Method validate

src/cpu/operators/CpuWinogradConv2d.cpp:323–357

    }
}
Status CpuWinogradConv2d::validate(const ITensorInfo         *src,
                                   const ITensorInfo         *weights,
                                   const ITensorInfo         *biases,
                                   const ITensorInfo         *dst,
                                   const PadStrideInfo       &conv_info,
                                   const ActivationLayerInfo &act_info,
                                   bool                       enable_fast_math)
{
    ARM_COMPUTE_TRACE_EVENT(ARM_COMPUTE_PROF_CAT_CPU, ARM_COMPUTE_PROF_LVL_CPU, "CpuWinogradConv2d::validate");
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));

    // Disable winograd for fp16 if fast math is false.
    if (!enable_fast_math)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
    }

    const Tensor4DShape              kernel_shape{internal_get_shape(weights)};
    arm_conv::winograd::WinogradImpl winograd_impl{};

    std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
    const bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math,
                                                            &winograd_impl, conv_args);

    ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows,
                                        kernel_shape.n_cols);
    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n",
                                        winograd_impl.input_transform->get_name().c_str());
    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n",
                                        winograd_impl.input_transform->get_name().c_str());
    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n",
                                        winograd_impl.input_transform->get_name().c_str());
    return Status{};
}

void CpuWinogradConv2d::run(ITensorPack &tensors)
{