def run():
    args = parser.parse_args()
    nlayer = args.nlayer
    bidirection = args.bidirection
    file_path = args.file_path  #'/content/drive/My Drive/Master_Final_Project/Genetic_attack/Code/nlp_adversarial_example_master_pytorch/glove.840B.300d.txt'#'/lustre/scratch/scratch/ucabdc3/lstm_attack'
    save_path = os.path.join(file_path, 'results')
    MAX_VOCAB_SIZE = 50000
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    #    with open(os.path.join(file_path, 'dataset_%d.pkl' %MAX_VOCAB_SIZE), 'rb') as f:
    #        dataset = pickle.load(f)

    with open('aux_files/dataset_%d.pkl' % MAX_VOCAB_SIZE, 'rb') as f:
        dataset = pickle.load(f)


#    skip_list = np.load('aux_files/missed_embeddings_counter_%d.npy' %MAX_VOCAB_SIZE)
    embedding_matrix = np.load('aux_files/embeddings_glove_%d.npy' %
                               (MAX_VOCAB_SIZE))
    embedding_matrix = torch.tensor(embedding_matrix.T).to(device)
    dist = np.load(('aux_files/dist_counter_%d.npy' % (MAX_VOCAB_SIZE)))

    #    goog_lm = LM()

    # pytorch
    max_len = args.max_len
    #    padded_train_raw = pad_sequences(dataset.train_seqs2, maxlen = max_len, padding = 'post')
    padded_test_raw = pad_sequences(dataset.test_seqs2,
                                    maxlen=max_len,
                                    padding='post')
    #    # TrainSet
    #    data_set = Data_infor(padded_train_raw, dataset.train_y)
    #    num_train = len(data_set)
    #    indx = list(range(num_train))
    #    train_set = Subset(data_set, indx)

    # TestSet
    batch_size = 1
    SAMPLE_SIZE = args.sample_size
    data_set = Data_infor(padded_test_raw, dataset.test_y)
    num_test = len(data_set)
    indx = list(range(num_test))

    all_test_set = Subset(data_set, indx)
    #    indx = random.sample(indx, SAMPLE_SIZE)
    with open('seq_number.pkl', 'rb') as f:
        indx = pickle.load(f)

    test_set = Subset(data_set, indx)
    test_loader = DataLoader(test_set,
                             batch_size=batch_size,
                             shuffle=False,
                             pin_memory=True)
    all_test_loader = DataLoader(all_test_set, batch_size=128, shuffle=True)

    lstm_size = 128
    rnn_state_save = os.path.join(file_path, 'best_lstm_0.7_0.001_300')

    model = SentimentAnalysis(batch_size=batch_size,
                              embedding_matrix=embedding_matrix,
                              hidden_size=lstm_size,
                              kept_prob=0.7,
                              num_layers=nlayer,
                              bidirection=bidirection)

    model.load_state_dict(torch.load(rnn_state_save))
    model = model.to(device)

    model.eval()
    test_pred = torch.tensor([])
    test_targets = torch.tensor([])

    with torch.no_grad():
        for batch_index, (seqs, length, target) in enumerate(all_test_loader):
            seqs, target, length = seqs.to(device), target.to(
                device), length.to(device)
            seqs = seqs.type(torch.LongTensor)
            len_order = torch.argsort(length, descending=True)
            length = length[len_order]
            seqs = seqs[len_order]
            target = target[len_order]

            output, pred_out = model.pred(seqs, length, False)
            test_pred = torch.cat((test_pred, pred_out.cpu()), dim=0)
            test_targets = torch.cat(
                (test_targets, target.type(torch.float).cpu()))

        accuracy = model.evaluate_accuracy(test_pred.numpy(),
                                           test_targets.numpy())
    print('Test Accuracy:{:.4f}.'.format(accuracy))

    n1 = 8
    n2 = 4
    pop_size = 60
    max_iters = 20
    n_prefix = 5
    n_suffix = 5
    batch_model = SentimentAnalysis(batch_size=pop_size,
                                    embedding_matrix=embedding_matrix,
                                    hidden_size=lstm_size,
                                    kept_prob=0.7,
                                    num_layers=nlayer,
                                    bidirection=bidirection)

