Replace Darknet's Reorg to permute layer

modules/dnn/src/layers/permute_layer.cpp

@@ -57,23 +57,6 @@ namespace dnn
 class PermuteLayerImpl CV_FINAL : public PermuteLayer
 {
 public:
-    void checkCurrentOrder(int currentOrder)
-    {
-        if(currentOrder < 0 || currentOrder > 3)
-        {
-            CV_Error(
-                     Error::StsBadArg,
-                     "Orders of dimensions in Permute layer parameter"
-                     "must be in [0...3] interval");
-        }
-
-        if(std::find(_order.begin(), _order.end(), currentOrder) != _order.end())
-        {
-            CV_Error(Error::StsBadArg,
-                     "Permute layer parameter contains duplicated orders.");
-        }
-    }
-
     void checkNeedForPermutation()
     {
         _needsPermute = false;
@@ -96,19 +79,22 @@ public:
         }
 
         DictValue paramOrder = params.get("order");
-        if(paramOrder.size() > 4)
-        {
-            CV_Error(
-                     Error::StsBadArg,
-                     "Too many (> 4) orders of dimensions in Permute layer");
-        }
-
         _numAxes = paramOrder.size();
 
         for (size_t i = 0; i < _numAxes; i++)
         {
             int currentOrder = paramOrder.get<int>(i);
-            checkCurrentOrder(currentOrder);
+            if (currentOrder < 0 || currentOrder > _numAxes)
+            {
+                CV_Error(Error::StsBadArg,
+                         format("Orders of dimensions in Permute layer parameter"
+                                "must be in [0...%d]", _numAxes - 1));
+            }
+            if (std::find(_order.begin(), _order.end(), currentOrder) != _order.end())
+            {
+                CV_Error(Error::StsBadArg,
+                         "Permute layer parameter contains duplicated orders.");
+            }
             _order.push_back(currentOrder);
         }
 

modules/dnn/src/layers/reorg_layer.cpp

@@ -85,6 +85,54 @@ public:
         return false;
     }
 
+    virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
+    {
+        std::vector<Mat> inputs, outputs;
+        inputs_arr.getMatVector(inputs);
+        outputs_arr.getMatVector(outputs);
+
+        Mat inp = inputs[0];
+        Mat out = outputs[0];
+        int batchSize = inp.size[0];
+
+        LayerParams permParams;
+        if (batchSize == 1)
+        {
+            int order[] = {1, 3, 0, 2};
+            permParams.set("order", DictValue::arrayInt(&order[0], 4));
+
+            permuteInpShape.resize(4);
+            permuteInpShape[0] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride);  // (channels*height)/(r*r)
+            permuteInpShape[1] = reorgStride;
+            permuteInpShape[2] = inp.size[3];  // width
+            permuteInpShape[3] = reorgStride;
+
+            permuteOutShape.resize(4);
+            for (int i = 0; i < 4; ++i)
+                permuteOutShape[i] = permuteInpShape[order[i]];
+        }
+        else
+        {
+            int order[] = {0, 2, 4, 1, 3};
+            permParams.set("order", DictValue::arrayInt(&order[0], 5));
+
+            permuteInpShape.resize(5);
+            permuteInpShape[0] = batchSize;
+            permuteInpShape[1] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride);  // (channels*height)/(r*r)
+            permuteInpShape[2] = reorgStride;
+            permuteInpShape[3] = inp.size[3];  // width
+            permuteInpShape[4] = reorgStride;
+
+            permuteOutShape.resize(5);
+            for (int i = 0; i < 5; ++i)
+                permuteOutShape[i] = permuteInpShape[order[i]];
+        }
+        permute = PermuteLayer::create(permParams);
+        std::vector<Mat> permuteInputs(1, inp.reshape(1, permuteInpShape));
+        std::vector<Mat> permuteOutputs(1, out.reshape(1, permuteOutShape));
+        permute->finalize(permuteInputs, permuteOutputs);
+    }
+
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
@@ -96,39 +144,13 @@ public:
         std::vector<UMat> inputs;
         std::vector<UMat> outputs;
 
