Move algos to NAS-Bench-201-algos
This commit is contained in:
D-X-Y
367
exps/NAS-Bench-201-algos/BOHB.py
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367
exps/NAS-Bench-201-algos/BOHB.py
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@@ -0,0 +1,367 @@
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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###################################################################
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
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# required to install hpbandster ##################################
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# pip install hpbandster ##################################
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###################################################################
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# bash ./scripts-search/algos/BOHB.sh -1 ##################
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###################################################################
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import os, sys, time, random, argparse
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from copy import deepcopy
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from pathlib import Path
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import torch
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from xautodl.config_utils import load_config
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from xautodl.datasets import get_datasets, SearchDataset
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from xautodl.procedures import prepare_seed, prepare_logger
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.models import CellStructure, get_search_spaces
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from nas_201_api import NASBench201API as API
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
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import ConfigSpace
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from hpbandster.optimizers.bohb import BOHB
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import hpbandster.core.nameserver as hpns
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from hpbandster.core.worker import Worker
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def get_configuration_space(max_nodes, search_space):
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cs = ConfigSpace.ConfigurationSpace()
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# edge2index = {}
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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cs.add_hyperparameter(
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ConfigSpace.CategoricalHyperparameter(node_str, search_space)
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)
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return cs
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def config2structure_func(max_nodes):
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def config2structure(config):
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genotypes = []
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for i in range(1, max_nodes):
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xlist = []
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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op_name = config[node_str]
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xlist.append((op_name, j))
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genotypes.append(tuple(xlist))
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return CellStructure(genotypes)
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return config2structure
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class MyWorker(Worker):
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def __init__(
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self,
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*args,
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convert_func=None,
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dataname=None,
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nas_bench=None,
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time_budget=None,
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**kwargs
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):
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super().__init__(*args, **kwargs)
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self.convert_func = convert_func
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self._dataname = dataname
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self._nas_bench = nas_bench
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self.time_budget = time_budget
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self.seen_archs = []
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self.sim_cost_time = 0
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self.real_cost_time = 0
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self.is_end = False
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def get_the_best(self):
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assert len(self.seen_archs) > 0
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best_index, best_acc = -1, None
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for arch_index in self.seen_archs:
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info = self._nas_bench.get_more_info(
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arch_index, self._dataname, None, hp="200", is_random=True
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)
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vacc = info["valid-accuracy"]
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if best_acc is None or best_acc < vacc:
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best_acc = vacc
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best_index = arch_index
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assert best_index != -1
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return best_index
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def compute(self, config, budget, **kwargs):
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start_time = time.time()
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structure = self.convert_func(config)
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arch_index = self._nas_bench.query_index_by_arch(structure)
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info = self._nas_bench.get_more_info(
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arch_index, self._dataname, None, hp="200", is_random=True
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)
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cur_time = info["train-all-time"] + info["valid-per-time"]
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cur_vacc = info["valid-accuracy"]
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self.real_cost_time += time.time() - start_time
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if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
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self.sim_cost_time += cur_time
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self.seen_archs.append(arch_index)
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return {
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"loss": 100 - float(cur_vacc),
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"info": {
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"seen-arch": len(self.seen_archs),
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"sim-test-time": self.sim_cost_time,
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"current-arch": arch_index,
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},
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}
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else:
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self.is_end = True
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return {
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"loss": 100,
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"info": {
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"seen-arch": len(self.seen_archs),
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"sim-test-time": self.sim_cost_time,
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"current-arch": None,
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},
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}
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def main(xargs, nas_bench):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(xargs.workers)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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if xargs.dataset == "cifar10":
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dataname = "cifar10-valid"
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else:
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dataname = xargs.dataset
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(
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xargs.dataset, xargs.data_path, -1
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)
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split_Fpath = "configs/nas-benchmark/cifar-split.txt"
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log("Load split file from {:}".format(split_Fpath))
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(
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config_path, {"class_num": class_num, "xshape": xshape}, logger
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)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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logger.log(
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"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
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)
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)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {
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"config": config,
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"train_loader": train_loader,
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"valid_loader": valid_loader,
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}
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else:
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(config_path, None, logger)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {"config": config, "train_loader": None, "valid_loader": None}
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# nas dataset load
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assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset)
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search_space = get_search_spaces("cell", xargs.search_space_name)
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cs = get_configuration_space(xargs.max_nodes, search_space)
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config2structure = config2structure_func(xargs.max_nodes)
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hb_run_id = "0"
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NS = hpns.NameServer(run_id=hb_run_id, host="localhost", port=0)
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ns_host, ns_port = NS.start()
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num_workers = 1
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# nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
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# logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
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workers = []
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for i in range(num_workers):
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w = MyWorker(
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nameserver=ns_host,
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nameserver_port=ns_port,
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convert_func=config2structure,
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dataname=dataname,
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nas_bench=nas_bench,
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time_budget=xargs.time_budget,
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run_id=hb_run_id,
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id=i,
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)
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w.run(background=True)
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workers.append(w)
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start_time = time.time()
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bohb = BOHB(
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configspace=cs,
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run_id=hb_run_id,
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eta=3,
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min_budget=12,
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max_budget=200,
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nameserver=ns_host,
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nameserver_port=ns_port,
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num_samples=xargs.num_samples,
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random_fraction=xargs.random_fraction,
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bandwidth_factor=xargs.bandwidth_factor,
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ping_interval=10,
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min_bandwidth=xargs.min_bandwidth,
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)
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results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
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bohb.shutdown(shutdown_workers=True)
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NS.shutdown()
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real_cost_time = time.time() - start_time
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id2config = results.get_id2config_mapping()
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incumbent = results.get_incumbent_id()
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logger.log(
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"Best found configuration: {:} within {:.3f} s".format(
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id2config[incumbent]["config"], real_cost_time
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)
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)
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best_arch = config2structure(id2config[incumbent]["config"])
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info = nas_bench.query_by_arch(best_arch, "200")
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if info is None:
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logger.log("Did not find this architecture : {:}.".format(best_arch))
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else:
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logger.log("{:}".format(info))
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logger.log("-" * 100)
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logger.log(
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"workers : {:.1f}s with {:} archs".format(
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workers[0].time_budget, len(workers[0].seen_archs)
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)
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)
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch(best_arch), real_cost_time
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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"BOHB: Robust and Efficient Hyperparameter Optimization at Scale"
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)
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parser.add_argument("--data_path", type=str, help="Path to dataset")
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parser.add_argument(
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"--dataset",
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type=str,
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choices=["cifar10", "cifar100", "ImageNet16-120"],
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help="Choose between Cifar10/100 and ImageNet-16.",
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)
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# channels and number-of-cells
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parser.add_argument("--search_space_name", type=str, help="The search space name.")
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parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
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parser.add_argument("--channel", type=int, help="The number of channels.")
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parser.add_argument(
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"--num_cells", type=int, help="The number of cells in one stage."
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)
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parser.add_argument(
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"--time_budget",
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type=int,
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help="The total time cost budge for searching (in seconds).",
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)
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# BOHB
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parser.add_argument(
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"--strategy",
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default="sampling",
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type=str,
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nargs="?",
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help="optimization strategy for the acquisition function",
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)
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parser.add_argument(
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"--min_bandwidth",
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default=0.3,
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type=float,
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nargs="?",
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help="minimum bandwidth for KDE",
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)
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parser.add_argument(
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"--num_samples",
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default=64,
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type=int,
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nargs="?",
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help="number of samples for the acquisition function",
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)
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parser.add_argument(
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"--random_fraction",
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default=0.33,
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type=float,
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nargs="?",
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help="fraction of random configurations",
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)
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parser.add_argument(
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"--bandwidth_factor",
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default=3,
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type=int,
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nargs="?",
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help="factor multiplied to the bandwidth",
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)
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parser.add_argument(
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"--n_iters",
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default=100,
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type=int,
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nargs="?",
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help="number of iterations for optimization method",
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)
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# log
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parser.add_argument(
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"--workers",
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type=int,
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default=2,
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help="number of data loading workers (default: 2)",
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)
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parser.add_argument(
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"--save_dir", type=str, help="Folder to save checkpoints and log."