    batch_model.eval()
    batch_model.load_state_dict(torch.load(rnn_state_save))
    batch_model.to(device)

    neighbour_model = SentimentAnalysis(batch_size=n1,
                                        embedding_matrix=embedding_matrix,
                                        hidden_size=lstm_size,
                                        kept_prob=0.7,
                                        num_layers=nlayer,
                                        bidirection=bidirection)

    neighbour_model.eval()
    neighbour_model.load_state_dict(torch.load(rnn_state_save))
    neighbour_model.to(device)
    lm_model = gpt_2_get_words_probs()
    use_lm = args.use_lm
    ga_attack = GeneticAttack_pytorch(model,
                                      batch_model,
                                      neighbour_model,
                                      dist,
                                      lm_model,
                                      max_iters=max_iters,
                                      dataset=dataset,
                                      pop_size=pop_size,
                                      n1=n1,
                                      n2=n2,
                                      n_prefix=n_prefix,
                                      n_suffix=n_suffix,
                                      use_lm=use_lm,
                                      use_suffix=True)

    TEST_SIZE = args.test_size
    order_pre = 0
    n = 0
    seq_success = []
    seq_orig = []
    seq_orig_label = []
    word_varied = []
    orig_list = []
    adv_list = []
    dist_list = []
    l_list = []
    label_list = []

    # if order_pre != 0:
    #   seq_success = np.load(os.path.join(save_path,'seq_success.npy'), allow_pickle = True).tolist()
    #   seq_orig = np.load(os.path.join(save_path,'seq_orig.npy')).tolist()
    #   seq_orig_label = np.load(os.path.join(save_path,'seq_orig_label.npy')).tolist()
    #   word_varied = np.load(os.path.join(save_path,'word_varied.npy'), allow_pickle = True).tolist()
    #   n = len(seq_success)

    for order, (seq, l, target) in enumerate(test_loader):

        if order >= order_pre:

            seq_len = np.sum(np.sign(seq.numpy()))
            seq, l = seq.to(device), l.to(device)
            seq = seq.type(torch.LongTensor)
            model.eval()
            with torch.no_grad():
                preds = model.pred(seq, l, False)[1]
                orig_pred = np.argmax(preds.cpu().detach().numpy())
            if orig_pred != target.numpy()[0]:
                #print('Wrong original prediction')
                #print('----------------------')
                continue
            if seq_len > args.max_len:
                #print('Sequence is too long')
                #print('----------------------')
                continue
            print('Sequence number:{}'.format(order))
            print('Length of sentence: {}, Number of samples:{}'.format(
                l.item(), n + 1))
            print(preds)
            seq_orig.append(seq[0].numpy())
            seq_orig_label.append(target.numpy()[0])
            target = 1 - target.numpy()[0]
            # seq_success.append(ga_attack.attack(seq, target, l))

            # if None not in np.array(seq_success[n]):
            #   w_be = [dataset.inv_dict[seq_orig[n][i]] for i in list(np.where(seq_success[n] != seq_orig[n])[0])]
            #   w_to = [dataset.inv_dict[seq_success[n][i]] for i in list(np.where(seq_success[n] != seq_orig[n])[0])]
            #   for i in range(len(w_be)):
            #     print('{} ----> {}'.format(w_be[i], w_to[i]))
            #   word_varied.append([w_be]+[w_to])
            # else:
            #   print('Fail')
            # print('----------------------')
            # n += 1

            # np.save(os.path.join(save_path,'seq_success_1000.npy'), np.array(seq_success))
            # np.save(os.path.join(save_path,'seq_orig_1000.npy'), np.array(seq_orig))
            # np.save(os.path.join(save_path,'seq_orig_label_1000.npy'), np.array(seq_orig_label))
            # np.save(os.path.join(save_path,'word_varied_1000.npy'), np.array(word_varied))

