Prototype generic nas model (cont.).
This commit is contained in:
D-X-Y
@@ -4,6 +4,14 @@
|
||||
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 1
|
||||
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1
|
||||
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
|
||||
####
|
||||
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v2 --rand_seed 1
|
||||
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v2
|
||||
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v2
|
||||
####
|
||||
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas --rand_seed 1
|
||||
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo gdas
|
||||
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo gdas
|
||||
######################################################################################
|
||||
import os, sys, time, random, argparse
|
||||
import numpy as np
|
||||
@@ -22,7 +30,7 @@ from models import get_cell_based_tiny_net, get_search_spaces
|
||||
from nas_201_api import NASBench201API as API
|
||||
|
||||
|
||||
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
|
||||
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, algo, logger):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
@@ -30,15 +38,26 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
|
||||
network.train()
|
||||
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
|
||||
scheduler.update(None, 1.0 * step / len(xloader))
|
||||
base_inputs = base_inputs.cuda(non_blocking=True)
|
||||
arch_inputs = arch_inputs.cuda(non_blocking=True)
|
||||
base_targets = base_targets.cuda(non_blocking=True)
|
||||
arch_targets = arch_targets.cuda(non_blocking=True)
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
|
||||
# update the weights
|
||||
sampled_arch = network.module.dync_genotype(True)
|
||||
network.module.set_cal_mode('dynamic', sampled_arch)
|
||||
#network.module.set_cal_mode( 'urs' )
|
||||
# Update the weights
|
||||
if algo == 'setn':
|
||||
sampled_arch = network.dync_genotype(True)
|
||||
network.set_cal_mode('dynamic', sampled_arch)
|
||||
elif algo == 'gdas':
|
||||
network.set_cal_mode('gdas', None)
|
||||
elif algo.startswith('darts'):
|
||||
network.set_cal_mode('joint', None)
|
||||
elif algo == 'random':
|
||||
network.set_cal_mode('urs', None)
|
||||
else:
|
||||
raise ValueError('Invalid algo name : {:}'.format(algo))
|
||||
|
||||
network.zero_grad()
|
||||
_, logits = network(base_inputs)
|
||||
base_loss = criterion(logits, base_targets)
|
||||
@@ -51,7 +70,16 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
|
||||
base_top5.update (base_prec5.item(), base_inputs.size(0))
|
||||
|
||||
# update the architecture-weight
|
||||
network.module.set_cal_mode( 'joint' )
|
||||
if algo == 'setn':
|
||||
network.set_cal_mode('joint')
|
||||
elif algo == 'gdas':
|
||||
network.set_cal_mode('gdas', None)
|
||||
elif algo.startswith('darts'):
|
||||
network.set_cal_mode('joint', None)
|
||||
elif algo == 'random':
|
||||
network.set_cal_mode('urs', None)
|
||||
else:
|
||||
raise ValueError('Invalid algo name : {:}'.format(algo))
|
||||
network.zero_grad()
|
||||
_, logits = network(arch_inputs)
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
@@ -73,36 +101,38 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
|
||||
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)
|
||||
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)
|
||||
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
|
||||
#print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
|
||||
#print (network.module.arch_parameters)
|
||||
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
|
||||
|
||||
|
||||
def get_best_arch(xloader, network, n_samples):
|
||||
def get_best_arch(xloader, network, n_samples, algo):
|
||||
with torch.no_grad():
|
||||
network.eval()
|
||||
archs, valid_accs = network.module.return_topK(n_samples), []
|
||||
#print ('obtain the top-{:} architectures'.format(n_samples))
|
||||
if algo == 'random':
|
||||
archs, valid_accs = network.return_topK(n_samples, True), []
|
||||
elif algo == 'setn':
|
||||
archs, valid_accs = network.return_topK(n_samples, False), []
|
||||
elif algo.startswith('darts') or algo == 'gdas':
|
||||
arch = network.genotype
|
||||
archs, valid_accs = [arch], []
|
||||
else:
|
||||
raise ValueError('Invalid algorithm name : {:}'.format(algo))
|
||||
loader_iter = iter(xloader)
|
||||
for i, sampled_arch in enumerate(archs):
|
||||
network.module.set_cal_mode('dynamic', sampled_arch)
|
||||
network.set_cal_mode('dynamic', sampled_arch)
|
||||
try:
|
||||
inputs, targets = next(loader_iter)
|
||||
except:
|
||||
loader_iter = iter(xloader)
|
||||
inputs, targets = next(loader_iter)
|
||||
|
||||
_, logits = network(inputs)
|
||||
_, logits = network(inputs.cuda(non_blocking=True))
|
||||
val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
|
||||
|
||||
valid_accs.append(val_top1.item())
|
||||
|
||||
best_idx = np.argmax(valid_accs)
|
||||
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
|
||||
return best_arch, best_valid_acc
