345 lines
18 KiB
Python
345 lines
18 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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##########################################################################
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# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
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##########################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np
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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, get_nas_search_loaders
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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 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 train_shared_cnn(xloader, shared_cnn, controller, criterion, scheduler, optimizer, epoch_str, print_freq, logger):
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data_time, batch_time = AverageMeter(), AverageMeter()
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losses, top1s, top5s, xend = AverageMeter(), AverageMeter(), AverageMeter(), time.time()
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shared_cnn.train()
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controller.eval()
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for step, (inputs, targets) in enumerate(xloader):
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scheduler.update(None, 1.0 * step / len(xloader))
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targets = targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - xend)
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with torch.no_grad():
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_, _, sampled_arch = controller()
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optimizer.zero_grad()
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shared_cnn.module.update_arch(sampled_arch)
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_, logits = shared_cnn(inputs)
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loss = criterion(logits, targets)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
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optimizer.step()
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# record
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1s.update (prec1.item(), inputs.size(0))
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top5s.update (prec5.item(), inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - xend)
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xend = time.time()
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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = '*Train-Shared-CNN* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
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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)
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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)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
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return losses.avg, top1s.avg, top5s.avg
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def train_controller(xloader, shared_cnn, controller, criterion, optimizer, config, epoch_str, print_freq, logger):
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# config. (containing some necessary arg)
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# baseline: The baseline score (i.e. average val_acc) from the previous epoch
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data_time, batch_time = AverageMeter(), AverageMeter()
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GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
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shared_cnn.eval()
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controller.train()
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controller.zero_grad()
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#for step, (inputs, targets) in enumerate(xloader):
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loader_iter = iter(xloader)
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for step in range(config.ctl_train_steps * config.ctl_num_aggre):
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try:
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inputs, targets = next(loader_iter)
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except:
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loader_iter = iter(xloader)
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inputs, targets = next(loader_iter)
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targets = targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - xend)
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log_prob, entropy, sampled_arch = controller()
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with torch.no_grad():
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shared_cnn.module.update_arch(sampled_arch)
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_, logits = shared_cnn(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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val_top1 = val_top1.view(-1) / 100
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reward = val_top1 + config.ctl_entropy_w * entropy
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if config.baseline is None:
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baseline = val_top1
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else:
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baseline = config.baseline - (1 - config.ctl_bl_dec) * (config.baseline - reward)
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loss = -1 * log_prob * (reward - baseline)
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# account
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RewardMeter.update(reward.item())
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BaselineMeter.update(baseline.item())
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ValAccMeter.update(val_top1.item()*100)
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LossMeter.update(loss.item())
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EntropyMeter.update(entropy.item())
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# Average gradient over controller_num_aggregate samples
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loss = loss / config.ctl_num_aggre
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loss.backward(retain_graph=True)
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# measure elapsed time
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batch_time.update(time.time() - xend)
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xend = time.time()
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if (step+1) % config.ctl_num_aggre == 0:
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grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), 5.0)
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GradnormMeter.update(grad_norm)
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optimizer.step()
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controller.zero_grad()
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if step % print_freq == 0:
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Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre)
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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)
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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)
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Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val, EntropyMeter.avg)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)
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return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item()
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def get_best_arch(controller, shared_cnn, xloader, n_samples=10):
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with torch.no_grad():
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controller.eval()
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shared_cnn.eval()
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archs, valid_accs = [], []
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loader_iter = iter(xloader)
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for i in range(n_samples):
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try:
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inputs, targets = next(loader_iter)
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except:
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loader_iter = iter(xloader)
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inputs, targets = next(loader_iter)
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_, _, sampled_arch = controller()
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arch = shared_cnn.module.update_arch(sampled_arch)
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_, logits = shared_cnn(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
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archs.append( arch )
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valid_accs.append( val_top1.item() )
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best_idx = np.argmax(valid_accs)
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best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
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return best_arch, best_valid_acc
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def valid_func(xloader, network, criterion):
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data_time, batch_time = AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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network.eval()
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end = time.time()
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with torch.no_grad():
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for step, (arch_inputs, arch_targets) in enumerate(xloader):
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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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# prediction
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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arch_losses.update(arch_loss.item(), arch_inputs.size(0))
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arch_top1.update (arch_prec1.item(), arch_inputs.size(0))
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arch_top5.update (arch_prec5.item(), arch_inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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return arch_losses.avg, arch_top1.avg, arch_top5.avg
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def main(xargs):
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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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train_data, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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logger.log('use config from : {:}'.format(xargs.config_path))
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config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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_, train_loader, valid_loader = get_nas_search_loaders(train_data, test_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
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# since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader
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valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform)
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if hasattr(valid_loader.dataset, 'transforms'):
