def validate(val_loader, model, criterion):
logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')
batch_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
top1_meter = AverageMeter()
top5_meter = AverageMeter()
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
data_time.update(time.time() - end)
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
with torch.no_grad():
output = model(input)
loss = criterion(output, target)
top1, top5 = cal_accuracy(output, target, topk=(1, 5))
n = input.size(0)
loss_meter.update(loss.item(), n), top1_meter.update(top1.item(), n), top5_meter.update(top5.item(), n)
output = output.max(1)[1]
intersection, union, target = intersectionAndUnionGPU(output, target, args.classes, args.ignore_label)
intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()
intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)
accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
logger.info('Test: [{}/{}] '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f}) '
'Accuracy {accuracy:.4f} '
'Acc@1 {top1.val:.3f} ({top1.avg:.3f}) '
'Acc@5 {top5.val:.3f} ({top5.avg:.3f}).'.format(i + 1, len(val_loader),
data_time=data_time,
batch_time=batch_time,
loss_meter=loss_meter,
accuracy=accuracy,
top1=top1_meter,
top5=top5_meter))
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
logger.info('Val result: mIoU/mAcc/allAcc/top1/top5 {:.4f}/{:.4f}/{:.4f}/{:.4f}/{:.4f}.'.format(mIoU, mAcc, allAcc, top1_meter.avg, top5_meter.avg))
for i in range(args.classes):
logger.info('Class_{} Result: iou/accuracy {:.4f}/{:.4f}.'.format(i, iou_class[i], accuracy_class[i]))
logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<')
return loss_meter.avg, mIoU, mAcc, allAcc, top1_meter.avg, top5_meter.avg
def triplet_train(train_loader, model, criterion, optimizer, epoch,
classif_criterion):
iter_time = AverageMeter()
data_time = AverageMeter()
transfer_time = AverageMeter()
batch_time = AverageMeter()
metric_time = AverageMeter()
loss_meter = AverageMeter()
loss_sketch_meter = AverageMeter()
loss_positive_meter = AverageMeter()
loss_negative_meter = AverageMeter()
loss_triplet_meter = AverageMeter()
top1_meter_sketch = AverageMeter()
top1_meter_positive = AverageMeter()
top1_meter_negative = AverageMeter()
model.train()
end = time.time()
sum_epochs = args.contras_epochs + args.triplet_epochs
max_iter = sum_epochs * len(train_loader)
for i, (sketch_input, positive_input, negative_input, sketch_target,
negative_target) in enumerate(train_loader):
data_time.update(time.time() - end)
if args.time_breakdown:
last = time.time()
sketch_input = sketch_input.cuda(non_blocking=True)
positive_input = positive_input.cuda(non_blocking=True)
negative_input = negative_input.cuda(non_blocking=True)
sketch_target = sketch_target.cuda(non_blocking=True)
negative_target = negative_target.cuda(non_blocking=True)
if args.time_breakdown:
torch.cuda.synchronize()
transfer_time.update(time.time() - last)
last = time.time()
(sketchFeatVec, positiveFeatVec,
negativeFeatVec), (sketch_output, positive_output,
negative_output) = model(sketch_input,
positive_input,
negative_input)
loss_sketch = smooth_loss(
sketch_output, sketch_target, args.label_smoothing
) if args.label_smoothing else classif_criterion(
sketch_output, sketch_target)
loss_positive = smooth_loss(
positive_output, sketch_target, args.label_smoothing
) if args.label_smoothing else classif_criterion(
positive_output, sketch_target)
loss_negative = smooth_loss(
negative_output, negative_target, args.label_smoothing
) if args.label_smoothing else classif_criterion(
negative_output, negative_target)
sketchFeatVec = F.normalize(sketchFeatVec)
positiveFeatVec = F.normalize(positiveFeatVec)
negativeFeatVec = F.normalize(negativeFeatVec)
loss_triplet = criterion(sketchFeatVec, positiveFeatVec,
negativeFeatVec)
