def test(epoch, test_loader, save=True):
global best_acc
net.eval()
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
outputs = net(inputs)
loss = criterion(outputs, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
# timing
batch_time.update(time.time() - end)
end = time.time()
progress_bar(
batch_idx, len(test_loader),
'Loss: {:.3f} | Acc1: {:.3f}% | Acc5: {:.3f}%'.format(
losses.avg, top1.avg, top5.avg))
if save:
writer.add_scalar('loss/test', losses.avg, epoch)
writer.add_scalar('acc/test_top1', top1.avg, epoch)
writer.add_scalar('acc/test_top5', top5.avg, epoch)
is_best = False
if top1.avg > best_acc:
best_acc = top1.avg
is_best = True
print('Current best acc: {}'.format(best_acc))
save_checkpoint(
{
'epoch':
epoch,
'model':
args.model,
'dataset':
args.dataset,
'state_dict':
net.module.state_dict()
if isinstance(net, nn.DataParallel) else net.state_dict(),
'acc':
top1.avg,
'optimizer':
optimizer.state_dict(),
},
is_best,
checkpoint_dir=log_dir)
def infer(self, model, epoch=0):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
data_time = utils.AverageMeter()
batch_time = utils.AverageMeter()
model.eval()
start = time.time()
prefetcher = data_prefetcher(self.val_data)
input, target = prefetcher.next()
step = 0
while input is not None:
step += 1
data_t = time.time() - start
n = input.size(0)
logits, logits_aux = model(input)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
batch_t = time.time() - start
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
data_time.update(data_t)
batch_time.update(batch_t)
if step % self.report_freq == 0:
logging.info('Val epoch %03d step %03d | top1_acc %.2f top5_acc %.2f | batch_time %.3f data_time %.3f', epoch, step, top1.avg, top5.avg, batch_time.avg, data_time.avg)
start = time.time()
input, target = prefetcher.next()
logging.info('EPOCH%d Valid_acc top1 %.2f top5 %.2f batch_time %.3f data_time %.3f', epoch, top1.avg, top5.avg, batch_time.avg, data_time.avg)
return top1.avg, top5.avg, batch_time.avg, data_time.avg
def validate(val_queue, model, criterion):
objs = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# model.eval()
# disable moving average
model.train()
for step, (x, target) in enumerate(val_queue):
x = x.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
with torch.no_grad():
logits, _ = model(x, sampling=True, mode='gumbel')
loss = criterion(logits, target)
# reset switches of log_alphas
model.module.reset_switches()
prec1, prec5 = accuracy(logits, target, topk=(1, 5))
n = x.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.print_freq == 0:
logging.info('VALIDATE Step: %04d Objs: %f R1: %f R5: %f', step,
objs.avg, top1.avg, top5.avg)
return top1.avg
def validate(logger, writer, device, config, valid_loader, model, epoch, cur_step):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
model.eval()
with torch.no_grad():
for step, (X, y) in enumerate(valid_loader):
X, y = X.to(device, non_blocking=True), y.to(device, non_blocking=True)
N = X.size(0)
logits = model(X)
loss = model.module.criterion(logits, y)
prec1, prec5 = utils.accuracy(logits, y, topk=(1, 5))
losses.update(loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
if step % config.print_freq == 0 or step == len(valid_loader)-1:
logger.info(
"Valid: [{:2d}/{}] Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch+1, config.epochs, step, len(valid_loader)-1, losses=losses,
top1=top1, top5=top5))
writer.add_scalar('val/loss', losses.avg, cur_step)
writer.add_scalar('val/top1', top1.avg, cur_step)
writer.add_scalar('val/top5', top5.avg, cur_step)
logger.info("Valid: [{:2d}/{}] Final Prec@1 {:.4%}".format(epoch+1, config.epochs, top1.avg))
return top1.avg
def validate(val_queue, model, criterion):
objs = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.eval()
for step, data in enumerate(val_queue):
x = data[0].cuda(non_blocking=True)
target = data[1].cuda(non_blocking=True)
with torch.no_grad():
logits = model(x)
loss = criterion(logits, target)
prec1, prec5 = accuracy(logits, target, topk=(1, 5))
n = x.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.print_freq == 0:
duration = 0 if step == 0 else time.time() - duration_start
duration_start = time.time()
logging.info(
'VALID Step: %03d Objs: %e R1: %f R5: %f Duration: %ds', step,
objs.avg, top1.avg, top5.avg, duration)
return top1.avg, top5.avg, objs.avg
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, model: ImageClassifier,
domain_adv: DomainAdversarialLoss, optimizer: SGD,
lr_scheduler: StepwiseLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':5.2f')
data_time = AverageMeter('Data', ':5.2f')
losses = AverageMeter('Loss', ':6.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
domain_accs = AverageMeter('Domain Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, cls_accs, domain_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
