def validate(valid_loader, model, epoch, cur_step, writer, logger, config):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
model.eval()
device = torch.device("cuda")
criterion = nn.CrossEntropyLoss().to(device)
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,
layer_idx=0,
super_flag=True,
pretrain_flag=True)
loss = criterion(logits, y)
prec1, prec5 = utils.accuracy(logits, y, topk=(1, 5))
if config.distributed:
reduced_loss = utils.reduce_tensor(loss.data,
config.world_size)
prec1 = utils.reduce_tensor(prec1, config.world_size)
prec5 = utils.reduce_tensor(prec5, config.world_size)
else:
reduced_loss = loss.data
losses.update(reduced_loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
torch.cuda.synchronize()
step_num = len(valid_loader)
if (step % config.print_freq == 0
or step == step_num - 1) and config.local_rank == 0:
logger.info(
"Valid: Epoch {:2d}/{} Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch + 1,
config.epochs,
step,
step_num,
losses=losses,
top1=top1,
top5=top5))
if config.local_rank == 0:
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: Epoch {:2d}/{} Final Prec@1 {:.4%}".format(
epoch + 1, config.epochs, top1.avg))
return top1.avg, top5.avg
def train_one_epoch(model,
epoch,
optimizer,
scheduler,
trainloader,
warm_up,
criterion_xent,
criterion_htri,
use_gpu=True):
model.train()
losses = util.AverageMeter()
n_batch = len(trainloader)
epoch_iter = 0
for batch_idx, (imgs, pids, _, _) in enumerate(trainloader):
epoch_iter += 1
total_iter = epoch * n_batch + epoch_iter
if warm_up > 0 and epoch < warm_up:
update_lr(scheduler, epoch, total_iter)
if use_gpu:
imgs, pids = imgs.cuda(), pids.cuda() # (N T C H W)
outputs, features = model(imgs)
# combine hard triplet loss with cross entropy loss
xent_loss = criterion_xent(outputs, pids)
htri_loss = criterion_htri(features, pids)
loss = xent_loss + htri_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(loss.item(), pids.size(0))
if total_iter % 100 == 0:
print("batch {}/{} loss {:.3f}({:.3f})".format(batch_idx + 1, len(trainloader), losses.val, losses.avg))
def train(train_loader, model, optimizer, epoch, writer, logger, config):
device = torch.device("cuda")
if config.label_smooth > 0:
criterion = CrossEntropyLabelSmooth(config.n_classes,
config.label_smooth).to(device)
else:
criterion = nn.CrossEntropyLoss().to(device)
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
step_num = len(train_loader)
cur_step = epoch * step_num
cur_lr = optimizer.param_groups[0]['lr']
if config.local_rank == 0:
logger.info("Train 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)
X, target_a, target_b, lam = data_utils.mixup_data(X,
y,
config.mixup_alpha,
use_cuda=True)
optimizer.zero_grad()
logits, logits_aux = model(X,
layer_idx=0,
super_flag=True,
pretrain_flag=True)
loss = data_utils.mixup_criterion(criterion, logits, target_a,
target_b, lam)
if config.aux_weight > 0:
# loss_aux = criterion(logits_aux, y)
loss_aux = data_utils.mixup_criterion(criterion, logits_aux,
target_a, target_b, lam)
loss = loss + config.aux_weight * loss_aux
if config.use_amp:
from apex import amp
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# gradient clipping
nn.utils.clip_grad_norm_(model.module.parameters(), config.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, y, topk=(1, 5))
if config.distributed:
reduced_loss = utils.reduce_tensor(loss.data, config.world_size)
prec1 = utils.reduce_tensor(prec1, config.world_size)
prec5 = utils.reduce_tensor(prec5, config.world_size)
else:
reduced_loss = loss.data
losses.update(reduced_loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
torch.cuda.synchronize()
if config.local_rank == 0 and (step % config.print_freq == 0
or step == step_num):
logger.info(
"Train: Epoch {:2d}/{} Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
epoch + 1,
config.epochs,
