269 lines
9.8 KiB
Python
269 lines
9.8 KiB
Python
##################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
|
|
#####################################################################################################
|
|
# modified from https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py #
|
|
#####################################################################################################
|
|
# python ./exps/NATS-algos/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.01
|
|
# python ./exps/NATS-algos/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.01
|
|
# python ./exps/NATS-algos/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.01
|
|
# python ./exps/NATS-algos/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.01
|
|
# python ./exps/NATS-algos/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.01
|
|
# python ./exps/NATS-algos/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.01
|
|
#####################################################################################################
|
|
import os, sys, time, glob, random, argparse
|
|
import numpy as np, collections
|
|
from copy import deepcopy
|
|
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 nats_bench import create
|
|
|
|
|
|
class PolicyTopology(nn.Module):
|
|
def __init__(self, search_space, max_nodes=4):
|
|
super(PolicyTopology, 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 PolicySize(nn.Module):
|
|
def __init__(self, search_space):
|
|
super(PolicySize, self).__init__()
|
|
self.candidates = search_space["candidates"]
|
|
self.numbers = search_space["numbers"]
|
|
self.arch_parameters = nn.Parameter(
|
|
1e-3 * torch.randn(self.numbers, len(self.candidates))
|
|
)
|
|
|
|
def generate_arch(self, actions):
|
|
channels = [str(self.candidates[i]) for i in actions]
|
|
return ":".join(channels)
|
|
|
|
def genotype(self):
|
|
channels = []
|
|
for i in range(self.numbers):
|
|
index = self.arch_parameters[i].argmax().item()
|
|
channels.append(str(self.candidates[index]))
|
|
return ":".join(channels)
|
|
|
|
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, api):
|
|
# torch.set_num_threads(4)
|
|
prepare_seed(xargs.rand_seed)
|
|
logger = prepare_logger(args)
|
|
|
|
search_space = get_search_spaces(xargs.search_space, "nats-bench")
|
|
if xargs.search_space == "tss":
|
|
policy = PolicyTopology(search_space)
|
|
else:
|
|
policy = PolicySize(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 api : {:}".format(time_string(), api))
|
|
api.reset_time()
|
|
|
|
# REINFORCE
|
|
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, [], []
|
|
current_best_index = []
|
|
while len(total_costs) == 0 or total_costs[-1] < xargs.time_budget:
|
|
start_time = time.time()
|
|
log_prob, action = select_action(policy)
|
|
arch = policy.generate_arch(action)
|
|
reward, _, _, current_total_cost = api.simulate_train_eval(
|
|
arch, xargs.dataset, hp="12"
|
|
)
|
|
trace.append((reward, arch))
|
|
total_costs.append(current_total_cost)
|
|
|
|
baseline.update(reward)
|
|
# calculate loss
|
|
policy_loss = (-log_prob * (reward - baseline.value())).sum()
|
|
optimizer.zero_grad()
|
|
policy_loss.backward()
|
|
optimizer.step()
|
|
# accumulate time
|
|
total_steps += 1
|
|
logger.log(
|
|
"step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}".format(
|
|
total_steps, baseline.value(), policy_loss.item(), policy.genotype()
|
|
)
|
|
)
|
|
# to analyze
|
|
current_best_index.append(
|
|
api.query_index_by_arch(max(trace, key=lambda x: x[0])[1])
|
|
)
|
|
# 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[-1], time.time() - x_start_time
|
|
)
|
|
)
|
|
info = api.query_info_str_by_arch(
|
|
best_arch, "200" if xargs.search_space == "tss" else "90"
|
|
)
|
|
logger.log("{:}".format(info))
|
|
logger.log("-" * 100)
|
|
logger.close()
|
|
|
|
return logger.log_dir, current_best_index, total_costs
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser("The REINFORCE Algorithm")
|
|
parser.add_argument(
|
|
"--dataset",
|
|
type=str,
|
|
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
|
help="Choose between Cifar10/100 and ImageNet-16.",
|
|
)
|
|
parser.add_argument(
|
|
"--search_space",
|
|
type=str,
|
|
choices=["tss", "sss"],
|
|
help="Choose the search space.",
|
|
)
|
|
parser.add_argument(
|
|
"--learning_rate", type=float, help="The learning rate for REINFORCE."
|
|
)
|
|
parser.add_argument(
|
|
"--EMA_momentum", type=float, default=0.9, help="The momentum value for EMA."
|
|
)
|
|
parser.add_argument(
|
|
"--time_budget",
|
|
type=int,
|
|
default=20000,
|
|
help="The total time cost budge for searching (in seconds).",
|
|
)
|
|
parser.add_argument(
|
|
"--loops_if_rand", type=int, default=500, help="The total runs for evaluation."
|
|
)
|
|
# log
|
|
parser.add_argument(
|
|
"--save_dir",
|
|
type=str,
|
|
default="./output/search",
|
|
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()
|
|
|
|
api = create(None, args.search_space, fast_mode=True, verbose=False)
|
|
|
|
args.save_dir = os.path.join(
|
|
"{:}-{:}".format(args.save_dir, args.search_space),
|
|
"{:}-T{:}".format(args.dataset, args.time_budget),
|
|
"REINFORCE-{:}".format(args.learning_rate),
|
|
)
|
|
print("save-dir : {:}".format(args.save_dir))
|
|
|
|
if args.rand_seed < 0:
|
|
save_dir, all_info = None, collections.OrderedDict()
|
|
for i in range(args.loops_if_rand):
|
|
print("{:} : {:03d}/{:03d}".format(time_string(), i, args.loops_if_rand))
|
|
args.rand_seed = random.randint(1, 100000)
|
|
save_dir, all_archs, all_total_times = main(args, api)
|
|
all_info[i] = {"all_archs": all_archs, "all_total_times": all_total_times}
|
|
save_path = save_dir / "results.pth"
|
|
print("save into {:}".format(save_path))
|
|
torch.save(all_info, save_path)
|
|
else:
|
|
main(args, api)
|