domain_adv.train()
end = time.time()
for i in range(args.iters_per_epoch):
lr_scheduler.step()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_t, _ = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
# compute output
x = torch.cat((x_s, x_t), dim=0)
y, f = model(x)
y_s, y_t = y.chunk(2, dim=0)
f_s, f_t = f.chunk(2, dim=0)
cls_loss = F.cross_entropy(y_s, labels_s)
transfer_loss = domain_adv(f_s, f_t)
domain_acc = domain_adv.domain_discriminator_accuracy
loss = cls_loss + transfer_loss * args.trade_off
cls_acc = accuracy(y_s, labels_s)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
domain_accs.update(domain_acc.item(), x_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
data_time = utils.AvgrageMeter()
batch_time = utils.AvgrageMeter()
model.eval()
start = time.time()
for step, (input, target) in enumerate(valid_queue):
data_t = time.time() - start
input = input.cuda()
target = target.cuda()
n = input.size(0)
logits = model(input)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
batch_t = time.time() - start
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
data_time.update(data_t)
batch_time.update(batch_t)
if step != 0 and step % args.report_freq == 0:
logging.info(
'Val step %03d | top1_acc %.2f top5_acc %.2f | batch_time %.3f data_time %.3f',
step, top1.avg, top5.avg, batch_time.avg, data_time.avg)
start = time.time()
return top1.avg, top5.avg, objs.avg, batch_time.avg
def train(self, model, epoch, optim_obj='Weights', search_stage=0):
assert optim_obj in ['Weights', 'Arch']
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
sub_obj_avg = utils.AverageMeter()
data_time = utils.AverageMeter()
batch_time = utils.AverageMeter()
model.train()
start = time.time()
if optim_obj == 'Weights':
prefetcher = data_prefetcher(self.train_data)
elif optim_obj == 'Arch':
prefetcher = data_prefetcher(self.val_data)
input, target = prefetcher.next()
step = 0
while input is not None:
input, target = input.cuda(), target.cuda()
data_t = time.time() - start
n = input.size(0)
if optim_obj == 'Weights':
self.scheduler.step()
if step == 0:
logging.info(
'epoch %d weight_lr %e', epoch,
self.search_optim.weight_optimizer.param_groups[0]
['lr'])
logits, loss, sub_obj = self.search_optim.weight_step(
input, target, model, search_stage)
elif optim_obj == 'Arch':
if step == 0:
logging.info(
'epoch %d arch_lr %e', epoch,
self.search_optim.arch_optimizer.param_groups[0]['lr'])
logits, loss, sub_obj = self.search_optim.arch_step(
input, target, model, search_stage)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
del logits, input, target
batch_t = time.time() - start
objs.update(loss, n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
sub_obj_avg.update(sub_obj)
data_time.update(data_t)
batch_time.update(batch_t)
if step != 0 and step % self.args.report_freq == 0:
logging.info(
'Train%s epoch %03d step %03d | loss %.4f %s %.2f top1_acc %.2f top5_acc %.2f | batch_time %.3f data_time %.3f',
optim_obj, epoch, step, objs.avg, self.sub_obj_type,
sub_obj_avg.avg, top1.avg, top5.avg, batch_time.avg,
data_time.avg)
start = time.time()
step += 1
input, target = prefetcher.next()
return top1.avg, top5.avg, objs.avg, sub_obj_avg.avg, batch_time.avg
def validate(val_loader, model, criterion):
objs = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.eval()
for step, data in enumerate(val_loader):
x = data[0].cuda(non_blocking=True)
target = data[1].cuda(non_blocking=True)
with torch.no_grad():
logits = model(x)
loss = criterion(logits, target)
prec1, prec5 = accuracy(logits, target, topk=(1, 5))
if args.distributed:
reduced_loss = reduce_tensor(loss.data)
prec1 = reduce_tensor(prec1)
prec5 = reduce_tensor(prec5)
else:
reduced_loss = loss.data
objs.update(reduced_loss.item(), x.size(0))
top1.update(prec1.item(), x.size(0))
top5.update(prec5.item(), x.size(0))
if args.local_rank == 0 and step % args.print_freq == 0:
duration = 0 if step == 0 else time.time() - duration_start
duration_start = time.time()
logging.info(
'VALIDATE Step: %03d Objs: %e R1: %f R5: %f Duration: %ds',
step, objs.avg, top1.avg, top5.avg, duration)
return top1.avg, top5.avg, objs.avg
def train(train_loader, model, criterion, optimizer):
objs = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
model.train()
end = time.time()
for step, data in enumerate(train_loader):
data_time.update(time.time() - end)
x = data[0].cuda(non_blocking=True)
target = data[1].cuda(non_blocking=True)
# forward
batch_start = time.time()
logits = model(x)
loss = criterion(logits, target)
# backward
optimizer.zero_grad()
if args.opt_level is not None:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if args.grad_clip > 0:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.grad_clip)
optimizer.step()
batch_time.update(time.time() - batch_start)
if step % args.print_freq == 0:
