add those code by your self:
for epoch in range(0, Config.train_number_epochs):
for i, data in enumerate(train_dataloader, 0):
img0, img1, label = data
img0, img1, label = img0.cuda(), img1.cuda(), label.cuda()
optimizer.zero_grad()
output1, output2 = net(img0, img1)
loss_contrastive = criterion(output1, output2, label)
loss_contrastive.backward()
optimizer.step()
if i % 10 == 0:
print("Epoch number {}\n Current loss {}\n".format(epoch, loss_contrastive.item()))
iteration_number += 10
counter.append(iteration_number)
loss_history.append(loss_contrastive.item())

show_plot(counter, loss_history)

save the training res

PATH = "./net.pt"
torch.save(net.state_dict(),PATH)

from facial-similarity-with-siamese-networks-in-pytorch.