logger.info(open(args.config).read()) parse_config(args.config) model = VideoCompressor() if args.pretrain != '': print("loading pretrain : ", args.pretrain) global_step = load_model(model, args.pretrain) net = model.cuda() net = torch.nn.DataParallel(net, list(range(gpu_num))) bp_parameters = net.parameters() optimizer = optim.Adam(bp_parameters, lr=base_lr) # save_model(model, 0) global train_dataset, test_dataset if args.testuvg: test_dataset = UVGDataSet(refdir=ref_i_dir, testfull=True) print('testing UVG') testuvg(0, testfull=True) exit(0) tb_logger = SummaryWriter('./events') train_dataset = DataSet("data/vimeo_septuplet/test.txt") # test_dataset = UVGDataSet(refdir=ref_i_dir) stepoch = global_step // (train_dataset.__len__() // (gpu_per_batch))# * gpu_num)) for epoch in range(stepoch, tot_epoch): adjust_learning_rate(optimizer, global_step) if global_step > tot_step: save_model(model, global_step) break global_step = train(epoch, global_step) save_model(model, global_step)
def main(): opts = get_train_args() print("load data ...") data = DataSet('data/modified_triples.txt') dataloader = DataLoader(data, shuffle=True, batch_size=opts.batch_size) print("load model ...") if opts.model_type == 'transe': model = TransE(opts, data.ent_tot, data.rel_tot) elif opts.model_type == "distmult": model = DistMult(opts, data.ent_tot, data.rel_tot) if opts.optimizer == 'Adam': optimizer = optim.Adam(model.parameters(), lr=opts.lr) elif opts.optimizer == 'SGD': optimizer = optim.SGD(model.parameters(), lr=opts.lr) model.cuda() model.relation_normalize() loss = torch.nn.MarginRankingLoss(margin=opts.margin) print("start training") for epoch in range(1, opts.epochs + 1): print("epoch : " + str(epoch)) model.train() epoch_start = time.time() epoch_loss = 0 tot = 0 cnt = 0 for i, batch_data in enumerate(dataloader): optimizer.zero_grad() batch_h, batch_r, batch_t, batch_n = batch_data batch_h = torch.LongTensor(batch_h).cuda() batch_r = torch.LongTensor(batch_r).cuda() batch_t = torch.LongTensor(batch_t).cuda() batch_n = torch.LongTensor(batch_n).cuda() pos_score, neg_score, dist = model.forward(batch_h, batch_r, batch_t, batch_n) pos_score = pos_score.cpu() neg_score = neg_score.cpu() dist = dist.cpu() train_loss = loss(pos_score, neg_score, torch.ones(pos_score.size(-1))) + dist train_loss.backward() optimizer.step() batch_loss = torch.sum(train_loss) epoch_loss += batch_loss batch_size = batch_h.size(0) tot += batch_size cnt += 1 print('\r{:>10} epoch {} progress {} loss: {}\n'.format( '', epoch, tot / data.__len__(), train_loss), end='') end = time.time() time_used = end - epoch_start epoch_loss /= cnt print('one epoch time: {} minutes'.format(time_used / 60)) print('{} epochs'.format(epoch)) print('epoch {} loss: {}'.format(epoch, epoch_loss)) if epoch % opts.save_step == 0: print("save model...") model.entity_normalize() torch.save(model.state_dict(), 'model.pt') print("save model...") model.entity_normalize() torch.save(model.state_dict(), 'model.pt') print("[Saving embeddings of whole entities & relations...]") save_embeddings(model, opts, data.id2ent, data.id2rel) print("[Embedding results are saved successfully.]")