dnn(ocl): fix automatic globalsize adjusting

- if kernel code doesn't support that

modules/core/include/opencv2/core/ocl.hpp

@@ -562,7 +562,9 @@ public:
         i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
         i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this;
     }
-    /** @brief Run the OpenCL kernel.
+
+    /** @brief Run the OpenCL kernel (globalsize value may be adjusted)
+
     @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3.
     @param globalsize work items for each dimension. It is not the final globalsize passed to
       OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding
@@ -571,12 +573,26 @@ public:
     @param localsize work-group size for each dimension.
     @param sync specify whether to wait for OpenCL computation to finish before return.
     @param q command queue
+
+    @note Use run_() if your kernel code doesn't support adjusted globalsize.
     */
     bool run(int dims, size_t globalsize[],
              size_t localsize[], bool sync, const Queue& q=Queue());
+
+    /** @brief Run the OpenCL kernel
+     *
+     * @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3.
+     * @param globalsize work items for each dimension. This value is passed to OpenCL without changes.
+     * @param localsize work-group size for each dimension.
+     * @param sync specify whether to wait for OpenCL computation to finish before return.
+     * @param q command queue
+     */
+    bool run_(int dims, size_t globalsize[], size_t localsize[], bool sync, const Queue& q=Queue());
+
     bool runTask(bool sync, const Queue& q=Queue());
 
-    /** @brief Similar to synchronized run() call with returning of kernel execution time
+    /** @brief Similar to synchronized run_() call with returning of kernel execution time
+     *
      * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE)
      * @return Execution time in nanoseconds or negative number on error
      */

modules/core/src/ocl.cpp

@@ -3160,6 +3160,14 @@ bool Kernel::run(int dims, size_t _globalsize[], size_t _localsize[],
 }
 
 
+bool Kernel::run_(int dims, size_t _globalsize[], size_t _localsize[],
+                  bool sync, const Queue& q)
+{
+    CV_Assert(p);
+    return p->run(dims, _globalsize, _localsize, sync, NULL, q);
+}
+
+
 static bool isRaiseErrorOnReuseAsyncKernel()
 {
     static bool initialized = false;

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -231,7 +231,7 @@ public:
                 kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_weight));
                 kernel.set(5, ocl::KernelArg::PtrReadOnly(umat_bias));
                 kernel.set(6, ocl::KernelArg::PtrWriteOnly(dst));
-                bool ret = kernel.run(2, global, NULL, false);
+                bool ret = kernel.run_(2, global, NULL, false);
                 if (!ret)
                     return false;
             }

modules/dnn/src/layers/mvn_layer.cpp

@@ -191,7 +191,7 @@ public:
             k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_weight));
             k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_bias));
             k1.set(argId++, ocl::KernelArg::PtrWriteOnly(outMat));
-            ret = k1.run(1, globalsize, localsize, false);
+            ret = k1.run_(1, globalsize, localsize, false);
             if (!ret)
                 return false;
         }

modules/dnn/src/layers/slice_layer.cpp

@@ -482,7 +482,7 @@ public:
                     ocl::KernelArg::PtrReadOnly(input),
                     ocl::KernelArg::PtrWriteOnly(output)
                 )
-                .run(2, (size_t*)ocl.global_size, (size_t*)ocl.local_size, false);
+                .run_(2, (size_t*)ocl.global_size, (size_t*)ocl.local_size, false);
             if (!ret)
                 return false;
         }  // for outputs.size()

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

@@ -116,6 +116,7 @@ ocl::Image2D ocl4dnnGEMMCopyBufferToImage(UMat buffer, int offset,
                 .args(
                     ocl::KernelArg::PtrReadOnly(buffer),
                     image, offset,
+                    padded_width, padded_height,
                     width, height,
                     ld)
                 .run(2, global_copy, NULL, false);

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

@@ -769,12 +769,11 @@ bool OCL4DNNConvSpatial<Dtype>::swizzleWeight(const UMat &weight,
             swizzled_factor
         );
 
-        size_t global_work_size_copy[3] = {
+        size_t global_work_size_copy[1] = { (size_t)(alignSize(num_output_, swizzled_factor) * channels * kernel_w_ * kernel_h_) };
-            (size_t) (alignSize(num_output_, swizzled_factor) * channels * kernel_w_ * kernel_h_), 1, 1 };
 