# For better performance, don't accumulate these metrics every iteration,
# since they may incur an allreduce and some host<->device syncs.
prec1, prec5 = accuracy(logits, target, topk=(1, 5))
if args.distributed:
reduced_loss = reduce_tensor(loss.data)
prec1 = reduce_tensor(prec1)
prec5 = reduce_tensor(prec5)
else:
reduced_loss = loss.data
objs.update(reduced_loss.item(), x.size(0))
top1.update(prec1.item(), x.size(0))
top5.update(prec5.item(), x.size(0))
torch.cuda.synchronize()
duration = 0 if step == 0 else time.time() - duration_start
duration_start = time.time()
if args.local_rank == 0:
logging.info(
'TRAIN Step: %03d Objs: %e R1: %f R5: %f Duration: %ds BTime: %.3fs DTime: %.4fs',
step, objs.avg, top1.avg, top5.avg, duration,
batch_time.avg, data_time.avg)
end = time.time()
return top1.avg, objs.avg
def train(logger, writer, device, config, train_loader, model, optimizer,
criterion, epoch):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
cur_step = epoch * len(train_loader)
cur_lr = optimizer.param_groups[0]['lr']
logger.info("Epoch {} LR {}".format(epoch, cur_lr))
writer.add_scalar('train/lr', cur_lr, cur_step)
model.train()
for step, (X, y) in enumerate(train_loader):
X, y = X.to(device, non_blocking=True), y.to(device, non_blocking=True)
N = X.size(0)
optimizer.zero_grad()
logits, aux_logits = model(X)
loss = criterion(logits, y)
if config.aux_weight > 0.:
loss += config.aux_weight * criterion(aux_logits, y)
loss.backward()
# gradient clipping
nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
if config.dist_privacy:
# privice gradient clipping
clipping_dispatcher(model.module.named_weights(),
config.max_weights_grad_norm, config.var_gamma,
device, logger)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, y, topk=(1, 5))
losses.update(loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
if step % config.print_freq == 0 or step == len(train_loader) - 1:
logger.info(
"Train: [{:3d}/{}] Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch + 1,
config.epochs,
step,
len(train_loader) - 1,
losses=losses,
top1=top1,
top5=top5))
writer.add_scalar('train/loss', loss.item(), cur_step)
writer.add_scalar('train/top1', prec1.item(), cur_step)
writer.add_scalar('train/top5', prec5.item(), cur_step)
cur_step += 1
logger.info("Train: [{:3d}/{}] Final Prec@1 {:.4%}".format(
epoch + 1, config.epochs, top1.avg))
def train(self, model, epoch):
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
data_time = utils.AverageMeter()
batch_time = utils.AverageMeter()
model.train()
start = time.time()
prefetcher = data_prefetcher(self.train_data)
input, target = prefetcher.next()
step = 0
while input is not None:
data_t = time.time() - start
self.scheduler.step()
n = input.size(0)
if step == 0:
logging.info('epoch %d lr %e', epoch,
self.optimizer.param_groups[0]['lr'])
self.optimizer.zero_grad()
logits = model(input)
if self.config.optim.label_smooth:
loss = self.criterion(logits, target,
self.config.optim.smooth_alpha)
else:
loss = self.criterion(logits, target)
loss.backward()
if self.config.optim.use_grad_clip:
nn.utils.clip_grad_norm_(model.parameters(),
self.config.optim.grad_clip)
self.optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
batch_t = time.time() - start
start = time.time()
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
data_time.update(data_t)
batch_time.update(batch_t)
if step != 0 and step % self.report_freq == 0:
logging.info(
'Train epoch %03d step %03d | loss %.4f top1_acc %.2f top5_acc %.2f | batch_time %.3f data_time %.3f',
epoch, step, objs.avg, top1.avg, top5.avg, batch_time.avg,
data_time.avg)
input, target = prefetcher.next()
step += 1
logging.info(
'EPOCH%d Train_acc top1 %.2f top5 %.2f batch_time %.3f data_time %.3f',
epoch, top1.avg, top5.avg, batch_time.avg, data_time.avg)
return top1.avg, top5.avg, objs.avg, batch_time.avg, data_time.avg
def train(logger, writer, device, config, train_loader, valid_loader, model, architect, w_optim, alpha_optim, lr, epoch):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
cur_step = epoch*len(train_loader)
writer.add_scalar('train/lr', lr, cur_step)
model.train()
for step, ((trn_X, trn_y), (val_X, val_y)) in enumerate(zip(train_loader, valid_loader)):
trn_X, trn_y = trn_X.to(device, non_blocking=True), trn_y.to(device, non_blocking=True)
val_X, val_y = val_X.to(device, non_blocking=True), val_y.to(device, non_blocking=True)
N = trn_X.size(0)
# phase 2. architect step (alpha)
alpha_optim.zero_grad()
architect.unrolled_backward(config, trn_X, trn_y, val_X, val_y, lr, w_optim)
alpha_optim.step()
# phase 1. child network step (w)
w_optim.zero_grad()
logits = model(trn_X)
loss = model.module.criterion(logits, trn_y)
loss.backward()
# gradient clipping
nn.utils.clip_grad_norm_(model.module.weights(), config.w_grad_clip)
if config.dist_privacy:
# privice gradient clipping
clipping_dispatcher(model.module.named_weights(),
config.max_weights_grad_norm,
config.var_gamma,
device,
logger
)
w_optim.step()
prec1, prec5 = utils.accuracy(logits, trn_y, topk=(1, 5))
losses.update(loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
if step % config.print_freq == 0 or step == len(train_loader)-1:
logger.info(
"Train: [{:2d}/{}] Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch+1, config.epochs, step, len(train_loader)-1, losses=losses,
top1=top1, top5=top5))
