218 lines
9.3 KiB
Python
218 lines
9.3 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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#####################################################################################################
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# modified from https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py #
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#####################################################################################################
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# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.001
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# python ./exps/algos-v2/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.001
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#####################################################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np, collections
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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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import torch.nn as nn
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from torch.distributions import Categorical
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from nas_201_api import NASBench201API, NASBench301API
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from models import CellStructure, get_search_spaces
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class PolicyTopology(nn.Module):
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def __init__(self, search_space, max_nodes=4):
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super(PolicyTopology, self).__init__()
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self.max_nodes = max_nodes
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self.search_space = deepcopy(search_space)
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self.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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self.edge2index[ node_str ] = len(self.edge2index)
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self.arch_parameters = nn.Parameter(1e-3*torch.randn(len(self.edge2index), len(search_space)))
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def generate_arch(self, actions):
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genotypes = []
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for i in range(1, self.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 = self.search_space[ actions[ self.edge2index[ 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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def genotype(self):
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genotypes = []
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for i in range(1, self.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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with torch.no_grad():
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weights = self.arch_parameters[ self.edge2index[node_str] ]
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op_name = self.search_space[ weights.argmax().item() ]
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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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def forward(self):
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alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
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return alphas
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class PolicySize(nn.Module):
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def __init__(self, search_space):
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super(PolicySize, self).__init__()
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self.candidates = search_space['candidates']
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self.numbers = search_space['numbers']
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self.arch_parameters = nn.Parameter(1e-3*torch.randn(self.numbers, len(self.candidates)))
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def generate_arch(self, actions):
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channels = [str(self.candidates[i]) for i in actions]
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return ':'.join(channels)
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def genotype(self):
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channels = []
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for i in range(self.numbers):
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index = self.arch_parameters[i].argmax().item()
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channels.append(str(self.candidates[index]))
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return ':'.join(channels)
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def forward(self):
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alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
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return alphas
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class ExponentialMovingAverage(object):
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"""Class that maintains an exponential moving average."""
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def __init__(self, momentum):
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self._numerator = 0
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self._denominator = 0
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self._momentum = momentum
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def update(self, value):
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self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
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self._denominator = self._momentum * self._denominator + (1 - self._momentum)
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def value(self):
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"""Return the current value of the moving average"""
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return self._numerator / self._denominator
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def select_action(policy):
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probs = policy()
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m = Categorical(probs)
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action = m.sample()
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# policy.saved_log_probs.append(m.log_prob(action))
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return m.log_prob(action), action.cpu().tolist()
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def main(xargs, api):
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torch.set_num_threads(4)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
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if xargs.search_space == 'tss':
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policy = PolicyTopology(search_space)
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else:
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policy = PolicySize(search_space)
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optimizer = torch.optim.Adam(policy.parameters(), lr=xargs.learning_rate)
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#optimizer = torch.optim.SGD(policy.parameters(), lr=xargs.learning_rate)
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eps = np.finfo(np.float32).eps.item()
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baseline = ExponentialMovingAverage(xargs.EMA_momentum)
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logger.log('policy : {:}'.format(policy))
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logger.log('optimizer : {:}'.format(optimizer))
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logger.log('eps : {:}'.format(eps))
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# nas dataset load
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logger.log('{:} use api : {:}'.format(time_string(), api))
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api.reset_time()
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# REINFORCE
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x_start_time = time.time()
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logger.log('Will start searching with time budget of {:} s.'.format(xargs.time_budget))
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total_steps, total_costs, trace = 0, [], []
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current_best_index = []
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while len(total_costs) == 0 or total_costs[-1] < xargs.time_budget:
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start_time = time.time()
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log_prob, action = select_action( policy )
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arch = policy.generate_arch( action )
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reward, _, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
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trace.append((reward, arch))
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total_costs.append(current_total_cost)
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baseline.update(reward)
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# calculate loss
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policy_loss = ( -log_prob * (reward - baseline.value()) ).sum()
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optimizer.zero_grad()
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policy_loss.backward()
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optimizer.step()
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# accumulate time
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total_steps += 1
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logger.log('step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}'.format(total_steps, baseline.value(), policy_loss.item(), policy.genotype()))
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# to analyze
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current_best_index.append(api.query_index_by_arch(max(trace, key=lambda x: x[0])[1]))
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# best_arch = policy.genotype() # first version
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best_arch = max(trace, key=lambda x: x[0])[1]
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logger.log('REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).'.format(total_steps, total_costs[-1], time.time()-x_start_time))
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info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
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logger.log('{:}'.format(info))
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logger.log('-'*100)
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logger.close()
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return logger.log_dir, current_best_index, total_costs
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("The REINFORCE Algorithm")
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parser.add_argument('--data_path', type=str, help='Path to dataset')
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parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
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parser.add_argument('--learning_rate', type=float, help='The learning rate for REINFORCE.')
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parser.add_argument('--EMA_momentum', type=float, default=0.9, help='The momentum value for EMA.')
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parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).')
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parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
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# log
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parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
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parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')
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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, default=-1, help='manual seed')
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args = parser.parse_args()
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if args.search_space == 'tss':
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api = NASBench201API(verbose=False)
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elif args.search_space == 'sss':
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api = NASBench301API(verbose=False)
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else:
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raise ValueError('Invalid search space : {:}'.format(args.search_space))
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args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'REINFORCE-{:}'.format(args.learning_rate))
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print('save-dir : {:}'.format(args.save_dir))
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if args.rand_seed < 0:
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save_dir, all_info = None, collections.OrderedDict()
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for i in range(args.loops_if_rand):
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print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
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args.rand_seed = random.randint(1, 100000)
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save_dir, all_archs, all_total_times = main(args, api)
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all_info[i] = {'all_archs': all_archs,
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'all_total_times': all_total_times}
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save_path = save_dir / 'results.pth'
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print('save into {:}'.format(save_path))
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torch.save(all_info, save_path)
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else:
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main(args, api)
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