AP loss Backpropagation

[Zenobia7]

@almazan
How do you calculate the derivative of the similarity matrix S and matrix D?I calculated it automatically through pytorch, but the parameter update seems to be a bit problematic.
'''
desc_db = Variable(torch.cuda.FloatTensor(desc_db),requires_grad=True)
scores = torch.matmul(desc_db,desc_db.t())
vaild_index = np.arange(batch_sizeindex,batch_size(index+1),1)
Y = np.array(Y_all)[vaild_index][:,vaild_index]
Y = torch.cuda.FloatTensor(np.array(Y))
rank_loss = criterion(scores, Y)
rank_loss.backward()
loss += rank_loss.item()
net.train()
for i,img in enumerate(imgs):
img = Variable(img.cuda(),requires_grad=True)
desc = net(img.unsqueeze(dim=0))
one_grad = desc_db.grad[i].unsqueeze(0)
desc.unsqueeze(0).backward(one_grad)
optimizer.step()
scheduler_mul.step()
optimizer.zero_grad()
lr = scheduler_mul.get_lr()[0]
'''