def nao_train(train_queue, model, optimizer):
objs = utils.AvgrageMeter()
mse = utils.AvgrageMeter()
nll = utils.AvgrageMeter()
model.train()
for step, sample in enumerate(train_queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_input = sample['decoder_input']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda().requires_grad_()
decoder_input = decoder_input.cuda()
decoder_target = decoder_target.cuda()
optimizer.zero_grad()
predict_value, log_prob, arch = model(encoder_input, decoder_input)
loss_1 = F.mse_loss(predict_value.squeeze(), encoder_target.squeeze())
loss_2 = F.nll_loss(log_prob.contiguous().view(-1, log_prob.size(-1)),
decoder_target.view(-1))
loss = args.trade_off * loss_1 + (1 - args.trade_off) * loss_2
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_bound)
optimizer.step()
n = encoder_input.size(0)
objs.update(loss.data, n)
mse.update(loss_1.data, n)
nll.update(loss_2.data, n)
return objs.avg, mse.avg, nll.avg
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
with torch.no_grad():
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda(non_blocking=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data, n)
top1.update(prec1.data, n)
top5.update(prec5.data, n)
if step % args.report_freq == 0:
end_time = time.time()
if step == 0:
duration = 0
start_time = time.time()
else:
duration = end_time - start_time
start_time = 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 infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
with torch.no_grad():
for step, (input, target) in enumerate(valid_queue):
# input = Variable(input, volatile=True).cuda()
# target = Variable(target, volatile=True).cuda(async=True)
input = input.to(device)
target = target.to(device)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
loss_data = loss.data[0] if loss.dim() != 0 else loss.item()
prec1_data = prec1.data[0] if prec1.dim() != 0 else prec1.item()
prec5_data = prec5.data[0] if prec5.dim() != 0 else prec5.item()
objs.update(loss_data, n)
top1.update(prec1_data, n)
top5.update(prec5_data, n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, objs.avg
def train(train_queue, model, criterion, optimizer):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.train()
for step, (input, target) in enumerate(train_queue):
target = target.cuda(async=True)
input = input.cuda()
input = Variable(input)
target = Variable(target)
optimizer.zero_grad()
logits, logits_aux = model(input)
loss = criterion(logits, target)
if args.auxiliary:
loss_aux = criterion(logits_aux, target)
loss += args.auxiliary_weight * loss_aux
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
with torch.no_grad():
for step, (data, target) in enumerate(valid_queue):
data = data.cuda()
target = target.cuda()
logits, _ = model(data)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = data.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, top5.avg, objs.avg
def infer(valid_queue, model, criterion):
global is_multi_gpu
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
# nn.BatchNorm layers will use their running stats (in the default mode) and nn.Dropout will be deactivated.
model.eval()
for step, (input, target) in enumerate(valid_queue):
with torch.no_grad():
input = input.cuda()
target = target.cuda(non_blocking=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def train(train_queue, model, criterion, optimizer):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
model.train()
for step, (input, target) in enumerate(train_queue):
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
optimizer.zero_grad()
logits, logits_aux = model(input)
loss = criterion(logits, target)
if args.auxiliary:
loss_aux = criterion(logits_aux, target)
loss += args.auxiliary_weight * loss_aux
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
prec1, _ = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
if step % args.report_freq == 0:
logging.info('Train Step: %03d Objs: %e Acc: %f', step, objs.avg,
top1.avg)
return top1.avg, objs.avg
def infer(valid_queue, model, criterion, writer_dict):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
# tensorboard logger
writer = writer_dict['writer']
val_step = writer_dict['val_steps']
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
# target = Variable(target, volatile=True).cuda(async=True)
target = Variable(target, volatile=True).cuda()
logits = model(input)
loss = criterion(logits, target)
writer.add_scalar('val_loss', loss.data, val_step)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
writer.add_scalar('val_prec1', prec1.data, val_step)
writer.add_scalar('val_prec5', prec5.data, val_step)
n = input.size(0)
objs.update(loss.data, n)
