cleanup ocl4dnn spatial convolution kernels

remove unused macros and half definition macros,
also remove unused ocl::Queue
Signed-off-by: Li Peng <peng.li@intel.com>

modules/dnn/src/ocl4dnn/src/math_functions.cpp

@@ -65,8 +65,6 @@ ocl::Image2D ocl4dnnGEMMCopyBufferToImage(UMat buffer, int offset,
                                           int padded_width, int height,
                                           int width, int ld)
 {
-    ocl::Context ctx = ocl::Context::getDefault();
-    ocl::Queue queue = ocl::Queue::getDefault();
     ocl::Image2D image;
 
     if (!is_matrix_a && transpose)
@@ -192,9 +190,6 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
     // just padding one line is enough as the sub group block read
     // will clamp to edge according to the spec.
 
-    ocl::Context ctx = ocl::Context::getDefault();
-    ocl::Queue queue = ocl::Queue::getDefault();
-
     ocl::Image2D ImA;
     ocl::Image2D ImB;
 
@@ -446,7 +441,6 @@ bool ocl4dnnGEMV<float>(const CBLAS_TRANSPOSE TransA,
                  const int32_t offx, const float beta, UMat y,
                  const int32_t offy)
 {
-    ocl::Queue queue = ocl::Queue::getDefault();
     bool ret = false;
 
     if (TransA == CblasNoTrans)
@@ -507,8 +501,6 @@ bool ocl4dnnAXPY(const int32_t N, const Dtype alpha,
                  const UMat X, const int32_t offX, UMat Y,
                  const int32_t offY)
 {
-    ocl::Context ctx = ocl::Context::getDefault();
-
     ocl::Kernel oclk_axpy(CL_KERNEL_SELECT("axpy"), cv::ocl::dnn::math_oclsrc);
     if (oclk_axpy.empty())
         return false;

modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp

@@ -184,8 +184,6 @@ void OCL4DNNConvSpatial<Dtype>::collectCommonInformation()
     addDef("as_Dtype2", "as_float2");
     addDef("as_Dtype4", "as_float4");
     addDef("as_Dtype8", "as_float8");
-    addDef("Dtype_ID", (int)CV_32F);
-    addDef("Dtype_SIZE", (int)sizeof(Dtype));
 }
 
 typedef enum {

modules/dnn/src/ocl4dnn/src/ocl4dnn_lrn.cpp

@@ -92,7 +92,6 @@ bool OCL4DNNLRN<Dtype>::Forward(const UMat& bottom, UMat& top)
 template<typename Dtype>
 bool OCL4DNNLRN<Dtype>::crossChannelForward(const UMat& bottom, UMat& top)
 {
-    ocl::Queue queue = ocl::Queue::getDefault();
     CHECK_EQ(phase_test_, true) << "Only support forward inference.";
 
     cl_uint argIdx = 0;

modules/dnn/src/ocl4dnn/src/ocl4dnn_pool.cpp

@@ -97,7 +97,6 @@ bool OCL4DNNPool<Dtype>::Forward(const UMat& bottom,
                                  UMat& top_mask)
 {
     bool ret = true;
-    ocl::Queue queue = ocl::Queue::getDefault();
     size_t global[] = { 128 * 128 };
     size_t local[] = { 128 };
     cl_uint argIdx = 0;

modules/dnn/src/ocl4dnn/src/ocl4dnn_softmax.cpp

@@ -83,7 +83,6 @@ template<typename Dtype>
 bool OCL4DNNSoftmax<Dtype>::Forward(const UMat& bottom, UMat& top)
 {
     bool ret = false;
-    ocl::Queue queue = ocl::Queue::getDefault();
     bool intel_subgroup = ocl::Device::getDefault().intelSubgroupsSupport();
     if (intel_subgroup && inner_num_ < 128)
     {

modules/dnn/src/opencl/conv_layer_spatial.cl

@@ -82,7 +82,6 @@
 #define LOOP(N, VAR, STMT) CAT(LOOP, N)((VAR), (STMT))
 
 #if defined(convolve_simd) || defined(Conv_Interleaved)
-#if Dtype_SIZE == 4
 #define INT_TYPE uint
 #define INT_TYPE2 uint2
 #define INT_TYPE4 uint4
@@ -91,9 +90,6 @@
 #define SUB_GROUP_BLOCK_READ4 intel_sub_group_block_read4
 #define SUB_GROUP_BLOCK_READ8 intel_sub_group_block_read8
 #define SUB_GROUP_BLOCK_READ intel_sub_group_block_read
-#else
-#error "Unsupported type"
-#endif
 #endif
 
 #ifdef KERNEL_BASIC
@@ -176,11 +172,7 @@ __kernel void ConvolveBasic(
 
 #elif defined KERNEL_IDLF
 
-#if TYPE == TYPE_HALF
-#define VLOAD4(_v, _p) do { (_v).s0 = *(_p); (_v).s1 = *(_p + 1); (_v).s2 = *(_p + 2); (_v).s3 = *(_p + 3); } while(0)
-#else
 #define VLOAD4(_v, _p) do { _v = vload4(0, _p); } while(0)
-#endif
 
 // Each work-item computes a OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT region of one output map.
 // Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the imput image.