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)
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parser.add_argument(
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"--arch_nas_dataset",
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type=str,
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help="The path to load the architecture dataset (tiny-nas-benchmark).",
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)
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parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
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parser.add_argument("--rand_seed", type=int, help="manual seed")
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args = parser.parse_args()
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# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
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nas_bench = None
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else:
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print(
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"{:} build NAS-Benchmark-API from {:}".format(
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time_string(), args.arch_nas_dataset
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)
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)
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nas_bench = API(args.arch_nas_dataset)
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if args.rand_seed < 0:
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save_dir, all_indexes, num, all_times = None, [], 500, []
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for i in range(num):
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print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
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args.rand_seed = random.randint(1, 100000)
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save_dir, index, ctime = main(args, nas_bench)
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all_indexes.append(index)
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all_times.append(ctime)
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print("\n average time : {:.3f} s".format(sum(all_times) / len(all_times)))
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torch.save(all_indexes, save_dir / "results.pth")
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else:
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main(args, nas_bench)
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417
exps/NAS-Bench-201-algos/DARTS-V1.py
Normal file
417
exps/NAS-Bench-201-algos/DARTS-V1.py
Normal file
@@ -0,0 +1,417 @@
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##################################################
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||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
########################################################
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# DARTS: Differentiable Architecture Search, ICLR 2019 #
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########################################################
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import sys, time, random, argparse
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from copy import deepcopy
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import torch
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from pathlib import Path
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from xautodl.config_utils import load_config, dict2config, configure2str
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from xautodl.datasets import get_datasets, get_nas_search_loaders
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from xautodl.procedures import (
|
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prepare_seed,
|
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prepare_logger,
|
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save_checkpoint,
|
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copy_checkpoint,
|
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get_optim_scheduler,
|
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)
|
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from xautodl.utils import get_model_infos, obtain_accuracy
|
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.models import get_cell_based_tiny_net, get_search_spaces
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from nas_201_api import NASBench201API as API
|
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def search_func(
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xloader,
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network,
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criterion,
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scheduler,
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w_optimizer,
|
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a_optimizer,
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epoch_str,
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print_freq,
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logger,
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gradient_clip,
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):
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data_time, batch_time = AverageMeter(), AverageMeter()
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base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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network.train()
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end = time.time()
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for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
|
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xloader
|
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):
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scheduler.update(None, 1.0 * step / len(xloader))
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base_targets = base_targets.cuda(non_blocking=True)
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arch_targets = arch_targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - end)
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|
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# update the weights
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w_optimizer.zero_grad()
|
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_, logits = network(base_inputs)
|
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base_loss = criterion(logits, base_targets)
|
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base_loss.backward()
|
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if gradient_clip > 0:
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torch.nn.utils.clip_grad_norm_(network.parameters(), gradient_clip)
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w_optimizer.step()
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# record
|
||||
base_prec1, base_prec5 = obtain_accuracy(
|
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logits.data, base_targets.data, topk=(1, 5)
|
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)
|
||||
base_losses.update(base_loss.item(), base_inputs.size(0))
|
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base_top1.update(base_prec1.item(), base_inputs.size(0))
|
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base_top5.update(base_prec5.item(), base_inputs.size(0))
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||||
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||||
# update the architecture-weight
|
||||
a_optimizer.zero_grad()
|
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
|
||||
arch_loss.backward()
|
||||
a_optimizer.step()
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
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logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
|
||||
if step % print_freq == 0 or step + 1 == len(xloader):
|
||||
Sstr = (
|
||||
"*SEARCH* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=base_losses, top1=base_top1, top5=base_top5
|
||||
)
|
||||
Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=arch_losses, top1=arch_top1, top5=arch_top5
|
||||
)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
|
||||
return base_losses.avg, base_top1.avg, base_top5.avg
|
||||
|
||||
|
||||
def valid_func(xloader, network, criterion):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.eval()
|
||||
end = time.time()
|
||||
with torch.no_grad():
|
||||
for step, (arch_inputs, arch_targets) in enumerate(xloader):
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
# prediction
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
return arch_losses.avg, arch_top1.avg, arch_top5.avg
|
||||
|
||||
|
||||
def main(xargs):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
# config_path = 'configs/nas-benchmark/algos/DARTS.config'
|
||||
config = load_config(
|
||||
xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
search_loader, _, valid_loader = get_nas_search_loaders(
|
||||
train_data,
|
||||
valid_data,
|
||||
xargs.dataset,
|
||||
"configs/nas-benchmark/",
|
||||
config.batch_size,
|
||||
xargs.workers,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(search_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
if xargs.model_config is None:
|
||||
model_config = dict2config(
|
||||
{
|
||||
"name": "DARTS-V1",
|
||||
"C": xargs.channel,
|
||||
"N": xargs.num_cells,
|
||||
"max_nodes": xargs.max_nodes,
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
else:
|
||||
model_config = load_config(
|
||||
xargs.model_config,
|
||||
{
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
search_model = get_cell_based_tiny_net(model_config)
|
||||
logger.log("search-model :\n{:}".format(search_model))
|
||||
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
|
||||
search_model.get_weights(), config
|
||||
)
|
||||
a_optimizer = torch.optim.Adam(
|
||||
search_model.get_alphas(),
|
||||
lr=xargs.arch_learning_rate,
|
||||
betas=(0.5, 0.999),
|
||||
weight_decay=xargs.arch_weight_decay,
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("a-optimizer : {:}".format(a_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
flop, param = get_model_infos(search_model, xshape)
|
||||
# logger.log('{:}'.format(search_model))
|
||||
logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
|
||||
if xargs.arch_nas_dataset is None:
|
||||
api = None
|
||||
else:
|
||||
api = API(xargs.arch_nas_dataset)
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||
)
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
last_info, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes = (
|
||||
0,
|
||||
{"best": -1},
|
||||
{-1: search_model.genotype()},
|
||||
)
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = (
|
||||
time.time(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
config.epochs + config.warmup,
|
||||
)
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
w_scheduler.update(epoch, 0.0)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, LR={:}".format(
|
||||
epoch_str, need_time, min(w_scheduler.get_lr())
|
||||
)
|
||||
)
|
||||
|
||||
search_w_loss, search_w_top1, search_w_top5 = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
xargs.gradient_clip,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
|
||||
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
|
||||
)
|
||||
)
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
||||
valid_loader, network, criterion
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
|
||||
)
|
||||
)
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
if valid_a_top1 > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = valid_a_top1
|
||||
genotypes["best"] = search_model.genotype()
|
||||
find_best = True
|
||||
else:
|
||||
find_best = False
|
||||
|
||||
genotypes[epoch] = search_model.genotype()
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
|
||||
)
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"search_model": search_model.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"a_optimizer": a_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
if find_best:
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
|
||||
epoch_str, valid_a_top1
|
||||
)
|
||||
)
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
with torch.no_grad():
|
||||
# logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
|
||||
logger.log("{:}".format(search_model.show_alphas()))
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
logger.log("\n" + "-" * 100)
|
||||
logger.log(
|
||||
"DARTS-V1 : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
total_epoch, search_time.sum, genotypes[total_epoch - 1]
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200")))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("DARTS first order")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
parser.add_argument("--config_path", type=str, help="The config path.")
|
||||
parser.add_argument(
|
||||
"--model_config",
|
||||
type=str,
|
||||
help="The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.",
|
||||
)
|
||||
parser.add_argument("--gradient_clip", type=float, default=5, help="")
|
||||
# architecture leraning rate
|
||||
parser.add_argument(
|
||||
"--arch_learning_rate",
|
||||
type=float,
|
||||
default=3e-4,
|
||||
help="learning rate for arch encoding",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_weight_decay",
|
||||
type=float,
|
||||
default=1e-3,
|
||||
help="weight decay for arch encoding",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
main(args)
|
||||
496
exps/NAS-Bench-201-algos/DARTS-V2.py
Normal file
496
exps/NAS-Bench-201-algos/DARTS-V2.py
Normal file
@@ -0,0 +1,496 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
########################################################
|
||||
# DARTS: Differentiable Architecture Search, ICLR 2019 #
|
||||
########################################################
|
||||
import os, sys, time, glob, random, argparse
|
||||
import numpy as np
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, get_nas_search_loaders