            # if n>TEST_SIZE:
            #   break

            x_adv = ga_attack.attack(seq, target, l)
            orig_list.append(seq[0].numpy())
            adv_list.append(x_adv)
            label_list.append(1 - target)
            l_list.append(l.cpu().numpy()[0])
            if x_adv is None:
                print('%d failed' % (order))
                dist_list.append(100000)
            else:
                num_changes = np.sum(seq[0].numpy() != x_adv)
                print('%d - %d changed.' % (order, num_changes))
                dist_list.append(num_changes)
                # display_utils.visualize_attack(sess, model, dataset, x_orig, x_adv)
            print('--------------------------')

            n += 1
            #if n>TEST_SIZE:
            #  break
            orig_len = [np.sum(np.sign(x)) for x in orig_list]
            normalized_dist_list = [
                dist_list[i] / orig_len[i] for i in range(len(orig_list))
            ]
            SUCCESS_THRESHOLD = 0.25
            successful_attacks = [
                x < SUCCESS_THRESHOLD for x in normalized_dist_list
            ]
            print('Attack success rate : {:.2f}%'.format(
                np.mean(successful_attacks) * 100))
            SUCCESS_THRESHOLD = 0.2
            successful_attacks = [
                x < SUCCESS_THRESHOLD for x in normalized_dist_list
            ]
            print('Attack success rate : {:.2f}%'.format(
                np.mean(successful_attacks) * 100))
    output_path = 'attack_results_final_300_AL_' + str(max_len) + '.pkl'
    with open(output_path, 'wb') as f:
        pickle.dump(
            (orig_list, adv_list, l_list, label_list, normalized_dist_list), f)
def train():
    args = parser.parse_args()
    learning_rate = args.learning_rate
    nlayer = args.nlayer
    bidirection = args.bidirection
    save_path = args.save_path
    kept_prob = args.kept_prob

    MAX_VOCAB_SIZE = 50000
    with open(('aux_files/dataset_%d.pkl' % MAX_VOCAB_SIZE), 'rb') as f:
        dataset = pickle.load(f)

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    embedding_matrix = np.load('aux_files/embeddings_glove_%d.npy' %
                               (MAX_VOCAB_SIZE))
    embedding_matrix = torch.tensor(embedding_matrix.T).to(device)

    # pytorch
    max_len = 100
    padded_train_raw = pad_sequences(dataset.train_seqs2,
                                     maxlen=max_len,
                                     padding='post')
    padded_test_raw = pad_sequences(dataset.test_seqs2,
                                    maxlen=max_len,
                                    padding='post')

    # TrainSet
    data_set_train = Data_infor(padded_train_raw, dataset.train_y)
    num_train = len(data_set_train)
    indx = list(range(num_train))
    all_train_set = Subset(data_set_train, indx)
    train_indx = random.sample(indx, int(num_train * 0.8))
    vali_indx = [i for i in indx if i not in train_indx]
    train_set = Subset(data_set_train, train_indx)
    vali_set = Subset(data_set_train, vali_indx)

    # TestSet
    data_set_test = Data_infor(padded_test_raw, dataset.test_y)
    num_test = len(data_set_test)
    indx = list(range(num_test))
    # indx = random.sample(indx, SAMPLE_SIZE)
    test_set = Subset(data_set_test, indx)

    batch_size = 64
    hidden_size = 128
    all_train_loader = DataLoader(all_train_set,
                                  batch_size=batch_size,
                                  shuffle=True)
    train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
    vali_loader = DataLoader(vali_set, batch_size=len(vali_indx) // batch_size)
    test_loader = DataLoader(test_set,
                             batch_size=int(num_test / 10),
                             shuffle=True)
    best_save_path = os.path.join(
        save_path, 'best_lstm_' + str(kept_prob) + '_' + str(learning_rate) +
        '_' + str(max_len))

    rnn = SentimentAnalysis(batch_size, embedding_matrix, hidden_size,
                            kept_prob, nlayer, bidirection)
    rnn = rnn.to(device)
    # class my_loss(nn.Module):
    #   def __init__(self):
    #     super().__init__()
    #     self.relu = nn.ReLU()