writer.add_scalar('train/loss', loss.item(), cur_step)
writer.add_scalar('train/top1', prec1.item(), cur_step)
writer.add_scalar('train/top5', prec5.item(), cur_step)
cur_step += 1
logger.info("Train: [{:2d}/{}] Final Prec@1 {:.4%}".format(epoch+1, config.epochs, top1.avg))
def validate(val_loader: DataLoader, G: nn.Module, F1: ImageClassifierHead,
F2: ImageClassifierHead,
args: argparse.Namespace) -> Tuple[float, float]:
batch_time = AverageMeter('Time', ':6.3f')
top1_1 = AverageMeter('Acc_1', ':6.2f')
top1_2 = AverageMeter('Acc_2', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_time, top1_1, top1_2],
prefix='Test: ')
# switch to evaluate mode
G.eval()
F1.eval()
F2.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.to(device)
target = target.to(device)
# compute output
g = G(images)
y1, y2 = F1(g), F2(g)
# measure accuracy and record loss
acc1, = accuracy(y1, target)
acc2, = accuracy(y2, target)
top1_1.update(acc1[0], images.size(0))
top1_2.update(acc2[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print(' * Acc1 {top1_1.avg:.3f} Acc2 {top1_2.avg:.3f}'.format(
top1_1=top1_1, top1_2=top1_2))
return top1_1.avg, top1_2.avg
def forward_loss(self, out, label, **kwargs):
loss = self.criterion(out, label)
acc = accuracy(
out,
label) # notice: out should be reshaped to (n*c) and label to (n)
# log your train status on your screen
self.logger.update(
'Loss', loss.data[0], out.size(0)
) # out.size(0) is the number of this batch data for calculate average loss
self.logger.update('acc', acc[0].data[0], out.size(0))
return loss
def train(train_source_iter: ForeverDataIterator, model: Classifier,
optimizer: SGD, lr_sheduler: StepwiseLR, epoch: int,
args: argparse.Namespace):
batch_time = AverageMeter('Time', ':4.2f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
progress = ProgressMeter(args.iters_per_epoch,
[batch_time, data_time, losses, cls_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i in range(args.iters_per_epoch):
if lr_sheduler is not None:
lr_sheduler.step()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_s = x_s.to(device)
labels_s = labels_s.to(device)
# compute output
y_s, f_s = model(x_s)
cls_loss = F.cross_entropy(y_s, labels_s)
loss = cls_loss
cls_acc = accuracy(y_s, labels_s)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def validate(val_queue, model):
top1 = AverageMeter()
top5 = AverageMeter()
model.eval()
for data in tqdm.tqdm(val_queue):
x = data[0].cuda(non_blocking=True)
target = data[1].cuda(non_blocking=True)
with torch.no_grad():
logits = model(x)
prec1, prec5 = accuracy(logits, target, topk=(1, 5))
n = x.size(0)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
return top1.avg, top5.avg
def train(train_queue, model, criterion, optimizer):
objs = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
model.train()
end = time.time()
for step, data in enumerate(train_queue):
data_time.update(time.time() - end)
x = data[0].cuda(non_blocking=True)
target = data[1].cuda(non_blocking=True)
# forward
batch_start = time.time()
logits = model(x)
loss = criterion(logits, target)
# backward
optimizer.zero_grad()
loss.backward()
if args.grad_clip > 0:
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
batch_time.update(time.time() - batch_start)
prec1, prec5 = accuracy(logits, target, topk=(1, 5))
n = x.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.print_freq == 0:
duration = 0 if step == 0 else time.time() - duration_start
duration_start = time.time()
logging.info(
'TRAIN Step: %03d Objs: %e R1: %f R5: %f Duration: %ds BTime: %.3fs DTime: %.4fs',
step, objs.avg, top1.avg, top5.avg, duration, batch_time.avg,
data_time.avg)
end = time.time()
return top1.avg, objs.avg
def train_ssl(inferred_dataloader: DataLoader, model: ImageClassifier,
optimizer: SGD, lr_scheduler: StepwiseLR, epoch: int,
args: argparse.Namespace):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(inferred_dataloader),
[batch_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i, (x, labels) in enumerate(inferred_dataloader):
lr_scheduler.step()
x = x.to(device)
labels = labels.to(device)
# compute output
output, _ = model(x)
loss = F.cross_entropy(output, labels)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, labels, topk=(1, 5))
losses.update(loss.item(), x.size(0))
top1.update(acc1[0], x.size(0))
top5.update(acc5[0], x.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def validate(val_loader: DataLoader, model: ImageClassifier, args: argparse.Namespace) -> float:
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(
len(val_loader),
[batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
images = images.to(device)
target = target.to(device)
# compute output
output, _ = model(images)
loss = F.cross_entropy(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg
def train(data_loader, model, criterion, optimizer, epoch, stage, logger,
args):
loss_avg = utils.AverageMeter()
top1_res = utils.AverageMeter()
top5_res = utils.AverageMeter()
global_step = epoch * len(data_loader)
model.train()
logger.log("stage: {}".format(stage))
for step, (images, labels) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
num_samples = images.size(0)
optimizer.zero_grad()
logits = model(images)
loss = criterion(logits, labels)
prec1_res, prec5_res = utils.accuracy(logits.detach(),
labels,
topk=(1, 5))
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
