def validate(validloader, model, criterion, args, tasks, meter, device):
model.eval()
with torch.no_grad():
for step, (data, target) in enumerate(validloader):
target = _wrap_target(target)
data = darts.to_device(data, device)
target = darts.to_device(target, device)
batch_size = data.size(0)
logits = model(data)
loss = darts.multitask_loss(target,
logits,
criterion,
reduce='mean')
prec1 = darts.multitask_accuracy_topk(logits, target, topk=(1, ))
meter.update_batch_loss(loss.item(), batch_size)
meter.update_batch_accuracy(prec1, batch_size)
if step % args.log_interval == 0:
logger.info(
f'>> Validation: {step} loss: {meter.loss_meter.avg:.4}')
meter.update_epoch()
meter.save(args.save_path)
def infer(validloader, model, criterion, args, tasks, device, meter):
model.eval()
with torch.no_grad():
for step, (data, target) in enumerate(validloader):
data = data.to(device)
for task, label in target.items():
target[task] = target[task].to(device)
batch_size = data.size(0)
logits = model(data)
loss = darts.multitask_loss(target, logits, criterion, reduce='mean')
prec1 = darts.multitask_accuracy_topk(logits, target)
meter.update_batch_loss(loss.item(), batch_size)
meter.update_batch_accuracy(prec1, batch_size)
if step % args.log_interval == 0:
print(f'>> Validation: {step} loss: {meter.loss_meter.avg:.4}')
meter.update_epoch()
meter.save(args.save_path)
return meter.loss_meter.avg
def train(trainloader, model, architecture, criterion, optimizer, scheduler,
args, tasks, meter, genotype, genotype_store, device):
valid_iter = iter(trainloader)
min_accuracy = 0.0
for step, (data, target) in enumerate(trainloader):
batch_size = data.size(0)
model.train()
data = darts.to_device(data, device)
target = darts.to_device(target, device)
x_search, target_search = next(valid_iter)
x_search = darts.to_device(x_search, device)
target_search = darts.to_device(target_search, device)
lr = scheduler.get_lr()[0]
# 1. update alpha
architecture.step(data,
target,
x_search,
target_search,
lr,
optimizer,
unrolled=False)
logits = model(data)
loss = darts.multitask_loss(target, logits, criterion, reduce='mean')
# 2. update weight
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
scheduler.step()
prec1 = darts.multitask_accuracy_topk(logits, target, topk=(1, ))
meter.update_batch_loss(loss.item(), batch_size)
meter.update_batch_accuracy(prec1, batch_size)
accuracy_avg = meter.acc_meter.get_avg_accuracy('response')
if accuracy_avg > min_accuracy:
genotype_store.save_genotype(genotype)
min_accuracy = accuracy_avg
if step % args.log_interval == 0:
logger.info(f'Step: {step} loss: {meter.loss_meter.avg:.4}')
meter.update_epoch()
meter.save(args.save_path)
def train(trainloader, validloader, model, architecture, criterion, optimizer, lr, args, tasks, device, meter):
valid_iter = iter(trainloader)
for step, (data, target) in enumerate(trainloader):
batch_size = data.size(0)
model.train()
data = data.to(device)
for task, label in target.items():
target[task] = target[task].to(device)
x_search, target_search = next(valid_iter)
x_search = x_search.to(device)
for task, label in target_search.items():
target_search[task] = target_search[task].to(device)
# 1. update alpha
architecture.step(
data,
target,
x_search,
target_search,
lr,
optimizer,
unrolled=args.unrolled
)
logits = model(data)
loss = darts.multitask_loss(target, logits, criterion, reduce='mean')
# 2. update weight
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
prec1 = darts.multitask_accuracy_topk(logits, target)
meter.update_batch_loss(loss.item(), batch_size)
meter.update_batch_accuracy(prec1, batch_size)
if step % args.log_interval == 0:
print(f'Step: {step} loss: {meter.loss_meter.avg:.4}')
meter.update_epoch()
meter.save(args.save_path)