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import get_cell_based_tiny_net, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
def _concat(xs):
|
||||
return torch.cat([x.view(-1) for x in xs])
|
||||
|
||||
|
||||
def _hessian_vector_product(
|
||||
vector, network, criterion, base_inputs, base_targets, r=1e-2
|
||||
):
|
||||
R = r / _concat(vector).norm()
|
||||
for p, v in zip(network.module.get_weights(), vector):
|
||||
p.data.add_(R, v)
|
||||
_, logits = network(base_inputs)
|
||||
loss = criterion(logits, base_targets)
|
||||
grads_p = torch.autograd.grad(loss, network.module.get_alphas())
|
||||
|
||||
for p, v in zip(network.module.get_weights(), vector):
|
||||
p.data.sub_(2 * R, v)
|
||||
_, logits = network(base_inputs)
|
||||
loss = criterion(logits, base_targets)
|
||||
grads_n = torch.autograd.grad(loss, network.module.get_alphas())
|
||||
|
||||
for p, v in zip(network.module.get_weights(), vector):
|
||||
p.data.add_(R, v)
|
||||
return [(x - y).div_(2 * R) for x, y in zip(grads_p, grads_n)]
|
||||
|
||||
|
||||
def backward_step_unrolled(
|
||||
network,
|
||||
criterion,
|
||||
base_inputs,
|
||||
base_targets,
|
||||
w_optimizer,
|
||||
arch_inputs,
|
||||
arch_targets,
|
||||
):
|
||||
# _compute_unrolled_model
|
||||
_, logits = network(base_inputs)
|
||||
loss = criterion(logits, base_targets)
|
||||
LR, WD, momentum = (
|
||||
w_optimizer.param_groups[0]["lr"],
|
||||
w_optimizer.param_groups[0]["weight_decay"],
|
||||
w_optimizer.param_groups[0]["momentum"],
|
||||
)
|
||||
with torch.no_grad():
|
||||
theta = _concat(network.module.get_weights())
|
||||
try:
|
||||
moment = _concat(
|
||||
w_optimizer.state[v]["momentum_buffer"]
|
||||
for v in network.module.get_weights()
|
||||
)
|
||||
moment = moment.mul_(momentum)
|
||||
except:
|
||||
moment = torch.zeros_like(theta)
|
||||
dtheta = (
|
||||
_concat(torch.autograd.grad(loss, network.module.get_weights()))
|
||||
+ WD * theta
|
||||
)
|
||||
params = theta.sub(LR, moment + dtheta)
|
||||
unrolled_model = deepcopy(network)
|
||||
model_dict = unrolled_model.state_dict()
|
||||
new_params, offset = {}, 0
|
||||
for k, v in network.named_parameters():
|
||||
if "arch_parameters" in k:
|
||||
continue
|
||||
v_length = np.prod(v.size())
|
||||
new_params[k] = params[offset : offset + v_length].view(v.size())
|
||||
offset += v_length
|
||||
model_dict.update(new_params)
|
||||
unrolled_model.load_state_dict(model_dict)
|
||||
|
||||
unrolled_model.zero_grad()
|
||||
_, unrolled_logits = unrolled_model(arch_inputs)
|
||||
unrolled_loss = criterion(unrolled_logits, arch_targets)
|
||||
unrolled_loss.backward()
|
||||
|
||||
dalpha = unrolled_model.module.arch_parameters.grad
|
||||
vector = [v.grad.data for v in unrolled_model.module.get_weights()]
|
||||
[implicit_grads] = _hessian_vector_product(
|
||||
vector, network, criterion, base_inputs, base_targets
|
||||
)
|
||||
|
||||
dalpha.data.sub_(LR, implicit_grads.data)
|
||||
|
||||
if network.module.arch_parameters.grad is None:
|
||||
network.module.arch_parameters.grad = deepcopy(dalpha)
|
||||
else:
|
||||
network.module.arch_parameters.grad.data.copy_(dalpha.data)
|
||||
return unrolled_loss.detach(), unrolled_logits.detach()
|
||||
|
||||
|
||||
def search_func(
|
||||
xloader,
|
||||
network,
|
||||
criterion,
|
||||
scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
print_freq,
|
||||
logger,
|
||||
):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.train()
|
||||
end = time.time()
|
||||
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
|
||||
xloader
|
||||
):
|
||||
scheduler.update(None, 1.0 * step / len(xloader))
|
||||
base_targets = base_targets.cuda(non_blocking=True)
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
|
||||
# update the architecture-weight
|
||||
a_optimizer.zero_grad()
|
||||
arch_loss, arch_logits = backward_step_unrolled(
|
||||
network,
|
||||
criterion,
|
||||
base_inputs,
|
||||
base_targets,
|
||||
w_optimizer,
|
||||
arch_inputs,
|
||||
arch_targets,
|
||||
)
|
||||
a_optimizer.step()
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
arch_logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
|
||||
# update the weights
|
||||
w_optimizer.zero_grad()
|
||||
_, logits = network(base_inputs)
|
||||
base_loss = criterion(logits, base_targets)
|
||||
base_loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
|
||||
w_optimizer.step()
|
||||
# record
|
||||
base_prec1, base_prec5 = obtain_accuracy(
|
||||
logits.data, base_targets.data, topk=(1, 5)
|
||||
)
|
||||
base_losses.update(base_loss.item(), base_inputs.size(0))
|
||||
base_top1.update(base_prec1.item(), base_inputs.size(0))
|
||||
base_top5.update(base_prec5.item(), base_inputs.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
|
||||
if step % print_freq == 0 or step + 1 == len(xloader):
|
||||
Sstr = (
|
||||
"*SEARCH* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=base_losses, top1=base_top1, top5=base_top5
|
||||
)
|
||||
Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=arch_losses, top1=arch_top1, top5=arch_top5
|
||||
)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
|
||||
return base_losses.avg, base_top1.avg, base_top5.avg
|
||||
|
||||
|
||||
def valid_func(xloader, network, criterion):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.eval()
|
||||
end = time.time()
|
||||
with torch.no_grad():
|
||||
for step, (arch_inputs, arch_targets) in enumerate(xloader):
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
# prediction
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
return arch_losses.avg, arch_top1.avg, arch_top5.avg
|
||||
|
||||
|
||||
def main(xargs):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
config = load_config(
|
||||
xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
search_loader, _, valid_loader = get_nas_search_loaders(
|
||||
train_data,
|
||||
valid_data,
|
||||
xargs.dataset,
|
||||
"configs/nas-benchmark/",
|
||||
config.batch_size,
|
||||
xargs.workers,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(search_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
model_config = dict2config(
|
||||
{
|
||||
"name": "DARTS-V2",
|
||||
"C": xargs.channel,
|
||||
"N": xargs.num_cells,
|
||||
"max_nodes": xargs.max_nodes,
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
search_model = get_cell_based_tiny_net(model_config)
|
||||
logger.log("search-model :\n{:}".format(search_model))
|
||||
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
|
||||
search_model.get_weights(), config
|
||||
)
|
||||
a_optimizer = torch.optim.Adam(
|
||||
search_model.get_alphas(),
|
||||
lr=xargs.arch_learning_rate,
|
||||
betas=(0.5, 0.999),
|
||||
weight_decay=xargs.arch_weight_decay,
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("a-optimizer : {:}".format(a_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
flop, param = get_model_infos(search_model, xshape)
|
||||
# logger.log('{:}'.format(search_model))
|
||||
logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
|
||||
if xargs.arch_nas_dataset is None:
|
||||
api = None
|
||||
else:
|
||||
api = API(xargs.arch_nas_dataset)
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||
)
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
last_info, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes = (
|
||||
0,
|
||||
{"best": -1},
|
||||
{-1: search_model.genotype()},
|
||||
)
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = (
|
||||
time.time(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
config.epochs + config.warmup,
|
||||
)
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
w_scheduler.update(epoch, 0.0)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
|
||||
min_LR = min(w_scheduler.get_lr())
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, LR={:}".format(
|
||||
epoch_str, need_time, min_LR
|
||||
)
|
||||
)
|
||||
|
||||
search_w_loss, search_w_top1, search_w_top5 = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
|
||||
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
|
||||
)
|
||||
)
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
||||
valid_loader, network, criterion
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
|
||||
)
|
||||
)
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
if valid_a_top1 > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = valid_a_top1
|
||||
genotypes["best"] = search_model.genotype()
|
||||
find_best = True
|
||||
else:
|
||||
find_best = False
|
||||
|
||||
genotypes[epoch] = search_model.genotype()
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
|
||||
)
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"search_model": search_model.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"a_optimizer": a_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
if find_best:
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
|
||||
epoch_str, valid_a_top1
|
||||
)
|
||||
)
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
with torch.no_grad():
|
||||
logger.log(
|
||||
"arch-parameters :\n{:}".format(
|
||||
nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu()
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
logger.log("\n" + "-" * 100)
|
||||
# check the performance from the architecture dataset
|
||||
logger.log(
|
||||
"DARTS-V2 : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
total_epoch, search_time.sum, genotypes[total_epoch - 1]
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1]), "200"))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("DARTS Second Order")
|
||||
parser.add_argument("--data_path", type=str, help="The path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--config_path", type=str, help="The config path.")
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
# architecture leraning rate
|
||||
parser.add_argument(
|
||||
"--arch_learning_rate",
|
||||
type=float,
|
||||
default=3e-4,
|
||||
help="learning rate for arch encoding",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_weight_decay",
|
||||
type=float,
|
||||
default=1e-3,
|
||||
help="weight decay for arch encoding",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
main(args)
|
||||
578
exps/NAS-Bench-201-algos/ENAS.py
Normal file
578
exps/NAS-Bench-201-algos/ENAS.py
Normal file
@@ -0,0 +1,578 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
##########################################################################
|
||||
# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
|
||||
##########################################################################
|
||||
import os, sys, time, glob, random, argparse
|
||||
import numpy as np
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, get_nas_search_loaders
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import get_cell_based_tiny_net, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
def train_shared_cnn(
|
||||
xloader,
|
||||
shared_cnn,
|
||||
controller,
|
||||
criterion,
|
||||
scheduler,
|
||||
optimizer,
|
||||
epoch_str,
|
||||
print_freq,
|
||||
logger,
|
||||
):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
losses, top1s, top5s, xend = (
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
time.time(),
|
||||
)
|
||||
|
||||
shared_cnn.train()
|
||||
controller.eval()
|
||||
|
||||
for step, (inputs, targets) in enumerate(xloader):
|
||||
scheduler.update(None, 1.0 * step / len(xloader))
|
||||
targets = targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - xend)
|
||||
|
||||
with torch.no_grad():
|
||||
_, _, sampled_arch = controller()
|
||||
|
||||
optimizer.zero_grad()
|
||||
shared_cnn.module.update_arch(sampled_arch)
|
||||
_, logits = shared_cnn(inputs)
|
||||
loss = criterion(logits, targets)
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
|
||||
optimizer.step()
|
||||
# record
|
||||
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
|
||||
losses.update(loss.item(), inputs.size(0))
|
||||
top1s.update(prec1.item(), inputs.size(0))
|
||||
top5s.update(prec5.item(), inputs.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - xend)
|
||||
xend = time.time()
|
||||
|
||||
if step % print_freq == 0 or step + 1 == len(xloader):
|
||||
Sstr = (
|
||||
"*Train-Shared-CNN* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=losses, top1=top1s, top5=top5s
|
||||
)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr)
|
||||
return losses.avg, top1s.avg, top5s.avg
|
||||
|
||||
|
||||
def train_controller(
|
||||
xloader,
|
||||
shared_cnn,
|
||||
controller,
|
||||
criterion,
|
||||
optimizer,
|
||||
config,
|
||||
epoch_str,
|
||||
print_freq,
|
||||
logger,
|
||||
):
|
||||
# config. (containing some necessary arg)
|
||||
# baseline: The baseline score (i.e. average val_acc) from the previous epoch
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
(
|
||||
GradnormMeter,
|
||||
LossMeter,
|
||||
ValAccMeter,
|
||||
EntropyMeter,
|
||||
BaselineMeter,
|
||||
RewardMeter,
|
||||
xend,
|
||||
) = (
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
time.time(),
|
||||
)
|
||||
|
||||
shared_cnn.eval()
|
||||
controller.train()
|
||||
controller.zero_grad()
|
||||
# for step, (inputs, targets) in enumerate(xloader):
|
||||
loader_iter = iter(xloader)
|
||||
for step in range(config.ctl_train_steps * config.ctl_num_aggre):
|
||||
try:
|
||||
inputs, targets = next(loader_iter)
|
||||
except:
|
||||
loader_iter = iter(xloader)
|
||||
inputs, targets = next(loader_iter)
|
||||
targets = targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - xend)
|
||||
|
||||
log_prob, entropy, sampled_arch = controller()
|
||||
with torch.no_grad():
|
||||
shared_cnn.module.update_arch(sampled_arch)
|
||||
_, logits = shared_cnn(inputs)
|
||||
val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
|
||||
val_top1 = val_top1.view(-1) / 100
|
||||
reward = val_top1 + config.ctl_entropy_w * entropy
|
||||
if config.baseline is None:
|
||||
baseline = val_top1
|
||||
else:
|
||||
baseline = config.baseline - (1 - config.ctl_bl_dec) * (
|
||||
config.baseline - reward
|
||||
)
|
||||
|
||||
loss = -1 * log_prob * (reward - baseline)
|
||||
|
||||
# account
|
||||
RewardMeter.update(reward.item())
|
||||
BaselineMeter.update(baseline.item())
|
||||
