Prototype generic nas model.
This commit is contained in:
D-X-Y
@@ -14,7 +14,7 @@ do
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python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
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python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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python exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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done
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done
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290
exps/algos-v2/search-cell.py
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290
exps/algos-v2/search-cell.py
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@@ -0,0 +1,290 @@
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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######################################################################################
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# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 1
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# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1
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# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
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######################################################################################
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import os, sys, time, 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 count_parameters_in_MB, 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 search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
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data_time, batch_time = AverageMeter(), AverageMeter()
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base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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end = time.time()
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network.train()
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for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
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scheduler.update(None, 1.0 * step / len(xloader))
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base_targets = base_targets.cuda(non_blocking=True)
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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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# update the weights
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sampled_arch = network.module.dync_genotype(True)
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network.module.set_cal_mode('dynamic', sampled_arch)
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#network.module.set_cal_mode( 'urs' )
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network.zero_grad()
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_, logits = network(base_inputs)
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base_loss = criterion(logits, base_targets)
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base_loss.backward()
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w_optimizer.step()
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# record
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base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
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base_losses.update(base_loss.item(), base_inputs.size(0))
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base_top1.update (base_prec1.item(), base_inputs.size(0))
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base_top5.update (base_prec5.item(), base_inputs.size(0))
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# update the architecture-weight
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network.module.set_cal_mode( 'joint' )
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network.zero_grad()
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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arch_loss.backward()
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a_optimizer.step()
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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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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = '*SEARCH* ' + 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 = 'Base [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=base_losses, top1=base_top1, top5=base_top5)
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Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
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#print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
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#print (network.module.arch_parameters)
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return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
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def get_best_arch(xloader, network, n_samples):
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with torch.no_grad():
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network.eval()
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archs, valid_accs = network.module.return_topK(n_samples), []
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#print ('obtain the top-{:} architectures'.format(n_samples))
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loader_iter = iter(xloader)
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for i, sampled_arch in enumerate(archs):
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network.module.set_cal_mode('dynamic', sampled_arch)
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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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_, logits = network(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
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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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end = time.time()
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with torch.no_grad():
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network.eval()
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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, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
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(config.batch_size, config.test_batch_size), xargs.workers)
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logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_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(xargs.search_space, 'nas-bench-301')
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model_config = dict2config(
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dict(name='generic', C=xargs.channel, N=xargs.num_cells, max_nodes=xargs.max_nodes, num_classes=class_num,
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space=search_space, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None)
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logger.log('search space : {:}'.format(search_space))
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logger.log('model config : {:}'.format(model_config))
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search_model = get_cell_based_tiny_net(model_config)
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search_model.set_algo(xargs.algo)
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
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a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
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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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params = count_parameters_in_MB(search_model)
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logger.log('The parameters of the search model = {:.2f} MB'.format(params))
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logger.log('search-space : {:}'.format(search_space))
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api = API()
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logger.log('{:} create API = {:} done'.format(time_string(), api))
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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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# network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
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network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
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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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valid_accuracies = checkpoint['valid_accuracies']
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search_model.load_state_dict( checkpoint['search_model'] )
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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 = 0, {'best': -1}, {}
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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={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
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import pdb; pdb.set_trace()
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search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
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= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
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search_time.update(time.time() - start_time)
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logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
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logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
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genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
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network.module.set_cal_mode('dynamic', genotype)
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valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
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#search_model.set_cal_mode('urs')
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#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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#logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
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#search_model.set_cal_mode('joint')
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#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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#logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
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#search_model.set_cal_mode('select')
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#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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#logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
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# check the best accuracy
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valid_accuracies[epoch] = valid_a_top1
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genotypes[epoch] = genotype
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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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'search_model': search_model.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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with torch.no_grad():
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logger.log('{:}'.format(search_model.show_alphas()))
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if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '200')))
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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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# the final post procedure : count the time
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start_time = time.time()
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genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
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search_time.update(time.time() - start_time)
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network.module.set_cal_mode('dynamic', genotype)
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valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))
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logger.log('\n' + '-'*100)
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# check the performance from the architecture dataset
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logger.log('SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotype))
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if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '200') ))
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logger.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
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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, default='tss', choices=['tss'], help='The search space name.')
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parser.add_argument('--algo' , type=str, choices=['darts-v1', 'darts-v2', 'gdas', 'setn', 'random', 'enas'], help='The search space name.')
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# channels and number-of-cells
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parser.add_argument('--max_nodes' , type=int, default=4, help='The maximum number of nodes.')
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parser.add_argument('--channel' , type=int, default=16, help='The number of channels.')
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parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.')
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#
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parser.add_argument('--eval_candidate_num', type=int, help='The number of selected architectures to evaluate.')
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#
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parser.add_argument('--track_running_stats',type=int, default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
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parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.')
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parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
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# architecture leraning rate
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parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
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parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
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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, default='./output/search', help='Folder to save checkpoints and log.')
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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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args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, args.algo)
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main(args)
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