step,
step_num,
losses=losses,
top1=top1,
top5=top5))
if config.local_rank == 0:
writer.add_scalar('train/loss', reduced_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
if config.local_rank == 0:
logger.info("Train: Epoch {:2d}/{} Final Prec@1 {:.4%}".format(
epoch + 1, config.epochs, top1.avg))
def search(train_loader, valid_loader, model, optimizer, w_optim, alpha_optim, layer_idx, epoch, writer, logger, config):
# interactive retrain and kl
device = torch.device("cuda")
criterion = nn.CrossEntropyLoss().to(device)
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
losses_interactive = utils.AverageMeter()
losses_cls = utils.AverageMeter()
losses_reg = utils.AverageMeter()
step_num = len(train_loader)
step_num = int(step_num * config.sample_ratio)
cur_step = epoch*step_num
cur_lr_search = w_optim.param_groups[0]['lr']
cur_lr_main = optimizer.param_groups[0]['lr']
if config.local_rank == 0:
logger.info("Train Epoch {} Search LR {}".format(epoch, cur_lr_search))
logger.info("Train Epoch {} Main LR {}".format(epoch, cur_lr_main))
writer.add_scalar('retrain/lr', cur_lr_search, cur_step)
model.train()
for step, ((trn_X, trn_y), (val_X, val_y)) in enumerate(zip(train_loader, valid_loader)):
if step > step_num:
break
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)
#use valid data
alpha_optim.zero_grad()
optimizer.zero_grad()
logits_search, emsemble_logits_search = model(val_X, layer_idx, super_flag=True)
logits_main, emsemble_logits_main= model(val_X, layer_idx, super_flag=False)
loss_cls = (criterion(logits_search, val_y) + criterion(logits_main, val_y)) / config.loss_alpha
loss_interactive = Loss_interactive(emsemble_logits_search, emsemble_logits_main, config.loss_T, config.interactive_type) * config.loss_alpha
loss_regular = 0 * loss_cls
if config.regular:
reg_decay = max(config.regular_coeff * (1 - float(epoch-config.pretrain_epochs)/((config.search_iter-config.pretrain_epochs)*config.search_iter_epochs*config.regular_ratio)), 0)
# normal cell
op_opt = ['max_pool_3x3', 'avg_pool_3x3', 'skip_connect']
op_groups = []
for idx in range(layer_idx, 3):
for op_dx in op_opt:
op_groups.append((idx - layer_idx, op_dx))
loss_regular = loss_regular + model.module.add_alpha_regularization(op_groups, weight_decay=reg_decay, method='L1', reduce=False)
# reduction cell
# op_opt = []
op_opt = ['max_pool_3x3', 'avg_pool_3x3', 'skip_connect']
op_groups = []
for i in range(layer_idx, 3):
for op_dx in op_opt:
op_groups.append((i - layer_idx, op_dx))
loss_regular = loss_regular + model.module.add_alpha_regularization(op_groups, weight_decay=reg_decay, method='L1', normal=False)
loss = loss_cls + loss_interactive + loss_regular
loss.backward()
nn.utils.clip_grad_norm_(model.module.parameters(), config.w_grad_clip)
optimizer.step()
alpha_optim.step()
prec1, prec5 = utils.accuracy(logits_main, val_y, topk=(1, 5))
if config.distributed:
reduced_loss = utils.reduce_tensor(loss.data, config.world_size)
reduced_loss_interactive = utils.reduce_tensor(loss_interactive.data, config.world_size)
reduced_loss_cls = utils.reduce_tensor(loss_cls.data, config.world_size)
reduced_loss_reg = utils.reduce_tensor(loss_regular.data, config.world_size)
prec1 = utils.reduce_tensor(prec1, config.world_size)
prec5 = utils.reduce_tensor(prec5, config.world_size)
else:
reduced_loss = loss.data
reduced_loss_interactive = loss_interactive.data
reduced_loss_cls = loss_cls.data
reduced_loss_reg = loss_regular.data
losses.update(reduced_loss.item(), N)
losses_interactive.update(reduced_loss_interactive.item(), N)
losses_cls.update(reduced_loss_cls.item(), N)
losses_reg.update(reduced_loss_reg.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
torch.cuda.synchronize()
if config.local_rank == 0 and (step % config.print_freq == 0 or step == step_num):
logger.info(
