Function save_to_npy

utils/Utils.h:642–689

 */
template <typename T, typename U = float>
void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order)
{
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::F32, arm_compute::DataType::QASYMM8);

    std::ofstream fs;
    try
    {
        fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
        fs.open(npy_filename, std::ios::out | std::ios::binary);

        std::vector<npy::ndarray_len_t> shape(tensor.info()->num_dimensions());

        for (unsigned int i = 0, j = tensor.info()->num_dimensions() - 1; i < tensor.info()->num_dimensions(); ++i, --j)
        {
            shape[i] = tensor.info()->tensor_shape()[!fortran_order ? j : i];
        }

        // Map buffer if creating a CLTensor
        map(tensor, true);

        using typestring_type = typename std::conditional<std::is_floating_point<U>::value, float, qasymm8_t>::type;

        std::vector<typestring_type> tmp; /* Used only to get the typestring */
        const npy::dtype_t           dtype = npy::dtype_map.at(std::type_index(typeid(tmp)));

        std::ofstream stream(npy_filename, std::ofstream::binary);
        npy::header_t header{dtype, fortran_order, shape};
        npy::write_header(stream, header);

        arm_compute::Window window;
        window.use_tensor_dimensions(tensor.info()->tensor_shape());

        arm_compute::Iterator in(&tensor, window);

        arm_compute::execute_window_loop(
            window,
            [&](const arm_compute::Coordinates &)
            { stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(typestring_type)); },
            in);

        // Unmap buffer if creating a CLTensor
        unmap(tensor);
    }
    catch (const std::ofstream::failure &e)
    {
        ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", npy_filename.c_str(), e.what());
    }
}

/** Load the tensor with pre-trained data from a binary file
 *