-        bool use_half = (inps.depth() == CV_16S);
         inps.getUMatVector(inputs);
         outs.getUMatVector(outputs);
-        String buildopt= format("-DDtype=%s ", use_half ? "half" : "float");
-
-        for (size_t i = 0; i < inputs.size(); i++)
-        {
-            ocl::Kernel kernel("reorg", ocl::dnn::reorg_oclsrc, buildopt);
-            if (kernel.empty())
-                return false;
-
-            UMat& srcBlob = inputs[i];
-            UMat& dstBlob = outputs[0];
-
-            int batch_size = srcBlob.size[0];
-            int channels = srcBlob.size[1];
-            int height = srcBlob.size[2];
-            int width = srcBlob.size[3];
-
-            size_t nthreads = batch_size * channels * height * width;
-
-            kernel.set(0, (int)nthreads);
-            kernel.set(1, ocl::KernelArg::PtrReadOnly(srcBlob));
-            kernel.set(2, (int)channels);
-            kernel.set(3, (int)height);
-            kernel.set(4, (int)width);
-            kernel.set(5, (int)reorgStride);
-            kernel.set(6, ocl::KernelArg::PtrWriteOnly(dstBlob));
-
-            if (!kernel.run(1, &nthreads, NULL, false))
-                return false;
-        }
 
+        inputs[0] = inputs[0].reshape(1, permuteInpShape.size(), &permuteInpShape[0]);
+        outputs[0] = outputs[0].reshape(1, permuteOutShape.size(), &permuteOutShape[0]);
+        permute->preferableTarget = preferableTarget;
+        permute->forward(inputs, outputs, internals);
         return true;
     }
 #endif
@@ -152,34 +174,9 @@ public:
         inputs_arr.getMatVector(inputs);
         outputs_arr.getMatVector(outputs);
 
-        for (size_t i = 0; i < inputs.size(); i++)
+        inputs[0] = inputs[0].reshape(1, permuteInpShape);
-        {
+        outputs[0] = outputs[0].reshape(1, permuteOutShape);
-            Mat srcBlob = inputs[i];
+        permute->forward(inputs, outputs, internals_arr);
-            MatShape inputShape = shape(srcBlob), outShape = shape(outputs[i]);
-            float *dstData = outputs[0].ptr<float>();
-            const float *srcData = srcBlob.ptr<float>();
-
-            int channels = inputShape[1], height = inputShape[2], width = inputShape[3];
-            int sample_size = channels*height*width;
-            int batch_size = inputShape[0];
-
-            int out_c = channels / (reorgStride*reorgStride);
-            for (int b = 0; b < batch_size; ++b) {
-                for (int k = 0; k < channels; ++k) {
-                    for (int j = 0; j < height; ++j) {
-                        for (int i = 0; i < width; ++i) {
-                            int out_index = i + width*(j + height*k);
-                            int c2 = k % out_c;
-                            int offset = k / out_c;
-                            int w2 = i*reorgStride + offset % reorgStride;
-                            int h2 = j*reorgStride + offset / reorgStride;
-                            int in_index = w2 + width*reorgStride*(h2 + height*reorgStride*c2);
-                            dstData[b*sample_size + out_index] = srcData[b*sample_size + in_index];
-                        }
-                    }
-                }
-            }
-        }
     }
 
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
@@ -208,6 +205,10 @@ public:
         }
         return flops;
     }
+
+private:
+    Ptr<PermuteLayer> permute;
+    std::vector<int> permuteInpShape, permuteOutShape;
 };
 