"Train_2: Layer {}/{} Epoch {:2d}/{} Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Loss_interactive {losses_interactive.avg:.3f} Losses_cls {losses_cls.avg:.3f} Losses_reg {losses_reg.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
layer_idx+1, config.layer_num, epoch+1, config.search_iter*config.search_iter_epochs, step,
step_num, losses=losses, losses_interactive=losses_interactive, losses_cls=losses_cls,
losses_reg=losses_reg, top1=top1, top5=top5))
if config.local_rank == 0:
writer.add_scalar('retrain/loss', reduced_loss.item(), cur_step)
writer.add_scalar('retrain/top1', prec1.item(), cur_step)
writer.add_scalar('retrain/top5', prec5.item(), cur_step)
cur_step += 1
w_optim.zero_grad()
logits_search_train, _ = model(trn_X, layer_idx, super_flag=True)
loss_cls_train = criterion(logits_search_train, trn_y)
loss_train = loss_cls_train
loss_train.backward()
# gradient clipping
nn.utils.clip_grad_norm_(model.module.parameters(), config.w_grad_clip)
# only update w
w_optim.step()
# alpha_optim.step()
if config.distributed:
reduced_loss_cls_train = utils.reduce_tensor(loss_cls_train.data, config.world_size)
reduced_loss_train = utils.reduce_tensor(loss_train.data, config.world_size)
else:
reduced_loss_cls_train = reduced_loss_cls_train.data
reduced_loss_train = reduced_loss_train.data
if config.local_rank == 0 and (step % config.print_freq == 0 or step == step_num-1):
logger.info(
"Train_1: Loss_cls: {:.3f} Loss: {:.3f}".format(
reduced_loss_cls_train.item(), reduced_loss_train.item())
)
if config.local_rank == 0:
logger.info("Train_2: Layer {}/{} Epoch {:2d}/{} Final Prec@1 {:.4%}".format(
layer_idx+1, config.layer_num, epoch+1, config.search_iter*config.search_iter_epochs, top1.avg))
def retrain_warmup(valid_loader, model, optimizer, layer_idx, epoch, writer, logger, super_flag, retrain_epochs, config):
device = torch.device("cuda")
criterion = nn.CrossEntropyLoss().to(device)
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
losses = utils.AverageMeter()
step_num = len(valid_loader)
step_num = int(step_num * config.sample_ratio)
cur_step = epoch*step_num
cur_lr = optimizer.param_groups[0]['lr']
if config.local_rank == 0:
logger.info("Warmup Epoch {} LR {:.3f}".format(epoch+1, cur_lr))
writer.add_scalar('warmup/lr', cur_lr, cur_step)
model.train()
for step, (val_X, val_y) in enumerate(valid_loader):
if step > step_num:
break
val_X, val_y = val_X.to(device, non_blocking=True), val_y.to(device, non_blocking=True)
N = val_X.size(0)
optimizer.zero_grad()
logits_main, _ = model(val_X, layer_idx, super_flag=super_flag)
loss = criterion(logits_main, val_y)
loss.backward()
nn.utils.clip_grad_norm_(model.module.parameters(), config.w_grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits_main, val_y, topk=(1, 5))
if config.distributed:
reduced_loss = utils.reduce_tensor(loss.data, config.world_size)
prec1 = utils.reduce_tensor(prec1, config.world_size)
prec5 = utils.reduce_tensor(prec5, config.world_size)
else:
reduced_loss = loss.data
losses.update(reduced_loss.item(), N)
top1.update(prec1.item(), N)
top5.update(prec5.item(), N)
torch.cuda.synchronize()
if config.local_rank == 0 and (step % config.print_freq == 0 or step == step_num):
logger.info(
"Warmup: Layer {}/{} Epoch {:2d}/{} Step {:03d}/{:03d} Loss {losses.avg:.3f} "
"Prec@(1,5) ({top1.avg:.1%}, {top5.avg:.1%})".format(
layer_idx+1, config.layer_num, epoch+1, retrain_epochs, step,
step_num, losses=losses, top1=top1, top5=top5))
if config.local_rank == 0:
writer.add_scalar('retrain/loss', reduced_loss.item(), cur_step)
writer.add_scalar('retrain/top1', prec1.item(), cur_step)
writer.add_scalar('retrain/top5', prec5.item(), cur_step)
cur_step += 1
if config.local_rank == 0:
logger.info("Warmup: Layer {}/{} Epoch {:2d}/{} Final Prec@1 {:.4%}".format(
layer_idx+1, config.layer_num, epoch+1, retrain_epochs, top1.avg))
