269 lines
14 KiB
Python
269 lines
14 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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##################################################################
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# Regularized Evolution for Image Classifier Architecture Search #
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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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import torch
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import torch.nn as nn
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from pathlib import Path
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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 as API
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from models import CellStructure, get_search_spaces
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class Model(object):
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def __init__(self):
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self.arch = None
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self.accuracy = None
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def __str__(self):
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"""Prints a readable version of this bitstring."""
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return '{:}'.format(self.arch)
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# This function is to mimic the training and evaluatinig procedure for a single architecture `arch`.
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# The time_cost is calculated as the total training time for a few (e.g., 12 epochs) plus the evaluation time for one epoch.
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# For use_012_epoch_training = True, the architecture is trained for 12 epochs, with LR being decaded from 0.1 to 0.
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# In this case, the LR schedular is converged.
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# For use_012_epoch_training = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
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#
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def train_and_eval(arch, nas_bench, extra_info, dataname='cifar10-valid', use_012_epoch_training=True):
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if use_012_epoch_training and nas_bench is not None:
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arch_index = nas_bench.query_index_by_arch( arch )
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assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
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info = nas_bench.get_more_info(arch_index, dataname, None, True)
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valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
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#_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
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elif not use_012_epoch_training and nas_bench is not None:
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# Please contact me if you want to use the following logic, because it has some potential issues.
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# Please use `use_012_epoch_training=False` for cifar10 only.
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# It did return values for cifar100 and ImageNet16-120, but it has some potential issues. (Please email me for more details)
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arch_index, nepoch = nas_bench.query_index_by_arch( arch ), 25
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assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
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xoinfo = nas_bench.get_more_info(arch_index, 'cifar10-valid', None, True)
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xocost = nas_bench.get_cost_info(arch_index, 'cifar10-valid', False)
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info = nas_bench.get_more_info(arch_index, dataname, nepoch, False, True) # use the validation accuracy after 25 training epochs, which is used in our ICLR submission (not the camera ready).
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cost = nas_bench.get_cost_info(arch_index, dataname, False)
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# The following codes are used to estimate the time cost.
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# When we build NAS-Bench-201, architectures are trained on different machines and we can not use that time record.
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# When we create checkpoints for converged_LR, we run all experiments on 1080Ti, and thus the time for each architecture can be fairly compared.
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nums = {'ImageNet16-120-train': 151700, 'ImageNet16-120-valid': 3000,
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'cifar10-valid-train' : 25000, 'cifar10-valid-valid' : 25000,
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'cifar100-train' : 50000, 'cifar100-valid' : 5000}
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estimated_train_cost = xoinfo['train-per-time'] / nums['cifar10-valid-train'] * nums['{:}-train'.format(dataname)] / xocost['latency'] * cost['latency'] * nepoch
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estimated_valid_cost = xoinfo['valid-per-time'] / nums['cifar10-valid-valid'] * nums['{:}-valid'.format(dataname)] / xocost['latency'] * cost['latency']
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try:
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valid_acc, time_cost = info['valid-accuracy'], estimated_train_cost + estimated_valid_cost
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except:
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valid_acc, time_cost = info['valtest-accuracy'], estimated_train_cost + estimated_valid_cost
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else:
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# train a model from scratch.
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raise ValueError('NOT IMPLEMENT YET')
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return valid_acc, time_cost
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def random_architecture_func(max_nodes, op_names):
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# return a random architecture
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def random_architecture():
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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 = random.choice( op_names )
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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 random_architecture
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def mutate_arch_func(op_names):
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"""Computes the architecture for a child of the given parent architecture.
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The parent architecture is cloned and mutated to produce the child architecture. The child architecture is mutated by randomly switch one operation to another.
