fp16 ocl support for more layers

Signed-off-by: Li Peng <peng.li@intel.com>

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -120,12 +120,16 @@ public:
         std::vector<UMat> inputs;
         std::vector<UMat> outputs;
 
+        bool use_half = (inputs_.depth() == CV_16S);
         inputs_.getUMatVector(inputs);
         outputs_.getUMatVector(outputs);
 
         CV_Assert(blobs.size() >= 2);
         CV_Assert(inputs.size() == 1);
 
+        if (use_half && inputs[0].dims == 2)
+            return false;
+
         if (umat_weight.empty())
         {
             umat_weight = weights_.getUMat(ACCESS_READ);
@@ -139,6 +143,7 @@ public:
         int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
         int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
 
+        String opts = (use_half) ? " -DDtype=half" : " -DDtype=float";
         for (size_t ii = 0; ii < outputs.size(); ii++)
         {
             if (inpBlob.dims == 2)
@@ -154,8 +159,12 @@ public:
                 UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
                 UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
                 int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
-                String buildopt = format("-DNUM=%d", number);
+                String buildopt = format("-DNUM=%d", number) + opts;
                 String kname = format("batch_norm%d", number);
+                if (number == 1)
+                    buildopt += format(" -Dconvert_T=convert_%s", use_half ? "half" : "float");
+                else
+                    buildopt += format(" -Dconvert_T=convert_%s%d", use_half ? "half" : "float", number);
                 ocl::Kernel kernel(kname.c_str(), ocl::dnn::batchnorm_oclsrc, buildopt);
                 if (kernel.empty())
                     return false;
@@ -181,7 +190,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/blank_layer.cpp

@@ -95,7 +95,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/detection_output_layer.cpp

@@ -307,8 +307,24 @@ public:
         std::vector<UMat> inputs;
         std::vector<UMat> outputs;
 
+        bool use_half = (inps.depth() == CV_16S);
+        if (use_half)
+        {
+            std::vector<UMat> orig_inputs;
+            std::vector<UMat> orig_outputs;
+
+            inps.getUMatVector(orig_inputs);
+            outs.getUMatVector(orig_outputs);
+
+            inputs.resize(orig_inputs.size());
+            for (size_t i = 0; i < orig_inputs.size(); i++)
+                convertFp16(orig_inputs[i], inputs[i]);
+        }
+        else
+        {
             inps.getUMatVector(inputs);
             outs.getUMatVector(outputs);
+        }
 
         std::vector<LabelBBox> allDecodedBBoxes;
         std::vector<Mat> allConfidenceScores;
@@ -342,6 +358,12 @@ public:
         {
             // Set confidences to zeros.
             Range ranges[] = {Range::all(), Range::all(), Range::all(), Range(2, 3)};
+            if (use_half)
+            {
+                std::vector<UMat> orig_outputs;
+                outs.getUMatVector(orig_outputs);
+                orig_outputs[0](ranges).setTo(0);
+            } else
                 outputs[0](ranges).setTo(0);
             return true;
         }
@@ -360,9 +382,23 @@ public:
             }
             CV_Assert(count == numKept);
         }
+
+        if (use_half)
+        {
+            UMat half_umat;
+            convertFp16(umat, half_umat);
+
+            std::vector<UMat> orig_outputs;
+            outs.getUMatVector(orig_outputs);
+            orig_outputs.clear();
+            orig_outputs.push_back(half_umat);
+            outs.assign(orig_outputs);
+        } else {
             outputs.clear();
             outputs.push_back(umat);
             outs.assign(outputs);
+        }
+
         return true;
     }
 #endif
@@ -372,7 +408,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -87,6 +87,9 @@ public:
         std::vector<UMat> outputs;
         std::vector<UMat> internals;
 
+        if (inputs_.depth() == CV_16S)
+            return false;
+
         inputs_.getUMatVector(inputs);
         outputs_.getUMatVector(outputs);
         internals_.getUMatVector(internals);
@@ -162,7 +165,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/permute_layer.cpp

@@ -288,9 +288,11 @@ public:
         if (!_needsPermute)
             return false;
 
