Add GELU layer for vision transformers

[fengyuentau]

This PR adds the CPU and OCL kernels of GELU and GELU-Approximation layers.

Merge with opencv/opencv_extra#1044

References:

• https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py
• https://pytorch.org/docs/stable/generated/torch.nn.GELU.html

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• I agree to contribute to the project under Apache 2 License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
• The PR is proposed to the proper branch
• There is a reference to the original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake

[alalek]

Looks good!

[alalek]

Thank you!

[fengyuentau]

@rogday Could you review if possible?

[alalek]

Please use this OpenCL code:

__kernel void GeluForward(const int n, __global T* in, __global T* out)
{
    int index = get_global_id(0);
    if (index < n)
    {
        T x = in[index];
        out[index] = (T)0.5f * x * ( (T)1.f + erf(x * M_SQRT1_2) );
    }
}

__kernel void GeluApproximationForward(const int n, __global T* in, __global T* out)
{
    // see GeluApproximationConstants from .cpp
    const T sqrt_2_pi = 0.7978845834732056f;
    const T coef_sqrt_2_pi = 0.044714998453855515f * sqrt_2_pi;

    int index = get_global_id(0);
    if(index < n)
    {
        T x = in[index];
        out[index] = (T)0.5f * x * ( (T)1.f + tanh(x * (sqrt_2_pi + coef_sqrt_2_pi * x * x)) );
    }
}

and drop setKernelParams() method.

[fengyuentau]

Okay