top1.update(prec1.data, n)
top5.update(prec5.data, n)
val_step += 1
writer_dict['val_steps'] += 1
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, objs.avg
def train(train_queue, model, criterion, optimizer, logger):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
n = input.size(0)
input = Variable(input.float(), requires_grad=False).to(device)
target = Variable(target.long(), requires_grad=False).to(device)
logits = model(input)
loss = criterion(logits, target)
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
if step % args.report_freq == 0:
logger.info('time = %s, train %03d %e %f',
str(utils.get_unix_timestamp()), step, objs.avg,
top1.avg)
print('time = {}, train {} {}'.format(
str(utils.get_unix_timestamp()), step, objs.avg))
return objs.avg, top1.avg
def infer(valid_queue, model, criterion, args, gpu):
'''
在最后一个epoch后打印验证集计算结果
'''
print('正在进行测试！！！！')
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
logits = model(input) # 计算预测结果
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if gpu == 0:
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
print('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def infer(valid_queue, model, criterion, temperature, logger=None, batch_num=-1,
log_frequence=10):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
# logger.info("Start new epoch inference")
tic = time.time()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda()
logits , _, _ = model(input , temperature)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data , n)
top1.update(prec1.data , n)
top5.update(prec5.data , n)
if logger is not None:
if step > 0 and step % log_frequence == 0:
toc = time.time()
speed = 1.0 * log_frequence * n / (toc - tic)
if batch_num > 0:
logger.info("Step[%d/%d] speed[%.4f samples/s] loss[%.6f] acc[%.4f]" % (step, batch_num,
speed, loss.detach().cpu().numpy(), \
prec1.detach().cpu().numpy() / 100.0))
else:
logger.info("Step[%d] speed[%.4f samples/s] loss[%.6f] acc[%.4f]" % (step,
speed, loss.detach().cpu().numpy(), \
prec1.detach().cpu().numpy() / 100.0))
tic = time.time()
return top1.avg, top5.avg, objs.avg
def test(test_queue, model, criterion, device):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
correct = 0
total = 0
for step, (inputs, targets) in enumerate(test_queue):
with torch.no_grad():
inputs, targets = Variable(inputs.to(device)), Variable(
targets.to(device))
logits = model(inputs)
loss = criterion(logits, targets)
total += inputs.size(0)
_, predict = torch.max(logits.data, 1)
correct += predict.eq(targets.data).cpu().sum().item()
prec1, prec5 = utils.accuracy(logits, targets, topk=(1, 5))
n = inputs.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('test %03d loss: %.3f top1: %f top5: %f', step,
objs.avg, top1.avg, top5.avg)
logging.info('Testing: Acc=%.3f(%d/%d)' %
(correct / total, correct, total))
return top1.avg, objs.avg
def infer(valid_queue, model, criterion):
root = logging.getLogger()
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
logits, _ = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.report_freq == 0:
root.info('%s %03d %e %f %f', valid_queue.name, step, objs.avg,
top1.avg, top5.avg)
return top1.avg, objs.avg
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
with torch.no_grad():
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda(non_blocking=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data, n)
top1.update(prec1.data, n)
top5.update(prec5.data, n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
if 'debug' in args.save:
break
return top1.avg, objs.avg
def train_step(train_queue, optimizer):
objs = utils.AvgrageMeter()
nll = utils.AvgrageMeter()
for step, sample in enumerate(train_queue):
fw_adjs = sample['fw_adjs']
bw_adjs = sample['bw_adjs']
operations = sample['operations']
num_nodes = sample['num_nodes']
sequence = sample['sequence']
optimizer.zero_grad()
log_prob, predicted_value = model(fw_adjs,
bw_adjs,
operations,
num_nodes,
targets=sequence)
# print("input: {} output : {}".format(log_prob.size(), sequence.size()))
loss = F.nll_loss(
log_prob.contiguous().view(-1, log_prob.size(-1)),
sequence.view(-1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
conf.grad_bound)
optimizer.step()
n = sequence.size(0)
objs.update(loss.data, n)
nll.update(loss.data, n)
# logging.info("step : %04d, objs: %.6f, nll : %.6f", step, objs,avgs, nll)
return objs.avg, nll.avg
def test(test_queue, model, criterion, logger):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
with torch.no_grad():
for step, (input, target) in enumerate(test_queue):
n = input.size(0)
input = Variable(input.float()).to(device)
