def train_PEM(data_loader,model,optimizer,epoch,writer,opt):
model.train()
epoch_iou_loss = 0
for n_iter,(input_data,label_iou,is_whole_lenght) in enumerate(data_loader):
PEM_output = model(input_data)
iou_loss = PEM_loss_function(PEM_output,[label_iou, is_whole_lenght],model,opt)
optimizer.zero_grad()
iou_loss.backward()
optimizer.step()
epoch_iou_loss += iou_loss.cpu().detach().numpy()
writer.add_scalars('data/iou_loss', {'train': epoch_iou_loss/(n_iter+1)}, epoch)
print "PEM training loss(epoch %d): iou - %.04f" %(epoch,epoch_iou_loss/(n_iter+1))
def train_PEM(data_loader, model, optimizer, epoch, writer, opt):
model.train()
epoch_iou_loss = 0
losses = AverageMeter()
for n_iter, (input_data, label_iou) in enumerate(data_loader):
PEM_output = model(input_data)
iou_loss = PEM_loss_function(PEM_output, label_iou, model, opt)
optimizer.zero_grad()
iou_loss.backward()
optimizer.step()
epoch_iou_loss += iou_loss.cpu().detach().numpy()
losses.update(iou_loss.item())
if (n_iter + 1) % opt['print_freq'] == 0:
print('[TRAIN] Epoch {}, iter {} / {}, loss: {}'.format(
epoch, n_iter + 1, len(data_loader), losses.avg))
writer.add_scalars('data/iou_loss',
{'train': epoch_iou_loss / (n_iter + 1)}, epoch)
print("PEM training loss(epoch %d): iou - %.04f" % (epoch, epoch_iou_loss /
(n_iter + 1)))
