region layer ocl implementation

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

diff --git a/modules/dnn/src/layers/region_layer.cpp b/modules/dnn/src/layers/region_layer.cpp
index bc12e8b..94993fa 100644 (file)
--- a/modules/dnn/src/layers/region_layer.cpp
+++ b/modules/dnn/src/layers/region_layer.cpp
@@ -44,6 +44,7 @@
 #include <opencv2/dnn/shape_utils.hpp>
 #include <opencv2/dnn/all_layers.hpp>
 #include <iostream>
+#include "opencl_kernels_dnn.hpp"
 
 namespace cv
 {
@@ -114,11 +115,83 @@ public:
         }
     }
 
+#ifdef HAVE_OPENCL
+    bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
+    {
+        std::vector<UMat> inputs;
+        std::vector<UMat> outputs;
+
+        inps.getUMatVector(inputs);
+        outs.getUMatVector(outputs);
+
+        if (useSoftmaxTree) {   // Yolo 9000
+            CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
+            return false;
+        }
+
+        CV_Assert(inputs.size() >= 1);
+        int const cell_size = classes + coords + 1;
+        UMat blob_umat = blobs[0].getUMat(ACCESS_READ);
+
+        for (size_t ii = 0; ii < outputs.size(); ii++)
+        {
+            UMat& inpBlob = inputs[ii];
+            UMat& outBlob = outputs[ii];
+
+            int rows = inpBlob.size[1];
+            int cols = inpBlob.size[2];
+
+            ocl::Kernel logistic_kernel("logistic_activ", ocl::dnn::region_oclsrc);
+            size_t global = rows*cols*anchors;
+            logistic_kernel.set(0, (int)global);
+            logistic_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
+            logistic_kernel.set(2, (int)cell_size);
+            logistic_kernel.set(3, ocl::KernelArg::PtrWriteOnly(outBlob));
+            logistic_kernel.run(1, &global, NULL, false);
+
+            if (useSoftmax)
+            {
+                // Yolo v2
+                // softmax activation for Probability, for each grid cell (X x Y x Anchor-index)
+                ocl::Kernel softmax_kernel("softmax_activ", ocl::dnn::region_oclsrc);
+                size_t nthreads = rows*cols*anchors;
+                softmax_kernel.set(0, (int)nthreads);
+                softmax_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
+                softmax_kernel.set(2, ocl::KernelArg::PtrReadOnly(blob_umat));
+                softmax_kernel.set(3, (int)cell_size);
+                softmax_kernel.set(4, (int)classes);
+                softmax_kernel.set(5, (int)classfix);
+                softmax_kernel.set(6, (int)rows);
+                softmax_kernel.set(7, (int)cols);
+                softmax_kernel.set(8, (int)anchors);
+                softmax_kernel.set(9, (float)thresh);
+                softmax_kernel.set(10, ocl::KernelArg::PtrWriteOnly(outBlob));
+                if (!softmax_kernel.run(1, &nthreads, NULL, false))
+                    return false;
+            }
+
+            if (nmsThreshold > 0) {
+                Mat mat = outBlob.getMat(ACCESS_WRITE);
+                float *dstData = mat.ptr<float>();
+                do_nms_sort(dstData, rows*cols*anchors, nmsThreshold);
+                //do_nms(dstData, rows*cols*anchors, nmsThreshold);
+            }
+
+        }
+
+        return true;
+    }
+#endif
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
     {
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
+        CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
+                   OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
+                   forward_ocl(inputs_arr, outputs_arr, internals_arr))
+
         Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
     }
 

diff --git a/modules/dnn/src/opencl/region.cl b/modules/dnn/src/opencl/region.cl
new file mode 100644 (file)
index 0000000..d33ac78
--- /dev/null
+++ b/modules/dnn/src/opencl/region.cl
@@ -0,0 +1,109 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#define Dtype float
+
+__kernel void logistic_activ(const int count,
+                             __global const Dtype* src,
+                             const int cell_size,
+                             __global Dtype* dst)
+{
+    for (int i = get_global_id(0); i < count; i += get_global_size(0))
+    {
+        int index = cell_size * i;
+        Dtype x = src[index + 4];
+        dst[index + 4] = 1.f / (1.f + exp(-x));
+    }
+}
+
+__kernel void softmax_activ(const int count,
+                            __global const Dtype* src,
+                            __global const Dtype* biasData,
+                            const int cell_size,
+                            const int classes,
+                            const int classfix,
+                            const int rows,
+                            const int cols,
+                            const int anchors,
+                            const float thresh,
+                            __global Dtype* dst)
+{
+    for (int index = get_global_id(0); index < count; index += get_global_size(0))
+    {
+        int box_index = index * cell_size;
+        float largest = -FLT_MAX;
+        __global const Dtype *input = src + box_index + 5;
+        __global Dtype *output = dst + box_index + 5;
+
+        for (int i = 0; i < classes; ++i)
+            largest = fmax(largest, input[i]);
+
+        float sum = 0;
+        for (int i = 0; i < classes; ++i)
+        {
+            float e = exp((input[i] - largest));
+            sum += e;
+            output[i] = e;
+        }
+
+        int y = index / anchors / cols;
+        int x = index / anchors % cols;
+        int a = index - anchors * (x + y * cols);
+        float scale = dst[box_index + 4];
+        if (classfix == -1 && scale < .5) scale = 0;
+
+        float v1 = src[box_index + 0];
+        float v2 = src[box_index + 1];
+        float l1 = 1.f / (1.f + exp(-v1));
+        float l2 = 1.f / (1.f + exp(-v2));
+
+        dst[box_index + 0] = (x + l1) / cols;
+        dst[box_index + 1] = (y + l2) / rows;
+        dst[box_index + 2] = exp(src[box_index + 2]) * biasData[2 * a] / cols;
+        dst[box_index + 3] = exp(src[box_index + 3]) * biasData[2 * a + 1] / rows;
+
+        for (int i = 0; i < classes; ++i)
+        {
+            float prob = scale * output[i] / sum;
+            output[i] = (prob > thresh) ? prob : 0;
+        }
+    }
+}