+        bool use_half = (inps.depth() == CV_16S);
+        String opts = format("-DDtype=%s", use_half ? "half" : "float");
         for (size_t i = 0; i < inputs.size(); i++)
         {
-            ocl::Kernel kernel("permute", ocl::dnn::permute_oclsrc);
+            ocl::Kernel kernel("permute", ocl::dnn::permute_oclsrc, opts);
 
             kernel.set(0, (int)_count);
             kernel.set(1, ocl::KernelArg::PtrReadOnly(inputs[i]));
@@ -313,7 +315,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/prior_box_layer.cpp

@@ -316,6 +316,7 @@ public:
         std::vector<UMat> inputs;
         std::vector<UMat> outputs;
 
+        bool use_half = (inps.depth() == CV_16S);
         inps.getUMatVector(inputs);
         outs.getUMatVector(outputs);
 
@@ -340,9 +341,15 @@ public:
             heights.copyTo(umat_heights);
         }
 
+        String opts;
+        if (use_half)
+            opts = "-DDtype=half -DDtype4=half4 -Dconvert_T=convert_half4";
+        else
+            opts = "-DDtype=float -DDtype4=float4 -Dconvert_T=convert_float4";
+
         size_t nthreads = _layerHeight * _layerWidth;
+        ocl::Kernel kernel("prior_box", ocl::dnn::prior_box_oclsrc, opts);
 
-        ocl::Kernel kernel("prior_box", ocl::dnn::prior_box_oclsrc);
         kernel.set(0, (int)nthreads);
         kernel.set(1, (float)_stepX);
         kernel.set(2, (float)_stepY);
@@ -375,7 +382,7 @@ public:
 
         // set the variance.
         {
-            ocl::Kernel kernel("set_variance", ocl::dnn::prior_box_oclsrc);
+            ocl::Kernel kernel("set_variance", ocl::dnn::prior_box_oclsrc, opts);
             int offset = total(shape(outputs[0]), 2);
             size_t nthreads = _layerHeight * _layerWidth * _numPriors;
             kernel.set(0, (int)nthreads);
@@ -395,7 +402,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/proposal_layer.cpp

@@ -158,6 +158,9 @@ public:
         std::vector<UMat> outputs;
         std::vector<UMat> internals;
 
+        if (inputs_.depth() == CV_16S)
+            return false;
+
         inputs_.getUMatVector(inputs);
         outputs_.getUMatVector(outputs);
         internals_.getUMatVector(internals);
@@ -237,7 +240,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/region_layer.cpp

@@ -127,7 +127,7 @@ public:
         std::vector<UMat> outputs;
 
         // TODO: implement a logistic activation to classification scores.
-        if (useLogistic)
+        if (useLogistic || inps.depth() == CV_16S)
             return false;
 
         inps.getUMatVector(inputs);
@@ -191,7 +191,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/reorg_layer.cpp

@@ -96,9 +96,10 @@ public:
         std::vector<UMat> inputs;
         std::vector<UMat> outputs;
 
+        bool use_half = (inps.depth() == CV_16S);
         inps.getUMatVector(inputs);
         outs.getUMatVector(outputs);
-        String buildopt = String("-DDtype=") + ocl::typeToStr(inputs[0].type()) + String(" ");
+        String buildopt= format("-DDtype=%s ", use_half ? "half" : "float");
 
         for (size_t i = 0; i < inputs.size(); i++)
         {
@@ -134,7 +135,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/layers/reshape_layer.cpp

@@ -219,7 +219,7 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
                    OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 

modules/dnn/src/opencl/batchnorm.cl

@@ -40,24 +40,27 @@
 //
 //M*/
 
-#define Dtype float
-#define Dtype4 float4
-#define Dtype8 float8
+#if defined(cl_khr_fp16)
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#endif
 
 #if NUM == 8
     #define load(src, index) vload8(0, src + index)
     #define store(vec, dst, index) vstore8(vec, 0, dst + index)
-    #define vec_type Dtype8
+    #define float_type float8
+    #define convert_f convert_float8
     #define BATCH_NORM batch_norm8
 #elif NUM == 4
     #define load(src, index) vload4(0, src + index)
     #define store(vec, dst, index) vstore4(vec, 0, dst + index)
-    #define vec_type Dtype4
+    #define float_type float4
+    #define convert_f convert_float4
     #define BATCH_NORM batch_norm4
 #elif NUM == 1
     #define load(src, index) src[index]
     #define store(vec, dst, index) dst[index] = vec
-    #define vec_type Dtype
+    #define float_type float
+    #define convert_f convert_float
     #define BATCH_NORM batch_norm1
 #endif
 