    #   def forward(self, x, y):
    #     loss = torch.mean((1-y)*x+torch.log(1+torch.exp(-abs(x)))+self.relu(-x))
    #     return loss
    criterion = nn.CrossEntropyLoss()
    optimiser = torch.optim.AdamW(rnn.parameters(), lr=learning_rate)
    # optimiser = torch.optim.SGD(rnn.parameters(), lr = learning_rate)

    epoches = 20
    best_epoch = 0
    best_acc = 0
    patience = 15

    for epoch in range(epoches):
        test_pred = torch.tensor([])
        test_targets = torch.tensor([])
        train_pred = torch.tensor([])
        train_targets = torch.tensor([])
        test_loss = []
        train_loss = []

        rnn.train()
        for batch_index, (seqs, length, target) in enumerate(all_train_loader):

            seqs = seqs.type(torch.LongTensor)
            len_order = torch.argsort(length, descending=True)
            length = length[len_order]
            seqs = seqs[len_order]
            target = target[len_order].type(torch.LongTensor)
            optimiser.zero_grad()
            seqs, target, length = seqs.to(device), target.to(
                device), length.to(device)
            output, pred_out = rnn(seqs, length, True)
            loss = criterion(output, target)
            loss.backward()
            optimiser.step()

            train_pred = torch.cat(
                (train_pred, pred_out.type(torch.float).cpu()), dim=0)
            train_targets = torch.cat(
                (train_targets, target.type(torch.float).cpu()))
            train_loss.append(loss)

            if batch_index % 100 == 0:
                print('Train Batch:{}, Train Loss:{:.4f}.'.format(
                    batch_index, loss.item()))
        train_accuracy = rnn.evaluate_accuracy(train_pred.detach().numpy(),
                                               train_targets.detach().numpy())
        print(
            'Epoch:{}, Train Accuracy:{:.4f}, Train Mean loss:{:.4f}.'.format(
                epoch, train_accuracy,
                sum(train_loss) / len(train_loss)))

        rnn.eval()
        with torch.no_grad():
            for batch_index, (seqs, length, target) in enumerate(test_loader):

                seqs = seqs.type(torch.LongTensor)
                len_order = torch.argsort(length, descending=True)
                length = length[len_order]
                seqs = seqs[len_order]
                target = target[len_order].type(torch.LongTensor)
                seqs, target, length = seqs.to(device), target.to(
                    device), length.to(device)
                output, pred_out = rnn(seqs, length, False)
                test_pred = torch.cat(
                    (test_pred, pred_out.type(torch.float).cpu()), dim=0)
                test_targets = torch.cat(
                    (test_targets, target.type(torch.float).cpu()))
                loss = criterion(output, target)
                test_loss.append(loss.item())
                if batch_index % 100 == 0:
                    print('Vali Batch:{}, Validation Loss:{:.4f}.'.format(
                        batch_index, loss.item()))
            accuracy = rnn.evaluate_accuracy(test_pred.numpy(),
                                             test_targets.numpy())
            print('Epoch:{}, Vali Accuracy:{:.4f}, Vali Mean loss:{:.4f}.'.
                  format(epoch, accuracy,
                         sum(test_loss) / len(test_loss)))
            print('\n\n')
            # # best save
            # if accuracy > best_acc:
            #   best_acc = accuracy
            #   best_epoch = epoch
            #   torch.save(rnn.state_dict(), best_save_path)
            # # early stop
            # if epoch-best_epoch >=patience:
            #   print('Early stopping')
            #   print('Best epoch: {}, Best accuracy: {:.4f}.'.format(best_epoch, best_acc))
            #   break
    torch.save(rnn.state_dict(), best_save_path)
    rnn.load_state_dict(torch.load(best_save_path))
    rnn.to(device)
    rnn.eval()
    test_pred = torch.tensor([])
    test_targets = torch.tensor([])
    test_loss = []
    with torch.no_grad():
        for batch_index, (seqs, length, target) in enumerate(test_loader):