ValAccMeter.update(val_top1.item() * 100)
|
||||
LossMeter.update(loss.item())
|
||||
EntropyMeter.update(entropy.item())
|
||||
|
||||
# Average gradient over controller_num_aggregate samples
|
||||
loss = loss / config.ctl_num_aggre
|
||||
loss.backward(retain_graph=True)
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - xend)
|
||||
xend = time.time()
|
||||
if (step + 1) % config.ctl_num_aggre == 0:
|
||||
grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), 5.0)
|
||||
GradnormMeter.update(grad_norm)
|
||||
optimizer.step()
|
||||
controller.zero_grad()
|
||||
|
||||
if step % print_freq == 0:
|
||||
Sstr = (
|
||||
"*Train-Controller* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(
|
||||
epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre
|
||||
)
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})".format(
|
||||
loss=LossMeter,
|
||||
top1=ValAccMeter,
|
||||
reward=RewardMeter,
|
||||
basel=BaselineMeter,
|
||||
)
|
||||
Estr = "Entropy={:.4f} ({:.4f})".format(EntropyMeter.val, EntropyMeter.avg)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Estr)
|
||||
|
||||
return (
|
||||
LossMeter.avg,
|
||||
ValAccMeter.avg,
|
||||
BaselineMeter.avg,
|
||||
RewardMeter.avg,
|
||||
baseline.item(),
|
||||
)
|
||||
|
||||
|
||||
def get_best_arch(controller, shared_cnn, xloader, n_samples=10):
|
||||
with torch.no_grad():
|
||||
controller.eval()
|
||||
shared_cnn.eval()
|
||||
archs, valid_accs = [], []
|
||||
loader_iter = iter(xloader)
|
||||
for i in range(n_samples):
|
||||
try:
|
||||
inputs, targets = next(loader_iter)
|
||||
except:
|
||||
loader_iter = iter(xloader)
|
||||
inputs, targets = next(loader_iter)
|
||||
|
||||
_, _, sampled_arch = controller()
|
||||
arch = shared_cnn.module.update_arch(sampled_arch)
|
||||
_, logits = shared_cnn(inputs)
|
||||
val_top1, val_top5 = obtain_accuracy(
|
||||
logits.cpu().data, targets.data, topk=(1, 5)
|
||||
)
|
||||
|
||||
archs.append(arch)
|
||||
valid_accs.append(val_top1.item())
|
||||
|
||||
best_idx = np.argmax(valid_accs)
|
||||
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
|
||||
return best_arch, best_valid_acc
|
||||
|
||||
|
||||
def valid_func(xloader, network, criterion):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.eval()
|
||||
end = time.time()
|
||||
with torch.no_grad():
|
||||
for step, (arch_inputs, arch_targets) in enumerate(xloader):
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
# prediction
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
return arch_losses.avg, arch_top1.avg, arch_top5.avg
|
||||
|
||||
|
||||
def main(xargs):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
train_data, test_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
logger.log("use config from : {:}".format(xargs.config_path))
|
||||
config = load_config(
|
||||
xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
_, train_loader, valid_loader = get_nas_search_loaders(
|
||||
train_data,
|
||||
test_data,
|
||||
xargs.dataset,
|
||||
"configs/nas-benchmark/",
|
||||
config.batch_size,
|
||||
xargs.workers,
|
||||
)
|
||||
# since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader
|
||||
valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform)
|
||||
if hasattr(valid_loader.dataset, "transforms"):
|
||||
valid_loader.dataset.transforms = deepcopy(train_loader.dataset.transforms)
|
||||
# data loader
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
model_config = dict2config(
|
||||
{
|
||||
"name": "ENAS",
|
||||
"C": xargs.channel,
|
||||
"N": xargs.num_cells,
|
||||
"max_nodes": xargs.max_nodes,
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
shared_cnn = get_cell_based_tiny_net(model_config)
|
||||
controller = shared_cnn.create_controller()
|
||||
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
|
||||
shared_cnn.parameters(), config
|
||||
)
|
||||
a_optimizer = torch.optim.Adam(
|
||||
controller.parameters(),
|
||||
lr=config.controller_lr,
|
||||
betas=config.controller_betas,
|
||||
eps=config.controller_eps,
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("a-optimizer : {:}".format(a_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
# flop, param = get_model_infos(shared_cnn, xshape)
|
||||
# logger.log('{:}'.format(shared_cnn))
|
||||
# logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
|
||||
logger.log("search-space : {:}".format(search_space))
|
||||
if xargs.arch_nas_dataset is None:
|
||||
api = None
|
||||
else:
|
||||
api = API(xargs.arch_nas_dataset)
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
shared_cnn, controller, criterion = (
|
||||
torch.nn.DataParallel(shared_cnn).cuda(),
|
||||
controller.cuda(),
|
||||
criterion.cuda(),
|
||||
)
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||
)
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
baseline = checkpoint["baseline"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
shared_cnn.load_state_dict(checkpoint["shared_cnn"])
|
||||
controller.load_state_dict(checkpoint["controller"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
last_info, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes, baseline = 0, {"best": -1}, {}, None
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = (
|
||||
time.time(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
config.epochs + config.warmup,
|
||||
)
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
w_scheduler.update(epoch, 0.0)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}".format(
|
||||
epoch_str, need_time, min(w_scheduler.get_lr()), baseline
|
||||
)
|
||||
)
|
||||
|
||||
cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn(
|
||||
train_loader,
|
||||
shared_cnn,
|
||||
controller,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, cnn_loss, cnn_top1, cnn_top5
|
||||
)
|
||||
)
|
||||
ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline = train_controller(
|
||||
valid_loader,
|
||||
shared_cnn,
|
||||
controller,
|
||||
criterion,
|
||||
a_optimizer,
|
||||
dict2config(
|
||||
{
|
||||
"baseline": baseline,
|
||||
"ctl_train_steps": xargs.controller_train_steps,
|
||||
"ctl_num_aggre": xargs.controller_num_aggregate,
|
||||
"ctl_entropy_w": xargs.controller_entropy_weight,
|
||||
"ctl_bl_dec": xargs.controller_bl_dec,
|
||||
},
|
||||
None,
|
||||
),
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}, time-cost={:.1f} s".format(
|
||||
epoch_str,
|
||||
ctl_loss,
|
||||
ctl_acc,
|
||||
ctl_baseline,
|
||||
ctl_reward,
|
||||
baseline,
|
||||
search_time.sum,
|
||||
)
|
||||
)
|
||||
best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader)
|
||||
shared_cnn.module.update_arch(best_arch)
|
||||
_, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion)
|
||||
|
||||
genotypes[epoch] = best_arch
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = best_valid_acc
|
||||
if best_valid_acc > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = best_valid_acc
|
||||
genotypes["best"] = best_arch
|
||||
find_best = True
|
||||
else:
|
||||
find_best = False
|
||||
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
|
||||
)
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"baseline": baseline,
|
||||
"shared_cnn": shared_cnn.state_dict(),
|
||||
"controller": controller.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"a_optimizer": a_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
if find_best:
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
|
||||
epoch_str, best_valid_acc
|
||||
)
|
||||
)
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
logger.log("\n" + "-" * 100)
|
||||
logger.log(
|
||||
"During searching, the best architecture is {:}".format(genotypes["best"])
|
||||
)
|
||||
logger.log("Its accuracy is {:.2f}%".format(valid_accuracies["best"]))
|
||||
logger.log(
|
||||
"Randomly select {:} architectures and select the best.".format(
|
||||
xargs.controller_num_samples
|
||||
)
|
||||
)
|
||||
start_time = time.time()
|
||||
final_arch, _ = get_best_arch(
|
||||
controller, shared_cnn, valid_loader, xargs.controller_num_samples
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
shared_cnn.module.update_arch(final_arch)
|
||||
final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion)
|
||||
logger.log("The Selected Final Architecture : {:}".format(final_arch))
|
||||
logger.log(
|
||||
"Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%".format(
|
||||
final_loss, final_top1, final_top5
|
||||
)
|
||||
)
|
||||
logger.log(
|
||||
"ENAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
total_epoch, search_time.sum, final_arch
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(final_arch)))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("ENAS")
|
||||
parser.add_argument("--data_path", type=str, help="The path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--config_path", type=str, help="The config file to train ENAS."
|
||||
)
|
||||
parser.add_argument("--controller_train_steps", type=int, help=".")
|
||||
parser.add_argument("--controller_num_aggregate", type=int, help=".")
|
||||
parser.add_argument(
|
||||
"--controller_entropy_weight",
|
||||
type=float,
|
||||
help="The weight for the entropy of the controller.",
|
||||
)
|
||||
parser.add_argument("--controller_bl_dec", type=float, help=".")
|
||||
parser.add_argument("--controller_num_samples", type=int, help=".")
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
main(args)
|
||||
404
exps/NAS-Bench-201-algos/GDAS.py
Normal file
404
exps/NAS-Bench-201-algos/GDAS.py
Normal file
@@ -0,0 +1,404 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
###########################################################################
|
||||
# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
|
||||
###########################################################################
|
||||
import sys, time, random, argparse
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
from xautodl.config_utils import load_config, dict2config
|
||||
from xautodl.datasets import get_datasets, get_nas_search_loaders
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import get_cell_based_tiny_net, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
def search_func(
|
||||
xloader,
|
||||
network,
|
||||
criterion,
|
||||
scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
print_freq,
|
||||
logger,
|
||||
):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.train()
|
||||
end = time.time()
|
||||
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
|
||||
xloader
|
||||
):
|
||||
scheduler.update(None, 1.0 * step / len(xloader))
|
||||
base_targets = base_targets.cuda(non_blocking=True)
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
|
||||
# update the weights
|
||||
w_optimizer.zero_grad()
|
||||
_, logits = network(base_inputs)
|
||||
base_loss = criterion(logits, base_targets)
|
||||
base_loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
|
||||
w_optimizer.step()
|
||||
# record
|
||||
base_prec1, base_prec5 = obtain_accuracy(
|
||||
logits.data, base_targets.data, topk=(1, 5)
|
||||
)
|
||||
base_losses.update(base_loss.item(), base_inputs.size(0))
|
||||
base_top1.update(base_prec1.item(), base_inputs.size(0))
|
||||
base_top5.update(base_prec5.item(), base_inputs.size(0))
|
||||
|
||||
# update the architecture-weight
|
||||
a_optimizer.zero_grad()
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
arch_loss.backward()
|
||||
a_optimizer.step()
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
|
||||
if step % print_freq == 0 or step + 1 == len(xloader):
|
||||
Sstr = (
|
||||
"*SEARCH* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=base_losses, top1=base_top1, top5=base_top5
|
||||
)
|
||||
Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=arch_losses, top1=arch_top1, top5=arch_top5
|
||||
)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
|
||||
return (
|
||||
base_losses.avg,
|
||||
base_top1.avg,
|
||||
base_top5.avg,
|
||||
arch_losses.avg,
|
||||
arch_top1.avg,
|
||||
arch_top5.avg,
|
||||
)
|
||||
|
||||
|
||||
def main(xargs):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
# config_path = 'configs/nas-benchmark/algos/GDAS.config'
|
||||
config = load_config(
|
||||
xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
search_loader, _, valid_loader = get_nas_search_loaders(
|
||||
train_data,
|
||||
valid_data,
|
||||
xargs.dataset,
|
||||
"configs/nas-benchmark/",
|
||||
config.batch_size,
|
||||
xargs.workers,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(search_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
if xargs.model_config is None:
|
||||
model_config = dict2config(
|
||||
{
|
||||
"name": "GDAS",
|
||||
"C": xargs.channel,
|
||||
"N": xargs.num_cells,
|
||||
"max_nodes": xargs.max_nodes,
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
else:
|
||||
model_config = load_config(
|
||||
xargs.model_config,
|
||||
{
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
search_model = get_cell_based_tiny_net(model_config)
|
||||
logger.log("search-model :\n{:}".format(search_model))
|
||||
logger.log("model-config : {:}".format(model_config))
|
||||
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
|
||||
search_model.get_weights(), config
|
||||
)
|
||||
a_optimizer = torch.optim.Adam(
|
||||
search_model.get_alphas(),
|
||||
lr=xargs.arch_learning_rate,
|
||||
betas=(0.5, 0.999),
|
||||
weight_decay=xargs.arch_weight_decay,
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("a-optimizer : {:}".format(a_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
flop, param = get_model_infos(search_model, xshape)
|
||||
logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
|
||||
logger.log("search-space [{:} ops] : {:}".format(len(search_space), search_space))
|
||||
if xargs.arch_nas_dataset is None:
|
||||
api = None
|
||||
else:
|
||||
api = API(xargs.arch_nas_dataset)
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||
)
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
last_info, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes = (
|
||||
0,
|
||||
{"best": -1},
|
||||
{-1: search_model.genotype()},
|
||||
)
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = (
|
||||
time.time(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
config.epochs + config.warmup,