@@ -65,8 +68,8 @@ __kernel void BATCH_NORM(__global const Dtype* src,
                          const int rows,
                          const int cols,
                          const int channels,
-                         __global const Dtype* weight,
-                         __global const Dtype* bias,
+                         __global const float* weight,
+                         __global const float* bias,
                          __global Dtype* dst)
 {
     int x = get_global_id(0);
@@ -76,9 +79,9 @@ __kernel void BATCH_NORM(__global const Dtype* src,
     if (x >= rows || y >= cols)
         return;
 
-    Dtype w = weight[x % channels];
-    Dtype b = bias[x % channels];
-    vec_type src_vec = load(src, index);
-    vec_type dst_vec = src_vec * w + (vec_type)b;
-    store(dst_vec, dst, index);
+    float w = weight[x % channels];
+    float b = bias[x % channels];
+    float_type src_vec = convert_f(load(src, index));
+    float_type dst_vec = src_vec * w + (float_type)b;
+    store(convert_T(dst_vec), dst, index);
 }

modules/dnn/src/opencl/permute.cl

@@ -40,7 +40,9 @@
 //
 //M*/
 
-#define Dtype float
+#if defined(cl_khr_fp16)
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#endif
 
 __kernel void permute(const int nthreads,
                       __global Dtype* bottom_data,

modules/dnn/src/opencl/prior_box.cl

@@ -39,17 +39,18 @@
 //
 //M*/
 
-#define Dtype float
-#define Dtype4 float4
+#if defined(cl_khr_fp16)
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#endif
 
 __kernel void prior_box(const int nthreads,
-                        const Dtype stepX,
-                        const Dtype stepY,
-                        __global const Dtype* _offsetsX,
-                        __global const Dtype* _offsetsY,
+                        const float stepX,
+                        const float stepY,
+                        __global const float* _offsetsX,
+                        __global const float* _offsetsY,
                         const int offsetsX_size,
-                        __global const Dtype* _widths,
-                        __global const Dtype* _heights,
+                        __global const float* _widths,
+                        __global const float* _heights,
                         const int widths_size,
                         __global Dtype* dst,
                         const int _layerHeight,
@@ -65,7 +66,7 @@ __kernel void prior_box(const int nthreads,
 
         outputPtr = dst + index * 4 * offsetsX_size * widths_size;
 
-        Dtype _boxWidth, _boxHeight;
+        float _boxWidth, _boxHeight;
         Dtype4 vec;
         for (int i = 0; i < widths_size; ++i)
         {
@@ -73,8 +74,8 @@ __kernel void prior_box(const int nthreads,
             _boxHeight = _heights[i];
             for (int j = 0; j < offsetsX_size; ++j)
             {
-                float center_x = (w + _offsetsX[j]) * stepX;
-                float center_y = (h + _offsetsY[j]) * stepY;
+                Dtype center_x = (w + _offsetsX[j]) * (Dtype)stepX;
+                Dtype center_y = (h + _offsetsY[j]) * (Dtype)stepY;
 
                 vec.x = (center_x - _boxWidth * 0.5f) / imgWidth;    // xmin
                 vec.y = (center_y - _boxHeight * 0.5f) / imgHeight;  // ymin
@@ -91,7 +92,7 @@ __kernel void prior_box(const int nthreads,
 __kernel void set_variance(const int nthreads,
                            const int offset,
                            const int variance_size,
-                           __global const Dtype* variance,
+                           __global const float* variance,
                            __global Dtype* dst)
 {
     for (int index = get_global_id(0); index < nthreads; index += get_global_size(0))
@@ -101,7 +102,7 @@ __kernel void set_variance(const int nthreads,
         if (variance_size == 1)
             var_vec = (Dtype4)(variance[0]);
         else
-            var_vec = vload4(0, variance);
+            var_vec = convert_T(vload4(0, variance));
 
         vstore4(var_vec, 0, dst + offset + index * 4);
     }

modules/dnn/src/opencl/reorg.cl

@@ -39,6 +39,10 @@
 //
 //M*/
 
+#if defined(cl_khr_fp16)
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#endif
+
 __kernel void reorg(const int count,
                     __global const Dtype* src,
                     const int channels,