iicd/xautodl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Prototype generic nas model (cont.) for ENAS.

Browse Source
This commit is contained in:
D-X-Y
2020-07-19 11:25:37 +00:00
parent b9a5d2880f
commit 16c5651bdc
2 changed files with 172 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

111
exps/algos-v2/search-cell.py
View File

@@ -20,6 +20,10 @@
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo random --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo random
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas
######################################################################################
import os, sys, time, random, argparse
import numpy as np
@@ -130,6 +134,11 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
network.set_cal_mode('joint', None)
elif algo == 'random':
network.set_cal_mode('urs', None)
elif algo == 'enas':
with torch.no_grad():
network.controller.eval()
_, _, sampled_arch = network.controller()
network.set_cal_mode('dynamic', sampled_arch)
else:
raise ValueError('Invalid algo name : {:}'.format(algo))
@@ -153,16 +162,21 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
network.set_cal_mode('joint', None)
elif algo == 'random':
network.set_cal_mode('urs', None)
else:
elif algo != 'enas':
raise ValueError('Invalid algo name : {:}'.format(algo))
network.zero_grad()
if algo == 'darts-v2':
arch_loss, logits = backward_step_unrolled(network, criterion, base_inputs, base_targets, w_optimizer, arch_inputs, arch_targets)
a_optimizer.step()
elif algo == 'random' or algo == 'enas':
with torch.no_grad():
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
else:
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
arch_loss.backward()
a_optimizer.step()
a_optimizer.step()
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
@@ -182,6 +196,76 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
def train_controller(xloader, network, criterion, optimizer, prev_baseline, epoch_str, print_freq, logger):
# config. (containing some necessary arg)
# baseline: The baseline score (i.e. average val_acc) from the previous epoch
data_time, batch_time = AverageMeter(), AverageMeter()
GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
controller_num_aggregate = 20
controller_train_steps = 50
controller_bl_dec = 0.99
controller_entropy_weight = 0.0001
network.eval()
network.controller.train()
network.controller.zero_grad()
loader_iter = iter(xloader)
for step in range(controller_train_steps * controller_num_aggregate):
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
inputs = inputs.cuda(non_blocking=True)
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - xend)
log_prob, entropy, sampled_arch = network.controller()
with torch.no_grad():
network.set_cal_mode('dynamic', sampled_arch)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
val_top1 = val_top1.view(-1) / 100
reward = val_top1 + controller_entropy_weight * entropy
if prev_baseline is None:
baseline = val_top1
else:
baseline = prev_baseline - (1 - controller_bl_dec) * (prev_baseline - reward)
loss = -1 * log_prob * (reward - baseline)
# account
RewardMeter.update(reward.item())
BaselineMeter.update(baseline.item())
ValAccMeter.update(val_top1.item()*100)
LossMeter.update(loss.item())
EntropyMeter.update(entropy.item())
# Average gradient over controller_num_aggregate samples
loss = loss / controller_num_aggregate
loss.backward(retain_graph=True)
# measure elapsed time
batch_time.update(time.time() - xend)
xend = time.time()
if (step+1) % controller_num_aggregate == 0:
grad_norm = torch.nn.utils.clip_grad_norm_(network.controller.parameters(), 5.0)
GradnormMeter.update(grad_norm)
optimizer.step()
network.controller.zero_grad()
if step % print_freq == 0:
Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, controller_train_steps * controller_num_aggregate)
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)
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)
Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val, EntropyMeter.avg)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)
return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg
def get_best_arch(xloader, network, n_samples, algo):
with torch.no_grad():
network.eval()
@@ -192,6 +276,11 @@ def get_best_arch(xloader, network, n_samples, algo):
elif algo.startswith('darts') or algo == 'gdas':
arch = network.genotype
archs, valid_accs = [arch], []
elif algo == 'enas':
archs, valid_accs = [], []
for _ in range(n_samples):
_, _, sampled_arch = network.controller()
archs.append(sampled_arch)
else:
raise ValueError('Invalid algorithm name : {:}'.format(algo))
loader_iter = iter(xloader)
@@ -245,7 +334,7 @@ def main(xargs):
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
(config.batch_size, config.test_batch_size), xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
@@ -263,7 +352,7 @@ def main(xargs):
logger.log('{:}'.format(search_model))
w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, eps=xargs.arch_eps)
logger.log('w-optimizer : {:}'.format(w_optimizer))
logger.log('a-optimizer : {:}'.format(a_optimizer))
logger.log('w-scheduler : {:}'.format(w_scheduler))
@@ -288,6 +377,8 @@ def main(xargs):
start_epoch = last_info['epoch']
checkpoint = torch.load(last_info['last_checkpoint'])
genotypes = checkpoint['genotypes']
if xargs.algo == 'enas':
baseline = checkpoint['baseline']
valid_accuracies = checkpoint['valid_accuracies']
search_model.load_state_dict( checkpoint['search_model'] )
w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
@@ -297,6 +388,7 @@ def main(xargs):
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: network.return_topK(1, True)[0]}
baseline = None
# start training
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
@@ -312,9 +404,13 @@ def main(xargs):
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))
if xargs.algo == 'enas':
ctl_loss, ctl_acc, baseline, ctl_reward \
= train_controller(valid_loader, network, criterion, a_optimizer, baseline, epoch_str, xargs.print_freq, logger)
logger.log('[{:}] controller : loss={:}, acc={:}, baseline={:}, reward={:}'.format(epoch_str, ctl_loss, ctl_acc, baseline, ctl_reward))
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
if xargs.algo == 'setn':
if xargs.algo == 'setn' or xargs.algo == 'enas':
network.set_cal_mode('dynamic', genotype)
elif xargs.algo == 'gdas':
network.set_cal_mode('gdas', None)
@@ -333,6 +429,7 @@ def main(xargs):
# save checkpoint
save_path = save_checkpoint({'epoch' : epoch + 1,
'args' : deepcopy(xargs),
'baseline' : baseline,
'search_model': search_model.state_dict(),
'w_optimizer' : w_optimizer.state_dict(),
'a_optimizer' : a_optimizer.state_dict(),
@@ -377,7 +474,6 @@ def main(xargs):
logger.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
parser.add_argument('--data_path' , type=str, help='Path to dataset')
@@ -396,7 +492,8 @@ if __name__ == '__main__':
parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
# architecture leraning rate
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
parser.add_argument('--arch_weight_decay' , type=float, default=1e-3, help='weight decay for arch encoding')
parser.add_argument('--arch_eps' , type=float, default=1e-8, help='weight decay for arch encoding')
parser.add_argument('--drop_path_rate' , type=float, help='The drop path rate.')
# log
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')