|
||||
)
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
w_scheduler.update(epoch, 0.0)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
|
||||
search_model.set_tau(
|
||||
xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
|
||||
)
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}".format(
|
||||
epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr())
|
||||
)
|
||||
)
|
||||
|
||||
(
|
||||
search_w_loss,
|
||||
search_w_top1,
|
||||
search_w_top5,
|
||||
valid_a_loss,
|
||||
valid_a_top1,
|
||||
valid_a_top5,
|
||||
) = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
|
||||
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
|
||||
)
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
|
||||
)
|
||||
)
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
if valid_a_top1 > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = valid_a_top1
|
||||
genotypes["best"] = search_model.genotype()
|
||||
find_best = True
|
||||
else:
|
||||
find_best = False
|
||||
|
||||
genotypes[epoch] = search_model.genotype()
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
|
||||
)
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"search_model": search_model.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"a_optimizer": a_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
if find_best:
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
|
||||
epoch_str, valid_a_top1
|
||||
)
|
||||
)
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
with torch.no_grad():
|
||||
logger.log("{:}".format(search_model.show_alphas()))
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
logger.log("\n" + "-" * 100)
|
||||
# check the performance from the architecture dataset
|
||||
logger.log(
|
||||
"GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
total_epoch, search_time.sum, genotypes[total_epoch - 1]
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200")))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("GDAS")
|
||||
parser.add_argument("--data_path", type=str, help="The path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config_path", type=str, help="The path of the configuration."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_config",
|
||||
type=str,
|
||||
help="The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.",
|
||||
)
|
||||
# architecture leraning rate
|
||||
parser.add_argument(
|
||||
"--arch_learning_rate",
|
||||
type=float,
|
||||
default=3e-4,
|
||||
help="learning rate for arch encoding",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_weight_decay",
|
||||
type=float,
|
||||
default=1e-3,
|
||||
help="weight decay for arch encoding",
|
||||
)
|
||||
parser.add_argument("--tau_min", type=float, help="The minimum tau for Gumbel")
|
||||
parser.add_argument("--tau_max", type=float, help="The maximum tau for Gumbel")
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
main(args)
|
||||
382
exps/NAS-Bench-201-algos/RANDOM-NAS.py
Normal file
382
exps/NAS-Bench-201-algos/RANDOM-NAS.py
Normal file
@@ -0,0 +1,382 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
##############################################################################
|
||||
# Random Search and Reproducibility for Neural Architecture Search, UAI 2019 #
|
||||
##############################################################################
|
||||
import os, sys, time, glob, random, argparse
|
||||
import numpy as np
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, get_nas_search_loaders
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import get_cell_based_tiny_net, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
def search_func(
|
||||
xloader, network, criterion, scheduler, w_optimizer, epoch_str, print_freq, logger
|
||||
):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.train()
|
||||
end = time.time()
|
||||
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
|
||||
xloader
|
||||
):
|
||||
scheduler.update(None, 1.0 * step / len(xloader))
|
||||
base_targets = base_targets.cuda(non_blocking=True)
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
|
||||
# update the weights
|
||||
network.module.random_genotype(True)
|
||||
w_optimizer.zero_grad()
|
||||
_, logits = network(base_inputs)
|
||||
base_loss = criterion(logits, base_targets)
|
||||
base_loss.backward()
|
||||
nn.utils.clip_grad_norm_(network.parameters(), 5)
|
||||
w_optimizer.step()
|
||||
# record
|
||||
base_prec1, base_prec5 = obtain_accuracy(
|
||||
logits.data, base_targets.data, topk=(1, 5)
|
||||
)
|
||||
base_losses.update(base_loss.item(), base_inputs.size(0))
|
||||
base_top1.update(base_prec1.item(), base_inputs.size(0))
|
||||
base_top5.update(base_prec5.item(), base_inputs.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
|
||||
if step % print_freq == 0 or step + 1 == len(xloader):
|
||||
Sstr = (
|
||||
"*SEARCH* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=base_losses, top1=base_top1, top5=base_top5
|
||||
)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr)
|
||||
return base_losses.avg, base_top1.avg, base_top5.avg
|
||||
|
||||
|
||||
def valid_func(xloader, network, criterion):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
network.eval()
|
||||
end = time.time()
|
||||
with torch.no_grad():
|
||||
for step, (arch_inputs, arch_targets) in enumerate(xloader):
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
# prediction
|
||||
|
||||
network.module.random_genotype(True)
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
return arch_losses.avg, arch_top1.avg, arch_top5.avg
|
||||
|
||||
|
||||
def search_find_best(xloader, network, n_samples):
|
||||
with torch.no_grad():
|
||||
network.eval()
|
||||
archs, valid_accs = [], []
|
||||
# print ('obtain the top-{:} architectures'.format(n_samples))
|
||||
loader_iter = iter(xloader)
|
||||
for i in range(n_samples):
|
||||
arch = network.module.random_genotype(True)
|
||||
try:
|
||||
inputs, targets = next(loader_iter)
|
||||
except:
|
||||
loader_iter = iter(xloader)
|
||||
inputs, targets = next(loader_iter)
|
||||
|
||||
_, logits = network(inputs)
|
||||
val_top1, val_top5 = obtain_accuracy(
|
||||
logits.cpu().data, targets.data, topk=(1, 5)
|
||||
)
|
||||
|
||||
archs.append(arch)
|
||||
valid_accs.append(val_top1.item())
|
||||
|
||||
best_idx = np.argmax(valid_accs)
|
||||
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
|
||||
return best_arch, best_valid_acc
|
||||
|
||||
|
||||
def main(xargs):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
config = load_config(
|
||||
xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
search_loader, _, valid_loader = get_nas_search_loaders(
|
||||
train_data,
|
||||
valid_data,
|
||||
xargs.dataset,
|
||||
"configs/nas-benchmark/",
|
||||
(config.batch_size, config.test_batch_size),
|
||||
xargs.workers,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(search_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
model_config = dict2config(
|
||||
{
|
||||
"name": "RANDOM",
|
||||
"C": xargs.channel,
|
||||
"N": xargs.num_cells,
|
||||
"max_nodes": xargs.max_nodes,
|
||||
"num_classes": class_num,
|
||||
"space": search_space,
|
||||
"affine": False,
|
||||
"track_running_stats": bool(xargs.track_running_stats),
|
||||
},
|
||||
None,
|
||||
)
|
||||
search_model = get_cell_based_tiny_net(model_config)
|
||||
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
|
||||
search_model.parameters(), config
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
if xargs.arch_nas_dataset is None:
|
||||
api = None
|
||||
else:
|
||||
api = API(xargs.arch_nas_dataset)
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||
)
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
last_info, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes = 0, {"best": -1}, {}
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = (
|
||||
time.time(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
config.epochs + config.warmup,
|
||||
)
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
w_scheduler.update(epoch, 0.0)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, LR={:}".format(
|
||||
epoch_str, need_time, min(w_scheduler.get_lr())
|
||||
)
|
||||
)
|
||||
|
||||
# selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num)
|
||||
search_w_loss, search_w_top1, search_w_top5 = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
|
||||
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
|
||||
)
|
||||
)
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
||||
valid_loader, network, criterion
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
|
||||
)
|
||||
)
|
||||
cur_arch, cur_valid_acc = search_find_best(
|
||||
valid_loader, network, xargs.select_num
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] find-the-best : {:}, accuracy@1={:.2f}%".format(
|
||||
epoch_str, cur_arch, cur_valid_acc
|
||||
)
|
||||
)
|
||||
genotypes[epoch] = cur_arch
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
if valid_a_top1 > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = valid_a_top1
|
||||
find_best = True
|
||||
else:
|
||||
find_best = False
|
||||
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"search_model": search_model.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
if find_best:
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
|
||||
epoch_str, valid_a_top1
|
||||
)
|
||||
)
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
logger.log("\n" + "-" * 200)
|
||||
logger.log("Pre-searching costs {:.1f} s".format(search_time.sum))
|
||||
start_time = time.time()
|
||||
best_arch, best_acc = search_find_best(valid_loader, network, xargs.select_num)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.".format(
|
||||
best_arch, best_acc, search_time.sum
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(best_arch, "200")))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Random search for NAS.")
|
||||
parser.add_argument("--data_path", type=str, help="The path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument(
|
||||
"--config_path", type=str, help="The path to the configuration."
|
||||
)
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--select_num",
|
||||
type=int,
|
||||
help="The number of selected architectures to evaluate.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
main(args)
|
||||
189
exps/NAS-Bench-201-algos/RANDOM.py
Normal file
189
exps/NAS-Bench-201-algos/RANDOM.py
Normal file
@@ -0,0 +1,189 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
##############################################################################
|
||||
import os, sys, time, glob, random, argparse
|
||||
import numpy as np, collections
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from pathlib import Path
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, SearchDataset
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import get_search_spaces
|
||||
|
||||
from nas_201_api import NASBench201API as API
|
||||
from R_EA import train_and_eval, random_architecture_func
|
||||
|
||||
|
||||
def main(xargs, nas_bench):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
if xargs.dataset == "cifar10":
|
||||
dataname = "cifar10-valid"
|
||||
else:
|
||||
dataname = xargs.dataset
|
||||
if xargs.data_path is not None:
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
split_Fpath = "configs/nas-benchmark/cifar-split.txt"
|
||||
cifar_split = load_config(split_Fpath, None, None)
|
||||
train_split, valid_split = cifar_split.train, cifar_split.valid
|
||||
logger.log("Load split file from {:}".format(split_Fpath))
|
||||
config_path = "configs/nas-benchmark/algos/R-EA.config"
|
||||
config = load_config(
|
||||
config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
# To split data
|
||||
train_data_v2 = deepcopy(train_data)
|
||||
train_data_v2.transform = valid_data.transform
|
||||
valid_data = train_data_v2
|
||||
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
|
||||
# data loader
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
|
||||
num_workers=xargs.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_loader = torch.utils.data.DataLoader(
|
||||
valid_data,
|
||||
batch_size=config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
|
||||
num_workers=xargs.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
extra_info = {
|
||||
"config": config,
|
||||
"train_loader": train_loader,
|
||||
"valid_loader": valid_loader,
|
||||
}
|
||||
else:
|
||||
config_path = "configs/nas-benchmark/algos/R-EA.config"
|
||||
config = load_config(config_path, None, logger)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
extra_info = {"config": config, "train_loader": None, "valid_loader": None}
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
random_arch = random_architecture_func(xargs.max_nodes, search_space)
|
||||
# x =random_arch() ; y = mutate_arch(x)
|
||||
x_start_time = time.time()
|
||||
logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
|
||||
best_arch, best_acc, total_time_cost, history = None, -1, 0, []
|
||||
# for idx in range(xargs.random_num):
|
||||
while total_time_cost < xargs.time_budget:
|
||||
arch = random_arch()
|
||||
accuracy, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
|
||||
if total_time_cost + cost_time > xargs.time_budget:
|
||||
break
|
||||
else:
|
||||
total_time_cost += cost_time
|
||||
history.append(arch)
|
||||
if best_arch is None or best_acc < accuracy:
|
||||
best_acc, best_arch = accuracy, arch
|
||||
logger.log(
|
||||
"[{:03d}] : {:} : accuracy = {:.2f}%".format(len(history), arch, accuracy)
|
||||
)
|
||||
logger.log(
|
||||
"{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s (real-cost = {:.3f} s).".format(
|
||||
time_string(),
|
||||
best_arch,
|
||||
best_acc,
|
||||
len(history),
|
||||
total_time_cost,
|
||||
time.time() - x_start_time,
|
||||
)
|
||||
)
|
||||
|
||||
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))
|
||||
logger.log("-" * 100)
|
||||
logger.close()
|
||||
return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Random NAS")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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('--random_num', type=int, help='The number of random selected architectures.')
