def test_PEM(data_loader, model, epoch, writer, opt):
model.eval()
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)
epoch_iou_loss += iou_loss.cpu().detach().numpy()
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"] + "/" + opt["arch"] + "_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"] + "/" + opt["arch"] + "_pem_best.pth.tar")
def test_BMN(data_loader, model, epoch, writer, opt):
model.eval()
epoch_pem_loss = 0
epoch_tem_loss = 0
epoch_loss = 0
for n_iter, (input_data, label_start, label_end, label_confidence) in enumerate(data_loader):
input_data = input_data.cuda()
label_start = label_start.cuda()
label_end = label_end.cuda()
label_confidence = label_confidence.cuda()
start_end, confidence_map = model(input_data)
tem_loss = TEM_loss_function(label_start, label_end, start_end, opt)
pem_loss = PEM_loss_function(label_confidence, confidence_map, confidence_mask, opt)
loss = tem_loss + pem_loss
epoch_pem_loss += pem_loss.cpu().detach().numpy()
epoch_tem_loss += tem_loss.cpu().detach().numpy()
epoch_loss += loss.cpu().detach().numpy()
writer.add_scalars('data/pem_loss', {'train': epoch_pem_loss / (n_iter + 1)}, epoch)
writer.add_scalars('data/tem_loss', {'train': epoch_tem_loss / (n_iter + 1)}, epoch)
writer.add_scalars('data/total_loss', {'train': epoch_loss / (n_iter + 1)}, epoch)
print("BMN testing loss(epoch %d): tem_loss: %.03f, pem_loss: %.03f, total_loss: %.03f" % (
epoch, epoch_tem_loss / (n_iter + 1),
epoch_pem_loss / (n_iter + 1),
epoch_loss / (n_iter + 1)))
state = {'epoch': epoch + 1,
'state_dict': model.state_dict()}
torch.save(state, opt["checkpoint_path"] + "/BMN_checkpoint.pth.tar")
if epoch_loss < model.best_loss:
model.best_loss = epoch_loss
torch.save(state, opt["checkpoint_path"] + "/BMN_best.pth.tar")
def train_BMN(data_loader, model, optimizer, epoch, writer, opt):
model.train()
epoch_pem_loss = 0
epoch_tem_loss = 0
epoch_loss = 0
for n_iter, (input_data, label_start, label_end, label_confidence) in enumerate(data_loader):
input_data = input_data.cuda()
label_start = label_start.cuda()
label_end = label_end.cuda()
label_confidence = label_confidence.cuda()
start_end, confidence_map = model(input_data)
tem_loss = TEM_loss_function(label_start, label_end, start_end, opt)
pem_loss = PEM_loss_function(label_confidence, confidence_map, confidence_mask, opt)
loss = tem_loss + pem_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_pem_loss += pem_loss.cpu().detach().numpy()
epoch_tem_loss += tem_loss.cpu().detach().numpy()
epoch_loss += loss.cpu().detach().numpy()
writer.add_scalars('data/pem_loss', {'train': epoch_pem_loss / (n_iter + 1)}, epoch)
writer.add_scalars('data/tem_loss', {'train': epoch_tem_loss / (n_iter + 1)}, epoch)
writer.add_scalars('data/total_loss', {'train': epoch_loss / (n_iter + 1)}, epoch)
print("BMN training loss(epoch %d): tem_loss: %.03f, pem_loss: %.03f, total_loss: %.03f" % (
epoch, epoch_tem_loss / (n_iter + 1),
epoch_pem_loss / (n_iter + 1),
epoch_loss / (n_iter + 1)))
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
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)))