 Ptr<ReorgLayer> ReorgLayer::create(const LayerParams& params)

modules/dnn/src/layers/shuffle_channel_layer.cpp

@@ -62,11 +62,40 @@ 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 (inputs[0].u != outputs[0].u)
+        {
+            if (!permute.empty())
+            {
+                inputs[0] = inputs[0].reshape(1, permuteInpShape.size(), &permuteInpShape[0]);
+                outputs[0] = outputs[0].reshape(1, permuteOutShape.size(), &permuteOutShape[0]);
+                permute->preferableTarget = preferableTarget;
+                permute->forward(inputs, outputs, internals);
+            }
+            else
+                inputs[0].copyTo(outputs[0]);
+        }
+        return true;
+    }
+#endif
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
+                   OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
+                   forward_ocl(inputs_arr, outputs_arr, internals_arr))
+
         if (inputs_arr.depth() == CV_16S)
         {
             forward_fallback(inputs_arr, outputs_arr, internals_arr);

modules/dnn/src/opencl/reorg.cl

@@ -1,70 +0,0 @@
-/*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*/
-
-#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,
-                    const int height,
-                    const int width,
-                    const int reorgStride,
-                    __global Dtype* dst)
-{
-    for (int index = get_global_id(0); index < count; index += get_global_size(0))
-    {
-        int sample_size = channels*height*width;
-        int b = index/sample_size;
-        int new_index = index%sample_size;
-        int k = new_index / (height * width);
-        int j = (new_index - (k * height * width)) / width;
-        int i = new_index % width;
-        int out_c = channels / (reorgStride*reorgStride);
-        int c2 = k % out_c;
-        int offset = k / out_c;
-        int w2 = i*reorgStride + offset % reorgStride;
-        int h2 = j*reorgStride + offset / reorgStride;
-        int in_index = w2 + width*reorgStride*(h2 + height*reorgStride*c2);
-        dst[index] = src[b*sample_size + in_index];
-    }
-}

modules/dnn/test/test_layers.cpp

@@ -1288,13 +1288,15 @@ TEST(Layer_Test_PoolingIndices, Accuracy)
     normAssert(indices, outputs[1].reshape(1, 5));
 }
 
-typedef testing::TestWithParam<tuple<Vec4i, int> > Layer_Test_ShuffleChannel;
+typedef testing::TestWithParam<tuple<Vec4i, int, tuple<Backend, Target> > > Layer_Test_ShuffleChannel;
 TEST_P(Layer_Test_ShuffleChannel, Accuracy)
 {
     Vec4i inpShapeVec = get<0>(GetParam());
     int group = get<1>(GetParam());
     ASSERT_EQ(inpShapeVec[1] % group, 0);
     const int groupSize = inpShapeVec[1] / group;
+    int backendId = get<0>(get<2>(GetParam()));
+    int targetId = get<1>(get<2>(GetParam()));
 
     Net net;
     LayerParams lp;
@@ -1308,21 +1310,25 @@ TEST_P(Layer_Test_ShuffleChannel, Accuracy)
     randu(inp, 0, 255);
 
     net.setInput(inp);
+    net.setPreferableBackend(backendId);
+    net.setPreferableTarget(targetId);
     Mat out = net.forward();
 
+    double l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-2 : 1e-5;
+    double lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 7e-2 : 1e-4;
     for (int n = 0; n < inpShapeVec[0]; ++n)
     {
         for (int c = 0; c < inpShapeVec[1]; ++c)
         {
             Mat outChannel = getPlane(out, n, c);
             Mat inpChannel = getPlane(inp, n, groupSize * (c % group) + c / group);
-            normAssert(outChannel, inpChannel);
+            normAssert(outChannel, inpChannel, "", l1, lInf);
         }
     }
 }
 INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_ShuffleChannel, Combine(
 /*input shape*/  Values(Vec4i(1, 6, 5, 7), Vec4i(3, 12, 1, 4)),
-/*group*/        Values(1, 2, 3, 6)
+/*group*/        Values(1, 2, 3, 6), dnnBackendsAndTargets(/*with IE*/ false)
 ));
 
 // Check if relu is not fused to convolution if we requested it's output