            seqs = seqs.type(torch.LongTensor)
            len_order = torch.argsort(length, descending=True)
            length = length[len_order]
            seqs = seqs[len_order]
            target = target[len_order]
            seqs, target, length = seqs.to(device), target.to(
                device), length.to(device)
            output, pred_out = rnn(seqs, length, False)
            test_pred = torch.cat(
                (test_pred, pred_out.type(torch.float).cpu()), dim=0)
            test_targets = torch.cat(
                (test_targets, target.type(torch.float).cpu()))
            loss = criterion(output, target)
            test_loss.append(loss.item())

        accuracy = rnn.evaluate_accuracy(test_pred.numpy(),
                                         test_targets.numpy())
    print('Test Accuracy:{:.4f}, Test Mean loss:{:.4f}.'.format(
        accuracy,
        sum(test_loss) / len(test_loss)))
Пример #3
0
def run():
    args = parser.parse_args()

    file_path = args.file_path  #'/content/drive/My Drive/Master_Final_Project/Genetic_attack/Code/nlp_adversarial_example_master_pytorch/glove.840B.300d.txt'#'/lustre/scratch/scratch/ucabdc3/lstm_attack'
    save_path = os.path.join(file_path, 'results')
    MAX_VOCAB_SIZE = 50000
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    #    with open(os.path.join(file_path, 'dataset_%d.pkl' %MAX_VOCAB_SIZE), 'rb') as f:
    #        dataset = pickle.load(f)

    with open('aux_files/dataset_%d.pkl' % MAX_VOCAB_SIZE, 'rb') as f:
        dataset = pickle.load(f)


#    skip_list = np.load('aux_files/missed_embeddings_counter_%d.npy' %MAX_VOCAB_SIZE)
    embedding_matrix = np.load('aux_files/embeddings_glove_%d.npy' %
                               (MAX_VOCAB_SIZE))
    embedding_matrix = torch.tensor(embedding_matrix.T).to(device)
    dist = np.load(('aux_files/dist_counter_%d.npy' % (MAX_VOCAB_SIZE)))
    #    goog_lm = LM()

    # pytorch
    max_len = 250
    #    padded_train_raw = pad_sequences(dataset.train_seqs2, maxlen = max_len, padding = 'post')
    padded_test_raw = pad_sequences(dataset.test_seqs2,
                                    maxlen=max_len,
                                    padding='post')
    #    # TrainSet
    #    data_set = Data_infor(padded_train_raw, dataset.train_y)
    #    num_train = len(data_set)
    #    indx = list(range(num_train))
    #    train_set = Subset(data_set, indx)

    # TestSet
    batch_size = 1
    SAMPLE_SIZE = args.sample_size
    data_set = Data_infor(padded_test_raw, dataset.test_y)
    num_test = len(data_set)
    indx = list(range(num_test))

    all_test_set = Subset(data_set, indx)
    indx = random.sample(indx, SAMPLE_SIZE)
    test_set = Subset(data_set, indx)
    test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)
    all_test_loader = DataLoader(all_test_set, batch_size=128, shuffle=True)

    lstm_size = 128
    rnn_state_save = os.path.join(file_path, 'best_lstm_0_73_0_0005')
    model = SentimentAnalysis(batch_size=batch_size,
                              embedding_matrix=embedding_matrix,
                              hidden_size=lstm_size,
                              kept_prob=0.73,
                              num_layers=2,
                              bidirection=True)
    model.eval()
    model.load_state_dict(torch.load(rnn_state_save))
    model = model.to(device)

    model.eval()
    test_pred = torch.tensor([])
    test_targets = torch.tensor([])

    with torch.no_grad():
        for batch_index, (seqs, length, target) in enumerate(all_test_loader):
            seqs, target, length = seqs.to(device), target.to(
                device), length.to(device)
            seqs = seqs.type(torch.LongTensor)
            len_order = torch.argsort(length, descending=True)
            length = length[len_order]
            seqs = seqs[len_order]
            target = target[len_order]

            output = model.pred(seqs, length)
            test_pred = torch.cat((test_pred, output.cpu()), dim=0)
            test_targets = torch.cat(
                (test_targets, target.type(torch.float).cpu()))