|
||||
parser.add_argument(
|
||||
"--time_budget",
|
||||
type=int,
|
||||
help="The total time cost budge for searching (in seconds).",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
|
||||
if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
|
||||
nas_bench = None
|
||||
else:
|
||||
print(
|
||||
"{:} build NAS-Benchmark-API from {:}".format(
|
||||
time_string(), args.arch_nas_dataset
|
||||
)
|
||||
)
|
||||
nas_bench = API(args.arch_nas_dataset)
|
||||
if args.rand_seed < 0:
|
||||
save_dir, all_indexes, num = None, [], 500
|
||||
for i in range(num):
|
||||
print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
save_dir, index = main(args, nas_bench)
|
||||
all_indexes.append(index)
|
||||
torch.save(all_indexes, save_dir / "results.pth")
|
||||
else:
|
||||
main(args, nas_bench)
|
||||
7
exps/NAS-Bench-201-algos/README.md
Normal file
7
exps/NAS-Bench-201-algos/README.md
Normal file
@@ -0,0 +1,7 @@
|
||||
# NAS Algorithms evaluated in NAS-Bench-201
|
||||
|
||||
The Python files in this folder are used to re-produce the results in our NAS-Bench-201 paper.
|
||||
|
||||
We have upgraded the codes to be more general and extendable at [NATS-algos](https://github.com/D-X-Y/AutoDL-Projects/tree/main/exps/NATS-algos).
|
||||
|
||||
**Notice** On 24 May 2021, the codes in `AutoDL` repo have been re-organized. If you find `module not found` error, please let me know. I will fix them ASAP.
|
||||
399
exps/NAS-Bench-201-algos/R_EA.py
Normal file
399
exps/NAS-Bench-201-algos/R_EA.py
Normal file
@@ -0,0 +1,399 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
##################################################################
|
||||
# Regularized Evolution for Image Classifier Architecture Search #
|
||||
##################################################################
|
||||
import os, sys, time, glob, random, argparse
|
||||
import numpy as np, collections
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from pathlib import Path
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, SearchDataset
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import CellStructure, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
class Model(object):
|
||||
def __init__(self):
|
||||
self.arch = None
|
||||
self.accuracy = None
|
||||
|
||||
def __str__(self):
|
||||
"""Prints a readable version of this bitstring."""
|
||||
return "{:}".format(self.arch)
|
||||
|
||||
|
||||
# This function is to mimic the training and evaluatinig procedure for a single architecture `arch`.
|
||||
# The time_cost is calculated as the total training time for a few (e.g., 12 epochs) plus the evaluation time for one epoch.
|
||||
# For use_012_epoch_training = True, the architecture is trained for 12 epochs, with LR being decaded from 0.1 to 0.
|
||||
# In this case, the LR schedular is converged.
|
||||
# For use_012_epoch_training = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
|
||||
#
|
||||
def train_and_eval(
|
||||
arch, nas_bench, extra_info, dataname="cifar10-valid", use_012_epoch_training=True
|
||||
):
|
||||
|
||||
if use_012_epoch_training and 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, dataname, iepoch=None, hp="12", is_random=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
|
||||
elif not use_012_epoch_training and nas_bench is not None:
|
||||
# Please contact me if you want to use the following logic, because it has some potential issues.
|
||||
# Please use `use_012_epoch_training=False` for cifar10 only.
|
||||
# It did return values for cifar100 and ImageNet16-120, but it has some potential issues. (Please email me for more details)
|
||||
arch_index, nepoch = nas_bench.query_index_by_arch(arch), 25
|
||||
assert arch_index >= 0, "can not find this arch : {:}".format(arch)
|
||||
xoinfo = nas_bench.get_more_info(
|
||||
arch_index, "cifar10-valid", iepoch=None, hp="12"
|
||||
)
|
||||
xocost = nas_bench.get_cost_info(arch_index, "cifar10-valid", hp="200")
|
||||
info = nas_bench.get_more_info(
|
||||
arch_index, dataname, nepoch, hp="200", is_random=True
|
||||
) # use the validation accuracy after 25 training epochs, which is used in our ICLR submission (not the camera ready).
|
||||
cost = nas_bench.get_cost_info(arch_index, dataname, hp="200")
|
||||
# The following codes are used to estimate the time cost.
|
||||
# When we build NAS-Bench-201, architectures are trained on different machines and we can not use that time record.
|
||||
# When we create checkpoints for converged_LR, we run all experiments on 1080Ti, and thus the time for each architecture can be fairly compared.
|
||||
nums = {
|
||||
"ImageNet16-120-train": 151700,
|
||||
"ImageNet16-120-valid": 3000,
|
||||
"cifar10-valid-train": 25000,
|
||||
"cifar10-valid-valid": 25000,
|
||||
"cifar100-train": 50000,
|
||||
"cifar100-valid": 5000,
|
||||
}
|
||||
estimated_train_cost = (
|
||||
xoinfo["train-per-time"]
|
||||
/ nums["cifar10-valid-train"]
|
||||
* nums["{:}-train".format(dataname)]
|
||||
/ xocost["latency"]
|
||||
* cost["latency"]
|
||||
* nepoch
|
||||
)
|
||||
estimated_valid_cost = (
|
||||
xoinfo["valid-per-time"]
|
||||
/ nums["cifar10-valid-valid"]
|
||||
* nums["{:}-valid".format(dataname)]
|
||||
/ xocost["latency"]
|
||||
* cost["latency"]
|
||||
)
|
||||
try:
|
||||
valid_acc, time_cost = (
|
||||
info["valid-accuracy"],
|
||||
estimated_train_cost + estimated_valid_cost,
|
||||
)
|
||||
except:
|
||||
valid_acc, time_cost = (
|
||||
info["valtest-accuracy"],
|
||||
estimated_train_cost + estimated_valid_cost,
|
||||
)
|
||||
else:
|
||||
# train a model from scratch.
|
||||
raise ValueError("NOT IMPLEMENT YET")
|
||||
return valid_acc, time_cost
|
||||
|
||||
|
||||
def random_architecture_func(max_nodes, op_names):
|
||||
# return a random architecture
|
||||
def random_architecture():
|
||||
genotypes = []
|
||||
for i in range(1, max_nodes):
|
||||
xlist = []
|
||||
for j in range(i):
|
||||
node_str = "{:}<-{:}".format(i, j)
|
||||
op_name = random.choice(op_names)
|
||||
xlist.append((op_name, j))
|
||||
genotypes.append(tuple(xlist))
|
||||
return CellStructure(genotypes)
|
||||
|
||||
return random_architecture
|
||||
|
||||
|
||||
def mutate_arch_func(op_names):
|
||||
"""Computes the architecture for a child of the given parent architecture.
|
||||
The parent architecture is cloned and mutated to produce the child architecture. The child architecture is mutated by randomly switch one operation to another.
|
||||
"""
|
||||
|
||||
def mutate_arch_func(parent_arch):
|
||||
child_arch = deepcopy(parent_arch)
|
||||
node_id = random.randint(0, len(child_arch.nodes) - 1)
|
||||
node_info = list(child_arch.nodes[node_id])
|
||||
snode_id = random.randint(0, len(node_info) - 1)
|
||||
xop = random.choice(op_names)
|
||||
while xop == node_info[snode_id][0]:
|
||||
xop = random.choice(op_names)
|
||||
node_info[snode_id] = (xop, node_info[snode_id][1])
|
||||
child_arch.nodes[node_id] = tuple(node_info)
|
||||
return child_arch
|
||||
|
||||
return mutate_arch_func
|
||||
|
||||
|
||||
def regularized_evolution(
|
||||
cycles,
|
||||
population_size,
|
||||
sample_size,
|
||||
time_budget,
|
||||
random_arch,
|
||||
mutate_arch,
|
||||
nas_bench,
|
||||
extra_info,
|
||||
dataname,
|
||||
):
|
||||
"""Algorithm for regularized evolution (i.e. aging evolution).
|
||||
|
||||
Follows "Algorithm 1" in Real et al. "Regularized Evolution for Image
|
||||
Classifier Architecture Search".
|
||||
|
||||
Args:
|
||||
cycles: the number of cycles the algorithm should run for.
|
||||
population_size: the number of individuals to keep in the population.
|
||||
sample_size: the number of individuals that should participate in each tournament.
|
||||
time_budget: the upper bound of searching cost
|
||||
|
||||
Returns:
|
||||
history: a list of `Model` instances, representing all the models computed
|
||||
during the evolution experiment.
|
||||
"""
|
||||
population = collections.deque()
|
||||
history, total_time_cost = (
|
||||
[],
|
||||
0,
|
||||
) # Not used by the algorithm, only used to report results.
|
||||
|
||||
# Initialize the population with random models.
|
||||
while len(population) < population_size:
|
||||
model = Model()
|
||||
model.arch = random_arch()
|
||||
model.accuracy, time_cost = train_and_eval(
|
||||
model.arch, nas_bench, extra_info, dataname
|
||||
)
|
||||
population.append(model)
|
||||
history.append(model)
|
||||
total_time_cost += time_cost
|
||||
|
||||
# Carry out evolution in cycles. Each cycle produces a model and removes
|
||||
# another.
|
||||
# while len(history) < cycles:
|
||||
while total_time_cost < time_budget:
|
||||
# Sample randomly chosen models from the current population.
|
||||
start_time, sample = time.time(), []
|
||||
while len(sample) < sample_size:
|
||||
# Inefficient, but written this way for clarity. In the case of neural
|
||||
# nets, the efficiency of this line is irrelevant because training neural
|
||||
# nets is the rate-determining step.
|
||||
candidate = random.choice(list(population))
|
||||
sample.append(candidate)
|
||||
|
||||
# The parent is the best model in the sample.
|
||||
parent = max(sample, key=lambda i: i.accuracy)
|
||||
|
||||
# Create the child model and store it.
|
||||
child = Model()
|
||||
child.arch = mutate_arch(parent.arch)
|
||||
total_time_cost += time.time() - start_time
|
||||
child.accuracy, time_cost = train_and_eval(
|
||||
child.arch, nas_bench, extra_info, dataname
|
||||
)
|
||||
if total_time_cost + time_cost > time_budget: # return
|
||||
return history, total_time_cost
|
||||
else:
|
||||
total_time_cost += time_cost
|
||||
population.append(child)
|
||||
history.append(child)
