使用capi实现conv_seq2seq模型的预测程序

[fmantianxing]

capi实现代码如下：

    paddle_arguments in_args = paddle_arguments_create_none();
    paddle_error ret1 = paddle_arguments_resize(in_args, 4);
    if (ret1 != kPD_NO_ERROR) {
        return false;
    }
    paddle_gradient_machine local_machine = nullptr;
    paddle_error local_mret = paddle_gradient_machine_create_shared_param(
            _machine, _config_buf, _config_size, &local_machine);
    if (local_mret != kPD_NO_ERROR) {
        return false;
    }
    std::vector<int> query_ids;
    std::vector<int> query_pos;
    gen_input(query, &query_ids, &query_pos);

    int* src_ids = (int*) query_ids.data();
    unsigned int src_num = query_ids.size();
    int* src_pos = (int*) query_pos.data();

    std::vector<int> trg_query_ids;
    std::vector<int> trg_query_pos;

    int trg_ids[WIN_SIZE]; //WORD_PADDING};
    int trg_pos[WIN_SIZE]; // = {POS_PADDING};

    for (int i = 0; i < WIN_SIZE; ++i) {
        trg_ids[i] = WORD_PADDING;
        trg_pos[i] = POS_PADDING;
    }
    trg_ids[WIN_SIZE-1] = 0;
    trg_pos[WIN_SIZE-1] = 0;

    int src_pos_array[] = {0, (int) src_num};
    paddle_ivector src_ids_vec = paddle_ivector_create(src_ids, src_num, false, false);
    CHECK(paddle_arguments_set_ids(in_args, 0, src_ids_vec));
    paddle_ivector src_seq_vec = paddle_ivector_create(src_pos_array, sizeof(src_pos_array) / sizeof(int), false, false);
    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 0, src_seq_vec));

    paddle_ivector src_pos_vec = paddle_ivector_create(src_pos, src_num, false, false);
    CHECK(paddle_arguments_set_ids(in_args, 1, src_pos_vec));
    paddle_ivector src_seq_pos_vec = paddle_ivector_create(src_pos_array, sizeof(src_pos_array) / sizeof(int), false, false);
    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 1, 0, src_seq_pos_vec));

    int trg_pos_array[] = {0, (int) WIN_SIZE};
    paddle_ivector trg_ids_vec = paddle_ivector_create(trg_ids, WIN_SIZE, false, false);
    CHECK(paddle_arguments_set_ids(in_args, 2, trg_ids_vec));
    paddle_ivector trg_seq_vec = paddle_ivector_create(trg_pos_array, sizeof(trg_pos_array) / sizeof(int), false, false);
    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 2, 0, trg_seq_vec));

    paddle_ivector trg_pos_vec = paddle_ivector_create(trg_pos, WIN_SIZE, false, false);
    CHECK(paddle_arguments_set_ids(in_args, 3, trg_pos_vec));
    paddle_ivector trg_seq_pos_vec = paddle_ivector_create(trg_pos_array, sizeof(trg_pos_array) / sizeof(int), false, false);
    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 3, 0, trg_seq_pos_vec));

    paddle_arguments out_args = paddle_arguments_create_none();
    paddle_error ret4 = paddle_gradient_machine_forward(local_machine, in_args, out_args, false);
    if (ret4 != kPD_NO_ERROR) {
        return false;
    }

线下模型地址：https://github.com/PaddlePaddle/models/tree/develop/conv_seq2seq 出现了如下错误： F0612 17:35:47.277493 34148 RecurrentGradientMachine.cpp:758] Check failed: idSize == size (1 vs. 12) Check failure stack trace: @ 0x7fcb239cb63e google::LogMessage::Fail() @ 0x7fcb239cb591 google::LogMessage::SendToLog() @ 0x7fcb239caf9f google::LogMessage::Flush() @ 0x7fcb239ce088 google::LogMessageFatal::~LogMessageFatal() @ 0x7fcb23829604 paddle::RecurrentGradientMachine::copyScattedId() @ 0x7fcb23829e9d paddle::RecurrentGradientMachine::createMemoryFrameInfo() @ 0x7fcb2382a807 paddle::RecurrentGradientMachine::forward() @ 0x7fcb23729391 paddle::RecurrentLayerGroup::forward() @ 0x7fcb23844d7c paddle::NeuralNetwork::forward() @ 0x7fcb236c0796 paddle_gradient_machine_forward

[nuoline]

没人跟吗

[lucywsq]

您好，此问题已经反馈至相关同学，正在排查跟进中，有进展会第一时间回复~

[shanyi15]

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