|
||||
|
||||
|
||||
def valid_func(xloader, network, criterion):
|
||||
def valid_func(xloader, network, criterion, algo, logger):
|
||||
data_time, batch_time = AverageMeter(), AverageMeter()
|
||||
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
end = time.time()
|
||||
@@ -113,7 +143,7 @@ def valid_func(xloader, network, criterion):
|
||||
# measure data loading time
|
||||
data_time.update(time.time() - end)
|
||||
# prediction
|
||||
_, logits = network(arch_inputs)
|
||||
_, logits = network(arch_inputs.cuda(non_blocking=True))
|
||||
arch_loss = criterion(logits, arch_targets)
|
||||
# record
|
||||
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
|
||||
@@ -166,7 +196,6 @@ def main(xargs):
|
||||
logger.log('{:} create API = {:} done'.format(time_string(), api))
|
||||
|
||||
last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
|
||||
# network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
|
||||
|
||||
last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
|
||||
@@ -185,7 +214,7 @@ def main(xargs):
|
||||
logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
|
||||
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: network.return_topK(1, True)[0]}
|
||||
|
||||
# start training
|
||||
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
|
||||
@@ -195,28 +224,25 @@ def main(xargs):
|
||||
epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
|
||||
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
|
||||
|
||||
import pdb; pdb.set_trace()
|
||||
|
||||
search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
|
||||
= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
|
||||
= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, xargs.algo, logger)
|
||||
search_time.update(time.time() - start_time)
|
||||
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))
|
||||
logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
|
||||
|
||||
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
|
||||
network.module.set_cal_mode('dynamic', genotype)
|
||||
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
|
||||
if xargs.algo == 'setn':
|
||||
network.set_cal_mode('dynamic', genotype)
|
||||
elif xargs.algo == 'gdas':
|
||||
network.set_cal_mode('gdas', None)
|
||||
elif xargs.algo.startswith('darts'):
|
||||
network.set_cal_mode('joint', None)
|
||||
elif xargs.algo == 'random':
|
||||
network.set_cal_mode('urs', None)
|
||||
else:
|
||||
raise ValueError('Invalid algorithm name : {:}'.format(xargs.algo))
|
||||
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, xargs.algo, logger)
|
||||
logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
|
||||
#search_model.set_cal_mode('urs')
|
||||
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
#logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
|
||||
#search_model.set_cal_mode('joint')
|
||||
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
#logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
|
||||
#search_model.set_cal_mode('select')
|
||||
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
#logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
|
||||
genotypes[epoch] = genotype
|
||||
@@ -245,15 +271,25 @@ def main(xargs):
|
||||
|
||||
# the final post procedure : count the time
|
||||
start_time = time.time()
|
||||
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
|
||||
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
|
||||
if xargs.algo == 'setn':
|
||||
network.set_cal_mode('dynamic', genotype)
|
||||
elif xargs.algo == 'gdas':
|
||||
network.set_cal_mode('gdas', None)
|
||||
elif xargs.algo.startswith('darts'):
|
||||
network.set_cal_mode('joint', None)
|
||||
elif xargs.algo == 'random':
|
||||
network.set_cal_mode('urs', None)
|
||||
else:
|
||||
raise ValueError('Invalid algorithm name : {:}'.format(xargs.algo))
|
||||
search_time.update(time.time() - start_time)
|
||||
network.module.set_cal_mode('dynamic', genotype)
|
||||
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
|
||||
|
||||
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, xargs.algo, logger)
|
||||
logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))
|
||||
|
||||
logger.log('\n' + '-'*100)
|
||||
# check the performance from the architecture dataset
|
||||
logger.log('SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotype))
|
||||
logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype))
|
||||
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '200') ))
|
||||
logger.close()
|
||||
|
||||
@@ -281,7 +317,7 @@ if __name__ == '__main__':
|
||||
# log
|
||||
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
|
||||
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
|
||||
parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
|
||||
parser.add_argument('--print_freq', type=int, default=200, help='print frequency (default: 200)')
|
||||
parser.add_argument('--rand_seed', type=int, help='manual seed')
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
|
||||
|
||||
Reference in New Issue
Block a user