loss_triplet, loss_sketch, loss_positive, loss_negative = loss_triplet, loss_sketch * 0.5, loss_positive * 0.25, loss_negative * 0.25
classif_decay = 0.5 * (1 + math.cos(epoch * math.pi / sum_epochs))
loss = 2.0 * loss_triplet * epoch + classif_decay * (
loss_sketch + loss_positive + loss_negative)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if args.time_breakdown:
torch.cuda.synchronize()
batch_time.update(time.time() - last)
last = time.time()
n = sketch_input.size(0)
if args.multiprocessing_distributed:
with torch.no_grad():
loss, loss_triplet, loss_sketch, loss_positive, loss_negative = loss.detach(
) * n, loss_triplet.detach() * n, loss_sketch.detach(
) * n, loss_positive.detach() * n, loss_negative.detach() * n
count = sketch_target.new_tensor([n], dtype=torch.long)
dist.all_reduce(loss), dist.all_reduce(count), dist.all_reduce(
loss_triplet), dist.all_reduce(
loss_sketch), dist.all_reduce(
loss_positive), dist.all_reduce(loss_negative)
n = count.item()
loss, loss_triplet, loss_sketch, loss_positive, loss_negative = loss / n, loss_triplet / n, loss_sketch / n, loss_positive / n, loss_negative / n
loss_meter.update(loss.item(), n)
loss_triplet_meter.update(loss_triplet.item(), n)
loss_sketch_meter.update(loss_sketch.item(), n)
loss_positive_meter.update(loss_positive.item(), n)
loss_negative_meter.update(loss_negative.item(), n)
# classification metrics
top1_s, = cal_accuracy(sketch_output, sketch_target, topk=(1, ))
top1_p, = cal_accuracy(positive_output, sketch_target, topk=(1, ))
top1_n, = cal_accuracy(negative_output, negative_target, topk=(1, ))
n = sketch_input.size(0)
if args.multiprocessing_distributed:
with torch.no_grad():
top1_s = top1_s * n
top1_p = top1_p * n
top1_n = top1_n * n
count = sketch_target.new_tensor([n], dtype=torch.long)
dist.all_reduce(top1_s), dist.all_reduce(
top1_p), dist.all_reduce(top1_n), dist.all_reduce(count)
n = count.item()
top1_s = top1_s / n
top1_p = top1_p / n
top1_n = top1_n / n
top1_meter_sketch.update(top1_s.item(), n), top1_meter_positive.update(
top1_p.item(), n), top1_meter_negative.update(top1_n.item(), n)
if args.time_breakdown:
torch.cuda.synchronize()
metric_time.update(time.time() - last)
iter_time.update(time.time() - end)
end = time.time()
# calculate remain time
current_iter = epoch * len(train_loader) + i + 1
remain_iter = max_iter - current_iter
remain_time = remain_iter * iter_time.avg
t_m, t_s = divmod(remain_time, 60)
t_h, t_m = divmod(t_m, 60)
remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m),
int(t_s))
if ((i + 1) % args.print_freq == 0) and main_process():
logger.info((
'Epoch: [{}/{}][{}/{}] '
'Time {iter_time.val:.3f} ({iter_time.avg:.3f}) '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) ' +
('Transfer {transfer_time.val:.3f} ({transfer_time.avg:.3f}) '
'Batch {batch_time.val:.4f} ({batch_time.avg:.4f}) '
'Metric {metric_time.val:.3f} ({metric_time.avg:.3f}) '
if args.time_breakdown else '') + 'Remain {remain_time} '
'Loss total/triplet/classifSketch/classifPos/classifNeg '
'{loss_meter.val:.4f}/{triplet_loss_meter.val:.4f}/{sketch_loss_meter.val:.4f}/{positive_loss_meter.val:.4f}/{negative_loss_meter.val:.4f} '
'Top1classif sketch/positive/negative {top1_s.val:.3f}/{top1_p.val:.3f}/{top1_n.val:.3f}'
).format(epoch + 1,
sum_epochs,
i + 1,
len(train_loader),
iter_time=iter_time,
data_time=data_time,
transfer_time=transfer_time,
batch_time=batch_time,
metric_time=metric_time,
remain_time=remain_time,
triplet_loss_meter=loss_triplet_meter,
sketch_loss_meter=loss_sketch_meter,
positive_loss_meter=loss_positive_meter,
negative_loss_meter=loss_negative_meter,
loss_meter=loss_meter,
top1_s=top1_meter_sketch,
top1_p=top1_meter_positive,
top1_n=top1_meter_negative))
if main_process():
logger.info(
'Train result at epoch [{}/{}]: Loss total/triplet/classifSketch/classifPos/classifNeg {:.4f}/{:.4f}/{:.4f}/{:.4f}/{:.4f}. '
