jd['cnn_filter_num'], jd['char_lstm_layers'], jd['char_lstm'],
jd['dropout_ratio'], jd['high_way'],
jd['highway_layers'], CRF_l_map['<start>'], CRF_l_map['<pad>'],
len(CRF_l_map), SCRF_l_map, jd['scrf_dense_dim'], in_doc_words,
jd['index_embeds_dim'], jd['allowspan'], SCRF_l_map['<START>'],
SCRF_l_map['<STOP>'], jd['grconv'])
print('load model')
model.load_state_dict(checkpoint_file['state_dict'])
#model.cuda()
model.to(local_device)
packer = Repack()
evaluator = evaluator(packer, CRF_l_map, SCRF_l_map)
print('dev...')
dev_f1_crf, dev_pre_crf, dev_rec_crf, dev_acc_crf, dev_f1_scrf, dev_pre_scrf, dev_rec_scrf, dev_acc_scrf, dev_f1_jnt, dev_pre_jnt, dev_rec_jnt, dev_acc_jnt = \
evaluator.calc_score(model, dev_dataset_loader)
print('test...')
test_f1_crf, test_pre_crf, test_rec_crf, test_acc_crf, test_f1_scrf, test_pre_scrf, test_rec_scrf, test_acc_scrf, test_f1_jnt, test_pre_jnt, test_rec_jnt, test_acc_jnt = \
evaluator.calc_score(model, test_dataset_loader)
print(' dev_f1: %.4f\n' % (dev_f1_crf))
print(' dev_f1_scrf: %.4f\n' % (dev_f1_scrf))
print(' dev_f1_jnt: %.4f\n' % (dev_f1_jnt))
print(' test_f1: %.4f\n' % (test_f1_crf))
print(' test_f1_scrf: %.4f\n' % (test_f1_scrf))
print(' test_f1_jnt: %.4f\n' % (test_f1_jnt))
loss = model(f_f, f_p, b_f, b_p, w_f, cnn_features, tg_v, mask_v,
mask_v.long().sum(0), SCRF_labels, mask_SCRF_labels, onlycrf=False)
epoch_loss += utils.to_scalar(loss)
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)
optimizer.step()
epoch_loss /= tot_length
print('epoch_loss: ', epoch_loss)
utils.adjust_learning_rate(optimizer, args.lr / (1 + (args.start_epoch + 1) * args.lr_decay))
dev_f1_crf, dev_pre_crf, dev_rec_crf, dev_acc_crf, dev_f1_scrf, dev_pre_scrf, dev_rec_scrf, dev_acc_scrf, dev_f1_jnt, dev_pre_jnt, dev_rec_jnt, dev_acc_jnt = \
evaluator.calc_score(model, dev_dataset_loader)
if dev_f1_jnt > best_dev_f1_jnt:
early_stop_epochs = 0
test_f1_crf, test_pre_crf, test_rec_crf, test_acc_crf, test_f1_scrf, test_pre_scrf, test_rec_scrf, test_acc_scrf, test_f1_jnt, test_pre_jnt, test_rec_jnt, test_acc_jnt = \
evaluator.calc_score(model, test_dataset_loader)
best_test_f1_crf = test_f1_crf
best_test_f1_scrf = test_f1_scrf
best_dev_f1_jnt = dev_f1_jnt
best_test_f1_jnt = test_f1_jnt
try:
utils.save_checkpoint({
'epoch': args.start_epoch,