-        if (!oclk_copy_weight.run(3, global_work_size_copy, NULL, false))
+        if (!oclk_copy_weight.run_(1, global_work_size_copy, NULL, false))
         {
-            std::cout << "Swizzle kernel run failed." << std::endl;
+            CV_LOG_ERROR(NULL, "DNN/OpenCL: Swizzle kernel run failed");
             return false;
         }
     } else {
@@ -937,7 +936,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
             kernel.set(argIdx++, (uint16_t)height_);
             kernel.set(argIdx++, (uint16_t)output_w_);
             kernel.set(argIdx++, (uint16_t)output_h_);
-            if (!kernel.run(3, config->global_work_size, config->local_work_size, false))
+            if (!kernel.run_(3, config->global_work_size, config->local_work_size, false))
             {
                 std::cout << "IDLF kernel run failed." << std::endl;
                 return false;
@@ -1056,7 +1055,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
             gy = alignSize(gy, blockK);
             size_t global_size[3] = { gx, gy, config->global_work_size[2] };
 
-            if (!kernel.run(3, global_size, config->local_work_size, false))
+            if (!kernel.run_(3, global_size, config->local_work_size, false))
             {
                 std::cout << "GEMM like kernel run failed." << std::endl;
                 return false;
@@ -1085,9 +1084,9 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
         global_size[1] = output_h_;
         global_size[2] = num_output_ * num_;
 
-        if (!kernel.run(3, global_size, NULL, false))
+        if (!kernel.run_(3, global_size, NULL, false))
         {
-            std::cout << "DWCONV kernel run failed." << std::endl;
+            CV_LOG_ERROR(NULL, "DNN/OpenCL: DWCONV kernel run failed");
             return false;
         }
     } else {
@@ -1127,11 +1126,11 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
                 kernel.set(argIdx++, (uint16_t)output_h_);
                 kernel.set(argIdx++, (uint16_t)pad_w_);
                 kernel.set(argIdx++, (uint16_t)pad_h_);
-                if (!kernel.run(3, config->global_work_size,
+                if (!kernel.run_(3, config->global_work_size,
                                 (config->use_null_local) ? NULL : config->local_work_size,
                                 false))
                 {
-                    std::cout << "Basic kernel run failed." << std::endl;
+                    CV_LOG_ERROR(NULL, "DNN/OpenCL: Basic kernel run failed");
                     return false;
                 }
             }

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

@@ -127,7 +127,7 @@ bool OCL4DNNSoftmax<Dtype>::Forward(const UMat& bottom, UMat& top)
             oclk_softmax_forward_kernel.set(argIdx++, ocl::KernelArg::PtrReadOnly(bottom));
             oclk_softmax_forward_kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(top));
         }
-        ret = oclk_softmax_forward_kernel.run(3, global_size, local_size, false);
+        ret = oclk_softmax_forward_kernel.run_(3, global_size, local_size, false);
     }
     return ret;
 }

modules/dnn/src/opencl/gemm_image.cl

@@ -954,6 +954,10 @@ __kernel void TEMPLATE(gemm_buffer_copy_image_transpose, Dtype)(
 {
     const int gidx = get_global_id(0);
     const int gidy = get_global_id(1);
+
+    if (gidx >= width || gidy >= height)
+        return;
+
     int2 coord_dst = (int2)(gidx, gidy);
     __global Dtype* A_off = A + offA;
     Dtype srcA = A_off[gidy * ldA + gidx];
@@ -968,12 +972,18 @@ __kernel void TEMPLATE(gemm_buffer_copy_image_no_transpose, Dtype)(
     __global Dtype* A,
     __write_only image2d_t ImA,
     int offA,
+    int padded_width,
+    int padded_height,
     int width,
     int height,
     int ldA)
 {
     const int gidx = get_global_id(0);
     const int gidy = get_global_id(1);
+
+    if (gidx >= padded_width || gidy >= padded_height)
+        return;
+
     int2 coord_dst = (int2)(gidx, gidy);
 #if TYPE == TYPE_HALF
     if (gidx >= width || gidy >= height) {