loss_avg.update(loss.detach().data.item(), num_samples)
loss.backward()
optimizer.step()
epochs = args.baseline_epochs
if step % 100 == 0 or step == len(data_loader) - 1:
logger.log("Train, Epoch: [{:3d}/{}], Step: [{:3d}/{}], " \
"Loss: {:.4f}, Prec@(res1, res5): {:.4%}, {:.4%}".format(
epoch, epochs, step, len(data_loader),
loss_avg.avg, top1_res.avg, top5_res.avg))
global_step += 1
logger.log("Train, Epoch: [{:3d}/{}], Step: [{:3d}/{}], " \
"Loss: {:.4f}, Prec@(res1, res5): {:.4%}, {:.4%}".format(
epoch, epochs, step, len(data_loader),
loss_avg.avg, top1_res.avg, top5_res.avg))
def infer(self, model, epoch):
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
sub_obj_avg = utils.AverageMeter()
data_time = utils.AverageMeter()
batch_time = utils.AverageMeter()
model.train() # don't use running_mean and running_var during search
start = time.time()
prefetcher = data_prefetcher(self.val_data)
input, target = prefetcher.next()
step = 0
while input is not None:
step += 1
data_t = time.time() - start
n = input.size(0)
logits, loss, sub_obj = self.search_optim.valid_step(
input, target, model)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
batch_t = time.time() - start
objs.update(loss, n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
sub_obj_avg.update(sub_obj)
data_time.update(data_t)
batch_time.update(batch_t)
if step % self.args.report_freq == 0:
logging.info(
'Val epoch %03d step %03d | loss %.4f %s %.2f top1_acc %.2f top5_acc %.2f | batch_time %.3f data_time %.3f',
epoch, step, objs.avg, self.sub_obj_type, sub_obj_avg.avg,
top1.avg, top5.avg, batch_time.avg, data_time.avg)
start = time.time()
input, target = prefetcher.next()
return top1.avg, top5.avg, objs.avg, sub_obj_avg.avg, batch_time.avg
def train_wo_arch(train_queue, model, criterion, optimizer_w):
objs_w = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.train()
for param in model.module.weight_parameters():
param.requires_grad = True
for param in model.module.arch_parameters():
param.requires_grad = False
for step, (x_w, target_w) in enumerate(train_queue):
x_w = x_w.cuda(non_blocking=True)
target_w = target_w.cuda(non_blocking=True)
logits_w_gumbel, _ = model(x_w, sampling=True, mode='gumbel')
loss_w_gumbel = criterion(logits_w_gumbel, target_w)
# reset switches of log_alphas
model.module.reset_switches()
optimizer_w.zero_grad()
loss_w_gumbel.backward()
if args.grad_clip > 0:
nn.utils.clip_grad_norm_(model.module.weight_parameters(),
args.grad_clip)
optimizer_w.step()
prec1, prec5 = accuracy(logits_w_gumbel, target_w, topk=(1, 5))
n = x_w.size(0)
objs_w.update(loss_w_gumbel.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.print_freq == 0:
logging.info('TRAIN wo_Arch Step: %04d Objs: %f R1: %f R5: %f',
step, objs_w.avg, top1.avg, top5.avg)
return top1.avg
def train(epoch, train_loader):
print('\nEpoch: %d' % epoch)
net.train()
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
for batch_idx, (inputs, targets) in enumerate(train_loader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
# timing
batch_time.update(time.time() - end)
end = time.time()
progress_bar(
batch_idx, len(train_loader),
'Loss: {:.3f} | Acc1: {:.3f}% | Acc5: {:.3f}%'.format(
losses.avg, top1.avg, top5.avg))
writer.add_scalar('loss/train', losses.avg, epoch)
writer.add_scalar('acc/train_top1', top1.avg, epoch)
writer.add_scalar('acc/train_top5', top5.avg, epoch)
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, model: ImageClassifier,
jmmd_loss: JointMultipleKernelMaximumMeanDiscrepancy, optimizer: SGD,
lr_sheduler: StepwiseLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':4.2f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':5.4f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
tgt_accs = AverageMeter('Tgt Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
jmmd_loss.train()
end = time.time()
for i in range(args.iters_per_epoch):
lr_sheduler.step()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_t, labels_t = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
labels_t = labels_t.to(device)
# compute output
x = torch.cat((x_s, x_t), dim=0)
y, f = model(x)
y_s, y_t = y.chunk(2, dim=0)
f_s, f_t = f.chunk(2, dim=0)
cls_loss = F.cross_entropy(y_s, labels_s)
transfer_loss = jmmd_loss((f_s, F.softmax(y_s, dim=1)),
(f_t, F.softmax(y_t, dim=1)))
loss = cls_loss + transfer_loss * args.trade_off
cls_acc = accuracy(y_s, labels_s)[0]
tgt_acc = accuracy(y_t, labels_t)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
tgt_accs.update(tgt_acc.item(), x_t.size(0))
trans_losses.update(transfer_loss.item(), x_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def train(train_loader, valid_loader, model, arch, w_optim, alpha_optim, lr,
epoch):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
cur_step = epoch * len(train_loader)
tb_writer.add_scalar('train/lr', lr, cur_step)
model.train()
for step, ((train_X, train_y),
(valid_X, valid_y)) in enumerate(zip(train_loader,
valid_loader)):
train_X, train_y = train_X.to(device, non_blocking=True), train_y.to(
device, non_blocking=True)
valid_X, valid_y = valid_X.to(device, non_blocking=True), valid_y.to(
device, non_blocking=True)
N = train_X.size(0)
# arch step (alpha training)
alpha_optim.zero_grad()
arch.unrolled_backward(train_X, train_y, valid_X, valid_y, lr, w_optim)
alpha_optim.step()
# child network step (w)
w_optim.zero_grad()