        accuracy = model.evaluate_accuracy(test_pred.numpy(),
                                           test_targets.numpy())
    print('Test Accuracy:{:.4f}.'.format(accuracy))
    np.save(os.path.join(save_path, 'accuracy.npy'), np.array(accuracy))
    print('\n')
    n1 = 8
    n2 = 4
    pop_size = 60
    max_iters = 20
    n_prefix = 4

    batch_model = SentimentAnalysis(batch_size=pop_size,
                                    embedding_matrix=embedding_matrix,
                                    hidden_size=lstm_size,
                                    kept_prob=0.73,
                                    num_layers=2,
                                    bidirection=True)

    batch_model.eval()
    batch_model.load_state_dict(torch.load(rnn_state_save))
    batch_model.to(device)

    neighbour_model = SentimentAnalysis(batch_size=batch_size,
                                        embedding_matrix=embedding_matrix,
                                        hidden_size=lstm_size,
                                        kept_prob=0.73,
                                        num_layers=2,
                                        bidirection=True)

    neighbour_model.eval()
    neighbour_model.load_state_dict(torch.load(rnn_state_save))
    neighbour_model.to(device)
    lm_model = gpt_2_get_words_probs()
    ga_attack = GeneticAttack_pytorch(model,
                                      batch_model,
                                      neighbour_model,
                                      dist,
                                      lm_model,
                                      max_iters=max_iters,
                                      dataset=dataset,
                                      pop_size=pop_size,
                                      n1=n1,
                                      n2=n2,
                                      n_prefix=n_prefix,
                                      use_lm=True,
                                      use_suffix=False)

    TEST_SIZE = args.test_size
    order_pre = 0
    n = 0
    seq_success = []
    seq_orig = []
    seq_orig_label = []
    word_varied = []

    seq_success_path = os.path.join(save_path, 'seq_success_gpt.npy')
    seq_orig_path = os.path.join(save_path, 'seq_orig_gpt.npy')
    seq_orig_label_path = os.path.join(save_path, 'seq_orig_label_gpt.npy')
    word_varied_path = os.path.join(save_path, 'word_varied_gpt.npy')

    #    if order_pre != 0:
    #      seq_success = np.load(seq_success_path, allow_pickle = True).tolist()
    #      seq_orig = np.load(seq_orig_path).tolist()
    #      seq_orig_label = np.load(seq_orig_label_path).tolist()
    #      word_varied = np.load(word_varied_path, allow_pickle = True).tolist()
    #      n = len(seq_success)

    for order, (seq, l, target) in enumerate(test_loader):

        if order >= order_pre:
            print('Sequence number:{}'.format(order))
            seq_len = np.sum(np.sign(seq.numpy()))
            seq, l = seq.to(device), l.to(device)
            seq = seq.type(torch.LongTensor)
            model.eval()
            with torch.no_grad():
                orig_pred = np.argmax(
                    model.pred(seq, l).cpu().detach().numpy())
            if orig_pred != target.numpy()[0]:
                print('Wrong original prediction')
                print('----------------------')
                continue
            if seq_len > 100:
                print('Sequence is too long')
                print('----------------------')
                continue

            print('Length of sentence: {}, Number of samples:{}'.format(
                l.item(), n + 1))
            seq_orig.append(seq[0].numpy())
            seq_orig_label.append(target.numpy()[0])
            target = 1 - target.numpy()[0]
            seq_success.append(
                ga_attack.attack(seq, target, l.type(torch.LongTensor)))

            if None not in np.array(seq_success[n]):
                w_be = [
                    dataset.inv_dict[seq_orig[n][i]]
                    for i in list(np.where(seq_success[n] != seq_orig[n])[0])
                ]
                w_to = [
                    dataset.inv_dict[seq_success[n][i]]
                    for i in list(np.where(seq_success[n] != seq_orig[n])[0])
                ]
                for i in range(len(w_be)):
                    print('{} ----> {}'.format(w_be[i], w_to[i]))
                word_varied.append([w_be] + [w_to])
            else:
                print('Fail')
            print('----------------------')
            n += 1

            np.save(seq_success_path, np.array(seq_success))
            np.save(seq_orig_path, np.array(seq_orig))
            np.save(seq_orig_label_path, np.array(seq_orig_label))
            np.save(word_varied_path, np.array(word_varied, dtype=object))