|
||||
|
||||
# Remove the oldest model.
|
||||
population.popleft()
|
||||
return history, total_time_cost
|
||||
|
||||
|
||||
def main(xargs, nas_bench):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
if xargs.dataset == "cifar10":
|
||||
dataname = "cifar10-valid"
|
||||
else:
|
||||
dataname = xargs.dataset
|
||||
if xargs.data_path is not None:
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
split_Fpath = "configs/nas-benchmark/cifar-split.txt"
|
||||
cifar_split = load_config(split_Fpath, None, None)
|
||||
train_split, valid_split = cifar_split.train, cifar_split.valid
|
||||
logger.log("Load split file from {:}".format(split_Fpath))
|
||||
config_path = "configs/nas-benchmark/algos/R-EA.config"
|
||||
config = load_config(
|
||||
config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
# To split data
|
||||
train_data_v2 = deepcopy(train_data)
|
||||
train_data_v2.transform = valid_data.transform
|
||||
valid_data = train_data_v2
|
||||
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
|
||||
# data loader
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
|
||||
num_workers=xargs.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_loader = torch.utils.data.DataLoader(
|
||||
valid_data,
|
||||
batch_size=config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
|
||||
num_workers=xargs.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
extra_info = {
|
||||
"config": config,
|
||||
"train_loader": train_loader,
|
||||
"valid_loader": valid_loader,
|
||||
}
|
||||
else:
|
||||
config_path = "configs/nas-benchmark/algos/R-EA.config"
|
||||
config = load_config(config_path, None, logger)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
extra_info = {"config": config, "train_loader": None, "valid_loader": None}
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
random_arch = random_architecture_func(xargs.max_nodes, search_space)
|
||||
mutate_arch = mutate_arch_func(search_space)
|
||||
# x =random_arch() ; y = mutate_arch(x)
|
||||
x_start_time = time.time()
|
||||
logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
|
||||
logger.log(
|
||||
"-" * 30
|
||||
+ " start searching with the time budget of {:} s".format(xargs.time_budget)
|
||||
)
|
||||
history, total_cost = regularized_evolution(
|
||||
xargs.ea_cycles,
|
||||
xargs.ea_population,
|
||||
xargs.ea_sample_size,
|
||||
xargs.time_budget,
|
||||
random_arch,
|
||||
mutate_arch,
|
||||
nas_bench if args.ea_fast_by_api else None,
|
||||
extra_info,
|
||||
dataname,
|
||||
)
|
||||
logger.log(
|
||||
"{:} regularized_evolution finish with history of {:} arch with {:.1f} s (real-cost={:.2f} s).".format(
|
||||
time_string(), len(history), total_cost, time.time() - x_start_time
|
||||
)
|
||||
)
|
||||
best_arch = max(history, key=lambda i: i.accuracy)
|
||||
best_arch = best_arch.arch
|
||||
logger.log("{:} best arch is {:}".format(time_string(), best_arch))
|
||||
|
||||
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))
|
||||
logger.log("-" * 100)
|
||||
logger.close()
|
||||
return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Regularized Evolution Algorithm")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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("--ea_cycles", type=int, help="The number of cycles in EA.")
|
||||
parser.add_argument("--ea_population", type=int, help="The population size in EA.")
|
||||
parser.add_argument("--ea_sample_size", type=int, help="The sample size in EA.")
|
||||
parser.add_argument(
|
||||
"--ea_fast_by_api",
|
||||
type=int,
|
||||
help="Use our API to speed up the experiments or not.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--time_budget",
|
||||
type=int,
|
||||
help="The total time cost budge for searching (in seconds).",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
|
||||
args.ea_fast_by_api = args.ea_fast_by_api > 0
|
||||
|
||||
if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
|
||||
nas_bench = None
|
||||
else:
|
||||
print(
|
||||
"{:} build NAS-Benchmark-API from {:}".format(
|
||||
time_string(), args.arch_nas_dataset
|
||||
)
|
||||
)
|
||||
nas_bench = API(args.arch_nas_dataset)
|
||||
if args.rand_seed < 0:
|
||||
save_dir, all_indexes, num = None, [], 500
|
||||
for i in range(num):
|
||||
print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
save_dir, index = main(args, nas_bench)
|
||||
all_indexes.append(index)
|
||||
torch.save(all_indexes, save_dir / "results.pth")
|
||||
else:
|
||||
main(args, nas_bench)
|
||||
476
exps/NAS-Bench-201-algos/SETN.py
Normal file
476
exps/NAS-Bench-201-algos/SETN.py
Normal file
@@ -0,0 +1,476 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
||||
######################################################################################
|
||||
# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
|
||||
######################################################################################
|
||||
import sys, time, random, argparse
|
||||
import numpy as np
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from pathlib import Path
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, get_nas_search_loaders
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import get_cell_based_tiny_net, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
def search_func(
|
||||
xloader,
|
||||
network,
|
||||
criterion,
|
||||
scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
print_freq,
|
||||
logger,
|
||||
):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
end = time.time()
|
||||
network.train()
|
||||
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
|
||||
xloader
|
||||
):
|
||||
scheduler.update(None, 1.0 * step / len(xloader))
|
||||
base_targets = base_targets.cuda(non_blocking=True)
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
|
||||
# update the weights
|
||||
sampled_arch = network.module.dync_genotype(True)
|
||||
network.module.set_cal_mode("dynamic", sampled_arch)
|
||||
# network.module.set_cal_mode( 'urs' )
|
||||
network.zero_grad()
|
||||
_, logits = network(base_inputs)
|
||||
base_loss = criterion(logits, base_targets)
|
||||
base_loss.backward()
|
||||
w_optimizer.step()
|
||||
# record
|
||||
base_prec1, base_prec5 = obtain_accuracy(
|
||||
logits.data, base_targets.data, topk=(1, 5)
|
||||
)
|
||||
base_losses.update(base_loss.item(), base_inputs.size(0))
|
||||
base_top1.update(base_prec1.item(), base_inputs.size(0))
|
||||
base_top5.update(base_prec5.item(), base_inputs.size(0))
|
||||
|
||||
# update the architecture-weight
|
||||
network.module.set_cal_mode("joint")
|
||||
network.zero_grad()
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
arch_loss.backward()
|
||||
a_optimizer.step()
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
|
||||
if step % print_freq == 0 or step + 1 == len(xloader):
|
||||
Sstr = (
|
||||
"*SEARCH* "
|
||||
+ time_string()
|
||||
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
|
||||
)
|
||||
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
|
||||
batch_time=batch_time, data_time=data_time
|
||||
)
|
||||
Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=base_losses, top1=base_top1, top5=base_top5
|
||||
)
|
||||
Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
|
||||
loss=arch_losses, top1=arch_top1, top5=arch_top5
|
||||
)
|
||||
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
|
||||
# print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
|
||||
# print (network.module.arch_parameters)
|
||||
return (
|
||||
base_losses.avg,
|
||||
base_top1.avg,
|
||||
base_top5.avg,
|
||||
arch_losses.avg,
|
||||
arch_top1.avg,
|
||||
arch_top5.avg,
|
||||
)
|
||||
|
||||
|
||||
def get_best_arch(xloader, network, n_samples):
|
||||
with torch.no_grad():
|
||||
network.eval()
|
||||
archs, valid_accs = network.module.return_topK(n_samples), []
|
||||
# print ('obtain the top-{:} architectures'.format(n_samples))
|
||||
loader_iter = iter(xloader)
|
||||
for i, sampled_arch in enumerate(archs):
|
||||
network.module.set_cal_mode("dynamic", sampled_arch)
|
||||
try:
|
||||
inputs, targets = next(loader_iter)
|
||||
except:
|
||||
loader_iter = iter(xloader)
|
||||
inputs, targets = next(loader_iter)
|
||||
|
||||
_, logits = network(inputs)
|
||||
val_top1, val_top5 = obtain_accuracy(
|
||||
logits.cpu().data, targets.data, topk=(1, 5)
|
||||
)
|
||||
|
||||
valid_accs.append(val_top1.item())
|
||||
|
||||
best_idx = np.argmax(valid_accs)
|
||||
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
|
||||
return best_arch, best_valid_acc
|
||||
|
||||
|
||||
def valid_func(xloader, network, criterion):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
end = time.time()
|
||||
with torch.no_grad():
|
||||
network.eval()
|
||||
for step, (arch_inputs, arch_targets) in enumerate(xloader):
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
# prediction
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(
|
||||
logits.data, arch_targets.data, topk=(1, 5)
|
||||
)
|
||||
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
||||
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
||||
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
return arch_losses.avg, arch_top1.avg, arch_top5.avg
|
||||
|
||||
|
||||
def main(xargs):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
config = load_config(
|
||||
xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
search_loader, _, valid_loader = get_nas_search_loaders(
|
||||
train_data,
|
||||
valid_data,
|
||||
xargs.dataset,
|
||||
"configs/nas-benchmark/",
|
||||
(config.batch_size, config.test_batch_size),
|
||||
xargs.workers,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(search_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
if xargs.model_config is None:
|
||||
model_config = dict2config(
|
||||
dict(
|
||||
name="SETN",
|
||||
C=xargs.channel,
|
||||
N=xargs.num_cells,
|
||||
max_nodes=xargs.max_nodes,
|
||||
num_classes=class_num,
|
||||
space=search_space,
|
||||
affine=False,
|
||||
track_running_stats=bool(xargs.track_running_stats),
|
||||
),
|
||||
None,
|
||||
)
|
||||
else:
|
||||
model_config = load_config(
|
||||
xargs.model_config,
|
||||
dict(
|
||||
num_classes=class_num,
|
||||
space=search_space,
|
||||
affine=False,
|
||||
track_running_stats=bool(xargs.track_running_stats),
|
||||
),
|
||||
None,
|
||||
)
|
||||
logger.log("search space : {:}".format(search_space))
|
||||
search_model = get_cell_based_tiny_net(model_config)
|
||||
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
|
||||
search_model.get_weights(), config
|
||||
)
|
||||
a_optimizer = torch.optim.Adam(
|
||||
search_model.get_alphas(),
|
||||
lr=xargs.arch_learning_rate,
|
||||
betas=(0.5, 0.999),
|
||||
weight_decay=xargs.arch_weight_decay,
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("a-optimizer : {:}".format(a_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
flop, param = get_model_infos(search_model, xshape)
|
||||
logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
|
||||
logger.log("search-space : {:}".format(search_space))
|
||||
if xargs.arch_nas_dataset is None:
|
||||
api = None
|
||||
else:
|
||||
api = API(xargs.arch_nas_dataset)
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||
)
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
last_info, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
init_genotype, _ = get_best_arch(valid_loader, network, xargs.select_num)
|
||||
start_epoch, valid_accuracies, genotypes = 0, {"best": -1}, {-1: init_genotype}
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = (
|
||||
time.time(),
|
||||
AverageMeter(),
|
||||
AverageMeter(),
|
||||
config.epochs + config.warmup,
|
||||
)
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
w_scheduler.update(epoch, 0.0)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, LR={:}".format(
|
||||
epoch_str, need_time, min(w_scheduler.get_lr())
|
||||
)
|
||||
)
|
||||
|
||||
(
|
||||
search_w_loss,
|
||||
search_w_top1,
|
||||
search_w_top5,
|
||||
search_a_loss,
|
||||
search_a_top1,
|
||||
search_a_top5,
|
||||
) = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
|
||||
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
|
||||
)
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, search_a_loss, search_a_top1, search_a_top5
|
||||
)
|
||||
)
|
||||
|
||||
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
|
||||
network.module.set_cal_mode("dynamic", genotype)
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
||||
valid_loader, network, criterion
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}".format(
|
||||
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype
|
||||
)
|
||||
)
|
||||
# search_model.set_cal_mode('urs')
|
||||
# valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
# logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
|
||||
# search_model.set_cal_mode('joint')
|
||||
# valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
# logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
|
||||
# search_model.set_cal_mode('select')
|
||||
# valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
# logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
|
||||
genotypes[epoch] = genotype
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
|
||||
)
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"search_model": search_model.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"a_optimizer": a_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
with torch.no_grad():
|
||||
logger.log("{:}".format(search_model.show_alphas()))
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
# the final post procedure : count the time
|
||||
start_time = time.time()
|
||||
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
|
||||
search_time.update(time.time() - start_time)
|
||||
network.module.set_cal_mode("dynamic", genotype)
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
||||
valid_loader, network, criterion
|
||||
)
|
||||
logger.log(
|
||||
"Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.".format(
|
||||
genotype, valid_a_top1
|
||||
)
|
||||
)
|
||||
|
||||
logger.log("\n" + "-" * 100)
|
||||
# check the performance from the architecture dataset
|
||||
logger.log(
|
||||
"SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
total_epoch, search_time.sum, genotype
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotype, "200")))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("SETN")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--select_num",
|
||||
type=int,
|
||||
help="The number of selected architectures to evaluate.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config_path", type=str, help="The path of the configuration."
