budge//3, max_budget=simulate_time_budge,
nameserver=ns_host,
nameserver_port=ns_port,
num_samples=xargs.num_samples,
random_fraction=xargs.random_fraction, bandwidth_factor=xargs.bandwidth_factor,
ping_interval=10, min_bandwidth=xargs.min_bandwidth)
# optimization_strategy=xargs.strategy, num_samples=xargs.num_samples,
results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
import pdb; pdb.set_trace()
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
real_cost_time = time.time() - start_time
import pdb; pdb.set_trace()
id2config = results.get_id2config_mapping()
incumbent = results.get_incumbent_id()
@@ -163,6 +190,7 @@ if __name__ == '__main__':
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.')
parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
parser.add_argument('--time_scale' , type=int, help='The time scale to accelerate the time budget.')
# BOHB
parser.add_argument('--strategy', default="sampling", type=str, nargs='?', help='optimization strategy for the acquisition function')
parser.add_argument('--min_bandwidth', default=.3, type=float, nargs='?', help='minimum bandwidth for KDE')

2
exps/algos/R_EA.py
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@@ -59,7 +59,7 @@ def train_and_eval(arch, nas_bench, extra_info):
if nas_bench is not None:
arch_index = nas_bench.query_index_by_arch( arch )
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
info = nas_bench.get_more_info(arch_index, 'cifar10-valid', True)
info = nas_bench.get_more_info(arch_index, 'cifar10-valid', None, True)
valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
#_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
else:

8
lib/nas_102_api/api.py
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@@ -147,14 +147,14 @@ class NASBench102API(object):
archresult = arch2infos[index]
return archresult.get_net_param(dataset, seed)
def get_more_info(self, index, dataset, use_12epochs_result=False):
def get_more_info(self, index, dataset, iepoch=None, use_12epochs_result=False):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
archresult = arch2infos[index]
if dataset == 'cifar10-valid':
train_info = archresult.get_metrics(dataset, 'train', is_random=True)
valid_info = archresult.get_metrics(dataset, 'x-valid', is_random=True)
test__info = archresult.get_metrics(dataset, 'ori-test', is_random=True)
train_info = archresult.get_metrics(dataset, 'train' , iepoch=iepoch, is_random=True)
valid_info = archresult.get_metrics(dataset, 'x-valid' , iepoch=iepoch, is_random=True)
test__info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=True)
total = train_info['iepoch'] + 1
return {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy'],

4
scripts-search/algos/BOHB.sh
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@@ -34,6 +34,6 @@ OMP_NUM_THREADS=4 python ./exps/algos/BOHB.py \
--dataset ${dataset} --data_path ${data_path} \
--search_space_name ${space} \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
--time_budget 12000 \
--n_iters 100 --num_samples 4 --random_fraction 0 \
--time_budget 12000 --time_scale 200 \
--n_iters 64 --num_samples 4 --random_fraction 0 \
--workers 4 --print_freq 200 --rand_seed ${seed}