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valid_loader.dataset.transforms = deepcopy(train_loader.dataset.transforms)
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# data loader
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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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search_space = get_search_spaces('cell', xargs.search_space_name)
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model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells,
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'max_nodes': xargs.max_nodes, 'num_classes': class_num,
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'space' : search_space,
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'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
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shared_cnn = get_cell_based_tiny_net(model_config)
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controller = shared_cnn.create_controller()
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(shared_cnn.parameters(), config)
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a_optimizer = torch.optim.Adam(controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps)
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logger.log('w-optimizer : {:}'.format(w_optimizer))
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logger.log('a-optimizer : {:}'.format(a_optimizer))
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logger.log('w-scheduler : {:}'.format(w_scheduler))
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logger.log('criterion : {:}'.format(criterion))
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#flop, param = get_model_infos(shared_cnn, xshape)
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#logger.log('{:}'.format(shared_cnn))
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#logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
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logger.log('search-space : {:}'.format(search_space))
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if xargs.arch_nas_dataset is None:
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api = None
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else:
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api = API(xargs.arch_nas_dataset)
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logger.log('{:} create API = {:} done'.format(time_string(), api))
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shared_cnn, controller, criterion = torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda()
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last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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last_info = torch.load(last_info)
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start_epoch = last_info['epoch']
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checkpoint = torch.load(last_info['last_checkpoint'])
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genotypes = checkpoint['genotypes']
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baseline = checkpoint['baseline']
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valid_accuracies = checkpoint['valid_accuracies']
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shared_cnn.load_state_dict( checkpoint['shared_cnn'] )
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controller.load_state_dict( checkpoint['controller'] )
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w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
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w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )
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a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )
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logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, genotypes, baseline = 0, {'best': -1}, {}, None
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# start training
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start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
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for epoch in range(start_epoch, total_epoch):
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w_scheduler.update(epoch, 0.0)
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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
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epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
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logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), baseline))
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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)
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logger.log('[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, cnn_loss, cnn_top1, cnn_top5))
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ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline \
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= train_controller(valid_loader, shared_cnn, controller, criterion, a_optimizer, \
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dict2config({'baseline': baseline,
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'ctl_train_steps': xargs.controller_train_steps, 'ctl_num_aggre': xargs.controller_num_aggregate,
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'ctl_entropy_w': xargs.controller_entropy_weight,
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'ctl_bl_dec' : xargs.controller_bl_dec}, None), \
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epoch_str, xargs.print_freq, logger)
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search_time.update(time.time() - start_time)
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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))
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best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader)
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shared_cnn.module.update_arch(best_arch)
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_, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion)
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genotypes[epoch] = best_arch
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# check the best accuracy
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valid_accuracies[epoch] = best_valid_acc
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if best_valid_acc > valid_accuracies['best']:
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valid_accuracies['best'] = best_valid_acc
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genotypes['best'] = best_arch
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find_best = True
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else: find_best = False
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logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
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# save checkpoint
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save_path = save_checkpoint({'epoch' : epoch + 1,
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'args' : deepcopy(xargs),
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'baseline' : baseline,
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'shared_cnn' : shared_cnn.state_dict(),
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'controller' : controller.state_dict(),
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'w_optimizer' : w_optimizer.state_dict(),
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'a_optimizer' : a_optimizer.state_dict(),
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'w_scheduler' : w_scheduler.state_dict(),
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'genotypes' : genotypes,
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'valid_accuracies' : valid_accuracies},
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model_base_path, logger)
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last_info = save_checkpoint({
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'epoch': epoch + 1,
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'args' : deepcopy(args),
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'last_checkpoint': save_path,
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}, logger.path('info'), logger)
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if find_best:
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logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, best_valid_acc))
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copy_checkpoint(model_base_path, model_best_path, logger)
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if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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logger.log('\n' + '-'*100)
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logger.log('During searching, the best architecture is {:}'.format(genotypes['best']))
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logger.log('Its accuracy is {:.2f}%'.format(valid_accuracies['best']))
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logger.log('Randomly select {:} architectures and select the best.'.format(xargs.controller_num_samples))
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start_time = time.time()
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final_arch, _ = get_best_arch(controller, shared_cnn, valid_loader, xargs.controller_num_samples)
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search_time.update(time.time() - start_time)
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shared_cnn.module.update_arch(final_arch)
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final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion)
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logger.log('The Selected Final Architecture : {:}'.format(final_arch))
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logger.log('Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%'.format(final_loss, final_top1, final_top5))
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logger.log('ENAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, final_arch))
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if api is not None: logger.log('{:}'.format( api.query_by_arch(final_arch) ))
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logger.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("ENAS")
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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('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
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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('--config_path', type=str, help='The config file to train ENAS.')
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parser.add_argument('--controller_train_steps', type=int, help='.')
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parser.add_argument('--controller_num_aggregate', type=int, help='.')
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parser.add_argument('--controller_entropy_weight', type=float, help='The weight for the entropy of the controller.')
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parser.add_argument('--controller_bl_dec' , type=float, help='.')
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parser.add_argument('--controller_num_samples' , type=int, help='.')
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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 (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, 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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main(args)
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