            if n > TEST_SIZE:
                break
def run():
    args = parser.parse_args()
    nlayer = args.nlayer
    bidirection = args.bidirection
    file_path = args.file_path#'/content/drive/My Drive/Master_Final_Project/Genetic_attack/Code/nlp_adversarial_example_master_pytorch/glove.840B.300d.txt'#'/lustre/scratch/scratch/ucabdc3/lstm_attack'
    save_path = os.path.join(file_path, 'results')
    MAX_VOCAB_SIZE = 50000
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#    with open(os.path.join(file_path, 'dataset_%d.pkl' %MAX_VOCAB_SIZE), 'rb') as f:
#        dataset = pickle.load(f)
        
    with open('aux_files/dataset_%d.pkl' %MAX_VOCAB_SIZE, 'rb') as f:
        dataset = pickle.load(f)

        
#    skip_list = np.load('aux_files/missed_embeddings_counter_%d.npy' %MAX_VOCAB_SIZE)
    embedding_matrix = np.load('aux_files/embeddings_glove_%d.npy' %(MAX_VOCAB_SIZE))
    embedding_matrix = torch.tensor(embedding_matrix.T).to(device)
    dist = np.load(('aux_files/dist_counter_%d.npy' %(MAX_VOCAB_SIZE)))

    
#    goog_lm = LM()
    
    # pytorch
    max_len = args.max_len
#    padded_train_raw = pad_sequences(dataset.train_seqs2, maxlen = max_len, padding = 'post')
    padded_test_raw = pad_sequences(dataset.test_seqs2, maxlen = max_len, padding = 'post')
#    # TrainSet
#    data_set = Data_infor(padded_train_raw, dataset.train_y)
#    num_train = len(data_set)
#    indx = list(range(num_train))
#    train_set = Subset(data_set, indx)
    
    # TestSet
    SAMPLE_SIZE = args.sample_size
    data_set = Data_infor(padded_test_raw, dataset.test_y)
    num_test = len(data_set)
    indx = list(range(num_test))
    
    all_test_set  = Subset(data_set, indx)
    #indx = random.sample(indx, SAMPLE_SIZE)
    with open('attack_results_final_200.pkl', 'rb') as f:
        results= pickle.load(f)
    seqs = []
    lens = []
    tgts = []
    for i in range(len(results[1])):
        if np.array(results[1][i]).shape == ():
            continue
        seqs.append(results[1][i])
        lens.append(results[2][i])
        tgts.append(results[3][i])
    seqs = torch.tensor(seqs)
    lens = torch.tensor(lens)
    tgts = torch.tensor(tgts)
    test_set = TensorDataset(seqs, lens, tgts)
    all_test_loader  = DataLoader(test_set, batch_size = 128, shuffle = True)
    
    lstm_size = 128
    rnn_state_save = os.path.join(file_path,'best_lstm_0.7_0.001_200')

    model = SentimentAnalysis(batch_size=lstm_size, embedding_matrix = embedding_matrix, hidden_size = lstm_size, kept_prob = 0.7, num_layers=nlayer, bidirection=bidirection)
    
    model.load_state_dict(torch.load(rnn_state_save))
    model = model.to(device)
    
    
    model.eval()
    test_pred = torch.tensor([])
    test_targets = torch.tensor([])

    with torch.no_grad():
      for batch_index, (seqs, length, target) in enumerate(all_test_loader):
        seqs, target, length = seqs.to(device), target.to(device), length.to(device)
        seqs = seqs.type(torch.LongTensor)
        len_order = torch.argsort(length, descending = True)
        length = length[len_order]
        seqs = seqs[len_order]
        target = target[len_order]

        output, pred_out = model.pred(seqs, length, False)
        test_pred = torch.cat((test_pred, pred_out.cpu()), dim = 0)
        test_targets = torch.cat((test_targets, target.type(torch.float).cpu()))

      accuracy = model.evaluate_accuracy(test_pred.numpy(), test_targets.numpy())
    print('Test Accuracy:{:.4f}.'.format(accuracy))