logits = model(train_X)
loss = model.criterion(logits, train_y)
loss.backward()
# gradient clipping
nn.utils.clip_grad_norm_(model.weights(), config.w_grad_clip)
w_optim.step()
prec1, prec5 = utils.accuracy(logits, train_y, topk=(1, 5))
losses.update(loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
if step % config.print_freq == 0 or step == len(train_loader) - 1:
print("\r", end="", flush=True)
logger.info(
"Train: [{:2d}/{}] Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch + 1,
config.epochs,
step,
len(train_loader) - 1,
losses=losses,
top1=top1,
top5=top5))
else:
print(
"\rTrain: [{:2d}/{}] Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch + 1,
config.epochs,
step,
len(train_loader) - 1,
losses=losses,
top1=top1,
top5=top5),
end="",
flush=True)
tb_writer.add_scalar('train/loss', loss.item(), cur_step)
tb_writer.add_scalar('train/top1', prec1.item(), cur_step)
tb_writer.add_scalar('train/top5', prec5.item(), cur_step)
if step % (config.print_freq // 5) == 0 or step == len(
train_loader) - 1: # not too much logging
for i, tensor in enumerate(model.alpha_normal):
for j, lsn in enumerate(F.softmax(tensor, dim=-1)):
tb_writer.add_scalars(
'alpha_normal/%d ~~ %d' % ((j - 2), i), {
'max_pl3': lsn[0],
'avg_pl3': lsn[1],
'skip_cn': lsn[2],
'sep_conv3': lsn[3],
'sep_conv5': lsn[4],
'dil_conv3': lsn[5],
'dil_conv5': lsn[6],
'none': lsn[7]
}, cur_step)
for i, tensor in enumerate(model.alpha_reduce):
for j, lsr in enumerate(F.softmax(tensor, dim=-1)):
tb_writer.add_scalars(
'alpha_reduce/%d ~~ %d' % ((j - 2), i), {
'max_pl3': lsr[0],
'avg_pl3': lsr[1],
'skip_cn': lsr[2],
'sep_conv3': lsr[3],
'sep_conv5': lsr[4],
'dil_conv3': lsr[5],
'dil_conv5': lsr[6],
'none': lsr[7]
}, cur_step)
cur_step += 1
logger.info("Train: [{:2d}/{}] Final Prec@1 {:.4%}".format(
epoch + 1, config.epochs, top1.avg))
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, classifier: ImageClassifier,
mdd: MarginDisparityDiscrepancy, optimizer: SGD,
lr_scheduler: StepwiseLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':3.1f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':3.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
tgt_accs = AverageMeter('Tgt Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
classifier.train()
mdd.train()
criterion = nn.CrossEntropyLoss().to(device)
end = time.time()
for i in range(args.iters_per_epoch):
lr_scheduler.step()
optimizer.zero_grad()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_t, labels_t = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
labels_t = labels_t.to(device)
# compute output
x = torch.cat((x_s, x_t), dim=0)
outputs, outputs_adv = classifier(x)
y_s, y_t = outputs.chunk(2, dim=0)
y_s_adv, y_t_adv = outputs_adv.chunk(2, dim=0)
# compute cross entropy loss on source domain
cls_loss = criterion(y_s, labels_s)
# compute margin disparity discrepancy between domains
transfer_loss = mdd(y_s, y_s_adv, y_t, y_t_adv)
loss = cls_loss + transfer_loss * args.trade_off
classifier.step()
cls_acc = accuracy(y_s, labels_s)[0]
tgt_acc = accuracy(y_t, labels_t)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
tgt_accs.update(tgt_acc.item(), x_t.size(0))
trans_losses.update(transfer_loss.item(), x_s.size(0))
# compute gradient and do SGD step
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def train(data_loader, model, criterion, optimizer_t, optimizer_s, epoch,
stage, logger, args):
[loss_avg, mse_avg, top1_cnn, top5_cnn, top1_res,
top5_res] = [utils.AverageMeter() for _ in range(6)]
global_step = epoch * len(data_loader)
model.train()
logger.log("stage: {}".format(stage))
m = Cosine(min_v=args.dc, max_v=1.0, epoch=epoch, epoch_max=60)
#m = 1.0
model.module.reset_margin()
for step, (images, labels) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
num_samples = images.size(0)
if optimizer_t is not None:
optimizer_t.zero_grad()
if optimizer_s is not None:
optimizer_s.zero_grad()
if "TA" in stage:
###train teacher#####################
model.module.teacher.eval()
logits_teacher, teacher_feas = model(images,
stage='RES_TA',
epoch=epoch)
#logits_teacher, _ = model(images, stage='RES_TA', epoch=epoch)
model.module.teacher.eval()
#####################################
logits_student, _, loss_dis = model(images,
stage=stage,
epoch=epoch,
teacher_feas=teacher_feas[-1])
loss = 0
loss_last = criterion(logits_student[-1], labels)
loss_avg.update(loss_last.detach().item(), num_samples)
loss += loss_last
loss += loss_dis[-1].mean() * args.dis_weight
#10^-3 for 32x32 image
#10^-4 for 224x224 scale classification task
#10^-5 for detection and segmentation task
if isinstance(logits_student, list):
prec1_cnn, prec5_cnn = utils.accuracy(
logits_student[-1].detach(), labels, topk=(1, 5))
else:
prec1_cnn, prec5_cnn = utils.accuracy(logits_student.detach(),
labels,
topk=(1, 5))
prec1_cnn, prec5_cnn = utils.accuracy(logits_student[-1].detach(),
labels,
topk=(1, 5))
prec1_res, prec5_res = utils.accuracy(logits_teacher.detach(),
labels,
topk=(1, 5))
### teacher is only updated by its own loss
loss.backward()
optimizer_s.step()
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
elif "KD" in stage:
###train teacher#####################
model.module.teacher.eval()
logits_teacher, teacher_feas = model(images,
stage='RES_TA',
epoch=epoch)
#logits_teacher, _ = model(images, stage='RES_TA', epoch=epoch)
model.module.teacher.eval()