'Top1classif sketch/positive/negative {top1_s.avg:.3f}/{top1_p.avg:.3f}/{top1_n.avg:.3f}'
.format(epoch + 1,
sum_epochs,
loss_meter.avg,
loss_triplet_meter.avg,
loss_sketch_meter.avg,
loss_positive_meter.avg,
loss_negative_meter.avg,
top1_s=top1_meter_sketch,
top1_p=top1_meter_positive,
top1_n=top1_meter_negative))
return loss_meter.avg
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
top1_meter = AverageMeter()
top5_meter = AverageMeter()
model.train()
# print(get_parameter_number(model))
# exit(0)
end = time.time()
max_iter = args.epochs * len(train_loader)
for i, (input, target) in enumerate(train_loader):
data_time.update(time.time() - end)
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
if args.mixup_alpha:
eps = args.label_smoothing if args.label_smoothing else 0.0
input, target_a, target_b, lam = mixup_data(
input, target, args.mixup_alpha)
output = model(input)
loss = mixup_loss(output, target_a, target_b, lam, eps)
else:
output = model(input)
loss = smooth_loss(
output, target,
args.label_smoothing) if args.label_smoothing else criterion(
output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
top1, top5 = cal_accuracy(output, target, topk=(1, 5))
n = input.size(0)
if args.multiprocessing_distributed:
with torch.no_grad():
loss, top1, top5 = loss.detach() * n, top1 * n, top5 * n
count = target.new_tensor([n], dtype=torch.long)
dist.all_reduce(loss), dist.all_reduce(top1), dist.all_reduce(
top5), dist.all_reduce(count)
n = count.item()
loss, top1, top5 = loss / n, top1 / n, top5 / n
loss_meter.update(loss.item(), n), top1_meter.update(
top1.item(), n), top5_meter.update(top5.item(), n)
output = output.max(1)[1]
intersection, union, target = intersectionAndUnionGPU(
output, target, args.classes, args.ignore_label)
if args.multiprocessing_distributed:
dist.all_reduce(intersection), dist.all_reduce(
union), dist.all_reduce(target)
intersection, union, target = intersection.cpu().numpy(), union.cpu(
).numpy(), target.cpu().numpy()
intersection_meter.update(intersection), union_meter.update(
union), target_meter.update(target)
accuracy = sum(
intersection_meter.val) / (sum(target_meter.val) + 1e-10)
batch_time.update(time.time() - end)
end = time.time()
# calculate remain time
current_iter = epoch * len(train_loader) + i + 1
remain_iter = max_iter - current_iter
remain_time = remain_iter * batch_time.avg
t_m, t_s = divmod(remain_time, 60)
t_h, t_m = divmod(t_m, 60)
remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m),
int(t_s))
if ((i + 1) % args.print_freq == 0) and main_process():
logger.info('Epoch: [{}/{}][{}/{}] '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Remain {remain_time} '
'Loss {loss_meter.val:.4f} '
'Accuracy {accuracy:.4f} '
'Acc@1 {top1.val:.3f} ({top1.avg:.3f}) '
'Acc@5 {top5.val:.3f} ({top5.avg:.3f}).'.format(
epoch + 1,
args.epochs,
i + 1,
len(train_loader),
data_time=data_time,
batch_time=batch_time,
remain_time=remain_time,
loss_meter=loss_meter,
accuracy=accuracy,
top1=top1_meter,
top5=top5_meter))
if main_process():
writer.add_scalar('train/loss', loss_meter.val, current_iter)
writer.add_scalar('train/mIoU',
np.mean(intersection / (union + 1e-10)),
current_iter)
writer.add_scalar('train/mAcc',
np.mean(intersection / (target + 1e-10)),
current_iter)
writer.add_scalar('train/allAcc', accuracy, current_iter)
writer.add_scalar('train/top1', top1, current_iter)
writer.add_scalar('train/top5', top5, current_iter)
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
if main_process():
logger.info(
'Train result at epoch [{}/{}]: mIoU/mAcc/allAcc/top1/top5 {:.4f}/{:.4f}/{:.4f}/{:.4f}/{:.4f}.'
.format(epoch + 1, args.epochs, mIoU, mAcc, allAcc, top1_meter.avg,
top5_meter.avg))
return loss_meter.avg, mIoU, mAcc, allAcc, top1_meter.avg, top5_meter.avg