35374bebada9b1d9bb50a48bce8cf939d9c6baf1,train_tb.py,,train,#Any#,34

Before Change

        fc_feats, att_feats, labels, masks = tmp
        
        optimizer.zero_grad()
        loss = crit(model(fc_feats, att_feats, labels)[:, 1:], labels[:,1:], masks[:,1:])
        loss.backward()
        utils.clip_gradient(optimizer, opt.grad_clip)
        optimizer.step()

After Change

        data = loader.get_batch("train")
        print("Read data:", time.time() - start)

        torch.cuda.synchronize()
        start = time.time()

        tmp = [data["fc_feats"], data["att_feats"], data["labels"], data["masks"]]
        tmp = [Variable(torch.from_numpy(_), requires_grad=False).cuda() for _ in tmp]
        fc_feats, att_feats, labels, masks = tmp
        
        optimizer.zero_grad()
        loss = crit(model(fc_feats, att_feats, labels), labels[:,1:], masks[:,1:])
        loss.backward()
        utils.clip_gradient(optimizer, opt.grad_clip)
        optimizer.step()
        train_loss = loss.data[0]
        torch.cuda.synchronize()
        end = time.time()
        print("iter {} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
            .format(iteration, epoch, train_loss, end - start))

Instances