274 lines
10 KiB
Python
274 lines
10 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/NATS-algos/reinforce.py --dataset cifar10 --search_space tss --learning_rate 0.01
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# python ./exps/NATS-algos/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.01
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# python ./exps/NATS-algos/reinforce.py --dataset ImageNet16-120 --search_space tss --learning_rate 0.01
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# python ./exps/NATS-algos/reinforce.py --dataset cifar10 --search_space sss --learning_rate 0.01
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# python ./exps/NATS-algos/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.01
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# python ./exps/NATS-algos/reinforce.py --dataset ImageNet16-120 --search_space sss --learning_rate 0.01
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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:
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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 (
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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 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 models import CellStructure, get_search_spaces
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from nats_bench import create
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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(
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1e-3 * torch.randn(len(self.edge2index), len(search_space))
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)
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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(
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1e-3 * torch.randn(self.numbers, len(self.candidates))
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)
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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 = (
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self._momentum * self._numerator + (1 - self._momentum) * value
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)
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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, "nats-bench")
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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(
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"Will start searching with time budget of {:} s.".format(xargs.time_budget)
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)
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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(
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arch, xargs.dataset, hp="12"
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)
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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(
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"step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}".format(
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total_steps, baseline.value(), policy_loss.item(), policy.genotype()
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)
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)
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# to analyze
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current_best_index.append(
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api.query_index_by_arch(max(trace, key=lambda x: x[0])[1])
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)
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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(
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"REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).".format(
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total_steps, total_costs[-1], time.time() - x_start_time
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)
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)
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info = api.query_info_str_by_arch(
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best_arch, "200" if xargs.search_space == "tss" else "90"
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)
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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(
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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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parser.add_argument(
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"--search_space",
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type=str,
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choices=["tss", "sss"],
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help="Choose the search space.",
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)
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parser.add_argument(
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"--learning_rate", type=float, help="The learning rate for REINFORCE."
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)
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parser.add_argument(
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"--EMA_momentum", type=float, default=0.9, help="The momentum value for EMA."
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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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default=20000,
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help="The total time cost budge for searching (in seconds).",
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)
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parser.add_argument(
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"--loops_if_rand", type=int, default=500, help="The total runs for evaluation."
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)
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# log
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./output/search",
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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, default=-1, help="manual seed")
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args = parser.parse_args()
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api = create(None, args.search_space, fast_mode=True, verbose=False)
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args.save_dir = os.path.join(
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"{:}-{:}".format(args.save_dir, args.search_space),
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"{:}-T{:}".format(args.dataset, args.time_budget),
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"REINFORCE-{:}".format(args.learning_rate),
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)
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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, "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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