target = Variable(target.long()).to(device)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
if step % args.report_freq == 0:
logger.info('time = %s, test %03d %e %f',
str(utils.get_unix_timestamp()), step, objs.avg,
top1.avg)
print('time = {}, test {} {}'.format(
str(utils.get_unix_timestamp()), step, objs.avg))
return objs.avg, top1.avg
def train(train_queue, model, criterion, optimizer):
global is_multi_gpu
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.train()
for step, (input, target) in enumerate(train_queue):
n = input.size(0)
input = input.cuda()
target = target.cuda(non_blocking=True)
optimizer.zero_grad()
logits, logits_aux = model(input)
loss = criterion(logits, target)
if args.auxiliary:
loss_aux = criterion(logits_aux, target)
loss += args.auxiliary_weight * loss_aux
loss.backward()
parameters = model.module.parameters() if is_multi_gpu else model.parameters()
nn.utils.clip_grad_norm_(parameters, args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
wandb.log({"evaluation_train_accuracy_avg": objs.avg}, step=step)
wandb.log({"evaluation_train_accuracy_top1": top1.avg}, step=step)
wandb.log({"evaluation_train_accuracy_top5": top5.avg}, step=step)
return top1.avg, objs.avg
def nao_valid(queue, model):
pa = utils.AvgrageMeter()
hs = utils.AvgrageMeter()
mse = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda()
decoder_target = decoder_target.cuda()
predict_value, logits, arch = model(encoder_input)
n = encoder_input.size(0)
pairwise_acc = utils.pairwise_accuracy(
encoder_target.data.squeeze().tolist(),
predict_value.data.squeeze().tolist())
hamming_dis = utils.hamming_distance(
decoder_target.data.squeeze().tolist(),
arch.data.squeeze().tolist())
mse.update(
F.mse_loss(predict_value.data.squeeze(),
encoder_target.data.squeeze()), n)
pa.update(pairwise_acc, n)
hs.update(hamming_dis, n)
return mse.avg, pa.avg, hs.avg
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
if args.debug:
break
return top1.avg, objs.avg
def infer(valid_loader, model, controller, criterion):
total_loss = utils.AvgrageMeter()
total_top1 = utils.AvgrageMeter()
model.eval()
controller.eval()
with torch.no_grad():
for step in range(20):
data, target = valid_loader.next_batch()
data = data.cuda()
target = target.cuda()
dag, _, _ = controller()
logits, auxs = model(dag, data)
loss = criterion(logits, target).cuda()
prec1 = utils.accuracy(logits, target)[0]
n = data.size(0)
total_loss.update(loss.item(), n)
total_top1.update(prec1.item(), n)
logging.info('valid {:0>3d} {:.6f} {:.3f}'.format(
step, loss.item(), prec1.item()))
logging.info('{}'.format([i for i in dag]))
with open(os.path.join(args.save, 'dag_all.txt'), 'a') as f:
f.write('{:.3f} {} infer\n'.format(prec1.item(),
[i for i in dag])) #
del loss, logits
return total_top1.avg
def infer(valid_queue, model, criterion):
global is_multi_gpu
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda()
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, objs.avg, loss
def train(epoch, train_queue, valid_queue, model, architect, criterion,
optimizer, lr):
global is_multi_gpu
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
# logging.info("epoch %d, step %d START" % (epoch, step))
model.train()
n = input.size(0)
model.set_tau(args.tau_max - epoch * 1.0 / args.epochs *
(args.tau_max - args.tau_min))
input = input.cuda()
target = target.cuda()
# get a random minibatch from the search queue with replacement
input_search, target_search = next(iter(valid_queue))
input_search = input_search.cuda()
target_search = target_search.cuda()
# Update architecture alpha by Adam-SGD
# logging.info("step %d. update architecture by Adam. START" % step)
#
if args.optimization == "AOS":
architect.step_AOS(input, target, input_search, target_search)
else:
architect.step_milenas(input, target, input_search, target_search,
1, 1)
# logging.info("step %d. update architecture by Adam. FINISH" % step)
# Update weights w by SGD, ignore the weights that gained during architecture training
# logging.info("step %d. update weight by SGD. START" % step)
optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
parameters = model.module.arch_parameters(
) if is_multi_gpu else model.arch_parameters()
nn.utils.clip_grad_norm_(parameters, args.grad_clip)
optimizer.step()
# logging.info("step %d. update weight by SGD. FINISH\n" % step)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, objs.avg
def train(train_queue, model, criterion, optimizer, epoch, init_lr,
warmup_epochs, global_step):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.train()
for step, (data, target) in enumerate(train_queue):
n = data.size(0)
data = data.cuda()
target = target.cuda()
