def test_PEM(data_loader, model, epoch, writer, opt):
model.eval()
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)
epoch_iou_loss += iou_loss.cpu().detach().numpy()
losses.update(iou_loss.item())
if (n_iter + 1) % opt['print_freq'] == 0:
print('[TEST] Epoch {}, iter {} / {}, loss: {}'.format(
epoch, n_iter + 1, len(data_loader), losses.avg))
writer.add_scalars('data/iou_loss',
{'validation': epoch_iou_loss / (n_iter + 1)}, epoch)
print("PEM testing loss(epoch %d): iou - %.04f" % (epoch, epoch_iou_loss /
(n_iter + 1)))
state = {'epoch': epoch + 1, 'state_dict': model.state_dict()}
torch.save(state, opt["checkpoint_path"] + "/pem_checkpoint.pth.tar")
if epoch_iou_loss < model.module.pem_best_loss:
model.module.pem_best_loss = np.mean(epoch_iou_loss)
torch.save(state, opt["checkpoint_path"] + "/pem_best.pth.tar")
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)))
