Python misc.utils.clip_gradient() Examples

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Example #1
Source File: train.py    From video-caption.pytorch with MIT License 4 votes vote down vote up 
def train(loader, model, crit, optimizer, lr_scheduler, opt, rl_crit=None):
    model.train()
    model = nn.DataParallel(model)
    for epoch in range(opt["epochs"]):
        lr_scheduler.step()

        iteration = 0
        # If start self crit training
        if opt["self_crit_after"] != -1 and epoch >= opt["self_crit_after"]:
            sc_flag = True
            init_cider_scorer(opt["cached_tokens"])
        else:
            sc_flag = False

        for data in loader:
            torch.cuda.synchronize()
            fc_feats = Variable(data['fc_feats']).cuda()
            labels = Variable(data['labels']).long().cuda()
            masks = Variable(data['masks']).cuda()

            optimizer.zero_grad()
            if not sc_flag:
                seq_probs, _ = model(fc_feats, labels, 'train')
                loss = crit(seq_probs, labels[:, 1:], masks[:, 1:])
            else:
                seq_probs, seq_preds = model(
                    fc_feats, mode='inference', opt=opt)
                reward = get_self_critical_reward(model, fc_feats, data,
                                                  seq_preds)
                print(reward.shape)
                loss = rl_crit(seq_probs, seq_preds,
                               Variable(
                                   torch.from_numpy(reward).float().cuda()))

            loss.backward()
            utils.clip_gradient(optimizer, opt["grad_clip"])
            optimizer.step()
            train_loss = loss.data[0]
            torch.cuda.synchronize()
            iteration += 1

            if not sc_flag:
                print("iter %d (epoch %d), train_loss = %.6f" %
                      (iteration, epoch, train_loss))
            else:
                print("iter %d (epoch %d), avg_reward = %.6f" %
                      (iteration, epoch, np.mean(reward[:, 0])))

        if epoch != 0 and epoch % opt["save_checkpoint_every"] == 0:
            model_path = os.path.join(opt["checkpoint_path"],
                                      'model_%d.pth' % (epoch))
            model_info_path = os.path.join(opt["checkpoint_path"],
                                           'model_score.txt')
            torch.save(model.state_dict(), model_path)
            print("model saved to %s" % (model_path))
            with open(model_info_path, 'a') as f:
                f.write("model_%d, loss: %.6f\n" % (epoch, train_loss))