# Change lr.
if epoch < warmup_epochs:
len_epoch = len(train_queue)
scale = float(1 + step + epoch * len_epoch) / \
(warmup_epochs * len_epoch)
lr = init_lr * scale
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# Forward.
optimizer.zero_grad()
logits, logits_aux = model(data)
loss = criterion(logits, target)
if args.auxiliary:
loss_aux = criterion(logits_aux, target)
loss += args.auxiliary_weight * loss_aux
# Backward and step.
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
############# APEX #############
# Calculate the accuracy.
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
reduced_loss = utils.reduce_tensor(loss.data, args.world_size)
prec1 = utils.reduce_tensor(prec1, args.world_size)
prec5 = utils.reduce_tensor(prec5, args.world_size)
objs.update(to_python_float(reduced_loss), n)
top1.update(to_python_float(prec1), n)
top5.update(to_python_float(prec5), n)
################################
if step % args.report_freq == 0:
current_lr = list(optimizer.param_groups)[0]['lr']
logging.info('train %03d %e %f %f lr: %e', step, objs.avg,
top1.avg, top5.avg, current_lr)
writer.add_scalar('train/loss', objs.avg, global_step)
writer.add_scalar('train/acc_top1', top1.avg, global_step)
writer.add_scalar('train/acc_top5', top5.avg, global_step)
writer.add_scalar('train/lr',
optimizer.state_dict()['param_groups'][0]['lr'],
global_step)
global_step += 1
return top1.avg, objs.avg, global_step
def infer(valid_loader, model, criterion):
"""
targert
valid loop and record loss ,accuracy
parameters
train_loader : training dataloader
model : training model
criterion : loss function
return
average top 1 accuracy
"""
total_loss = utils.AvgrageMeter()
total_top1 = utils.AvgrageMeter()
aux_nums = len(model.aux_ind)
total_loss_aux = [utils.AvgrageMeter() for i in range(aux_nums)]
total_top1_aux = [utils.AvgrageMeter() for i in range(aux_nums)]
model.eval()
for step, (data, target) in enumerate(valid_loader):
data = data.cuda()
target = target.cuda()
with torch.no_grad():
logits, auxs = model(data)
loss = F.cross_entropy(logits, target).cuda()
loss_aux = [F.cross_entropy(i, target).cuda() for i in auxs]
prec1 = utils.accuracy(logits, target)[0]
prec1_aux = [utils.accuracy(i, target)[0] for i in auxs]
n = data.size(0)
total_loss.update(loss.item(), n)
total_top1.update(prec1.item(), n)
[
total_loss_aux[ind].update(i.item(), n)
for ind, i in enumerate(loss_aux)
]
[
total_top1_aux[ind].update(i.item(), n)
for ind, i in enumerate(prec1_aux)
]
if (step + 1) % args.report_freq == 0:
[
logging.info('aux_{} {:0>3d} {:.6f} {:.3f}'.format(
i, step, total_loss_aux[i].avg, total_top1_aux[i].avg))
for i in range(aux_nums)
]
logging.info('\nvalid {:0>3d} {:.6f} {:.3f}'.format(
step, total_loss.avg, total_top1.avg))
del loss, logits, loss_aux
return total_top1.avg
def train(train_queue, valid_queue, model, architect, criterion, optimizer, lr,
epoch):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
model.train()
n = input.size(0)
input = input.cuda()
target = target.cuda(non_blocking=True)
# get a minibatch from the search queue with replacement
try:
input_search, target_search = next(valid_queue_iter)
except:
valid_queue_iter = iter(valid_queue)
input_search, target_search = next(valid_queue_iter)
input_search = input_search.cuda()
target_search = target_search.cuda(non_blocking=True)
