Method do_setup

examples/neon_cnn.cpp:39–225

{
public:
    bool do_setup(int argc, char **argv) override
    {
        ARM_COMPUTE_UNUSED(argc);
        ARM_COMPUTE_UNUSED(argv);

        // Create memory manager components
        // We need 2 memory managers: 1 for handling the tensors within the functions (mm_layers) and 1 for handling the input and output tensors of the functions (mm_transitions))
        auto lifetime_mgr0 = std::make_shared<BlobLifetimeManager>();                       // Create lifetime manager
        auto lifetime_mgr1 = std::make_shared<BlobLifetimeManager>();                       // Create lifetime manager
        auto pool_mgr0     = std::make_shared<PoolManager>();                               // Create pool manager
        auto pool_mgr1     = std::make_shared<PoolManager>();                               // Create pool manager
        auto mm_layers = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr0, pool_mgr0); // Create the memory manager
        auto mm_transitions =
            std::make_shared<MemoryManagerOnDemand>(lifetime_mgr1, pool_mgr1); // Create the memory manager

        // The weights and biases tensors should be initialized with the values inferred with the training

        // Set memory manager where allowed to manage internal memory requirements
        conv0   = std::make_unique<NEConvolutionLayer>(mm_layers);
        conv1   = std::make_unique<NEConvolutionLayer>(mm_layers);
        fc0     = std::make_unique<NEFullyConnectedLayer>(mm_layers);
        softmax = std::make_unique<NESoftmaxLayer>(mm_layers);

        /* [Initialize tensors] */

        // Initialize src tensor
        constexpr unsigned int width_src_image  = 32;
        constexpr unsigned int height_src_image = 32;
        constexpr unsigned int ifm_src_img      = 1;

        const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img);
        src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32));

        // Initialize tensors of conv0
        constexpr unsigned int kernel_x_conv0 = 5;
        constexpr unsigned int kernel_y_conv0 = 5;
        constexpr unsigned int ofm_conv0      = 8;

        const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0);
        const TensorShape biases_shape_conv0(weights_shape_conv0[3]);
        const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]);

        weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32));
        biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32));
        out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));

        // Initialize tensor of act0
        out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));

        // Initialize tensor of pool0
        TensorShape out_shape_pool0 = out_shape_conv0;
        out_shape_pool0.set(0, out_shape_pool0.x() / 2);
        out_shape_pool0.set(1, out_shape_pool0.y() / 2);
        out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32));

        // Initialize tensors of conv1
        constexpr unsigned int kernel_x_conv1 = 3;
        constexpr unsigned int kernel_y_conv1 = 3;