#####################################
logits_student, _, loss_dis = model(images,
stage=stage,
epoch=epoch,
teacher_feas=teacher_feas[-1])
loss = 0
loss += criterion(logits_student[-1], labels)
loss_avg.update(loss.detach().item(), num_samples)
if loss_dis is not None:
for loss_d in loss_dis[:-1]:
loss += loss_d.mean() * m * args.dis_weight
mse_avg.update(loss_dis[-1].detach().mean().item(),
num_samples)
loss += loss_dis[-1].mean() * args.dis_weight
#10^-3 for 32x32 image
#10^-4 for 224x224 scale classification task
#10^-5 for detection and segmentation task
if isinstance(logits_student, list):
prec1_cnn, prec5_cnn = utils.accuracy(
logits_student[-1].detach(), labels, topk=(1, 5))
else:
prec1_cnn, prec5_cnn = utils.accuracy(logits_student.detach(),
labels,
topk=(1, 5))
prec1_cnn, prec5_cnn = utils.accuracy(logits_student[-1].detach(),
labels,
topk=(1, 5))
prec1_res, prec5_res = utils.accuracy(logits_teacher.detach(),
labels,
topk=(1, 5))
### teacher is only updated by its own loss
loss.backward()
optimizer_s.step()
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
elif "KL" in stage:
###train teacher#####################
model.module.teacher.eval()
logits_teacher = model(images, stage='RES_NMT', epoch=epoch)
#logits_teacher, _ = model(images, stage='RES_TA', epoch=epoch)
model.module.teacher.eval()
#####################################
logits_student = model(images, stage="CNN_NMT", epoch=epoch)
loss = loss_KD_fn(criterion,
logits_student,
logits_teacher,
targets=labels,
alpha=args.alpha,
temperature=args.temperature)
#10^-3 for 32x32 image
#10^-4 for 224x224 scale classification task
#10^-5 for detection and segmentation task
if isinstance(logits_student, list):
prec1_cnn, prec5_cnn = utils.accuracy(logits_student.detach(),
labels,
topk=(1, 5))
else:
prec1_cnn, prec5_cnn = utils.accuracy(logits_student.detach(),
labels,
topk=(1, 5))
prec1_cnn, prec5_cnn = utils.accuracy(logits_student.detach(),
labels,
topk=(1, 5))
prec1_res, prec5_res = utils.accuracy(logits_teacher.detach(),
labels,
topk=(1, 5))
### teacher is only updated by its own loss
loss.backward()
optimizer_s.step()
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
elif "JOINT" in stage:
## teacher and student are jointly trained from scratch
###train teacher#####################
model.module.teacher.train()
optimizer_t.zero_grad()
logits_teacher, teacher_feas = model(images,
stage='RES_TA',
epoch=epoch)
#logits_teacher, _ = model(images, stage='RES_TA', epoch=epoch)
loss_teacher = criterion(logits_teacher, labels)
loss_teacher.backward()
optimizer_t.step()
model.module.teacher.eval()
#####################################
logits_student, _, loss_dis = model(images,
stage=stage,
epoch=epoch,
teacher_feas=teacher_feas[-1])
loss = 0
xishu = 1.0 / 4.
for logit_student in logits_student[:-1]:
KD_TRAIN = False
if KD_TRAIN:
loss += loss_KD_fn(
criterion,
logit_student,
logits_teacher,
targets=labels,
alpha=args.alpha,
temperature=args.temperature) * m * xishu
else:
loss += criterion(logit_student, labels) * m * xishu
loss_last = criterion(logits_student[-1], labels) * xishu
loss_avg.update(loss_last.detach().item(), num_samples)
loss += loss_last
if loss_dis is not None:
for loss_d in loss_dis[:-1]:
loss += loss_d.mean() * m * xishu * args.dis_weight
mse_avg.update(loss_dis[-1].detach().mean().item(),
num_samples)
loss += loss_dis[-1].mean() * args.dis_weight * xishu
#10^-3 for 32x32 image
#10^-4 for 224x224 scale classification task
#10^-5 for detection and segmentation task
if isinstance(logits_student, list):
prec1_cnn, prec5_cnn = utils.accuracy(
logits_student[-1].detach(), labels, topk=(1, 5))
else:
prec1_cnn, prec5_cnn = utils.accuracy(logits_student.detach(),
labels,
topk=(1, 5))
prec1_cnn, prec5_cnn = utils.accuracy(logits_student[-1].detach(),
labels,
topk=(1, 5))
prec1_res, prec5_res = utils.accuracy(logits_teacher.detach(),
labels,
topk=(1, 5))
### teacher is only updated by its own loss
loss.backward()
#for n, v in model.named_parameters():
# print(n)
# print(v.grad.mean())
#pdb.set_trace()
optimizer_s.step()
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
elif "RES_NMT" in stage:
logits = model(images, stage='RES_NMT')
loss = criterion(logits, labels)
prec1_res, prec5_res = utils.accuracy(logits.detach(),
labels,
topk=(1, 5))
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
loss_avg.update(loss.detach().data.item(), num_samples)
loss.backward()
optimizer_t.step()
elif "CNN_NMT" in stage:
logits = model(images, stage=stage)
loss = criterion(logits, labels)
prec1_cnn, prec5_cnn = utils.accuracy(logits.detach(),
labels,
topk=(1, 5))
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
loss_avg.update(loss.detach().data.item(), num_samples)
loss.backward()
optimizer_s.step()
elif "RES_KD" in stage:
logit_student, logits_teacher = model(images, stage=stage)
loss = loss_KD_fn(criterion,
logit_student,
logits_teacher,
targets=labels,
alpha=args.alpha,
temperature=args.temperature)
prec1_res, prec5_res = utils.accuracy(logit_student.detach(),
labels,
topk=(1, 5))
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
loss_avg.update(loss.detach().data.item(), num_samples)
else:
raise NameError("invalide stage nanme")
epochs = args.baseline_epochs
if step % 100 == 0 or step == len(data_loader) - 1:
logger.log("Train, Epoch: [{:3d}/{}], Step: [{:3d}/{}], " \