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"""
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def mutate_arch_func(parent_arch):
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child_arch = deepcopy( parent_arch )
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node_id = random.randint(0, len(child_arch.nodes)-1)
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node_info = list( child_arch.nodes[node_id] )
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snode_id = random.randint(0, len(node_info)-1)
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xop = random.choice( op_names )
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while xop == node_info[snode_id][0]:
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xop = random.choice( op_names )
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node_info[snode_id] = (xop, node_info[snode_id][1])
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child_arch.nodes[node_id] = tuple( node_info )
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return child_arch
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return mutate_arch_func
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def regularized_evolution(cycles, population_size, sample_size, time_budget, random_arch, mutate_arch, nas_bench, extra_info, dataname):
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"""Algorithm for regularized evolution (i.e. aging evolution).
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Follows "Algorithm 1" in Real et al. "Regularized Evolution for Image
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Classifier Architecture Search".
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Args:
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cycles: the number of cycles the algorithm should run for.
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population_size: the number of individuals to keep in the population.
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sample_size: the number of individuals that should participate in each tournament.
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time_budget: the upper bound of searching cost
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Returns:
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history: a list of `Model` instances, representing all the models computed
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during the evolution experiment.
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"""
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population = collections.deque()
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history, total_time_cost = [], 0 # Not used by the algorithm, only used to report results.
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# Initialize the population with random models.
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while len(population) < population_size:
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model = Model()
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model.arch = random_arch()
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model.accuracy, time_cost = train_and_eval(model.arch, nas_bench, extra_info, dataname)
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population.append(model)
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history.append(model)
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total_time_cost += time_cost
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# Carry out evolution in cycles. Each cycle produces a model and removes
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# another.
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#while len(history) < cycles:
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while total_time_cost < time_budget:
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# Sample randomly chosen models from the current population.
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start_time, sample = time.time(), []
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while len(sample) < sample_size:
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# Inefficient, but written this way for clarity. In the case of neural
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# nets, the efficiency of this line is irrelevant because training neural
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# nets is the rate-determining step.
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candidate = random.choice(list(population))
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sample.append(candidate)
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# The parent is the best model in the sample.
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parent = max(sample, key=lambda i: i.accuracy)
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# Create the child model and store it.
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child = Model()
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child.arch = mutate_arch(parent.arch)
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total_time_cost += time.time() - start_time
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child.accuracy, time_cost = train_and_eval(child.arch, nas_bench, extra_info, dataname)
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if total_time_cost + time_cost > time_budget: # return
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return history, total_time_cost
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else:
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total_time_cost += time_cost
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population.append(child)
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history.append(child)
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# Remove the oldest model.
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population.popleft()
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return history, total_time_cost
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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(xargs.dataset, xargs.data_path, -1)
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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(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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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(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split) , num_workers=xargs.workers, pin_memory=True)
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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)
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logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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extra_info = {'config': config, 'train_loader': train_loader, 'valid_loader': valid_loader}
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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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search_space = get_search_spaces('cell', xargs.search_space_name)
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random_arch = random_architecture_func(xargs.max_nodes, search_space)
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mutate_arch = mutate_arch_func(search_space)
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#x =random_arch() ; y = mutate_arch(x)
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x_start_time = time.time()
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logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
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logger.log('-'*30 + ' start searching with the time budget of {:} s'.format(xargs.time_budget))
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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)
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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))
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best_arch = max(history, key=lambda i: i.accuracy)
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best_arch = best_arch.arch
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logger.log('{:} best arch is {:}'.format(time_string(), best_arch))
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info = nas_bench.query_by_arch( best_arch )
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if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
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else : 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, nas_bench.query_index_by_arch( best_arch )
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Regularized Evolution 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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# 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('--num_cells', type=int, help='The number of cells in one stage.')
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parser.add_argument('--ea_cycles', type=int, help='The number of cycles in EA.')
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parser.add_argument('--ea_population', type=int, help='The population size in EA.')
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parser.add_argument('--ea_sample_size', type=int, help='The sample size in EA.')
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parser.add_argument('--ea_fast_by_api', type=int, help='Use our API to speed up the experiments or not.')
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parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_dir', type=str, 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.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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args.ea_fast_by_api = args.ea_fast_by_api > 0
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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 ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
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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 = 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 = main(args, nas_bench)
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all_indexes.append( index )
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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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