# Allow for warm starting of the one-shot model for more reliable architecture updates.
if epoch >= args.warm_start_epochs:
architect.step(input,
target,
input_search,
target_search,
lr,
optimizer,
unrolled=args.unrolled)
optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
if args.debug:
break
return top1.avg, objs.avg
def train_controller(reward_loader, model, controller, controller_optimizer):
global baseline
total_loss = utils.AvgrageMeter()
total_reward = utils.AvgrageMeter()
total_entropy = utils.AvgrageMeter()
controller.train()
model.eval()
for step in range(150):
data, target = reward_loader.next_batch()
n = data.size(0)
data = data.cuda()
target = target.cuda()
controller_optimizer.zero_grad()
dag, log_prob, entropy = controller()
log_prob = sum(log_prob)
entropy = sum(entropy)
with torch.no_grad():
logits, auxs = model(dag, data)
reward = utils.accuracy(logits, target)[0]
if args.entropy_weight is not None:
reward = reward + args.entropy_weight * entropy
log_prob = torch.sum(log_prob)
if baseline is None:
baseline = reward
baseline -= (1 - args.bl_dec) * (baseline - reward)
loss = (log_prob * (reward - baseline)).sum()
loss.backward()
controller_optimizer.step()
total_loss.update(loss.item(), n)
total_reward.update(reward.item(), n)
total_entropy.update(entropy.item(), n)
if (step + 1) % args.report_freq == 0:
#logging.info('controller %03d %e %f %f', step, loss.item(), reward.item(), baseline.item())
#logging.info(f'controller {step :0>3d} {total_loss.avg :.6f} {total_reward.avg :.3f} {baseline.item() :.3f}')
logging.info('controller {:0>3d} {:.6f} {:.3f} {:.3f}'.format(
step, total_loss.avg, total_reward.avg, baseline.item()))
# logging.info(f'{[i for i in dag]}')
logging.info('{}'.format([i for i in dag]))
with open(os.path.join(args.save, 'dag_all.txt'), 'a') as f:
#f.write(f'{reward.item() :.3f} {[i for i in dag]} controller\n')
f.write('{:.3f} {} controller\n'.format(reward.item(),
[i for i in dag]))
del loss, reward, entropy, logits
def train(train_loader, model, optimizer, criterion, start=False):
"""
targert
training loop and record loss, accuracy
parameters
train_loader : training dataloader
model : training model
optimizer : optimizer
criterion : loss function
start : in search stage needed , make all training stable
return
average top 1 accuracy
"""
total_loss = utils.AvgrageMeter()
total_top1 = utils.AvgrageMeter()
model.train()
aux_nums = len(model.aux_ind)
for step, (data, target) in enumerate(train_loader):
n = data.size(0)
data = data.cuda()
target = target.cuda()
# data, targets_a, targets_b, lam = mixup_data(data, target,args.alpha, True)
optimizer.zero_grad()
logits, auxs = model(data)
loss1 = criterion(logits, target).cuda()
#loss1 = mixup_criterion(criterion, logits, targets_a, targets_b, lam)
#stage loss not in paper , every block will return loss and have different weight
loss_aux = sum([
criterion(auxs[i], target).cuda() * 0.1 * (i + 1)
for i in range(aux_nums)
]) / sum((i + 1) * 0.1 for i in range(aux_nums))
#loss_aux = sum([mixup_criterion(criterion , auxs[i], targets_a, targets_b, lam).cuda()*0.1*(i+1) for i in range(aux_nums)])/sum((i+1)*0.1 for i in range(aux_nums))
loss = loss1 + 0.4 * loss_aux
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.grad_clip)
optimizer.step()
prec1 = utils.accuracy(logits, target)[0]
total_loss.update(loss.item(), n)
total_top1.update(prec1.item(), n)
if (step + 1) % args.report_freq == 0:
logging.info('train {:0>3d} {:.6f} {:.3f}'.format(