|
||||
)
|
||||
# architecture leraning rate
|
||||
parser.add_argument(
|
||||
"--arch_learning_rate",
|
||||
type=float,
|
||||
default=3e-4,
|
||||
help="learning rate for arch encoding",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_weight_decay",
|
||||
type=float,
|
||||
default=1e-3,
|
||||
help="weight decay for arch encoding",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
main(args)
|
||||
294
exps/NAS-Bench-201-algos/reinforce.py
Normal file
294
exps/NAS-Bench-201-algos/reinforce.py
Normal file
@@ -0,0 +1,294 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
||||
#####################################################################################################
|
||||
# modified from https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py #
|
||||
#####################################################################################################
|
||||
import os, sys, time, glob, random, argparse
|
||||
import numpy as np, collections
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.distributions import Categorical
|
||||
|
||||
from xautodl.config_utils import load_config, dict2config, configure2str
|
||||
from xautodl.datasets import get_datasets, SearchDataset
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
get_optim_scheduler,
|
||||
)
|
||||
from xautodl.utils import get_model_infos, obtain_accuracy
|
||||
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from xautodl.models import CellStructure, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
from R_EA import train_and_eval
|
||||
|
||||
|
||||
class Policy(nn.Module):
|
||||
def __init__(self, max_nodes, search_space):
|
||||
super(Policy, self).__init__()
|
||||
self.max_nodes = max_nodes
|
||||
self.search_space = deepcopy(search_space)
|
||||
self.edge2index = {}
|
||||
for i in range(1, max_nodes):
|
||||
for j in range(i):
|
||||
node_str = "{:}<-{:}".format(i, j)
|
||||
self.edge2index[node_str] = len(self.edge2index)
|
||||
self.arch_parameters = nn.Parameter(
|
||||
1e-3 * torch.randn(len(self.edge2index), len(search_space))
|
||||
)
|
||||
|
||||
def generate_arch(self, actions):
|
||||
genotypes = []
|
||||
for i in range(1, self.max_nodes):
|
||||
xlist = []
|
||||
for j in range(i):
|
||||
node_str = "{:}<-{:}".format(i, j)
|
||||
op_name = self.search_space[actions[self.edge2index[node_str]]]
|
||||
xlist.append((op_name, j))
|
||||
genotypes.append(tuple(xlist))
|
||||
return CellStructure(genotypes)
|
||||
|
||||
def genotype(self):
|
||||
genotypes = []
|
||||
for i in range(1, self.max_nodes):
|
||||
xlist = []
|
||||
for j in range(i):
|
||||
node_str = "{:}<-{:}".format(i, j)
|
||||
with torch.no_grad():
|
||||
weights = self.arch_parameters[self.edge2index[node_str]]
|
||||
op_name = self.search_space[weights.argmax().item()]
|
||||
xlist.append((op_name, j))
|
||||
genotypes.append(tuple(xlist))
|
||||
return CellStructure(genotypes)
|
||||
|
||||
def forward(self):
|
||||
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
|
||||
return alphas
|
||||
|
||||
|
||||
class ExponentialMovingAverage(object):
|
||||
"""Class that maintains an exponential moving average."""
|
||||
|
||||
def __init__(self, momentum):
|
||||
self._numerator = 0
|
||||
self._denominator = 0
|
||||
self._momentum = momentum
|
||||
|
||||
def update(self, value):
|
||||
self._numerator = (
|
||||
self._momentum * self._numerator + (1 - self._momentum) * value
|
||||
)
|
||||
self._denominator = self._momentum * self._denominator + (1 - self._momentum)
|
||||
|
||||
def value(self):
|
||||
"""Return the current value of the moving average"""
|
||||
return self._numerator / self._denominator
|
||||
|
||||
|
||||
def select_action(policy):
|
||||
probs = policy()
|
||||
m = Categorical(probs)
|
||||
action = m.sample()
|
||||
# policy.saved_log_probs.append(m.log_prob(action))
|
||||
return m.log_prob(action), action.cpu().tolist()
|
||||
|
||||
|
||||
def main(xargs, nas_bench):
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(xargs.workers)
|
||||
prepare_seed(xargs.rand_seed)
|
||||
logger = prepare_logger(args)
|
||||
|
||||
if xargs.dataset == "cifar10":
|
||||
dataname = "cifar10-valid"
|
||||
else:
|
||||
dataname = xargs.dataset
|
||||
if xargs.data_path is not None:
|
||||
train_data, valid_data, xshape, class_num = get_datasets(
|
||||
xargs.dataset, xargs.data_path, -1
|
||||
)
|
||||
split_Fpath = "configs/nas-benchmark/cifar-split.txt"
|
||||
cifar_split = load_config(split_Fpath, None, None)
|
||||
train_split, valid_split = cifar_split.train, cifar_split.valid
|
||||
logger.log("Load split file from {:}".format(split_Fpath))
|
||||
config_path = "configs/nas-benchmark/algos/R-EA.config"
|
||||
config = load_config(
|
||||
config_path, {"class_num": class_num, "xshape": xshape}, logger
|
||||
)
|
||||
# To split data
|
||||
train_data_v2 = deepcopy(train_data)
|
||||
train_data_v2.transform = valid_data.transform
|
||||
valid_data = train_data_v2
|
||||
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
|
||||
# data loader
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
|
||||
num_workers=xargs.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_loader = torch.utils.data.DataLoader(
|
||||
valid_data,
|
||||
batch_size=config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
|
||||
num_workers=xargs.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
logger.log(
|
||||
"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
|
||||
xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
extra_info = {
|
||||
"config": config,
|
||||
"train_loader": train_loader,
|
||||
"valid_loader": valid_loader,
|
||||
}
|
||||
else:
|
||||
config_path = "configs/nas-benchmark/algos/R-EA.config"
|
||||
config = load_config(config_path, None, logger)
|
||||
extra_info = {"config": config, "train_loader": None, "valid_loader": None}
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
search_space = get_search_spaces("cell", xargs.search_space_name)
|
||||
policy = Policy(xargs.max_nodes, search_space)
|
||||
optimizer = torch.optim.Adam(policy.parameters(), lr=xargs.learning_rate)
|
||||
# optimizer = torch.optim.SGD(policy.parameters(), lr=xargs.learning_rate)
|
||||
eps = np.finfo(np.float32).eps.item()
|
||||
baseline = ExponentialMovingAverage(xargs.EMA_momentum)
|
||||
logger.log("policy : {:}".format(policy))
|
||||
logger.log("optimizer : {:}".format(optimizer))
|
||||
logger.log("eps : {:}".format(eps))
|
||||
|
||||
# nas dataset load
|
||||
logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
|
||||
|
||||
# REINFORCE
|
||||
# attempts = 0
|
||||
x_start_time = time.time()
|
||||
logger.log(
|
||||
"Will start searching with time budget of {:} s.".format(xargs.time_budget)
|
||||
)
|
||||
total_steps, total_costs, trace = 0, 0, []
|
||||
# for istep in range(xargs.RL_steps):
|
||||
while total_costs < xargs.time_budget:
|
||||
start_time = time.time()
|
||||
log_prob, action = select_action(policy)
|
||||
arch = policy.generate_arch(action)
|
||||
reward, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
|
||||
trace.append((reward, arch))
|
||||
# accumulate time
|
||||
if total_costs + cost_time < xargs.time_budget:
|
||||
total_costs += cost_time
|
||||
else:
|
||||
break
|
||||
|
||||
baseline.update(reward)
|
||||
# calculate loss
|
||||
policy_loss = (-log_prob * (reward - baseline.value())).sum()
|
||||
optimizer.zero_grad()
|
||||
policy_loss.backward()
|
||||
optimizer.step()
|
||||
# accumulate time
|
||||
total_costs += time.time() - start_time
|
||||
total_steps += 1
|
||||
logger.log(
|
||||
"step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}".format(
|
||||
total_steps, baseline.value(), policy_loss.item(), policy.genotype()
|
||||
)
|
||||
)
|
||||
# logger.log('----> {:}'.format(policy.arch_parameters))
|
||||
# logger.log('')
|
||||
|
||||
# best_arch = policy.genotype() # first version
|
||||
best_arch = max(trace, key=lambda x: x[0])[1]
|
||||
logger.log(
|
||||
"REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).".format(
|
||||
total_steps, total_costs, time.time() - x_start_time
|
||||
)
|
||||
)
|
||||
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))
|
||||
logger.log("-" * 100)
|
||||
logger.close()
|
||||
return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("The REINFORCE Algorithm")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
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(
|
||||
"--learning_rate", type=float, help="The learning rate for REINFORCE."
|
||||
)
|
||||
# parser.add_argument('--RL_steps', type=int, help='The steps for REINFORCE.')
|
||||
parser.add_argument(
|
||||
"--EMA_momentum", type=float, help="The momentum value for EMA."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--time_budget",
|
||||
type=int,
|
||||
help="The total time cost budge for searching (in seconds).",
|
||||
)
|
||||
# log
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="number of data loading workers (default: 2)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset",
|
||||
type=str,
|
||||
help="The path to load the architecture dataset (tiny-nas-benchmark).",
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
|
||||
if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
|
||||
nas_bench = None
|
||||
else:
|
||||
print(
|
||||
"{:} build NAS-Benchmark-API from {:}".format(
|
||||
time_string(), args.arch_nas_dataset
|
||||
)
|
||||
)
|
||||
nas_bench = API(args.arch_nas_dataset)
|
||||
if args.rand_seed < 0:
|
||||
save_dir, all_indexes, num = None, [], 500
|
||||
for i in range(num):
|
||||
print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
save_dir, index = main(args, nas_bench)
|
||||
all_indexes.append(index)
|
||||
torch.save(all_indexes, save_dir / "results.pth")
|
||||
else:
|
||||
main(args, nas_bench)
|
||||
Reference in New Issue
Block a user