"Loss: {:.4f}, Loss_dis: {:.4f}, Prec@(cnn1, res1, cnn5, res5): {:.4%},{:.4%}, {:.4%}, {:.4%}".format(
epoch, epochs, step, len(data_loader),
loss_avg.avg, mse_avg.avg, top1_cnn.avg, top1_res.avg, top5_cnn.avg, top5_res.avg))
global_step += 1
logger.log("m is {}".format(m))
logger.log(
"Train, Epoch: [{:3d}/{}], Final Prec: cnn, res@1: {:.4%}, {:.4%}, Final Prec: cnn, res@5: {:.4%}, {:.4%} Loss: {:.4f}"
.format(epoch, epochs, top1_cnn.avg, top1_res.avg, top5_cnn.avg,
top5_res.avg, loss_avg.avg))
def train_w_arch(train_queue, val_queue, model, criterion, optimizer_w,
optimizer_a):
objs_a = AverageMeter()
objs_l = AverageMeter()
objs_w = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.train()
for step, (x_w, target_w) in enumerate(train_queue):
x_w = x_w.cuda(non_blocking=True)
target_w = target_w.cuda(non_blocking=True)
for param in model.module.weight_parameters():
param.requires_grad = True
for param in model.module.arch_parameters():
param.requires_grad = False
logits_w_gumbel, _ = model(x_w, sampling=True, mode='gumbel')
loss_w_gumbel = criterion(logits_w_gumbel, target_w)
logits_w_random, _ = model(x_w, sampling=True, mode='random')
loss_w_random = criterion(logits_w_random, target_w)
loss_w = loss_w_gumbel + loss_w_random
optimizer_w.zero_grad()
loss_w.backward()
if args.grad_clip > 0:
nn.utils.clip_grad_norm_(model.module.weight_parameters(),
args.grad_clip)
optimizer_w.step()
prec1, prec5 = accuracy(logits_w_gumbel, target_w, topk=(1, 5))
n = x_w.size(0)
objs_w.update(loss_w.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % 2 == 0:
# optimize a
try:
x_a, target_a = next(val_queue_iter)
except:
val_queue_iter = iter(val_queue)
x_a, target_a = next(val_queue_iter)
x_a = x_a.cuda(non_blocking=True)
target_a = target_a.cuda(non_blocking=True)
for param in model.module.weight_parameters():
param.requires_grad = False
for param in model.module.arch_parameters():
param.requires_grad = True
logits_a, lat = model(x_a, sampling=False)
loss_a = criterion(logits_a, target_a)
loss_l = torch.abs(lat / args.target_lat - 1.) * args.lambda_lat
loss = loss_a + loss_l
optimizer_a.zero_grad()
loss.backward()
if args.grad_clip > 0:
nn.utils.clip_grad_norm_(model.module.arch_parameters(),
args.grad_clip)
optimizer_a.step()
# ensure log_alphas to be a log probability distribution
for log_alphas in model.module.arch_parameters():
log_alphas.data = F.log_softmax(log_alphas.detach().data,
dim=-1)
n = x_a.size(0)
objs_a.update(loss_a.item(), n)
objs_l.update(loss_l.item(), n)
if step % args.print_freq == 0:
logging.info(
'TRAIN w_Arch Step: %04d Objs_W: %f R1: %f R5: %f Objs_A: %f Objs_L: %f',
step, objs_w.avg, top1.avg, top5.avg, objs_a.avg, objs_l.avg)
return top1.avg
def valid(data_loader, model, criterion, epoch, global_step, stage, logger,
args):
loss_avg = utils.AverageMeter()
top1_cnn = utils.AverageMeter()
top5_cnn = utils.AverageMeter()
top1_res = utils.AverageMeter()
top5_res = utils.AverageMeter()
global_step = epoch * len(data_loader)
model.eval()
logger.log("stage: {}".format(stage))
with torch.no_grad():
for step, (images, labels) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
num_samples = images.size(0)
if "TA" in stage or "JOINT" in stage or "KD" in stage or "KL" in stage:
with torch.no_grad():
logits = model(images, stage='CNN_NMT')
logits_teacher = model(images, stage='RES_NMT')
prec1_cnn, prec5_cnn = utils.accuracy(logits.detach(),
labels,
topk=(1, 5))
prec1_res, prec5_res = utils.accuracy(
logits_teacher.detach(), labels, topk=(1, 5))
loss = criterion(logits, labels)
loss_avg.update(loss.detach().item(), num_samples)
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
elif "RES_NMT" in stage:
logits = model(images, stage=stage)
loss = criterion(logits, labels)
prec1_res, prec5_res = utils.accuracy(logits,
labels,
topk=(1, 5))
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
loss_avg.update(loss.data.item(), num_samples)
elif "CNN_NMT" in stage:
logits = model(images, stage=stage)
loss = criterion(logits, labels)
prec1_cnn, prec5_cnn = utils.accuracy(logits,
labels,
topk=(1, 5))
top1_cnn.update(prec1_cnn.item(), num_samples)
top5_cnn.update(prec5_cnn.item(), num_samples)
loss_avg.update(loss.data.item(), num_samples)
elif "RES_KD" in stage:
logit_student, logits_teacher = model(images, stage=stage)
loss = loss_KD_fn(criterion,
logit_student,
logits_teacher,
targets=labels,
alpha=args.alpha,
temperature=args.temperature)
prec1_res, prec5_res = utils.accuracy(logit_student.detach(),
labels,
topk=(1, 5))
top1_res.update(prec1_res.item(), num_samples)
top5_res.update(prec5_res.item(), num_samples)
loss_avg.update(loss.detach().data.item(), num_samples)
else:
raise NameError("invalide stage nanme")
epochs = args.baseline_epochs
if step % 100 == 0 or step == len(data_loader) - 1:
logger.log("Valid, Epoch: [{:3d}/{}], Step: [{:3d}/{}], " \
"Loss: {:.4f}, Prec@(cnn1, res1, cnn5, res5): {:.4%},{:.4%}, {:.4%}, {:.4%}".format(
epoch, epochs, step, len(data_loader),
loss_avg.avg, top1_cnn.avg, top1_res.avg, top5_cnn.avg, top5_res.avg))
global_step += 1
logger.log(
"Valid, Epoch: [{:3d}/{}], Final Prec: cnn, res@1: {:.4%}, {:.4%}, Final Prec: cnn, res@5: {:.4%}, {:.4%} Loss: {:.4f}"
.format(epoch, epochs, top1_cnn.avg, top1_res.avg, top5_cnn.avg,
top5_res.avg, loss_avg.avg))
if "RES" in stage:
return top1_res.avg
else:
return top1_cnn.avg