step, total_loss.avg, total_top1.avg))
del loss, loss1, loss_aux
return total_top1.avg
def train(train_queue, valid_queue, model, architect, criterion, optimizer, lr,
epoch):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
model.train()
n = input.size(0)
input = input.cuda()
target = target.cuda(non_blocking=True)
# get a random minibatch from the search queue with replacement
input_search, target_search = next(iter(valid_queue))
input_search = input_search.cuda()
target_search = target_search.cuda(non_blocking=True)
if epoch >= 10:
architect.step(input,
target,
input_search,
target_search,
lr,
optimizer,
unrolled=args.unrolled)
optimizer.zero_grad()
architect.optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
optimizer.zero_grad()
architect.optimizer.zero_grad()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.data, n)
top1.update(prec1.data, n)
top5.update(prec5.data, n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
if 'debug' in args.save:
break
return top1.avg, objs.avg
def train(train_loader, model, controller, optimizer, criterion, start=False):
total_loss = utils.AvgrageMeter()
total_top1 = utils.AvgrageMeter()
#controller.eval()
model.eval()
controller.GA_training(10, copy.deepcopy(model))
model.train()
aux_nums = len(model.aux_ind)
for step, (data, target) in enumerate(train_loader):
n = data.size(0)
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
# with torch.no_grad():
# dag, _, _ = controller()
#with architecture different GPU memery usage is different when start with maximum memery usage architecutre sequence
dag = [seq_creater() for i in range(2)]
if ((step == 0) & (start == True)):
dag = [[
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1
]] * 2
logits, auxs = model(dag, data)
loss1 = criterion(logits, target).cuda()
loss_aux = sum([
criterion(auxs[i], target).cuda() * 0.1 * (i + 1)
for i in range(aux_nums)
]) / sum((i + 1) * 0.1 for i in range(aux_nums))
loss = loss1 + 0.4 * loss_aux
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
#loss.backward() without amp use this
optimizer.step()
prec1 = utils.accuracy(logits, target)[0]
total_loss.update(loss.item(), n)
total_top1.update(prec1.item(), n)
if (step + 1) % args.report_freq == 0:
logging.info('train {:0>3d} {:.6f} {:.3f}'.format(
step, total_loss.avg, total_top1.avg))
with open(os.path.join(args.save, 'dag_all.txt'), 'a') as f:
f.write('{:.3f} {} share_weight\n'.format(prec1.item(),
[i for i in dag])) #
del loss
return total_top1.avg
def train(epoch, train_queue, valid_queue, model, architect, criterion,
optimizer, lr):
global is_multi_gpu
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
# logging.info("epoch %d, step %d START" % (epoch, step))
model.train()
n = input.size(0)
input = input.cuda()
target = target.cuda()
# get a random minibatch from the search queue with replacement
input_search, target_search = next(iter(valid_queue))
input_search = input_search.cuda()
target_search = target_search.cuda()
architect.step_v2(input, target, input_search, target_search,
lambda_train_regularizer, lambda_valid_regularizer)
optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
parameters = model.module.arch_parameters(
) if is_multi_gpu else model.arch_parameters()
nn.utils.clip_grad_norm_(parameters, args.grad_clip)
optimizer.step()
# logging.info("step %d. update weight by SGD. FINISH\n" % step)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
# torch.cuda.empty_cache()
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, objs.avg, loss
